A hybrid artificial neural network–dynamic programming approach for feeder capacitor scheduling

A hybrid artificial neural network (ANN)–dynamic programming (DP) method for optimal feeder capacitor scheduling is presented in this paper. To overcome the time-consuming problem of full dynamic programming method, a strategy of ANN assisted partial DP is proposed. In this method, the DP procedures are performed on historical load data off-line. The results are managed and valuable knowledge is extracted by using cluster algorithms. And then, by the assistance of the extracted knowledge, a partial DP of reduced size is performed on-line to give the optimal schedule for the forecast load. Two types of clustering algorithms, hard clustering by Euclidean alogorithm and soft clustering by a unsupervised learning neural network, are studied and compared in the paper. The effectiveness of proposed algorithm is demonstrated by a typical feeder in Taipei City with its 365 days' load records. It is found that execution time of scheduling is highly reduced, while the cost is almost the same as the optimal one derived from full DP.