Research on Reactive Power Optimization Based on Immunity Genetic Algorithm
ABSTRACT This paper proposed a new kind of immune genetic algorithm (IGA) according to the current algorithms solving the reactive
power optimization. The hybrid algorithm is applied in reactive power optimization of power system. Adaptive crossover and
adaptive mutation are used according to the fitness of individual. The substitution of individuals is implemented and the
multiform of the population is kept to avoid falling into local optimum. The decimal integer encoding and reserving the elitist
are used to improve the accuracy and computation speed. The flow chart of improved algorithm is presented and the parameter
of the immune genetic algorithm is provided. The procedures of IGA algorithm are designed. A standard test system of IEEE
30-bus has been used to test. The results show that the improved algorithm in the paper is more feasible and effective than
current known algorithms.
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ABSTRACT: At present, the algorithm used in optimization calculation of the system static voltage stability margin can't meet the real-time requirements very well. For this reason, this paper presents a new method of static voltage stability margin calculation based on immune/Tabu search hybrid algorithm. This method combines improved immune algorithm and Tabu search algorithm. Through improved continuation power flow, the maximum static voltage stability margin of system can be quickly and accurately achieved by the hybrid algorithm. To a certain extent, the method makes up for efficiency deficiencies of traditional optimization algorithm. Using the new method several simulation for IEEE 30-bus system are conducted. Compared with improved genetic algorithm, metric Tabu search algorithm, genetic/Tabu search hybrid algorithm, and immune algorithm, the result shows that the method is feasible and effective.
Conference Paper: Immune Agent-Based Neural Networks Soft-Sensor and its Application[Show abstract] [Hide abstract]
ABSTRACT: Aiming at difficulty modeling of large amounts of industrial process data, a novel soft-sensor based on artificial immune multiagent and multiple model Radial Basis Function(RBF) networks is proposed. The method is to predict the qualities of manufactred products from process variables. Where, artificial immune T-cell and B-cell agents with different tasks of clustering work cooperatively to accomplish the common goal of soft-sensor model training. Immune memory and pattern recognition provide high efficiency of predicting. Multiple model technique is introduced to improve the computation and performance of soft-sensor. The prediction of dry point of naphtha produced in a practical industrial process is carried out as a case study. Results obtained indicate that proposed method provides oil quality prediction with high efficiency and accuracy which is capable of learning the relationship between process variables measured during the production.Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on; 01/2009