Hierarchical Coordinated Reactive Power Optimization of Wind Farm Clusters Using Environment-Aware Particle Swarm Optimization

Large-scale wind-farm clusters are characterized by intense power fluctuations, various turbine types and time-varying reactive power margins, along with conflicting goals of voltage security and network loss reduction. To address these issues, this paper proposes a hierarchical coordinated reactive power optimization strategy based on an Environment-Aware Particle Swarm Optimization (EAPSO) algorithm. In this study, “environment-aware” specifically refers to operating-condition awareness, including low-load degree, spatial power dispersion, and output imbalance degree of wind turbines. A three-layer framework consisting of cluster level, wind-farm level and unit level is established to coordinate wind-farm reactive power commands, SVG outputs, transformer tap positions, and turbine-level reactive power allocation. A comprehensive disturbance index is constructed from the low-load degree, spatial power dispersion, and output imbalance degree and is then mapped to the initialization strategy, learning factors, and mutation operation of EAPSO. A 24 h offline simulation is conducted using historical operating profiles of a practical large-scale wind-farm cluster in South China. The results show that the proposed strategy reduces the average absolute PCC voltage deviation from 0.013331 p.u. before optimization to 0.004120 p.u. it achieves an average network-loss reduction rate of 3.55%. Compared with Method 1, the transformer tap operation times decrease from 139 to 92, and the cumulative SVG reactive output decreases from 1077.32 Mvar to 208.39 Mvar.