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Renewable Energy 28 (2003) 139–152

Technical note
Development of an integrated data-acquisition
system for renewable energy sources systems
Eftichios Koutroulis *, Kostas Kalaitzakis
Department of Electronics and Computer Engineering, Technical University of Crete, GR-73100,
Chania, Greece

Received 1 October 2001; accepted 16 November 2001


Data-acquisition systems are widely used in renewable energy source (RES) applications in
order to collect data regarding the installed system performance, for evaluation purposes. In
this paper, the development of a computer-based system for RES systems monitoring is
described. The proposed system consists of a set of sensors for measuring both meteorological
(e.g. temperature, humidity etc.) and electrical parameters (photovoltaics voltage and current
etc.). The collected data are first conditioned using precision electronic circuits and then inter-
faced to a PC using a data-acquisition card. The LABVIEW program is used to further process,
display and store the collected data in the PC disk. The proposed architecture permits the
rapid system development and has the advantage of flexibility in the case of changes, while
it can be easily extended for controlling the RES system operation.  2002 Elsevier Science
Ltd. All rights reserved.

Keywords: Renewable energy sources; Data-acquisition system; Microcomputer; Sensors; LABVIEW

1. Introduction

The rapid evolution of renewable energy sources (RESs) during the last two dec-
ades resulted in the installation of many RES power systems all over the world. A
disadvantage of RES systems is that the installation cost is still high, so their design

* Corresponding author.

0960-1481/03/$ - see front matter  2002 Elsevier Science Ltd. All rights reserved.
PII: S 0 9 6 0 - 1 4 8 1 ( 0 1 ) 0 0 1 9 7 - 5
140 E. Koutroulis, K. Kalaitzakis / Renewable Energy 28 (2003) 139–152

optimization is desirable. However, such an effort requires detailed knowledge of

meteorological data of the site where the system will be installed and operational
results from similar systems, if available. Many data-acquisition systems have been
developed in order to collect and process such data, as well as monitor the perform-
ance of RES systems under operation, in order to evaluate their performance [1–3].
A data-acquisition system used for monitoring the performance of both photovol-
taic battery charging [4] and water-pumping systems [5] is shown in Fig. 1(a). An
A/D converter interfaced to a microcontroller-based unit records a set of sensors’
signals, while the collected data are stored in a local EPROM. The data collected
by the microcontroller are transmitted to a PC, with an RS-232 serial connection,
where they are stored for further processing. The same architecture has been
implemented in [6–8] for solar irradiation and ambient temperature measurements. In
all the above-mentioned cases, Windows or MS-DOS based software was developed
specifically for each application, in order to process and display the collected data on
the PC, but this approach is not flexible to changes, e.g. the addition of new sensors.
A different approach has been proposed in [9], shown in Fig. 1(b). A commercial
data-logging unit has been used to measure a set of meteorological and operational
parameters of a hybrid photovoltaic–diesel system. The collected data are transmitted
to a PC through an RS-232 serial interface, where they are processed using the
LABVIEW data acquisition software. However, a data logging unit lacks flexibility
compared with a data-acquisition card approach, while, in addition, it cannot be used
for RES system control.
A common characteristic of the design methods described above is that a
microcontroller-based data-logging unit is used to measure the signals of interest and
interface the collected data to a PC through an RS-232 serial interface. However,
serial data transmission limits the system performance if an advanced control capa-
bility is desired.
In this paper, a computer-based data-acquisition system for monitoring both
meteorological data and RES system operational parameters is proposed. A block
diagram of the proposed system is shown in Fig. 1(c). A set of sensors are used to
measure atmospheric and soil conditions, as well as quantities regarding the energy
produced by the hybrid photovoltaic/wind generator power system, such as the photo-
voltaic array voltage and current, the wind generator speed etc. The sensor signals
are first filtered and amplified using precision electronic circuits and then are inter-
faced to a PC, through the PCI bus, using a commercially available data-acquisition
card. The collected data are further processed, displayed on the monitor and stored
in the disk using the LABVIEW software. This method has the advantages of rapid
data-acquisition system development and flexibility in the case of changes, while it
can be easily extended to control the RES system operation. Also, the LABVIEW
provides an easy-to-use graphical environment that permits the system operators to
process easily the collected data, using complex data-processing algorithms, without
detailed knowledge of the data-acquisition system design [10]. The proposed data-
acquisition system has been interfaced to a pilot low-power RES system just for test
purposes, but this does not limit its use on large power capacity RES plants, which
is the main objective of such monitoring and control systems.

