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Reactive Power Optimization Based on Improved Quantum-Inspired Genetic Algorithm

Author: LiuHongWen
Tutor: ZhangGeXiang
School: Southwest Jiaotong University
Course: Proceedings of the
Keywords: Power system Reactive Power Optimization Quantum Genetic Algorithm Memetic Algorithm Improved quantum genetic algorithm
CLC: TM714
Type: Master's thesis
Year: 2009
Downloads: 367
Quote: 2
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Abstract


Power system reactive power optimization is both an effective means to guarantee power system security and economic operation, but also an important measure to reduce network losses and improve voltage quality. With the development of the power system, in particular, strengthen networking, reactive power optimization is becoming increasingly important. In essence, the reactive power optimization is a multi-variable, multi-constraint, mixed nonlinear combinatorial optimization scheduling problem, there are a large number of local minima, the optimization process is extremely complex. Reactive power optimization control system, reactive power optimization algorithm plays an important role in optimizing speed and quality, Therefore, reactive power optimization algorithm to get a better optimize performance, to reduce the grid active power loss and the purpose of improving the voltage quality. The main work and results are as follows: 1. Introduce reactive power optimization of the present situation and development of research in the field, the establishment of minimum active power loss as the objective function of reactive power optimization mathematical model. 2 introduces the basic genetic algorithm and its reactive power optimization applications, elaborate quantum genetic algorithm and its reactive power optimization process, and the IEEE-30 node system reactive power optimization using quantum genetic algorithm and genetic algorithm, experimental results show that quantum genetic algorithm lower active power loss than the genetic algorithm. (3) In order to solve the quantum genetic algorithm in solving the reactive power optimization easy to fall into local minima problems, proposed the reactive optimization method based on improved disaster quantum genetic algorithm (ICQGA), IEEE-6 and IEEE-30 node system Reactive Power Optimization example simulation results show that in the quantum genetic algorithm introduced population disaster strategy helped the quantum genetic algorithm jump out of the local minimum active power loss lower than the quantum genetic algorithm. Quantum genetic algorithm local search optimization ability problem of poor, asked the local search Quantum Genetic Algorithm (LSQGA) with two layers of quantum genetic algorithm optimization the outer quantum genetic algorithm for global optimization, global optimization search to The best solution after several iterations does not change, the small optimization interval in the vicinity of this solution, using the inner quantum genetic algorithm and local optimization. Complex function optimization and IEEE-30 node system reactive power optimization simulation results show that LSQGA the QGA and ICQGA optimization capability, convergence speed than. 5. In LSQGA based on the study proposed quantum genetic algorithm based on real observation Memetic Algorithm (Marq). MArQ is a hybrid algorithm that is added in real-observation quantum genetic algorithm local search operation, real-observation quantum genetic algorithm to search the entire optimization interval and the tabu search algorithm as a local search operation in embedded real-observation quantum genetic algorithm, which makes the algorithm a reasonable balance between global optimization and local optimization. Tabu search local search, each iteration only change in a decision-making component in the search for the best solution to generate candidate solutions in a small neighborhood, and the neighborhood radius decreases with the increase in the algebra of tabu search, In order to enhance the diversity of the initial population, the chaos initialization. The feasibility of high-dimensional continuous function optimization and IEEE-30 node system reactive power optimization simulation validation algorithm, experimental results show that the active power loss in Marq than ICQGA and LSQGA provide new ideas for solving the reactive power optimization . This work was supported by the National Natural Science Foundation of China (60702026) and the Ministry of Education Traffic Engineering Research Center Open Fund (2008), co-financed.

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Theory and Analysis > Load analysis
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