MILP approach for optimal coordination of directional overcurrent relays in interconnected power systems

• Published in: Electric power systems research 2018-05, Vol.158, p.267-274
• Main Authors: Damchi, Yaser, Dolatabadi, Mohammad, Mashhadi, Habib Rajabi, Sadeh, Javad
• Format: Article
• Language: English
• Subjects: Convexity; Directional overcurrent relay; Electric currents; Electric power; Heuristic; Heuristic methods; Integer programming; Linear programming; Mixed integer; Mixed integer linear programming; Non-linear and non-convex optimization; Nonlinear programming; Optimization; Overcurrent; Relay coordination; Variables
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2018.01.015

•A MILP is proposed for nonlinear DOCRs coordination problem formulation.•The proposed formulation is easier to be solved by branch and bound.•Pickup current and time multiplier settings of DOCRs are as optimization variables.•Proposed method is compared with some previous heuristics and NLP approaches.•Proposed method is guaranteed to converge to global optimal settings. The coordination problem of directional overcurrent relays (DOCRs) is a non-linear and non-convex optimization one. Until now, several methods based on the heuristics and non-linear programming (NLP) approaches have been proposed for solving the problem. Drawback of these methods is that they are likely to be trapped in local minima. In order to overcome the drawback, in this paper, the problem is formulated as a mixed integer linear programming (MILP) while pickup current setting (Iset) and time multiplier setting (TMS) of relays are considered as optimization variables. This formulation is easier to be solved by branch and bound (B&B) approach, because at each branch there is a linear and convex sub-problem. Furthermore, for the first time, the proposed method is guaranteed to converge to global optimal settings. The proposed method is evaluated using a 3-bus, an 8-bus, the IEEE 14-bus and the modified IEEE 30-bus test systems. The results are compared with some previous heuristics and NLP approaches. Based on the obtained results, it can be seen that, better optimal settings for DOCRs are obtained by using the proposed method in comparison with previous methods.