Fig. 1. Data-acquisition architectures for RES systems: (a) a microcontroller-based system, (b) a data-
logging unit connected to a PC and (c) the proposed arrangement block diagram.

This paper is organized as follows: an analysis of the sensors and the electronic
circuits developed is presented in Section 2, the data collection and processing inter-
faces are described in Section 3 and the experimental results of the proposed design
application in an operating hybrid RES system are given in Section 4.

2. The sensors and the interface circuits

A block diagram of the RES system under consideration is shown in Fig. 2. The
positions of the sensors used are also indicated in the figure. The power plant consists
of two photovoltaic (PV) arrays of 450 and 300 W, respectively, and a 2 kW perma-
nent magnet (PM) wind generator (W/G). Both the W/G and one of the PV arrays
are used to charge a 24 V battery stack, by means of appropriate battery chargers.
A DC/AC inverter is used to supply the load, which in this case is the lamp array
of a parking lot. The second PV array is interconnected to the electric grid through
a grid-connected-type inverter. The size of the above RES plant is experimental, but

Fig. 2. The RES system block diagram and the sensors used.

the proposed data-acquisition system can be interfaced to larger power capacity RES
plants, where the PC power consumption is negligible compared with that generated
by the RES system.
The voltages of both PV arrays and the W/G are measured with voltage dividers
connected to an AD625 precision instrumentation amplifier [Fig. 3(a)].
The amplifier output voltage is given by the following equation:
Vo ⫽ V (1)
R ⫹ RF in
where Vo is the output voltage, Vin is the measured PV or W/G voltage and R, RF
are the voltage divider resistances.
In RES systems equipped with batteries as storage units, the battery service time
depends on the estimated state of charge, which is highly sensitive to the current
measurement errors. In such cases, if the battery charge monitoring is based on
amp-hour measurements using Hall-effect sensors [11], a circuitry improving the
measurement accuracy is essential, as explained below.
All currents are measured with Honeywell Microswitch CSLA1CF type Hall-effect
sensors. The non-intrusive method of measurement and the very low power loss are
among the advantages of this type of sensor. A disadvantage is that the zero current
offset voltage has a temperature coefficient of ±0.05%/°C relative to the offset at
25°C. The measurement of currents as high as 100 A dictates the use of a single
wire turn wound around the magnetic core, causing relatively high errors when meas-
uring low currents, due to the sensor temperature coefficient. Thus, the development
of a temperature compensation arrangement is necessary. The Hall-effect sensors
compensation circuit developed is depicted in Fig. 3(b). The forward voltage tem-
perature drift of a small signal diode balances the Hall-effect sensor offset voltage
temperature drift. The Hall-effect sensors compensation circuit tested in the labora-
tory features a temperature coefficient of dVH / dT ⫽ ⫺0.8 mV / °C, while the com-
pensation diode has a coefficient of dVD / dT ⫽ ⫺2.3 mV / °C, where VH is the Hall-
effect sensor output voltage, VD is the compensation diode voltage and T is the tem-
The instrumentation amplifier output voltage Vc is given by the following equation:
Vc ⫽ (VH⫺Vr1).G1 ⫹ (Vr2⫺VD).G2 (2)
where G1, G2 are the amplifier A1, A2 gains, respectively, and Vr1, Vr2 are refer-
ence voltages.
Initially, both potentiometers are trimmed so that VH ⫽ Vr1 and VD ⫽ Vr2. Then,
the amplifier gain G1 is calculated as:
G1 ⫽ (3)
where Vc,max is the desired maximum output voltage of amplifier A1, SH is the sensor
sensitivity (V/A) and Imax is the maximum measured current.
The amplifier A2 gain is calculated differentiating Eq. (2), with respect to tempera-
ture, and setting dVc / dT ⫽ 0, resulting in:

