Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system
2022
Zhaoyang Qu | Yunchang Dong | Sylvère Mugemanyi | Tong Yu | Xiaoyong Bo | Huashun Li | Yang Li | François Xavier Rugema | Christophe Bananeza
Abstract Here, a novel Gaussian bare‐bones bat algorithm (GBBBA) and its modified version named as dynamic exploitation Gaussian bare‐bones bat algorithm (DeGBBBA) are proposed for solving optimal reactive power dispatch (ORPD) problem. The optimal reactive power dispatch (ORPD) plays a fundamental role in ensuring stable, secure, reliable as well as economical operation of the power system. The proposed GBBBA employs the Gaussian distribution in updating the bat positions in an effort to mitigate the premature convergence problem associated with the standard BA. DeGBBBA is an advanced variant of GBBBA in which a modified Gaussian distribution is introduced so as to allow the dynamic adaptation of exploitation and exploitation in the proposed algorithm. Both GBBBA and DeGBBBA are used to determine the optimal settings of generator bus voltages, tap setting transformers and shunt reactive sources in order to minimize the active power loss, total voltage deviations and voltage stability index. The performance and effectiveness of the proposed algorithms are demonstrated on three power system test bus including the standard IEEE 14‐bus, IEEE 57‐bus and IEEE 118‐bus. Simulation results show that GBBBA and DeGBBBA are robust and effective in solving the ORPD problem.
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