Abstract
This paper presents an artificial neural network-based approach (ANN) for reactive power (VAR) optimization of interconnected power systems. The reactive power resources are scheduled to minimize the total transmission losses of the network. The proposed ANN for this study is a three-layer feed-forward network with a sigmoidal transfer function. Different loading conditions of reactive power are used as input pattern to train the ANN. The desired output is the optimal voltages at the VAR-controlled buses. Since the resulting state from the ANN may not be feasible and some voltage limits are exceeded, a rule-based approach is used for control variable adjustments. Simplicity, high processing speed and ability to model non-linear functions using ANN make the proposed approach a viable option for VAR optimization. The proposed approach is applied on a real power system and the presented test results demonstrate its applicability for real-time VAR optimization.
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