Application of Tabu-Genetic Algorithm in Reactive Power Optimization

2014 ◽  
Vol 1006-1007 ◽  
pp. 1021-1025
Author(s):  
Song Tao Zhang ◽  
Gong Bao Wang ◽  
Hui Bo Wang
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Local Search ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Reactive Power Optimization ◽  
Simple Genetic Algorithm ◽  
Better Than

By using tabu search algorithm which has strong local search ability as mutation operator of genetic algorithm, the tabu-genetic algorithm is designed for reactive power optimization in this paper, the strong global search ability of genetic algorithm and strong local search ability of tabu search algorithm is combined, the disadvantage of weak local search ability of genetic algorithm is conquered. Otherwise, the over limit of population is recorded and filtered, to ensure the final individual is under limit and effective. The tabu-genetic algorithm and simple genetic algorithm are used for simulation of IEEE 14-bus system 500 times, the results indicate that the performance of the tabu-genetic algorithm is much better than the simple genetic algorithm, its local search ability is improved obviously, and the active power loss is reduced more.

scholarly journals Ant colony search algorithm for optimal reactive power optimization

2006 ◽  
Vol 3 (1) ◽  
pp. 77-88 ◽  
Author(s):  
K. Lenin ◽  
M.R. Mohan
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Ant Colony ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Ant Colonies

The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents? approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical) test bus system. The proposed approach is tested and compared to genetic algorithm (GA), Adaptive Genetic Algorithm (AGA).

scholarly journals An Enhanced Quantum-Behaved Particle Swarm Algorithm for Reactive Power Optimization considering Distributed Generation Penetration

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Runhai Jiao ◽  
Bo Li ◽  
Yuancheng Li ◽  
Lingzhi Zhu
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Active Power ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Distributed Generations ◽  
Better Than

This paper puts forward a novel particle swarm optimization algorithm with quantum behavior (QPSO) to solve reactive power optimization in power system with distributed generation. Moreover, differential evolution (DE) operators are applied to enhance the algorithm (DQPSO). This paper focuses on the minimization of active power loss, respectively, and uses QPSO and DQPSO to determine terminal voltage of generators, and ratio of transformers, switching group number of capacitors to achieve optimal reactive power flow. The proposed algorithms are validated through three IEEE standard examples. Comparing the results obtained from QPSO and DQPSO with those obtained from PSO, we find that our algorithms are more likely to get the global optimal solution and have a better convergence. What is more, DQPSO is better than QPSO. Furthermore, with the integration of distributed generation, active power loss has decreased significantly. Specifically, PV distributed generations can suppress voltage fluctuation better than PQ distributed generations.

Reactive Power Compensation of the Distribution Power System with Distributed Generation Using Improved Tabu Search Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 2503-2508
Author(s):  
Xiang Lei ◽  
Yan Li ◽  
Shao Rong Wang ◽  
Hong Zhao ◽  
Fen Zhou ◽  
...  
Keyword(s):  
Tabu Search ◽  
Distributed Generation ◽  
Power Loss ◽  
Cost Allocation ◽  
Reactive Power ◽  
Search Algorithm ◽  
Test System ◽  
Local Optimum ◽  
Tabu Search Algorithm ◽  
Optimal Position

Taking account of the mutual impacts of distributed generation and reactive power, to determine the optimal position and capacity of the compensation device to be installed, the paper proposed an improved Tabu search algorithm for reactive power optimization. The voltage quality is considered of the model using minimum network active power loss as objective Function. It is achieved by maintaining the whole system power loss as minimum thereby reducing cost allocation. On the basis of general Tabu search algorithm, the algorithm used memory guidance search strategy to focus on searching for a local optimum value, avoid a global search blindness. To deal with the neighborhood solution set properly and save algorithm storage space , some corresponding improvements are made, thus, it is easily to stop the iteration of partial optimization and it is more probable to achieve the global optimization by use of the improved algorithm. Simulations are carried out on standard IEEE 33 test system and results are presented.

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