scholarly journals The Approach of Genetic Algorithms Application on Reactive Power Optimization of Electric Power Systems

2018 ◽  
Author(s):  
RuiJin Zhu
Keyword(s):  
Genetic Algorithms ◽  
Power Systems ◽  
Electric Power ◽  
Reactive Power ◽  
Power Optimization ◽  
Electric Power Systems ◽  
Reactive Power Optimization

Research on Reactive Power Optimization Based on Genetic Algorithms in Distribution Network

2014 ◽  
Vol 644-650 ◽  
pp. 2476-2478
Author(s):  
Lin Yuan Wang
Keyword(s):  
Genetic Algorithms ◽  
Mutation Rate ◽  
Exponential Function ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Adaptive Mutation ◽  
Initial Population ◽  
Reactive Power Optimization ◽  
Slow Convergence

The reactive power optimization is formulated based on genetic algorithms in distribution net work. SGA has defects of slow convergence and being prone to immature convergence. In order to eliminate the defects, an improved GA is proposed in this thesis. CIP scheme is presented, which can guarantee diversity of the population by designing the initial population to obtain all the values within the definition area. A parameter called individual distributing degree is defined to describe how individuals are distributed in the definition area. Adaptive mutation rate is defined as an exponential function of the retained generations of the Elitism, and it is in inverse proportion to individual distribution degree. It accelerates the convergent process.

scholarly journals Opposition-Based Improved PSO for Optimal Reactive Power Dispatch and Voltage Control

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shengrang Cao ◽  
Xiaoqun Ding ◽  
Qingyan Wang ◽  
Bingyan Chen
Keyword(s):  
Power Systems ◽  
Life Cycle Cost ◽  
Reactive Power ◽  
Power Optimization ◽  
Optimization Problems ◽  
Cost Model ◽  
Population Diversity ◽  
Reactive Power Optimization ◽  
Switching Operation ◽  
Global And Local

An opposition-based improved particle swarm optimization algorithm (OIPSO) is presented for solving multiobjective reactive power optimization problem. OIPSO uses the opposition learning to improve search efficiency, adopts inertia weight factors to balance global and local exploration, and takes crossover and mutation and neighborhood model strategy to enhance population diversity. Then, a new multiobjective model is built, which includes system network loss, voltage dissatisfaction, and switching operation. Based on the market cost prices, objective functions are converted to least-cost model. In modeling process, switching operation cost is described according to the life cycle cost of transformer, and voltage dissatisfaction penalty is developed considering different voltage quality requirements of customers. The experiment is done on the new mathematical model. Through the simulation of IEEE 30-, 118-bus power systems, the results prove that OIPSO is more efficient to solve reactive power optimization problems and the model is more accurate to reflect the real power system operation.

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