Fig. 3. The interface circuits: (a) the voltage conditioning circuit, (b) the Hall-effect device conditioning
and temperature compensation circuits, (c) the W/G speed measurement circuit and (d) the associated
waveforms of the circuit shown in (c).

dVH / dT
G2 ⫽ .G (4)
dVD / dT 1
The W/G rotational speed is measured using the circuit shown in Fig. 3(c). As
shown in Fig. 3(d), the sinusoidal voltage between two phases of the W/G (Vac) is
transformed into a TTL level digital signal (Vs) with frequency (fWG) proportional
to the W/G speed. This digital signal is connected to one of the data-acquisition card
counter inputs and the W/G speed is computed by means of the data-collection
software, as explained in the next section.
All other atmospheric and soil condition sensor signals are amplified differentially
using AD625 type instrumentation amplifiers, while the amplifier outputs are connec-
ted to the data-acquisition card analog input channels through low-pass RC filters
and protection diodes, as shown in Fig. 4.
The ambient temperature and humidity are measured using the Rotronic MP100A
type hygrometer, while the barometric pressure and the global irradiation are meas-
ured using the Delta-T Devices BS4 type barometer and the GS1 type pyranometer,
respectively. The wind speed and direction are measured with the Vector Instruments
A100R type anemometer and the W200P type wind vane, respectively. The wind
speed signal is connected to a counter input of the data-acquisition card (Fig. 4).
The soil temperature is measured with a PT100 Platinum resistance RTD type
sensor and the soil heat flux is measured with a Hukseflux HFP01 type heat flux
plate. The soil volumetric water content is measured with the Campell Scientific
CS615-L type water content reflectometer. Its output voltage period is proportional

Fig. 4. Block diagrams of the atmospheric and soil condition sensor interface circuits.

to the soil volumetric water content and is converted to an analog signal with the
LM331 precision frequency-to-voltage converter (Fig. 4).

3. The data collection and processing interfaces

The data-acquisition system uses the National Instruments PCI-6024E type DAQ
card installed in a PC. All instrumentation amplifier outputs and the digital signals
are connected to the card through the external CB-68LP R686 connector block.
The DAQ card has the following operational characteristics:

앫 16 single ended or eight differential analog input channels (software selectable

per channel) with a ±10 V operating range;
앫 a successive approximation A/D converter with a 12-bit accuracy and a 200 kS/s
maximum sampling rate;
앫 two analog output channels with 12-bit accuracy;
앫 eight digital input/output channels; and
앫 two up/down timer/counters.

The data-acquisition card is controlled by a properly developed interface, using

the LABVIEW software, running on the PC. It consists of two parts: (a) a graphical
environment with components such as displays, buttons and charts in order to provide
a convenient-to-use environment for the system operator, and (b) the program code,
which is in block-diagram format and consists of built-in virtual instruments (VIs),
performing functions such as analog channel sampling, mathematical operations, file
management etc. The LABVIEW software runs under the Windows 95/98/NT/2000
operating system and it requires a Pentium processor, a minimum RAM of 32 MB
and 60 MB of disk storage space.
The block diagram of the developed LABVIEW program is illustrated in Fig. 5.
Initially, all analog signals are sequentially sampled and the input voltage data are
calibrated to correspond to physical units. The calibration equations have the gen-
eral form:
yi ⫽ ai.xi ⫹ bi (5)
where yi is the ith sensor output in physical units, xi is the ith sample and ai, bi are
calibration constants.
The frequency of the anemometer digital output signal is used for calculation of
the wind speed, forming the corresponding LABVIEW built-in VI. The relation for
the conversion of frequency to wind speed is the following:
Na 60 × fa
vlin ⫽ ⫽ (6)
where vlin is the calculated speed (m/s), Na are the anemometer revolutions per minute
(rpm), D is a conversion constant given by the anemometer manufacturer and equal
to 47.7 rpm/m/s and fa is the measured frequency (Hz).

