Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit

Abstract

As main measure to stabilize fluctuation of power grid under renewable energy utilization, the flexible operation of thermal power unit reveals remarkable significant. To serve for peak shaving and fast load varying, a novel T-S fuzzy modeling approach based on fast adaptive moth-flame optimization (FAMFO) algorithm is proposed for dynamics investigation of supercritical unit. This modeling scheme is deemed as hierarchical process with data partition stage and parameter determination stage. Firstly, an automatic clustering strategy relying on FAMFO is fulfilled for more reasonable data partition result, which devotes to accuracy modeling without subjective interference. In this stage, the FAMFO is attained with assistance of four improvements and its merits in balancing search exploration and exploitation guarantee the width of covered operation range of supercritical unit for peak shaving. Secondly, inherent advantages of least square technics in parameter identification witness the successful implementation of a well-performed exponentially-weighted least squares algorithm in later stage of fuzzy modeling. In effectiveness verification of the proposed modeling approach, the superiorities of FAMFO in promoting search precision and speed are demonstrated via benchmark function test and Friedman test. Then, the flexible operation modeling performance is proved via extensive simulation experiments using on-site data of supercritical unit.