Open AccessA hybrid method for recloser and sectionalizer placement in distribution networks considering protection coordination, fault type and equipment malfunction
Author(s) -
Safari Majid,
Haghifam MahmoudReza,
Zangiabadi Mansoureh
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12164
Subject(s) - recloser , reliability engineering , robustness (evolution) , linear programming , computer science , matlab , reliability (semiconductor) , genetic algorithm , engineering , mathematical optimization , circuit breaker , algorithm , electrical engineering , mathematics , biochemistry , chemistry , power (physics) , physics , quantum mechanics , machine learning , gene , operating system
An effective way to improve distribution system reliability is to place switches and protective equipment in the optimal location. Commonly, in the placement problem, the use of equipment in the designated location is assumed to be possible. But in practice, to establish protection coordination between the equipment, it is necessary to remove or relocate some of the equipment. This paper aims to increase distribution companies' profits and reduce customer interruption costs through a feasible solution. A new hybrid method for equipment placement has been proposed that simultaneously solves the protection coordination problem. While determining the optimal number and location of reclosers and sectionalizers, the proposed method ensures protection coordination between equipment (new and existing devices). Furthermore, to achieve a more realistic and accurate model, factors such as equipment malfunction, fault types (transient and permanent), and the relationship between these faults have been carefully considered to formulate the proposed method. In this method, to solve the optimization problems related to equipment placement and protection coordination, genetic algorithm, and linear programming in MATLAB software have been used, respectively. A real‐life distribution network has been utilized to evaluate the proposed method, and the results show the capability and robustness of this method.