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

Journal Article · Sun May 01 04:00:00 EDT 1994 · IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States)

OSTI ID:7009716

Hsu, Y Y; Yang, C C ^[1]

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1. National Taiwan Univ., Taipei (Taiwan, Province of China). Dept. of Electrical Engineering

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 forecasted load. Two types of clustering algorithms, hard clustering by Euclidean algorithm and soft clustering by an 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.

• OSTI ID: 7009716
• Journal Information: IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States) Vol. 9:2; ISSN ITPSEG; ISSN 0885-8950
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
240100* -- Power Systems-- (1990-)
CAPACITORS
ELECTRICAL EQUIPMENT
ENERGY SYSTEMS
EQUIPMENT
NEURAL NETWORKS
POWER SYSTEMS
STABILIZATION
USES