Particle swarm optimized extreme learning machine for feature classification in power quality data mining

This paper proposes enhanced particle swarm optimization (PSO) with craziness factor based extreme learning machine (ELM) for feature classification of single and combined power quality disturbances, in the proposed method, an S-transform technique is applied for feature extraction, PSO with craziness factor is applied to adjust the input weight and hidden biases of ELM. To test the effectiveness of the proposed approach, eight possible combinations of single and combined power quality disturbances are assumed in the sampled form and the performance of the proposed approach is investigated. In addition white gaussian noise of different signal-to-noise ratio is added to the signals and the performance of the algorithm is analysed. The results indicate that the proposed approach can be effectively applied for classification of power quality disturbances.