Fig. 5. The LABVIEW measurement program flowchart.

The wind speed is further corrected using the R30 Rotor calibration data. The
corresponding correction factor F is calculated by interpolation, using a look-up table
and the wind speed vlin calculated in Eq. (6). The correct wind speed va (m/s) is then
derived from the following equation:
va ⫽ (7)
Since the wind speed anemometer is located on a mast at a height of 12 m, while

the W/G turbine is at 15 m, the following equation is used to calculate the wind
speed at the height of the turbine:

vWG ⫽ va 冉 冊

where va is the measured speed at the height of the anemometer ha, vWG is the
estimated wind speed at the turbine height hWG and P ⫽ 1 is the stability constant.
The W/G rotational speed NWG is calculated from the corresponding digital signal
frequency, as described in the previous section, using the following equation:

Fig. 6. Atmospheric and soil condition measurements for a specific day.


Fig. 7. PV and battery voltage and current measurements for a specific day.

NWG ⫽ × 60 (9)
where NWG is the rotational speed (rpm), fWG is the measured frequency (Hz) and
p ⫽ 8 is the number of the W/G pole pairs.
When the calibration procedure is completed, the calculated values are displayed
on the monitor. Also, the measurements performed every 1 min are stored on the
PC hard disk, in files named with the current date. These files contain the exact time

Fig. 8. Wind speed, wind direction, barometric pressure and W/G speed, voltage and current measure-
ments for a specific day.

of the measurement along with each measured parameter identification and value.
The above described LABVIEW program runs continuously.

4. The experimental results

The monitoring system together with the power system described above is installed
in a small house close to the RES. The geographical location of the installation is
approximately: latitude: 35.53° (35° 31⬘ 48⬙ N); longitude: 24.06° (24° 03⬘ 35⬙ E);
altitude: 150 m (approx.) above sea level.

Fig. 9. Part of the developed LABVIEW program code.

The measurements of all sensors described in Section 2, collected in a specific

day, are illustrated in Figs. 6–8. A part of the LABVIEW program code is shown
in Fig. 9.
The Hall-effect sensors used are wound with a single turn, giving a sensitivity of
26 mV/A. The temperature compensation circuit, described in Section 2, was tested
in the range of 20–30°C. The zero current temperature drift measured is less than
3% of the sensor sensitivity, while if no temperature compensation is used, then the
maximum zero current temperature drift, in the same temperature range, would be
about 15% of the sensor sensitivity. These results verify the accuracy improvement
of the Hall-effect sensor current measurements by the compensation circuit.

5. Conclusions

Data-acquisition systems are used in RES systems in order not only to measure
the meteorological conditions, but also to collect data regarding the system perform-
ance for evaluation purposes. Most present data-acquisition systems collect the data
of interest and store them in a local memory until the system operator downloads
them to a computer.
In this paper, the development of an integrated computer-based data-acquisition
system for RES plants is described. The proposed method is based on precision

electronic circuits and an easy-to-use graphical environment, based on the LAB-

VIEW program, for processing, displaying and storing the collected data. The system
operator can easily process the measured parameters using any LABVIEW built-in
function available. The proposed architecture has the advantages of rapid develop-
ment and flexibility in the case of changes, while it can be easily extended for con-
trolling the RES system operation.


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