**1. Introduction**

Fruits and vegetables are highly perishable and have a very short shelf-life. During different handling and marketing operations, there is a huge postharvest loss of agricultural produce. Both qualitative and quantitative losses occur in horticultural commodities between harvest and consumption. Qualitative losses like loss inedibility, nutritional quality, calorific value, and consumer acceptability of fresh produce are much more difficult to assess than are quantitative losses [1]. Quantitative post-harvest losses in India estimated by different committees ranged between 25 and 33% depending upon the crop. The major cause of postharvest loss is the lack of proper infrastructure for processing and packing. These losses can only be minimized to some extent by proper marketing, handling, and processing of agricultural commodities. According to a national level study conducted under the All India Coordinated Research Project (AICRP) on Postharvest technology of the Indian Council of Agricultural Research (ICAR) the post-harvest losses during different farm handling operations like harvesting, sorting, grading, and packing accounts for about 13%, during farm storage about 6% and during storage at going down, wholesale and retail level about 12% of the produce goes waste. Thus, on average, about one-third of horticulture produce never reaches the ultimate consumer. This results in a considerable gap between gross food production and net availability [2]. Insufficient knowledge of pre and post-harvest operations and lack of proper facilities for handling like pre-cooling, grading, packaging, transport,

storage, processing, and marketing all together compound the post-harvest losses and wastage which in value terms accounts for more than 6,720,000.00 US dollars.

Keeping the huge postharvest losses in mind, there is an urgent need to reduce the postharvest losses of fresh commodities and increase the level of processing as a reduction in post-harvest losses is a complementary means of production [3]. The important strategies for loss prevention include the development of varieties (genotypes) that have longer postharvest life, use of integrated crop management system, and development of cost-effective adaptable technologies for post-harvest handling, value addition, and by-product waste utilization [4]. The value chain in post-harvest management of horticultural crops mainly comprises pre-harvest factors, harvesting, market preparation (pre-cooling, sorting, grading, packaging, and on-farm storage), transportation, storage, value addition, and by-product waste management. The status of R&D carried out pertaining to postharvest management (PHM) and processing in the country by different ICAR institutes like Central Institute of Post Harvest Technology (CIPHET) (Ludhiana) and State Agricultural University (SAUs) on different aspects of post-harvest management and processing of horticultural crops is given ahead. Depending upon the status report, research scientists can find out the gap/missing links in the available technology to suggest future priorities in the area of R&D.

Maturity is the state where the product is ready for picking. Proper identification of maturity of produce is essential so that the product is less prone to various physiological disorders and diseases [5]. Maturity indices have been developed for various fruits such as mango, pomegranate, apple, grapes, ber, aonla, Nagpur mandarin, etc. Technique to determine the maturity of mango on the tree (CIPHET) and non-destructive method for the maturity of Grand Naine banana (NRCB, Trichur) need to be popularized.

In recent years, rapid industrialization, population growth, and changed lifestyle led to increased demand for processed and packed foods. Currently, ready to eat packed food industry is growing very fast. Packaging is considered as the science, art, and technology of protecting the products during transportation, distribution, storage, sale, and use. Further, the packaging ensures safe and efficient delivery of the commodity to the consumer in good condition. Good packaging attracts the customer to buy the product. It also plays a vital role in reducing the security risks during shipment. Packaged products are easy in displaying, handling, storing, distributing, opening, reclosing, and reusing. Packaging performs four important functions, such as containment, protection, convenience, and communication. A wide variety of materials, such as cane baskets, wooden boxes, clay vessels, metal cans, China pots, paper bags, and plastics containers are still used for packaging the products in many areas of the world. The packaging material should not cause any environmental pollution. Hence, there is a need to undertake detailed studies to assess the impact of food packaging on the environment.

In this context, Paine and Paine [6] concluded that packaging contains, protects, and preserves as well as informs to create convenience to consumers. It is stated that many companies apply packaging to create values beyond the basic components of containing, protecting, preserving, and informing [7]. Recent progress in food packaging is resulting from the rising need for mild processed but with better shelflife food products by the consumers. An important reason for innovative packaging is the emergence of food-borne microbial outbreaks that demand packaging with anti-microbial products to ascertain quality and safety. No hazardous components must touch the food within the packaging, and the flavor of the food should not get affected. The food must not change its original appearance and taste. In addition, the food should not cause any discoloring in the packaging. It is pertinent to mention that high-quality films serve to protect a product during transportation,

*Advances in Postharvest Packaging Systems of Fruits and Vegetable DOI: http://dx.doi.org/10.5772/intechopen.101124*

distribution, and use. It seems that the public health impact of unhygienic packaging of food is not well studied. The new food packaging techniques, such as intelligent packaging, bio-active packaging, and active packaging, which engage deliberate contact with the food or its surroundings and influence on consumer's health have been the most important innovations in the field of packaging technology [8]. Therefore, the main objective of this article is to present an overview of the innovations in food packaging technology.


**4. Role of cytokine gene polymorphism in sepsis**

trauma has a multifactorial etiology, which can be divided in endogenous and exogenous factors [12]. The endogenous factors, such as genetic predisposition, form the basis of the patient's susceptibility for the development of organ failure. Recent studies have shown that genetic variations (e.g., TNF-α polymorphisms) are strongly associated with the development of organ failure [13]. The exogenous factors, such as injury itself (the "first hit" or "trauma load") and the resuscitation or surgical intervention (the "second hit" or "intervention load"), play a crucial role in the development of organ failure. Organ damage and subsequent organ

**3. Role of cytokines in development of sepsis-related complications**

Cytokines are low molecular weight polypeptides, and pharmacologically active molecules possess autocrine, paracrine, and juxtracrine effects [15]. These molecules are classified into several classes (i.e., interleukins, interferons, colony-stimulating factors, tumor necrosis factors,

**Figure 2.** The outcome of trauma patients depends on induced inflammatory response due to trauma such as migration of leukocytes, cellular activation, and effecter functions, which may subsequently depend on genetic background of

individuals.

failure are the result of dysfunctional immune system [11, 14].

