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This paper presents a novel optimization approach using Improved Harmony Search (IHS) algorithm to solve economic power dispatch problem. The proposed methodology easily takes care of different equality and inequality constraints of the power dispatch problem to find the optimal solution. To show its efficiency, the proposed algorithm is applied to single area and multi area system of four area having 16 units with and without Prohibited Operating Zones (POZ). The results are compared with other existing relevant approaches. The result obtained by the proposed method confirmed that the robustness and efficiency of the algorithms over other reported methods.

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This paper presents an extensive study of the advanced signal processing techniques for the classification of different Power Quality (PQ) disturbances. A detailed study of application of the signal processing techniques like Wavelet Transform (WT) and Wavelet Packet Transform (WPT) is carried out for the said purpose. These techniques are used to extract useful information from the raw signal in different frequency bands and give the time-frequency information. Hence, the statistical features are derived from this information and are used for the classification purpose. The features extracted are given to the Neural Network (NN) for training and subsequently it is tested for an effective classification. Three types of NN classifiers, namely, Multi Layer Feed Forward (MLFF), Probabilistic Neural Network (PNN) and Radial Basis Function (RBF) NN are analyzed for effective classification of PQ disturbances. For real-time implementation, one has to see the structural complexity of NN along with its capability of accurate classification. Hence, these NNs are compared with respect to classification performance and structural complexity. The simulation results show that the PNN offers acceptable classification accuracies.