Adaptive OCR coordination in distribution system with distributed energy resources contribution

More and more distributed energy resources (DERs) are being added to the medium-voltage (MV) or low-voltage (LV) radial distribution networks (RDNs). These distributed power sources will cause the redistribution of power flow and fault current, bringing new challenges to the coordination of power sy...
Ausführliche Beschreibung

More and more distributed energy resources (DERs) are being added to the medium-voltage (MV) or low-voltage (LV) radial distribution networks (RDNs). These distributed power sources will cause the redistribution of power flow and fault current, bringing new challenges to the coordination of power system protection. An adaptive protection coordination strategy is proposed in this paper. It will trace the connectivity of the system structure to determine the set of relay numbers as a tracking path. According to the topology of the system structure, the tracking path can be divided into two categories: the main feeder path and the branch path. The time multiplier setting (TMS) of each relay can be used to evaluate the operation time of the over-current relay (OCR), and the operation time of the relay can be used to evaluate the fitness of the TMS setting combination. Furthermore, the relay protection coordination problem can be modeled to minimize the accumulated summation of all primary and backup relay operation time (OT) subject to the coordination time interval (CTI) limitation. A modified particle swarm optimization (MPSO) algorithm with adaptive self-cognition and society operation scheme (ASSOS) was proposed and utilized to determine TMS for each relay on the tracking path. A 16-bus test MV system with distributed generators (DGs) will be applied to test the adaptive protection coordination approach proposed in this paper. The results show that the proposed MPSO algorithm reduces the overall OT and relieves the impact on protection coordination settings after DG joins the system. The paper also tests and compares the proposed MPSO with other metaheuristic intelligence-based random search algorithms to prove that MPSO possesses with increased efficiency and performance. Ausführliche Beschreibung