44 Infectious Process and Sepsis

Cytokine gene polymorphism, therefore, is advocated as the underlying reason to distinguish individual specific immune responses. The cytokine gene polymorphism studies may be considered as powerful biomarkers for the identification of trauma patients who have higher risk to develop sepsis complications in the ICU [20, 21]. Therefore, understanding the associations between genetic polymorphisms and sepsis or MODS may lead to the better understanding of sepsis. Nowadays the significance of genetic variations [particularly single-nucleotide polymorphisms (SNPs)] as key determinants of inter-individual variations in both inflammatory responses and clinical outcome in trauma patients is advocated [13, 22]. Single-nucleotide polymorphisms are the key factors to regulate the expressional variation of human genes and found to be associated with the disease susceptibility and progression. To understand the importance of cytokine gene polymorphism (CGP) while predicting the occurrence of sepsis, the SNPs in the promoter, coding, and noncoding regions of 13 cytokine genes with 22 loci are commonly used. Single-nucleotide polymorphisms of 13 cytokine genes including interleukin IL-1-α (T/C-889), IL-1-β (-511 C/T, T/C + 3962), IL-1 RA (T/C mspal 1100), IL-4 RA (G/A + 1902), IL-4(T/G-1098, T/C-590, T/C-33), IL-6 (G/C-174, G/A nt560), IL-10 (G/C-1082, C/T-819, C/A-592), IL-12 (C/A-1188), γIFN (+874 A/T), TGF-β1(C/T codon 10, G/C codon 25), TNF α (G/A - 308, G/A-238), and IL-2(T/G-330 G/T + 166), to investigate the susceptibility towards sepsis in trauma patients, are commonly used. All the cytokine genes and their polymorphic loci which are commonly used to investigate the genetic basis of susceptibility towards disease are shown in **Table 1.** Various studies also showed the associations of these SNPs in the development of sepsis and outcomes. The polymorphic loci in the promoter region of TNF-α-308 and TNF-α-238 have been reported by various studies and showed susceptibility and resistance between populations [23, 24]. These two polymorphisms are well recognized and associated with susceptibility for tuberculosis, heart disease, and Graves' disease [25–27]. The interleukin 6 is an important cytokine and plays a very important role in the activation of T17 cells. The polymorphisms in the promoter region of IL-6 (174 G/C) and structural region (+560) are well characterized in various diseases. However, the polymorphism in the promoter regions (174G/C) showed significant association with celiac disease, bowel syndrome, cancer, and autoimmunity [28–31]. Interestingly, the polymorphism in the promoter region of IL-6 (174 G/C) influenced the immunopathogenesis of sepsis and associated with outcomes in European population of trauma [32, 33]. We have also reported the association of IL-6 (174 G/ C) polymorphism and susceptibility of sepsis in the Indian trauma patients [34]. IL-10 is an important anti-inflammatory cytokine and plays a very crucial role in the inflammation, autoimmunity, and tolerance. Many studies suggested that elevated level of IL-10 activates the transcription factor Fox P3 which may subsequently lead to the production T regulatory cells. Polymorphism in the promoter regions of IL-10 gene 1082(G/A), 819(C/T), and 592 (C/A) is well established and showed significant association with various infectious diseases, autoimmunity, cancer D, and diabetic retinopathy [35–37]. These IL-10 promoters, 1082(G/ A), 819(C/T), and 592(C/A) polymorphisms, are associated with resistance to sepsis in the Caucasian population [38, 39]. Interleukin (IL-1) gene complex consists of IL-1α, IL-1β, IL-1R, and IL-RA genes with five potent polymorphic loci in the structural and promoter regions [40]. These polymorphic loci are associated with susceptibility for sepsis in trauma patients of the Chinese population [41, 42] and also associated with other diseases, such as cancer and autoimmunity [43, 44]. In our study, we have reported the changes in alleles and genotype frequency at the promoter region of IL-1β (511) gene. We have also reported the significant association of this polymorphism with susceptibility for sepsis in Indian trauma patients. The recently published work by Gupta et al., 2016, showed that polymorphisms in the structural and promoter regions of TNF-α, IL-β, IL-6, and IL-10 are significantly associated with susceptibility to sepsis and outcomes in trauma patients [34] (**Figure 3**).

**Table 1.** Twenty-two single-nucleotide polymorphism in 13 cytokine genes including structural and coding regions.

Cytokine Gene Polymorphism and Sepsis http://dx.doi.org/10.5772/intechopen.90572 47

**Figure 3.** This figure shows the cytokine genes and its polymorphic loci present in the structural and promoter regions

which are significantly involved in susceptibility for sepsis, septic shock, and death.

populations [23, 24]. These two polymorphisms are well recognized and associated with susceptibility for tuberculosis, heart disease, and Graves' disease [25–27]. The interleukin 6 is an important cytokine and plays a very important role in the activation of T17 cells. The polymorphisms in the promoter region of IL-6 (174 G/C) and structural region (+560) are well characterized in various diseases. However, the polymorphism in the promoter regions (174G/C) showed significant association with celiac disease, bowel syndrome, cancer, and autoimmunity [28–31]. Interestingly, the polymorphism in the promoter region of IL-6 (174 G/C) influenced the immunopathogenesis of sepsis and associated with outcomes in European population of trauma [32, 33]. We have also reported the association of IL-6 (174 G/ C) polymorphism and susceptibility of sepsis in the Indian trauma patients [34]. IL-10 is an important anti-inflammatory cytokine and plays a very crucial role in the inflammation, autoimmunity, and tolerance. Many studies suggested that elevated level of IL-10 activates the transcription factor Fox P3 which may subsequently lead to the production T regulatory cells. Polymorphism in the promoter regions of IL-10 gene 1082(G/A), 819(C/T), and 592 (C/A) is well established and showed significant association with various infectious diseases, autoimmunity, cancer D, and diabetic retinopathy [35–37]. These IL-10 promoters, 1082(G/ A), 819(C/T), and 592(C/A) polymorphisms, are associated with resistance to sepsis in the Caucasian population [38, 39]. Interleukin (IL-1) gene complex consists of IL-1α, IL-1β, IL-1R, and IL-RA genes with five potent polymorphic loci in the structural and promoter regions [40]. These polymorphic loci are associated with susceptibility for sepsis in trauma patients of the Chinese population [41, 42] and also associated with other diseases, such as cancer and autoimmunity [43, 44]. In our study, we have reported the changes in alleles and genotype frequency at the promoter region of IL-1β (511) gene. We have also reported the significant association of this polymorphism with susceptibility for sepsis in Indian trauma patients. The recently published work by Gupta et al., 2016, showed that polymorphisms in the structural and promoter regions of TNF-α, IL-β, IL-6, and IL-10 are significantly associated with suscep-

46 Infectious Process and Sepsis

tibility to sepsis and outcomes in trauma patients [34] (**Figure 3**).

**Table 1.** Twenty-two single-nucleotide polymorphism in 13 cytokine genes including structural and coding regions.

**Figure 3.** This figure shows the cytokine genes and its polymorphic loci present in the structural and promoter regions which are significantly involved in susceptibility for sepsis, septic shock, and death.


**Author details**

Antonio Alvarez Fernandez-Balbuena\*, Daniel Vázquez Moliní, Ángela Gómez-Manzanares, Juan Carlos Martínez-Antón and Santiago Mayorga Pinilla Universidad Complutense de Madrid, Madrid, Spain

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


2.2 Case of study 2: Evolution of glass bottled rosé wine as a function of closure (cork stopper with or without aluminum capsule), storage position, and brightness regime over a period of 12 months

2.2.1 Influence of storage conditions on antioxidant capacity of stored wine

Chemical composition of the rosé wine utilized for the experimental runs (t = 0).

Figure 2.

Table 8.

219

Case of study 2: graphical abstract—Experimental setup.

DOI: http://dx.doi.org/10.5772/intechopen.85672

To highlight the influence of storage conditions on the time evolution of the rosé wine, the antioxidant capacity of all the stored samples was determined after 6 and 12 months from bottling by the ABTS assay according to Sgherri et al. [58]. As shown in Figure 3, following the first observation period (6 months after bottling), only small changes in the antioxidant capacity of wines were observed, whereas after 12 months of storage, conditions significantly affected this parameter.

