Comparison of Various Learning Algorithms for Artificial Neural Network Based On-Line Load Flow Analysis
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This paper compares various learning algorithms for neural network based On-line Load Flow Analysis. Load flow analysis is a basic problem in real time power system planning, operation and is required to carry out further studies on the power system like economic scheduling. The conventional methods used for load flow studies are iterative techniques and needs longer time for data computation. Neural Network (NN) based model provide an alternative solution for on-line load flows. The on-line load flow analysis requires the NN model to be accurate, simple and structurally compact to ensure faster execution time so that the results of the analysis can be applied instantly to the real time control operations of the complex power systems. This desired performance to a large extent depends on the type of Neural Learning algorithms used to train the Neural Architecture for load flow analysis. The popular single layer feed forward neural architecture is chosen for study. The chosen architecture is trained off-line using three types of learning algorithms to solve load flow problem. Their performance is compared in terms of accuracy, computational complexity and structural compactness. The results are validated for IEEE 30 Bus system. The suitable off-line learning algorithm for on-line load analysis is identified. The promising simulation results obtained are presented
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