A Genetic Algorithm method for Standing Phase Angle reduction in power system restoration

Abstract

This paper presented a new approach to address the frequently encountered Standing Phase Angle (SPA) reduction problem during a power system restoration process. The proposed method was based on a modified Genetic Algorithm (GA). The problem of the SPA reduction was modeled as a mixed discrete-continuous optimization problem with various constraints. The objective function that needed to be minimized was the weighted sum of active power generation adjustments and load sheddings. Most inequality constraints were derived from the conditions under which the security of the power system operation was guaranteed. The acceptable limits of the SPA were also converted into additional constraints and integrated into the optimization problem. The modified GA was then used to solve the problem. In solving the problem, by incorporating the objective function and constraints, we applied a partial order relation defined by a better function to manage the constraints. Compared with a traditional penalty function method, this approach avoided the difficulties of setting coefficients for the penalty function. Simulations were carried out on the IEEE 118-bus system and the results proved the effectiveness of the proposed technique.Index Terms-genetic algorithm, mixed discrete-continuous optimization, power system restoration, and Standing Phase Angle. and the Ph.D. degree in computational analysis and modeling from Louisiana Tech University, Ruston, LA, in 1989, 1992, and 2002 He currently is Assistant Research Professor at Indiana University-Purdue University Indianapolis. His research interests include distributed generation, microgrid, pattern recognition, power system protections, mathematical models and computational techniques, and biometrics.

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