Parameter Mean value C.I. (p < 0.05)

Main Operating Conditions That Can Influence the Evolution of Wines during Long-Term Storage

Alcohol (%v/v) 11.33 0.06 pH 3.32 0.01 Titratable acidity (g/L as tartaric acid) 4.92 0.01 Net volatile acidity (g/L as acetic acid) 0.33 0.01 Total SO2 (g/L) 0.133 0.009 Total phenols (g/L as gallic acid) 0.332 0.004 Not flavonoid phenols (g/L as gallic acid) 0.219 0.009 Total anthocyanins (g/L as malvin) 0.087 0.002

In particular, the antioxidant capacity of wine was better preserved when the bottles were closed with capsules and stored in the dark in a horizontal position. Furthermore, the storage in the dark delayed the decrease of the antioxidant capacity of wine regardless of the other parameters. The influence exerted by the light

The samples reported in Figure 2 are identified by code letters composed of a capital letter, which represents the closure type (C = with capsule) and the storage position (H = horizontal; V = vertical) and of a small letter, which indicates the light conditions. In particular, the letter "d" indicates that wines were stored in the dark, while "l" means that wines were stored under a cool fluorescent lamp (645 lux), considered as the common lighting of most supermarkets (Table 8) [28].

Main Operating Conditions That Can Influence the Evolution of Wines during Long-Term Storage DOI: http://dx.doi.org/10.5772/intechopen.85672

#### Figure 2.

optimization, based on experimental data, could improve the shelf life of wine and

index of the aging degree of the red wine during storage, the TSO2 degradation kinetic constant (Table 5) was correlated for all packaging conditions with the sensory attributes (see Table 6). The correlation coefficients are reported in

Parameter kTSO2 Frankness 0.84 Harmony of odor 0.80 Aftertaste 0.53 Degree of oxidation 0.75 Overall pleasantness 0.80

According to Paula and Conti-Silva [57], a correlation coefficient of about 0.70

≥

0.6) are reported in boldface.

indicates a fairly strong correlation. Thus, data reported for this case of study evidenced that the TSO2 degradation rate (kTSO2) is strongly inversely correlated to positive sensorial attributes such as "frankness" and "harmony of odor" as well as the hedonic parameter "overall pleasantness," whereas the negative attribute

Correlation matrix relating the kinetic constant describing TSO2 degradation to wine attributes (storage

Based on the above observations, an integrated approach deriving from the merging of both chemical and sensorial data can be used to identify the best packaging and storage conditions necessary to extend the shelf life of red wines. In this context, kTSO2 represents a useful index to describe the chemical evolution of red wines in combination with the main sensorial attributes generally associated

The preliminary results obtained after 12 months of storage indicate that wine evolution during storage could be greatly influenced by the packaging characteristics (i.e., materials and volumes). Furthermore, also temperature imposed during the storage period seems to play a key role in the evolution of wine, since it can directly influence the oxygen permeability of the system "wine + package."

2.2 Case of study 2: Evolution of glass bottled rosé wine as a function of closure (cork stopper with or without aluminum capsule), storage position, and

The samples reported in Figure 2 are identified by code letters composed of a capital letter, which represents the closure type (C = with capsule) and the storage position (H = horizontal; V = vertical) and of a small letter, which indicates the light conditions. In particular, the letter "d" indicates that wines were stored in the dark, while "l" means that wines were stored under a cool fluorescent lamp (645 lux), considered as the common lighting of most supermarkets (Table 8) [28].

"degree of oxidation" is directly correlated with kTSO2.

Note: The correlation coefficients that indicate a strong correlation (

time = 12 months; T = 20°C; small volume packages).

brightness regime over a period of 12 months

with oxidative evolution.

Among all the experimental conditions, the rate of wine aging was higher when the volume of the containers decreased and storage temperature increased. Furthermore, after 12 months of storage, glass bottles generally better preserved wine from oxidation than multilayer materials, regardless of the closure characteristics. To highlight the fact that the rate of TSO2 degradation may represent a chemical

enhance the consumer's enjoyment during tasting.

Advances in Grape and Wine Biotechnology

Table 7.

Table 7.

218

Case of study 2: graphical abstract—Experimental setup.

Table 8.

Chemical composition of the rosé wine utilized for the experimental runs (t = 0).

#### 2.2.1 Influence of storage conditions on antioxidant capacity of stored wine

To highlight the influence of storage conditions on the time evolution of the rosé wine, the antioxidant capacity of all the stored samples was determined after 6 and 12 months from bottling by the ABTS assay according to Sgherri et al. [58]. As shown in Figure 3, following the first observation period (6 months after bottling), only small changes in the antioxidant capacity of wines were observed, whereas after 12 months of storage, conditions significantly affected this parameter.

In particular, the antioxidant capacity of wine was better preserved when the bottles were closed with capsules and stored in the dark in a horizontal position. Furthermore, the storage in the dark delayed the decrease of the antioxidant capacity of wine regardless of the other parameters. The influence exerted by the light

exposure reached its maximum when the bottles were closed with cork stoppers and stored in a vertical position.

from the closure system, the time evolution of the rosé wine during storage was

Main Operating Conditions That Can Influence the Evolution of Wines during Long-Term Storage

To confirm the convenience in using the rates of TSO2 and total anthocyanin degradation as parameters effectively describing the oxidative evolution during storage of a rosé wine, the kinetic constants kTSO2 and kAnt (Table 9) as well as their combination (kTSO2 + kAnt) were correlated with the wine antioxidant capacity. This was performed over time and for all storage conditions. The Pearson's correlation

) TEAC (L1

0.6) are reported in boldface.

)

The results (Table 10) highlight that all the degradation kinetic constants were

≥

Correlation matrix relating the kinetic constant describing TSO2 (kTSO2), total anthocyanins (kAnt), degradation, and a combination of them (kTSO2 + kAnt) to wine antioxidant capacity (storage

Notwithstanding kTAnt did not result a good index for monitoring the chemical evolution of a red wine stored in the same conditions used in this research study [27], the correlation between kTAnt and the antioxidant capacity showed by the rosé

Furthermore, kTSO2 confirmed to be a suitable index for the description of the oxidative evolution of different wines, regardless of the wine style (i.e., white, rosé, full-bodied red) and the operating conditions (i.e., packaging, storage or tasting

It can be concluded that also antioxidant capacity could be considered a useful index to describe the chemical evolution of the rosé wine under investigation, when correlated with the total anthocyanin decay rate constant (kTAnt) and, at a lower

Based on the analysis of recent papers available in international literature as well

Firstly, packaging characteristics (i.e., material and volume) deeply influence wine evolution: glass bottles generally preserved wine better than multilayer materials; larger volumes slow down the wine deterioration rate over time regardless the

Regardless the material utilized for packaging, the storage temperature plays a key role in the evolution of wine, since it can directly influence the oxygen permeability of the system "wine + package": lower temperature allows to improve the

as on the experimental results discussed above, the main issues related to wine storage could be outlined in some main topics useful to better clarify the role played by both packaging and storage conditions on the evolution of the most diffused

strictly inversely correlated with the antioxidant capacity of wine.

kTSO2 0.86 kAnt 0.94 kTSO2 + kAnt 0.93

wine was higher than that determined when kTSO2 was considered.

conditions), according to what is reported in [59–61].

extent, with the TSO2 decay rate constant (kTSO2).

