This paper proposes measures to improve the protection of MV distribution networks operating with feeders in a closed-loop arrangement. Bi-directional overcurrent relays (OCRs) are discussed, the selectivity of which is achieved through the timing coordination of their operation. The classic approach is formulated as a minimization of the operating times of all the OCRs. The proposed approach enhances the selectivity by considering the maximum operating time of substation OCRs and the unwanted trips of in-loop OCRs. Moreover, the sensitivity is also increased by introducing an objective function that minimizes the pickup-current settings of all the OCRs together with their operating times. Furthermore, to fulfill the demanding requirements for operating times, variable penalties are introduced. Thus, the optimization procedure is forced towards the region with viable solutions for the optimization problem. Two variants of self-adaptive differential evolution have been used that both show better convergence when compared to the classic differential evolution. Moreover, ten mutation strategies were tested, where 'rand/1/bin' showed the best results. A comparison with other methods for timing coordination shows that the proposed optimization results in a comparable value for the OCRs' operating times. In order to further reduce the operating times, GOOSE communications between the OCRs are adopted. The proposed measures for improved protection operation are fully confirmed through dynamic simulations of the faults in the discussed 20-kV network. Moreover, the proposed protection design is already implemented and permanently operates in a 20-kV network with more than 5000 customers, whereas the field results show selective and reliable protection operation.
CITATION STYLE
Polajžer, B., Pintarič, M., Rošer, M., & Štumberger, G. (2019). Protection of MV Closed-Loop Distribution Networks with Bi-Directional Overcurrent Relays and GOOSE Communications. IEEE Access, 7, 165884–165896. https://doi.org/10.1109/ACCESS.2019.2952934
Mendeley helps you to discover research relevant for your work.