A hybrid algorithm combining Regrouping Particle Swarm Optimization (RegPSO) with wavelet radial basis function neural network referred to as (RegPSO-WRBF-NN) algorithm is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on clean and polluted high-voltage glass insulators by using surface tracking and erosion test procedure of international electro-technical commission 60,587. A laboratory experiment was conducted by preparing the prototypes of the discharges. A very important step for the WRBF network training is to decide a proper number of hidden nodes, centers, spreads and the network weights can be viewed as a system identification problem. So PSO is used to optimize the WRBF neural network parameters in this work. Therefore, the combination method based on the WRBF neural network is adapted. A regrouping technique called as a Regrouping Particle Swarm Optimization (RegPSO) is also used to help the swarm escape from the state of premature convergence, RegPSO was able to solve the stagnation problem for the surface discharge dataset tested and approximate the true global minimizer. Testing results indicate that the proposed approach can make a quick response and yield accurate solutions as soon as the inputs are given. Comparisons of learning performance are made to the existing conventional networks. This learning method has proven to be effective by applying the wavelet radial basis function based on the RegPSO neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and revealed a very high classification rate. 
Hybrid regrouping PSO based wavelet neural networks for characterization of acoustic signals due to surface discharges on H.V. glass insulators