3. Conclusions

kinds of wines.

shelf life.

221

kind of packaging selected.

delayed when bottles were stored in the horizontal position.

2.2.3 Conclusions related to case of study 2

DOI: http://dx.doi.org/10.5772/intechopen.85672

coefficients are reported in Table 10.

Note: The correlation coefficients that indicate a strong correlation (

Kinetic constant (months1

Table 10.

time = 12 months).


**1. Introduction**

Bacteria of the genus *Cobetia* are Gram-negative, aerobic and halotolerant and belong to the *Halomonadaceae* family. For the first time, a bacterium of the genus *Cobetia* was described in 1970 by Cobet et al. [1] and was originally assigned to the species *Arthrobacter marinus* sp. nov. In further studies, it was assigned to various genera, such as *Pseudomonas* [2], *Deleya* [3], and *Halomonas* [4]. Then, based on an analysis of the 23S and 16S rRNA sequences and amending the description of the species *Halomonas marina*, including new features, the authors [5] proposed assigning it to a new genus, *Cobetia* gen. nov., within the *Halomonadaceae* family. The type species is *Cobetia marina*, with the type strain LMG 2217 (= CIP 104,765 = IAM 14,107 = CECT 4278 = DSM 4741 = NCIMB 1877 = CCUG 49,558 = NBRC 102,605 = JCM 21,022 = ATCC 25374).

At present, a few species are characterized for marine bacteria of the genus *Cobetia*, namely: *C. marina* [5], *C. crustatorum* [6], *C. amphilecti* [7]*, C. pacifica* [7], and *C. litoralis* [7]. The whole genome sequence analysis is presented only for three strains of the genus *Cobetia*, including a type strain *C. marina* JCM 21022T [8], and the non-type strains *C. amphilecti* KMM 296 (formerly *C. marina* KMM 296) [9] and *Cobetia* sp. cqz5-12 [10]. At the same time, more and more information appears about *Cobetia* isolates, which are promising sources of unique enzymes and secondary metabolites degrading oil [11], bacterial biofilms [12], alginate [10,13], and phenol [14]. However, their species identification are complicated due to the high level of identity for their 16S rRNA genes and the absence of whole genome sequences for the type strains of known species, excluding from *C. marina* JCM 21022T [8]. The 16S rRNA gene usually is highly specific to each bacterial species that makes it a good target for identification of both environmental and clinical bacterial isolates [15–17]. However, the 16S rRNA sequences were found to be indistinguishable for a few species [18].

**Citation:** Noskova, Y.; Seitkalieva, A.; Nedashkovskaya, O.; Shevchenko, L.; Tekutyeva, L.; Son, O.; Balabanova, L. Are the Closely Related *Cobetia* Strains of Different Species? *Molecules* **2021**, *26*, 690. https://doi.org/ 10.3390/molecules26030690

Academic Editors: Pavel S. Dmitrenok and Francesco Epifano Received: 18 December 2020 Accepted: 26 January 2021 Published: 28 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The usage of 16S rRNA gene as a marker is limited for the closely related species, which have a high percentage of the sequence similarity and lack enough variations. Thus, a conventional method of 16S rRNA phylogeny has often failed to correctly identify *Vibrio* species [19]. Similarly, we found that the *Cobetia* species have 99–100% identity of their compared 16S rRNA sequences. In this regard, searching for the additional molecular markers and/or methods for the species identification of the *Cobetia* isolates is especially relevant.

In this work, we have applied the genome-based found species-specific coding sequences for the essential enzymes from each functional category and different structural families, such as nucleases, proteases, phosphatases, and phospholipase, as the additional molecular markers in the polymerase chain reaction (PCR)-based method, to differentiate the species of the closely related isolates of the marine bacteria *Cobetia,* collected in the Pacific Ocean from various habitats. In general, we have raised the issue of the possible species reclassification, due to their highly homologous 16S rRNA sequences, PCR-patterns with the use of the additional molecular markers, which were suggested here, and a high similarity between two whole genome sequences of the closely related non-type strains.


Salp Swarm Optimization

**Chapter 5**

## Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design

*Mohamed A. Ebrahim, Reham M. Abdel Fattah, Ebtisam M. Saied, Samir M. Abdel Maksoud and Hisham El Khashab*

### **Abstract**

The collaboration of the various distributed generation (DG) units is required to meet the increasing electricity demand. To run parallel-connected inverters for microgrid load sharing, several control strategies have been developed. Among these methods, the droop control method was widely accepted in the research community due to the lack of important communication links between parallelconnected inverters to control the DG units within a microgrid. To help to solve the power-sharing process, keep to frequency and voltage constrained limits in islanded mode microgrid system. The parameter values must therefore be chosen accurately by using the optimization technique. Optimization techniques are a hot topic of researchers; hence This paper discusses the microgrid droop controller during islanding using the salp swarm inspired algorithm (SSIA). To obtain a better fine microgrid output reaction during islanding, SSIA-based droop control is used to optimally determine the PI gain and the coefficients of the prolapse control. The results of the simulation show that the SSIA-based droop control can control the power quality of the microgrid by ensuring that the keep to frequency and voltage constrained limits and deviation and proper power-sharing occurs during the microgrid island mode during a load change.

**Keywords:** Droop control, microgrid, salp swarm inspired algorithm, distributed generation, power-sharing

### **1. Introduction**

In the last decade, the electricity demand was increase and shortly, the electricity demand will be expected to rise significantly [1]. To meet this projected demand, there is a trend towards renewable energy sources to be used because they are environmentally sound and are considered economically better [1]. This transition in electricity generation from conventional to renewable energy sources (RES) [2]. This has culminated in the development of small-scale power generation systems named microgrids [2]. A microgrid that involves local loads and Distributed generation sources (DGs) [3]. DG systems are ideal for highly reliable electrical power

supply [3]. Various types of energy resources are currently available, such as wind turbines (WTs), photovoltaic systems (PVS), fuel cells (FC) [4]. It is difficult to connect these renewable resources directly to a utility grid [5]. To solve this problem, the microgrid is used to make the interface between the utility grid and distributed renewable resources [3]. Microgrids must be worked in the gridconnected mode as well as island mode contingency [3]. The power produced from most renewable resources is direct current (DC) but the utility grid is alternating current (AC). The inverter must be used to convert DC to AC. Therefore, an inverter is the main microgrid element [3]. In a microgrid, there are working Inverter parallel. The Inverters parallels are guaranteed to high reliability. Because if an inverter fails, the remaining modules can still supply the necessary power to the load [3]. The inverters control is intended to deliver the active and reactive energy while preserving the variability in frequency and voltage within the allowable limits [6]. To control inverters used the droop control technique. The droop control technique provides power-sharing, voltage and frequency constrained limits [7]. Such droop controllers are tuned with identical parameters in the d-axis and q-axis by trial and error method [8]. Nonetheless, in obtaining optimum parameters or even the right outcomes, this method has a major limitation.

