The Study Of Automatic Voltage Control In Large-scale Hydro Power Station Based On Chaos Particle Swarm Optimization

As one of the three indicators of electricity quality, which reflects the balance ofreactive power and reactive power demand, the stability of voltage has a direct impact onthe security and stability of the power system. To ensure the stability of the voltage,automatic voltage control is used to ensure that the central point voltage of the grid ismaintained at the given level. As one of the important voltage contral point andmonitoring point, which has a large installed capacity and takes a lot of load, the quality ofthe bus voltage of large-scale hydropower station determines whether or not the voltage ofpowered regional meets the requirements. Therefore, the automatic voltage control oflarge-scale hydropower station becomes important technical support for safe and stableoperation of the power system. In addition, the hydrogenerator has a flexible phasemodulation function, which can not only absorb excessive reactive power of the gird, butalso supply reactive power to the grid when it is inadequate. If the flexible phasemodulation function of hydrogenerator is maken full use of, it can greatly reduce theinvestment of reactive compensation equipment for the hydropower station. Therefore, thevoltage control based on hydrogenerator becomes the first choice of the voltage controlmethods for the power grid.The automatic voltage control method of large-scale hydropower station in the earlytime is “tentative adjustment”, which fails to establish effective linkages between the busvoltage and reactive power. Therefore, the voltage adjustment process is slow and requiresrepeated adjustment, which leads to low efficiency. Modern automatic voltage controlmethod establishs effective linkages between the bus voltage and reactive power by using“sensitive method”, which can acquire the total reactive power needed to eliminate the busvoltage bias. However, in terms of the dispatch of reactive power,“Average distributionmethod” and “average capacity proportion” are used, which don’t take the power loss oflarge-scale power station into account.“Average net loss by rate method” takes thepower loss of large-scale power station into account, however, for the hydrogeneratorswith different characterastics, this method can’t acquire the optimal value of the activepower loss with the problem of cross-border of the unit reactive power, which is not goodfor the secure and stable operation of the hydrogenerators.To solve the shorcomings of the automatic voltage control methods of large-scalehydropower station mentioned above and taking actual project needs into consideration, intelligent optimization methods(PSO and CPSO) are introduced to solve the automaticvoltage control module of large-scale hydropower station, with the reactive power balanceconstraint、 safe and stable operation restrictions of the hydrogenerator and otherrestrictions taken into consideration. The simulating results show that these methods candevelop the plan for optimal reactive power dispatching quickly and accurately, andreduce the transformer-loss to the most extent. The main works of this paper are asfollowing:（1）This paper has discussed the basic principles of particle swarm optimizationalgorithm and the methods to improve its performance. Of all the methods to improve itsperformance, Chaos particle swarm optimization is taken for example to discuss its basicprinciples. For several chaotic map models, the distribution characteristics of chaoticsequence in the chaotic mapping interval are studied. The performance of these chatic mapmodels are evaluated based on these characteristics. The chaos particle swarmoptimization based on logistic map and tent map are used to solve the automatic voltagecontrol model of large-scale hydropower station.（2）PSO、CPSO based on logistic map and tent map are used to solve the automaticvoltage control model of large-scale hydropower station based on the analysis of differentalgorithms, with all kinds of constraints taken into consideration. The results of differentalgorihms are compared and analyzed. The convergence characteristics are studied and thepreliminary conclusions of the performance of the three algorithms are given.（3）The research achivements are applied to the the “automatic voltage module” ofthe “Three Gorges Cascade Hydropower Joint optimal scheduling and decision supportingsystem” based on the study above. The function、operational processe of the “automaticvoltage control module” and simulating results are displayed and discussed in detail.