Application of Key Cutting Algorithms for Optimal Power Flow Problems

Uthen Leeton, Thanatchai Kulworawanichpong

Abstract


This paper illustrates an application of Key Cutting Algorithm (KCA) to optimal power flow (OPF) problems in comparative with some effective mathematical and evolutionary optimization methods. The KCA is one of intelligence algorithms (AI), which was just developed since 2009. This algorithm emulates the work of locksmiths to open the lock. The best key that matches a given lock is pretended to be an optimal solution of a relevant optimization problem. The basic structure of the key cutting algorithm is as simple as that of genetic algorithms in which a string of binary numbers is employed as a key to open the lock. The proposed algorithm was tested with four mathematical test functions and three standard IEEE test power systems (6-bus, 14-bus and 30-bus test systems). The test power systems were divided into two cases. The first test case was given by applying a quadratic function to generators’ fuel-cost curve whereas a non-smooth fuel-cost function was assigned to the second. The comparisons among solutions obtained by sequential quadratic programming (SQP), genetic algorithms (GA), particle swarm optimization (PSO) and key cutting algorithm (KCA) were carried out. As revealed from the simulated results, the effectiveness of the KCA algorithm for solving OPF problems was confirmed.

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