Before human existence on this planet, nature used evolution to constantly solve challenging problems. The researchers inspired a solution based on nature to solve the difficult problems and challenges facing them. In 1977, Holland introduced a revolutionary idea in the field of optimization when evolutionary ideas in nature were modeled in computers to solve optimization problems [9]. The emergence of a new form of heuristic algorithm is Genetic Algorithms (GA), which is the most common and famous [10]. Opening the door for researchers to research and study to find new ways to solve the problems and challenges facing them in different fields.

Heuristic algorithms treat the problem as a black square with a combination of inputs and outputs. Their inputs are the problem variables and the outputs are the goals or objectives. A heuristic search begins with the formation of a collection of randomized inputs as the solution to the problem. The search is followed by analyzing each solution, monitoring objective values, and modifying /mixing/ developing output-based solutions. These steps will be repeated until solve the problem [9].

Researchers have a propensity to use optimization algorithms to overcome many engineering problems and challenges, but there is a question for researchers "Is there an only technique of optimization that can solve all problems? ". Lately, several types of algorithms have emerged such as Harris hawks optimization (HHO) [11], Salp Swarm Inspired Algorithm (SSIA) [12–14], grasshopper optimization algorithm (GOA) [1, 15], Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA) [16–18], Moth-Flame Optimization Techniques [19], Gray Wolf Algorithm (GWO) [20–22], Ant Lion Optimizer (ALO) [23], Moth-Flame Optimization algorithm (MFO) [24, 25], particle swarm optimization (PSO) [26, 27] and Dragonfly Algorithm (DA) [28] were the outcome of GA's success. Any heuristic algorithm is flawed and the output is influenced by its limitations.

But there's a fundamental question of "why do researchers keep discovering or developing new algorithms?". There is a theory that explains the answer to this question, which is called the rule of no free lunch (NFL) [23]. Logically, this principle shows that no one can suggest an algorithm to solve all optimization issues. This does not mean that the effectiveness of a type of algorithm in solving a particular set of problems is that it can solve all the optimization problems. The NFL principle enables researchers to suggest new optimization algorithms or to enhance existing algorithms to solve problem subtypes in various fields [23].

*Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design DOI: http://dx.doi.org/10.5772/intechopen.97229*

The study proposes an SSIA-based controller to optimize the parameters of the PI controller and drop control coefficients under load change conditions to control the voltage, frequency and power-sharing of an islanded MG.

### **2. Salp swarm inspired algorithm (SSIA)**

There are several swarm algorithms, many of them inspired by food search behavior, that have emerged so far. A new swarm intelligence technique known as SSIA inspired by the action of salp was proposed by Mirjalili et al. [29]. Salps are from the Salpidae family. Salp is described as a body shaped like a barrel with a transparent body. Salps are strongly jellyfish-like. The salp environment in which he lives is difficult to reach and also difficult to provide this environment in the laboratory, so it is difficult to preserve. Biological studies of salp are therefore in its early stages. The salp forms the salp chain, a swarm that lives in the deep ocean [30]. The chains of salp are made up of two groups: leaders and followers. The leader is at the front of the chain, but the follower is named the remainder of salp. In an n-dimensional search space where n is the number of variables of a given problem, the location of salps is. It can be observed from the nature of the salp's actions that the leader salp goes around the source of food and the followers follow the leader. The leader changes his position in every iteration and the followers adopt it when finding food. In **Figure 1** The form and nature of the salp swarm.

**Figure 2** illustrates the leader and follower's movement around the food. In a two-dimensional matrix named x, the location of all salps is stored. It is also believed that in the search space there is a food source called F as the target of the swarm. To alter the location of the Leader, the following equation is proposed [29]:

$$x\_j^1 = \begin{cases} F\_j + c\_1 ( (ub\_j - lb\_j)c\_2 + lb\_j ) c\_3 \ge 0 \\ F\_j - c\_1 ( (ub\_j - lb\_j)c\_2 + lb\_j ) c\_3 < 0 \end{cases} \tag{1}$$

where

*x*1 *j* : first Salp (leader) position in the jth dimension, Fj: food source position of the jth dimension, ubj: upper bound of jth dimension, lbj: lower bound of jth dimension,

l: current iteration,

L: maximum number of iterations,

c1, c2, and c3: random numbers uniformly generated in the interval of [0,1].

### **Figure 1.**

*Shape and structure of salp swarm in the deep ocean. (a) Single salp, and (b) single salp chain.*

**Figure 2.**

*The swarm of Salps (Salps chain) [31].*

*xi j* : position of nth follower salp in ith dimension.

c1 is a significant coefficient that balances exploration and exploitation. The following equation is used to estimate c1 [12, 32]:

$$\mathbf{c}\_1 = \mathbf{2}e^{-\left(\frac{\mathbf{4}}{\mathbf{1}}\right)^2} \tag{2}$$

Newton's law of motion will be used to update the position of the followers as follows:

$$\mathbf{x}\_{j}^{i} = \frac{1}{2}\,\mathrm{c}t^{2} + \lambda\_{0}t \,\tag{3}$$

Where I ≥ 2 and *xi j* shows the position of ith follower salp in jth dimension, t is time, λ0 is the initial speed, and c = *λfinal λ*0 where λ = *x*�*x*0 *t* .

For restructuring, in an optimization problem, it can be inferred that t is the iteration; this equation can be represented as follows:

$$\boldsymbol{\omega}\_{j}^{i} = \frac{1}{2} \left( \boldsymbol{\omega}\_{j}^{i} + \boldsymbol{\omega}\_{j}^{i-1} \right) \tag{4}$$

The advantages of SSIA:

The SSIA pseudo-code algorithm is shown in **Figure 3**. **Figure 4** Performs flowchart for the SSIA.

*Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design DOI: http://dx.doi.org/10.5772/intechopen.97229*

### **Figure 3.**

*Pseudo code of the SSIA algorithm [12].*

**Figure 4.** *The flowchart of Salp swarm inspired algorithm.*

### **3. Concept droop control**

The ability of the inverter device is one of the fundamental objectives on which the wireless structure relies to regulate the output voltage and frequency while sharing the power and reactive demands. A key to wireless techniques is the use of droop control [33]. This is widely used in conventional power generation systems. A feature of this is that an external communication mechanism between inverters is not needed. In addition, its simple structure, based solely on the local voltage and current data, makes it possible for plug-and-play operations. The importance of droop control power in the island mode becomes obvious when it is possible to provide energy sharing across all units without the need to communicate with other units [3]. This method is built on the droop control of synchronous generators. The active and reactive power of each DG is determined with its nominal capacity and the droop coefficient. This is achieved by changing the droop coefficient, which increases the output resistance of the DG inverters, to regulate the amount of energy injected per DG on the grid. The output voltage and frequency of the inverter is controlled based on the reference active and reactive power of DGs, so the Q-V and P-f droop controllers are usually good candidates [34]. Therefore, the active power will be controlled according to the phase angle, whereas the voltage difference will regulate the reactive power. **Figure 5** demonstrates the relationship between P-ω and Q-V.

