A BPSO-Based Method for Optimal Voltage Sag Monitor Placement Considering Uncertainties of Transition Resistance

• Main Authors: Haiwei Jiang, Yonghai Xu, Ziteng Liu, Ning Ma, Wenqing Lu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Binary particle swarm optimization; conditional probability; observability indices; optimal monitor placement; random vector model; transition resistance
• Online Access: https://ieeexplore.ieee.org/document/9079530/

This study presents a new approach to the optimal placement of voltage sag monitors considering the uncertainties associated with transition resistance. The influence of transition resistance on the magnitude of voltage sags triggered by symmetrical and unsymmetrical faults is analyzed. Then the transition resistance interval set array for voltage sags is established, on the basis of which, a random vector model on voltage sag observability is proposed and related observability indices are defined in the form of conditional probability. The optimal placement model is established by taking the available number of monitors as the constraint condition and the maximum sag global observability index as the objective function. Binary particle swarm optimization (BPSO) is implemented to obtain the optimal placement results. Finally, simulation is carried out on IEEE 30-bus system, and it is shown that the proposed optimal monitor placement method is more applicable compared with the traditional MRA method.