Power System Reactive Power Optimization Including Wind Farms Based On Improved Differential Evolution Algorithm

Renewable energy has been increasingly embraced due to dwindling fuel reserves,and wind generation is one of the most prospective new energy. With the development of economy and the progress of technology, the new requirements of power quality from social, production and other aspects of life are put forward. Rational distribution of reactive power in system plays an important role which can ensure voltage quality, raise the revel of economic operation and protect the security and stability of the power system. However, the large-scale wind generation connected to grid has adverse effect on the safety of power systems. When a wind farm is incorporated into the power grid, the power systems will change flow and have influence on the system loss and voltage stability to some extent. Therefore, if the reliability of wind farm grid and stable operation is needed, the wind power system flow calculation is necessary.Reactive power optimization for wind power system is a non-linear problem with multi-state variables and multi-constraints. Compared with simulated annealing algorithm, genetic algorithm, particle swarm algorithm of the intelligent algorithms, the principle of differential evolution algorithm is simple, controlled parameter less, it is easy to understand and realization, high efficiency, strong stability in continuous space, implement randomized, parallel, direct the global search ability is strong, obtain approximate solution speed etc, in nonlinear function optimization is widely used. But as with other evolutionary algorithm, basic differential evolution algorithm is prone to the problems of slow convergence, local solution and so on. To solve these problems, this article adopt differential evolution algorithm, lead into the increasing quadratic function crossover operator to increase the convergence speed. When the algorithm run into prematurity, the random perturbation mutation strategy is adopted for the best individual and the random selected individual to help them out of local minimum. Results of a system simulation show that this algorithm can seek the best result of overall situation effectively, increase the convergence speed obviously, also has favorable self-adaptive characteristic.Firstly, this paper introduces the development of wind power and research status of reactive power optimization algorithm at home and abroad, compares the advantages and disadvantages of various optimization algorithms, adopts improved differential evolution algorithm for calculating the reactive power optimization. Secondly, this paper introduces the basic principle of wind power and the function of each component. On the basis of characteristics of wind power, this paper analyzes the influence of wind power generators on distribution network operation. T According to the basic differential evolution algorithm of variation, cross, choice and so on basic operation and fitness function forms and control parameters characteristics.It is put forward that an improved form and algorithm control strategy of differential evolution algorithm. Finally, improved differential evolution algorithm is applied to reactive power optimization with wind power generators. Simulation calculation on IEEE 30-bus system proofs the correctness and effectiveness of the improved differential evolution algorithm.