Comprehensive Evaluation of Power Quality Based on LSM-GA and Improved VFS

To address the uncertainty associated with evaluation indices and measured data processing in the comprehensive evaluation of power quality, a comprehensive evaluation method of power quality based on the least squares method (LSM), genetic algorithm (GA), and improved variable fuzzy set theory (VFS) is proposed. First, the power quality index system is established. Second, the subjective weight of each power quality index is calculated using the order relation analysis method (G1), while the objective weight of each power quality index is calculated using the criteria importance through intercriteria correlation (CRITIC) method. Third, LSM is employed to model the power quality index weights, and the GA is applied to solve the weight model. In this way, the comprehensive optimal weight of each power quality evaluation index is obtained, which avoids the limitations of single weighting methods. Subsequently, the set pair analysis theory (SPA) is used to improve the VFS, thereby avoiding the subjective randomness of the difference function. A comprehensive evaluation model of power quality based on LSM-GA and improved VFS is constructed. Finally, the results of the example verify the effectiveness and feasibility of the proposed method.