In the case of islands, the essential feature of droop control is to control the output power to achieve good power-sharing between transformers. This topology of the three-layer microgrid control strategy with its components will be addressed in detail in the following subsections.

### **3.1 Power circuit**

In the power circuit, four components are used the three-phase VSI, the filter for resistive-inductive-capacitive (RLC), the inductor coupling (L2), and the threephase load.

### **3.2 Droop control**

Droop control is a control technique that is usually applied to generators to allow parallel generators to be controlled by the microgrid. The relationship is

**Figure 5.** *P-ω and Q-V droop control curves [34].*

*Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design DOI: http://dx.doi.org/10.5772/intechopen.97229*

concentrated between the active power and the frequency and the reactive power and the voltage. Using the output voltage (*ν*0) and output current (I0) to calculate the active power (p) and the reactive power (q) before the filter, V0 and I0 are transformed to the dq reference frame for the calculation of (p and q) using below Equation [8, 35]:

$$p = \nu\_{0d} I\_{0d} + \nu\_{0q} I\_{0q} \tag{5}$$

$$q = \nu\_{0d} I\_{0q} + \nu\_{0q} I\_{0d} \tag{6}$$

where

*ν*od: the output voltage on the d reference frame. *ν*oq: the output voltage on the q reference frame. I0d: the output current on the d reference frame. I0q: the output current on the q reference frame.

P: the active power before the filter.

Q: the reactive power before the filter.

For enhancement, the p and q pass into a low pass filter and are renamed as P and Q. P and Q are determined in accordance with the following equation:

$$P = \frac{\alpha\_{\mathcal{E}}}{\mathcal{S} + \alpha\_{\mathcal{E}}} \left(\nu\_{0d} I\_{0d} + \nu\_{0q} I\_{0q}\right) \tag{7}$$

$$Q = \frac{\alpha\_{\mathcal{C}}}{\mathcal{S} + \alpha\_{\mathcal{C}}} \left(\nu\_{0d} I\_{0q} + \nu\_{0q} I\_{0d}\right) \tag{8}$$

where

*ωc*: the cut-off frequency of low-pass filters.

S: the Laplace transform parameter.

P: the measured active power.

Q: the measured reactive power.

After calculating P and Q, the reference angular frequency ω and reference voltage V will be calculated using the equation below:

$$
\alpha = \alpha\_n - m\_p \ast P \tag{9}
$$

$$V = V\_n - n\_q \* Q \tag{10}$$

where

*ω*: the reference angular frequency.

*V*: the reference voltage.

ωn: the constant coefficients of frequency characteristics.

*Vn*: the constant coefficients of voltage characteristics.

mp and nq: the droop coefficients.

### **3.3 Voltage: current controller**

An input to the voltage controller to find the reference current (Ii \* ) will be the reference voltage and frequency. The voltage controller output Ii \* will feed the current controller. The current controller output (V\* ) feeds the Pulse Width Modulation (PWM). The output of PWM is used to regulate VSI. Ii \* and V\* are determined by the equations below [8]:

$$I\_i^\* = -aC\_f V\_o^\* + k\_{pv} \left(V\_o^\* - V\_o\right) + \frac{K\_{iv}}{s} \left(V\_o^\* - V\_o\right) \tag{11}$$

$$V^\* = -\alpha L\_f I\_i + k\_{p\text{c}} \left( I\_i^\* - I\_i \right) + \frac{k\_{\text{ic}}}{S} \left( I\_i^\* - I\_i \right) \tag{12}$$

where

*L f* is the coupling inductor.

S is the Laplace transform parameter.

The voltage and current are controlled through the use of the PI controller. The gains of the PI controller and droop coefficients need to be exactly calculated. To determine PI gains and droop coefficients, there are many methods used, such as the trial and error method and the root locus method. However, these methods do not deal with the complicated nonlinear framework, such as microgrids, or even assess the controller's exact gains. Several studies are attempting to solve this issue because of the relevance of calculating PI gains and drop coefficients. So, the SSIA will be applied to obtain PI gains and drop coefficients.

### **4. SSIA application in microgrids**

The proposed SSIA technology will be used to determine the optimum control parameters and drop control coefficients. SSIA determines the control parameters and droop coefficients (Kp1, Ki1, Kp2, Ki2, Kp3, Ki3, Kp4, Ki4, nq, mp) for the realization of minimized voltage and frequency fluctuations. Every optimization technique requires an objective function to perform its assigned task.

The objective function is designed to minimize the error between the calculated and expected voltage. Integral of absolute error (IAE), integral of square error (ISE), integral of time absolute error (ITAE), integral of time square error (ITSE) are the four types of error benchmark objective functions [36]. ITAE is the most widely used feature in literature for reducing control objectives. This is because ITAE aims for easier implementation and provides improved efficiency compared to its rivals. The ITSE and ISE are aggressive criteria and produce unrealistic assessments due to squaring of the mistake made. In contrast to the ITAE, the IAE is also an ineffective choice, reflecting reasonable a more practical error-index due to the time-multiplying error feature. ITAE mathematically explains the equation below:

$$\text{ITAE} = \bigcap\_{0}^{\infty} t. |e(t)| \, dt \tag{13}$$

where ITAE: integral of time absolute error. t: time. e: error.

The multi-objective function is used in this case study to recognize both the frequency and voltage errors via the property of the accumulative sum. **Figure 6** illustration the test system diagram consists of two solar PV array systems (SPVAS), a DC-DC boost converter, two battery stations (BSs), a supercapacitor (SC), a three-phase VSI, a load, and a transmission line [31]. Owing to their fast charging and discharging characteristics, supercapacitors are also used to boost the microgrid's dynamic response. The DC-DC boost converter is fitted with maximum power point tracking (MPPT) based on incremental conductance (INC) to control the DC voltage of the SPVAS output terminals. **Table 1** reviews the parameters of the test system (islanded microgrid model) [8]. The detailed comparative analysis is given in **Table 2** for three optimization techniques. The findings indicate that SSIA

*Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design DOI: http://dx.doi.org/10.5772/intechopen.97229*

### **Figure 6.**

*Test system diagram [31].*

### **Table 1.**

*Test system parameters [8].*

**Table 2.**

*Results of the applied three optimization techniques.*

succeeded with minimal voltage and frequency errors in achieving the assigned control task. Three alternative optimization methods (SSIA, Particle Swarm Optimization (PSO), and ABC) are used to validate the quality of SSIA for a fair comparison.

### **5. Results of simulations**

On a microgrid test device, well-tuned controllers via SSIA are equipped to confirm the power-sharing between multiple sources as well as the voltage and frequency regulation. The two types of load: constant and continuous change loads are considered in this study with RERs variability such as (variable irradiance and temperature).

