Author: Lai, Wai-man Raymond Title: Application of genetic algorithm in reactive power/voltage control problem Degree: M.Sc. Year: 1998 Subject: Reactive power (Electrical engineering)Genetic algorithmsHong Kong Polytechnic University -- Dissertations Department: Multi-disciplinary Studies Pages: iii, 96 leaves : ill. ; 30 cm Language: English Abstract: Reactive power / voltage control is an very attractive topic for discussion. There were many different methods provided by researchers for solving the problem, such as using expert system, linear programming, non-linear programming and fuzzy linear programming. Reactive power / voltage control problem solved by a genetic algorithm (GA) has been developed and implemented in this project. A genetic algorithm is used as a tool of optimization, which is based on the mechanics of natural selection and natural genetics. A genetic algorithm is a simple optimization algorithm. It is very convenient to implement in any optimization problem. It is also system independent. It can be used for solving linear and non-linear problems. It can find a global or near global solution. So I chose it as an optimization tool to solve the reactive power / voltage control problem. The successful of this approach will be shown and discussed in this dissertation. In a power system, there is a certain value of voltage (V) and reactive power (Q) at each bus. For a generator bus, the value of Q is an dependent variable. Similarly, the value of V for a load bus is also an dependent variable. These variables are depended on the system situation. If these variables are out of their limited range, the system will be violated and the system operator must take an appropriate action as soon as possible in order to alleviate the problem. In this project, we consider a generator voltage control, reactive power compensator and tap change transformer as control variables. After the based case load flow calculation, we can check the limitation of each bus. If one of the bus is violated, we can use GA to alleviate it. During the optimization, we try to minimize the system real power loss and minimize the operation. At the same time the system constraints must be satisfied. We can also assign a penalty factor to each control variable. The large value of penalty factor for the control variable, the less change for that control variable is preferred and vice versa. It can let a system operator to set the priority for each system control variable.

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