Application Of Immune Genetic Algorithm Based On Vector Distance In Voltage Optimization And Control

The security and stability of the power system is confronted with new challenges, as the structure of modern power network complicated, the power load sharply increased, the higher requirements to the power quality by the users and the power system. Voltage quality is an important indicator of the power quality. So, improving the quality of voltage of the power system by voltage optimization adjusting and management not only is the basic requirements of security and stability of the power system and production safety of users, but also has economic significance on reducing the power loss of the distribution system, increasing the utilization of the equipments and increasing the quality of the product.The classical optimization algorithms and intelligent optimization algorithms all have issue that has slow convergence, easy to fall into local optimum and require too much memory, when used to solve voltage optimization and control problem, with the optimization decision variables and constraints increase., The mathematical model taking the minimize of the new reactive compensation as the objective function and the out-of-limit of node voltage and generator reactive power output as penalty function is established in this paper in order to improve the quality of voltage and the reduce the power loss.A new immune genetic algorithm based on the vector distance density for power system voltage optimization is proposed to avoid complex computing, slow convergence, and the constant factor set by the experience and so on, which may appear in the genetic algorithm based on information entropy or euclidean distance, by using the fitness function to define the antibody distance, using the vector distance to calculate the antibody concentration, and improving the concentration control method. It is used to solve the problem of low voltage control. The selection probability and expectation reproduction rate based on the antibody concentration is calculated to be the basis of the mutation rates of adaptive adjustment and the operation of cloning. Constructing the immune memory unit, saving the current outstanding individual can ensure the convergence of the algorithm, avoid the repeated flow calculation of a few individuals, and improve the formation rate of the global optimal solution.The proposed method is applied to IEEE30 buses system, which shows that this algorithm has the ability to find the global optimal solution. The proposed method can effectively improve the voltage quality and voltage qualified rate, and also reduce the network loss when used to the low voltage control of a real power grid. The probability of convergence to the global optimal solution in the 30 times optimization calculation gotten by the proposed method is higher, the evolution algebra of convergence to the global optimal solution is lesser, and the minimum convergence time and average convergence time is shorter compared with the genetic algorithm based on information entropy or euclidean distance. It shows that the proposed algorithm is effective and feasible.