A Knowledge-Based Cooperative Co-Evolutionary Algorithm for Non-Contact Voltage Measurement

• Published in: IEEE transactions on emerging topics in computational intelligence Vol. 8; no. 2; pp. 1142 - 1155
• Main Authors: Li, Hongbin, Ma, Chaojun, Zhang, Chuanji, Chen, Qing, He, Cheng, Jin, Yaochu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation algorithms; benchmark problem; Conductors; Cooperative co-evolution; Electrical measurement; Evolutionary algorithms; Genetic algorithms; Global optimization; Heuristic algorithms; high-voltage system; Knowledge based systems; Measurement methods; Metaheuristics; non-contact measurement; Optimization; Optimization algorithms; Physical properties; Smart grid; Voltage measurement
• ISSN: 2471-285X; 2471-285X
• DOI: 10.1109/TETCI.2023.3300526

Non-contact three-phase instantaneous voltage measurement is an emerging and challenging topic in modern smart grids. Existing measurement methods can hardly obtain accurate or instantaneous results attributed to the coupled three-phase information. Even though some advanced optimization algorithms have been developed, their performance should be further promoted. In this study, we first transform the measurement task into a single-objective optimization problem to address the deficiencies of existing methods. Then six problems with scalable numbers of decision variables and complexity of objectives are gathered to form a test suite for global optimization. Moreover, a knowledge-based cooperative co-evolutionary algorithm is proposed for solving the formulated problem. The main idea is to incorporate the physical properties and rules of the system into the design of an effective and efficient algorithm. By proposing the knowledge-based grouping and local search strategies, the proposed algorithm follows an iterated manner for balancing diversity maintenance and convergence enhancement during the cooperative co-evolution. Numerical studies comparing the proposed algorithm with 14 popular optimization algorithms demonstrate its effectiveness and efficiency. The practicability of the proposed modelling and optimization approach is validated on a hardware platform.