Adequacy assessment of composite power systems through hybridization of Monte Carlo simulation and artificial immune recognition system
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abstract
Adequacy assessment of power systems provides an effective mechanism to ensure proper or acceptable system performance in the presence of different uncertainties. Monte Carlo simulation (MCS) has been widely used to evaluate system adequacy of complex power systems. However, one major drawback of MCS is its high computational cost when power flow calculation is involved in determining system state. Also, the target problem is concerned with highly imbalanced data sets which may hinder the performance of general classifiers. In this investigation, we propose a novel hybrid algorithm by combining Monte Carlo simulation with artificial immune recognition system (AIRS). AIRS is inspired by the biological immune system and has shown to be an effective classifier. The hyridization significantly decreases the state evaluation time by first deriving a set of artificial recognition balls (ARBs) in the training process, which can then be used to classify system states without power flow analyses. A comparison is made with respect to artificial neural network based classifiers including standard backpropagation neural network (BPNN) and Self-Organizing Map (SOM), and it demonstrates a better performance of the proposed algorithm in terms of both classification accuracy and computational time.