### **5.1 Case I: islanding mode with fixed cyclic load variations scenario (IMFCLVS)**

In this case, for islanded MG with a 70 kW (0.7p.u.) constant load, RERs variability (variable solar irradiance and temperature) is regarded. The ramp-up/

**Figure 7.** *Solar irradiance variation pattern for all applied scenarios.*

**Figure 8.** *Solar temperature variation for all applied scenarios.*

*Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design DOI: http://dx.doi.org/10.5772/intechopen.97229*

**Figure 9.** *Active powers generated by two DGs IMFCLVS scenario.*

**Figure 10.** *Inverter frequency of IMFCLVS scenario.*

**Figure 11.** *Voltage magnitude of IMFCLVS scenario.*

down solar irradiance from 1000 W/m2 to 250 W/m2 is expressed in **Figure 7**. **Figure 8** establishes the temperature variation between 25°C and 50°C. For each source, the dynamic active power response is represented in **Figure 9**. It should be noted that for both sources, the active power is almost equal (0.35 p.u.), which confirms successful power-sharing. Remarkably, it is observed that solar radiation

**Figure 12.**

*Powers of SPVASs, BSs, and SC in IMFCLVS scenario.*

and temperature fluctuations do not impact power-sharing because the total power fed to the DC bus is constant due to the mechanism of energy management. **Figures 10** and **11** indicate the frequency and voltage responses during the applied case. **Figure 12** shows the process of energy management among the multi-sources of MG (SPVAS, BS, and SC). It is clear from the findings that the droop control strategy based on SSIA dealt successfully with the variability of RERs.

### **5.2 Case II: islanding mode with continuous cyclic load variations scenario (IMCCLVS)**

The microgrid is operated in islanding mode with continuous cyclic load variations under the variability of RERs as kW (0.7 p.u.) from 0 to 0.3 sec, then the load value increased to 110 kW (1.1 p.u.) at 0.3–0.7 sec, then the load value returned to 70 kW (0.7 p.u.) at 0.7–1.2 sec at the end of the load cycle. **Figure 13** shows that an equivalent amount of active power is injected into MG by each DG. The rate of power change is notably almost the same as the rate of load change, remarkably. **Figures 14** and **15** denote the transient response of frequency and voltage during the IMCCLVS scenario, respectively. Strikingly, **Figures 14** and **15**. show that the frequency response indicates that the rate of power change is highly influenced by the frequency response, while the voltage is slightly affected. Additionally,

**Figure 13.** *Active powers generated by DGs in IMCCLVS scenario.*

*Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design DOI: http://dx.doi.org/10.5772/intechopen.97229*

**Figure 14.** *Inverter frequency of IMCCLVS scenario.*

**Figure 15.** *Voltage magnitude IMCCLVS scenario.*

**Figure 16.** *Powers of SPVASs, BSs, and SC in IMCCLVS scenario.*

**Figure 16** demonstrates how SPVASs, BSs, and SC interact in dynamism with each other to preserve the continuity of supply.

### **6. Conclusions**

In this chapter, an optimal SSIA-based voltage and frequency control and the power-sharing scheme was presented for inverter-dependent DG units in an island microgrid. Two sources and each source of a microgrid test system are available. The solar PV array, supercapacitor and battery station are included. To determine the gains of the PI controllers and coefficients of the droop control system, the SSIA is used. The cost function includes four forms: IAE, ISE, ITAE, and ITSE. The best solution is found when implementing ITAE as an objective function. By comparing three different types of optimization techniques (SSIA, PSO and ABC) applied to the microgrid scheme, the quality of SSIA as an optimization method was verified. In two cases, the achieved gains of PI controllers and droop control coefficients (Kp1, Ki1, Kp2, Ki2, Kp3, Ki3, Kp4, Ki4, nq, mp) are implemented in the system. Fixed load and slow and fast changes are considered in the proposed cases, as well as abrupt variations in both renewable energy supplies and loads. The results of the simulation indicated that the power-sharing depends on SSIA between the parallel DGs and the droop control strategy. The frequency deviation is within the acceptable range and the DGs are easily followed by changes in load with a good dynamic response.

### **Conflict of interest**

The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

### **Author details**

Mohamed A. Ebrahim1 \*, Reham M. Abdel Fattah2 , Ebtisam M. Saied1,3, Samir M. Abdel Maksoud1 and Hisham El Khashab2

1 Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt

2 Power Electronics and Energy Conversion Department, Electronics Research Institute, Cairo, Egypt

3 Electrical Engineering Department-High Technological Institute (HTI) 10th of Ramadan City, Egypt

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design DOI: http://dx.doi.org/10.5772/intechopen.97229*

### **References**

[1] Jumani TA, Mustafa MW, Rasid MM, Mirjat NH, Leghari ZH, Salman Saeed M. Optimal voltage and frequency control of an islanded microgrid using grasshopper optimization algorithm. Energies 2018;11. https://doi.org/ 10.3390/en11113191.

[2] Khaledian A, Golkar MA. Analysis of droop control method in an autonomous microgrid 2017;15:371–7.

[3] Tayab UB, Roslan MA Bin, Hwai LJ, Kashif M. A review of droop control techniques for microgrid. Renew Sustain Energy Rev 2017;76:717–27. https://doi. org/10.1016/j.rser.2017.03.028.

[4] Goodarzi HM, Kazemi MH, Kazemi MH. An optimal autonomous microgrid cluster based on distributed generation droop parameter optimization and renewable energy sources using an improved grey wolf optimizer. Eng Optim 2017;0:1–21. h ttps://doi.org/10.1080/ 0305215X.2017.1355970.

[5] Zeng Z, Yang H, Zhao R. Study on small signal stability of microgrids: A review and a new approach. Renew Sustain Energy Rev 2011;15:4818–28. https://doi.org/10.1016/j.rser.2011.07.069.

[6] Shokoohi S, Bevrani H. An Intelligent Droop Control for Simultaneous Voltage and Frequency Regulation in Islanded Microgrids. IEEE Trans Smart Grid 2013;4:1505–13. https://doi.org/10.1016/ j.ijepes.2014.07.024.

[7] Zeng X, Tang C, Wang W, Tang X. Analysis of microgrid inverter droop controller with virtual output impedance under non-linear load condition. IET Power Electron 2014;7: 1547–56. https://doi.org/10.1049/iet-pe l.2013.0407.

[8] Kohansal M, Gharehpetian GB, Abedi M. An optimization to improve voltage response of VSI in islanded Microgrid considering reactive power sharing. 2012 2nd Iran Conf Renew Energy Distrib Gener ICREDG 2012 2012:127–31. https://doi.org/10.1109/ ICREDG.2012.6190447.

[9] Aljarah I. Grasshopper optimization algorithm for multi-objective optimization problems 2017. https://doi. org/10.1007/s10489-017-1019-8.

[10] Tsai C, Eberle W, Chu C. Knowledge-Based Systems Genetic algorithms in feature and instance selection. Knowledge-Based Syst 2013; 39:240–7. https://doi.org/10.1016/j. knosys.2012.11.005.

