Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

Abstract

Aiming at the problems of economy, security, and reliability of power system operation, the multiobjective gap evolutionary algorithm is introduced into power-sensitive early warning data conversion. A power-sensitive early warning data conversion (PSEWDC) method based on multiobjective gap evolutionary algorithm (MOGEA) is proposed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of defects and problems, the method quickly locates the problems and analyzes the causes, replaces and rearranges the original data by using the substitution data method to eliminate the nonlinear autocorrelation, and then, according to the convergence of the long-range correlation index, identifies the abnormal values that have no impact on the overall fluctuation of the original sequence, judges that the current sequence reaches the critical point of extreme events, and provides a reference threshold for real-time risk early warning. Finally, the example analysis shows that this method can finally determine the risk early warning threshold of power peak and valley load by analyzing the convergence of long-range correlation index, so as to avoid the blindness of subjective experience, provide a theoretical basis for power load risk early warning, effectively solve the problems of subjectivity, lack of dynamics, and lack of theoretical basis for setting risk threshold in traditional power risk early warning research, and have an intelligent insight into the security situation of power big data.