Reactive Power Optimization Of Power System With Wind Farm Based On Quantum-Particle Swarm Optimization

With the rapid development of the economy,the demand for electric is also increasing gradually,and the problem of the security and stability of power system is becoming more and more prominent.Power quality is the key to ensure the safe and stable operation of the whole power system and voltage quality is an important index to measure the quality of power.Therefore,it gradually becomes the focus of attention of the power workers.At the same time,the factors that affect the voltage quality include reactive power optimization,so it is very important to make a reasonable reactive power planning for the power system.The reasonable optimization of reactive power can not only improve the reactive power distribution of the whole power grid,but also reduce the active power loss of the system to a certain degree.Reactive power optimization of power system is a very complicated nonlinear programming problem.It contains not only multiple variables and constraints,but also the coexistence of discrete variables and continuous variables.For the equality constraints in the constraint conditions,namely the power flow equation.It is a high order equation,which also contains several variables and constraints.If the general mathematical method is used to solve this problem and the whole calculation process is very complex and low efficiency,so we should choose a reasonable method to solve the problem.In the whole process of reactive power optimization,we can adjust the voltage of generator,switching capacitors for reactive power compensation and the optimal capacity of the capacitor and achieve the goal of reactive power optimization.So we can reduce the active of the power system and the probability of the voltage exceeding,making the power system run stably.Through the analysis of the basic Particle Swarm Optimization and Quantum Particle Swarm Optimization and aim at solving the problems of easily falling into the local optimal position and slowly convergence rate at the later time of these two algorithm.In this paper,we proposed an improved quantum particle swarm algorithm which uses the bidirectional searching strategy based on the cross factor.The algorithm introduces the global worst particle position into the update formula of particle position and helps the particle jump out of the local optimum in the latter iterative process.Then this paper selects the loss of active power minimum as the objective function which also contains a penalty function for a model,and then applies this improved algorithm into reactive power optimization of wind farm.We adopt the PQ decomposition method for power flow equation calculation.At last,this paper gives the calculation steps of the reactive power optimization.In order to verify the effectiveness of the proposed algorithm,we adopt the IEEE-57 node standard test system.The improved algorithm was applied to this system.Comparing with the PSO(particle swarm optimization),QPSO(quantum particle swarm optimization),we can draw the conclusion that the improved Quantum Particle Swarm Optimization algorithm reduce the loss of active power effectively.