[11] Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications. Futur Gener Comput Syst 2019;97:849–72. https://doi.org/10.1016/ j.future.2019.02.028.

[12] Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 2017; 114:163–91. https://doi.org/10.1016/j.ad vengsoft.2017.07.002.

[13] Abbassi R, Abbassi A, Asghar A, Mirjalili S. An efficient salp swarminspired algorithm for parameters identification of photovoltaic cell models An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag 2018;179:362–72. https://doi.org/10.1016/j.enconma n.2018.10.069.

[14] Hegazy AE, Makhlouf MA, El-Tawel GS. Improved salp swarm algorithm for feature selection. J King Saud Univ - Comput Inf Sci 2018. https://doi.org/10.1016/j.jksuc i.2018.06.003.

[15] Saremi S, Mirjalili S, Lewis A. Grasshopper Optimisation Algorithm: Theory and application. Adv Eng Softw 2017;105:30–47. https://doi.org/10.1016/ j.advengsoft.2017.01.004.

[16] Mirjalili S, Lewis A. The Whale Optimization Algorithm. Adv Eng Softw 2016;95:51–67. https://doi.org/10.1016/j. advengsoft.2016.01.008.

[17] Ebrahim MA, Osama A, Kotb KM, Bendary F. Whale inspired algorithm based MPPT controllers for gridconnected solar photovoltaic system. Energy Procedia 2019;162:77–86. https:// doi.org/10.1016/j.egypro.2019.04.009.

[18] Mafarja MM, Mirjalili S. Hybrid Whale Optimization Algorithm with simulated annealing for feature selection. Neurocomputing 2017;260: 302–12. https://doi.org/10.1016/j.neuc om.2017.04.053.

[19] Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Syst 2015;89:228–49. https://doi.org/ 10.1016/j.knosys.2015.07.006.

[20] Mirjalili S, Mirjalili SM, Lewis A. grey wolf. Adv Eng Softw 2014;69:46– 61. https://doi.org/10.1016/j.advengsof t.2013.12.007.

[21] Ahmed Ebrahim M, Mohamed RG. Comparative Study and Simulation of Different Maximum Power Point Tracking (MPPT) Techniques Using Fractional Control and Grey Wolf Optimizer for Grid Connected PV System with Battery. Electr Power Convers 2019;2016:1–14. https://doi. org/10.5772/intechopen.82302.

[22] Soued S, Ebrahim MA, Ramadan HS, Becherif M. Optimal blade pitch control for enhancing the dynamic performance of wind power plants via metaheuristic optimisers. IET Electr Power Appl 2017; 11:1432–40. https://doi.org/10.1049/iet-e pa.2017.0214.

[23] Mirjalili S. The ant lion optimizer. Adv Eng Softw 2015;83:80–98. https://doi.org/ 10.1016/j.advengsoft.2015.01.010.

[24] Aouchiche N, Aitcheikh MS, Becherif M, Ebrahim MA. AI-based global MPPT for partial shaded grid connected PV plant via MFO approach. Sol Energy 2018;171:593–603. https://d oi.org/10.1016/j.solener.2018.06.109.

[25] Ebrahim MA, Becherif M, Abdelaziz AY. Dynamic performance enhancement for wind energy conversion system using Moth-Flame Optimization based blade pitch controller. Sustain Energy Technol Assessments 2018;27:206–12. https://d oi.org/10.1016/j.seta.2018.04.012.

[26] Ali AM, Ebrahim MA, Moustafa Hassan MA. Automatic voltage generation control for two area power system based on particle swarm optimization. Indones J Electr Eng Comput Sci 2016;2:132–44. https://doi. org/10.11591/ijeecs.v2.i1.pp132-144.

[27] Ebrahim MA, Mostafa HE, Gawish SA, Bendary FM. Design of decentralized load frequency based-PID controller using stochastic particle swarm optimization technique. 2009 Int Conf Electr Power Energy Convers Syst EPECS 2009 2009.

[28] Mirjalili S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 2016;27:1053–73. https://doi.org/ 10.1007/s00521-015-1920-1.

[29] Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 2017; 114:163–91. https://doi.org/10.1016/j.ad vengsoft.2017.07.002.

[30] Abbassi R, Abbassi A, Heidari AA, Mirjalili S. An efficient salp

*Salp Swarm Optimization with Self-Adaptive Mechanism for Optimal Droop Control Design DOI: http://dx.doi.org/10.5772/intechopen.97229*

swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag 2019;179:362–72. h ttps://doi.org/10.1016/j.enconma n.2018.10.069.

[31] Ebrahim MA, Fattah RMA, Saied EM, Maksoud SMA, El Khashab H. Real-Time Implementation of Self-Adaptive Salp Swarm Optimizationbased Microgrid Droop Control. IEEE Access 2020;8:1–1. https://doi.org/ 10.1109/access.2020.3030160.

[32] Qais MH, Hasanien HM, Alghuwainem S. Enhanced salp swarm algorithm: Application to variable speed wind generators. Eng Appl Artif Intell 2019;80:82–96. https://doi.org/10.1016/ j.engappai.2019.01.011.

[33] Lopes JAP, Moreira CL, Madureira AG. Defining control strategies for microgrids islanded operation. IEEE Trans Power Syst 2006; 21:916–24. https://doi.org/10.1109/ TPWRS.2006.873018.

[34] Zafari P, Zangeneh A, Moradzadeh M, Ghafouri A, Parazdeh MA. Various Droop Control Strategies in Microgrids. Power Syst 2020:527–54. https://doi.org/10.1007/ 978-3-030-23723-3\_22.

[35] Ebrahim MA, Aziz BA, Nashed MNF, Osman FA. A Novel Hybrid-HHOPSO Algorithm based Optimal Compensators of Four-Layer Cascaded Control for a New Structurally Modified AC Microgrid. IEEE Access 2020. https://doi.org/10.1109/ ACCESS.2020.3047876.

[36] Hussain KM, Zepherin RAR, Kumar MS, Kumar SMG. Comparison of PID Controller Tuning Methods with Genetic Algorithm for FOPTD System. J Eng Res Appl 2014;4:308–14.

### Section 6


*Edited by Amita Ray*

This book on gestational diabetes does not claim to cover all aspects of this complex and ever-evolving medical condition. It is an attempt by the group of authors to provide an overview, highlight important features, and bring to light certain recent advances in the diagnosis, screening, and understanding of gestational diabetes mellitus. As the book provides an overview of the condition, we are sure that reading it would provide medical undergraduates and postgraduates a quick revision for their exams. The current concepts section of the book may inspire more exploration into this area.It has been a pleasure to work with experts, both senior and junior, for this endeavor but we are particularly grateful to the publisher IntechOpen who have shown commitment and perseverance in completing this work. This new book deserves to be a success and we are sure it will be.

ISBN 978-1-78984-733-8

Gestational Diabetes Mellitus - An Overview with Some Recent Advances

Published in London, UK © 2020 IntechOpen © eriksvoboda / iStock

## Gestational Diabetes Mellitus An Overview with Some Recent Advances

*Edited by Amita Ray*