A novel fault location method for distribution networks with distributed generators based on improved seagull optimization algorithm

With the increasing penetration of distributed generators (DGs), the fault current of the connected distribution network (DN) becomes complex and variable. The overcurrent signals at the same location may be decided by the connected grid or solely by the DGs. As a result, the fault location methods based on intelligent optimization algorithms have low accuracy and poor fault tolerance, especially when the overcurrent signals are determined only by the DGs under simultaneous multi-area faults. Aimed at the aforementioned problems, a novel fault location method based on an improved seagull optimization algorithm is proposed for the distribution grid integrated with the DGs. The expression of the switch status function for DNs is firstly improved considering the impact of DGs on the overcurrent signals. An elite reverse learning strategy is introduced for the seagull optimization algorithm to the diversity of the initial seagull population. Both the Levy flight control and random walk strategies are used to increase the randomness of the optimization algorithm. It is good for avoiding the emergence of locally optimal results due to the variable overcurrent status of the feeder terminal units (FTUs). Finally, the proposed fault location method was validated using a simulation model of an active DN with photovoltaic DGs based on the IEEE 33 nodes. Based on the simulation results, it is verified that the proposed fault location method can identify single-point or multi-point faults in the case of distorted overcurrent signals. The proposed method is superior to the existing one in both high accuracy and high fault tolerance.