期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 13, 期 4, 页码 1692-1703出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2016.2614659
关键词
Back propagation neural network (BPNN); B-loss coefficients; dynamic economic dispatch (DED); linear programming estimator; particle swarm optimization (PSO); transmission loss estimation
The conventional transmission loss estimation methods used by power system utilities in scheduling problems rely on the exactness of the network model. However, the transmission network model in the system operator database is erroneous and not updated periodically. Therefore, the transmission losses calculated based on the erroneous network model is also erroneous. In this context, this paper proposes an auxiliary hybrid model using a back propagation neural network (BPNN) and a particle swarm optimization (PSO) technique to estimate transmission losses, while solving power system scheduling problems. Here, the historical information of the power system is processed by the BPNN and its control parameters are optimized using PSO. In the proposed PSO-BPNN loss estimator, power system variables such as real power generation levels, reactive power injection values, and ambient temperature are used as the input variables. The proposed loss estimator is validated using IEEE 30 bus system and Ontario power system.
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