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Multi-objective Reactive Power Optimization Of Hybrid AC-DC Power System Based On Quantum-behaved Particle Swarm Algorithm

Posted on:2013-11-29Degree:MasterType:Thesis
Country:ChinaCandidate:M Y YinFull Text:PDF
GTID:2232330374982088Subject:Power system and its automation
Abstract/Summary:PDF Full Text Request
As a sub-problem of optimal power flow, reactive power optimization is an important aspect of the security and economic operation of power system, and plays an important role in improving the voltage quality, ensuring safety system, reducing the operation cost and making the operation of system reliable and economical. With the development of our country’s power system, the interconnected regional power grids have become a hot topic in the planning of development. Because DC power system has many merits, the hybrid AC-DC power system has become an inevitable trend. The reactive power optimization proposed as a new model should cause our attention.Based on target function of minimum loss value and the best voltage quality, this paper establishes the mathematical model of reactive power optimization of hybrid AC-DC power system, and schedules the DC voltage, the DC current, the transmission power, the control angel of inverter of DC part and variables of AC part, so that the reactive power flow has a reasonable distribution to achieve optimal objective function.This paper summarizes some typical algorithms of reactive power optimization, and makes a detailed narration of the basic principle and realization of standard particle swarm optimization algorithm and standard quantum-behaved particle swarm optimization algorithm. Aiming at overcoming the shortcoming of being easy to get local optimums in QPSO algorithm, this paper puts forward an improved QPSO, which is based on QPSO, and considers particle’s neighborhood optimums, thus avoiding the algorithm easily falling into local optimums. Besides, in the earlier period of calculation, the new algorithm uses QPSO algorithm to look for optimums to improve convergence speed. The test functions validate the performance of the new algorithm, and the results showed that the new algorithm is better than QPSO and PSO in the ability of searching optimums and the stability of the convergence.This paper uses improved QPSO to solve the problem of reactive power optimization of hybrid AC-DC power system. In order to make the algorithm can get a better solution, we see discrete variables as continuous variables, and use interior point method to quickly get a solution, on the basis of which we obtain the improved initial solutions. Examples mainly include IEEE standard examples and Shandong power grid. IEEE30example mainly discusses the application effect of the new algorithm, and the result shows that the improved QPSO algorithm has stronger ability to get the optimum solution and better convergent stability. Moreover, the new algorithm reduces the grid loss and improves the voltage quality effectively. IEEE14example mainly discusses the interrelation between grid loss and voltage quality in multiple objective function. In the actual system, according to the different needs of power grid we can select different weights, and thus gain the optimization results required. In the example of Shandong power grid, this paper optimizes the reactive power on the basis of a simplified model. Due to the optimization, the voltage of the nodes whose voltage is not qualified can be up to standard, the loss can be reduced and the quality of the voltage can be improved.In essence, reactive power optimization is a mathematical optimization problem, which makes power grid as a model, and optimizes the relevant control variables to achieve optimal performance of some functions. Reactive power optimization is a complex nonlinear optimization problem with a large number of variables and constraints. With the continuous expansion and complexity of modern power system and the continuous development of HVDC technology, higher request of the solution of reactive power optimization, which should be paid attention to, will be made.
Keywords/Search Tags:reactive power optimization, hybrid AC-DC power system, multi-objective, improved quantum-behaved particle swarm optimizationalgorithm
PDF Full Text Request
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