Multi-objective adaptive clonal selection algorithm for solving optimal power flow considering multi-type FACTS devices and load uncertainty

• We propose new multi-objective methods using recently developed evolutionary algorithms.
• The developed new methods are implemented to solve single multi-objective OPF problem.
• A maiden attempt has been made to include load uncertainty in solving OPF problem.
• Multi type VSC based FACTS devices are incorporated in multi-objective optimisation.
• It is found that the MOACSA performs better than other methods reported in the literature.

This paper presents a solution to multi-objective optimal power flow (MOOPF) problem using an adaptive clonal selection algorithm (ACSA) to minimise generation cost, transmission loss and voltage stability index (L-index) in the presence of multi-type FACTS devices in power systems. The proposed approach utilizes clonal selection principle and evolutionary concept which performs cloning of antibodies followed by hyper maturation. In this algorithm, a non-dominated sorting and crowding distance have been used to find and manage Pareto optimal front. Various voltage source converter (VSC) based multi-type FACTS devices such as UPFC, IPFC and GUPFC are considered and incorporated as power injection models in multi-objective optimisation problem formulation. The proposed multi-objective adaptive clonal selection algorithm (MOACSA) has been tested on standard IEEE 30-bus test system with FACTS devices. The results obtained from the proposed MOACSA approach are compared with implementation of standard algorithms namely NSGA-II, MOPSO and MODE.

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