Genetic Algorithm Based Fuzzy Multi-Objective Approach to FACTS Devices Allocation in FARS Regional Electric Network

In this investigation, a novel approach is presented to find the optimum locations and capacityof flexible ac transmission systems (facts) devices in a power system using a fuzzymulti-objective function. maximising the fuzzy satisfaction allows the optimization algorithmto simultaneously consider the multiple objectives of the network to obtain active power lossreduction; i.e., new facts devices cost reduction, robustifying the security margin againstvoltage collapse, network loadability enhancement and a voltage deviation reduction of thepower system. a genetic algorithm (ga) optimization technique is then implemented tosolve the fuzzy multi-objective problem. operational and control constraints, as well as loadconstraints. are considered for optimum device allocation. also, an estimated annual load profilehas been utilized in a sequential quadratic programming (sqp) optimization sub-problem tofind the optimum location and capacity of facts devices, accurately. a thyristor controlledseries compensator (tcsc) and a static var compensator (svc) are utilized as series andshunt facts devices in this study. the fars regional electric network is selected as a practicalsystem to validate the performance and effectiveness of the proposed method.