Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

• Published in: IEEE access 2020-01, Vol.8 (99), p.97863-97872
• Main Authors: Tong, Ning, Jiang, Zhihao, Zhu, Lin, Liu, Yilu
• Format: Article
• Language: English
• Subjects: Dynamic model reduction; Dynamic models; Dynamic response; ENGINEERING; Frequency measurement; Heuristic algorithms; large-scale power system; Mathematical model; Model reduction; Optimization; Parameters; Particle swarm optimization; Power system dynamics; Power system stability; Reduced order systems; Trip generation; Wide area measurements
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2992624

To perform faster than the real-time dynamic simulation of large-scale power systems, it is necessary to reduce the simulated system size by using equivalents for surrounding areas of the study area, and existing dynamic model reduction approach could provide the needed structure of the reduced area. However, further parameter optimization is required to achieve the desired accuracy. In this paper, a particle swarm optimization (PSO) based approach is used to solve the above problem. Parameters for the individual dynamic elements in the reduced system are calibrated repeatedly until the wide-area measurements of the reduced model and the original model are very similar to each other with satisfactory accuracy. Results indicate that after optimization, the dynamic response of the reduced model matches better with that of the original one than using existing methods. Under both the generator-trip event and the bus-fault event, the reduced model has a higher frequency match and less power mismatch.