Collaborative control technology for restoring power resources caused by human operation faults in distribution network

To address the issues of low recovery efficiency and slow restoration speed in traditional self-healing control strategies when dealing with human operational errors, this paper proposes a self-healing control method for distribution networks based on an improved particle swarm optimization algorithm. A mathematical model is established for multi-objective collaborative optimization based on load recovery rate, switch action frequency, and power loss coefficient. Based on an improved particle swarm optimization algorithm, dynamic weight adjustment and global optimization are carried out to achieve a comprehensive balance between power supply capacity, operational economy, and operational feasibility. This method, through a two-stage coupling mechanism, not only ensures the rapid restoration of power demand for important loads, but also achieves overall optimization of the recovery benefits of the entire network. Considering the stochastic output of distributed generation, energy storage system constraints, and network topology requirements, a multi-objective optimization model is developed with objectives of maximizing load recovery rate while minimizing switch operations and power losses. The improved particle swarm optimization algorithm effectively avoids local optima through dynamic adjustment of particle concentration and velocity update strategies.