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Research On Reactive Power Optimization Control In District Power System

Posted on:2004-09-19Degree:DoctorType:Dissertation
Country:ChinaCandidate:G F GengFull Text:PDF
GTID:1102360182961516Subject:Agricultural Electrification and Automation
Abstract/Summary:PDF Full Text Request
With the development of economy and power system automation, power system requires higher stability, economy and quality. Local and static reactive power optimization cannot meet the practical needs. It is an impendence need and an inevitable trend for power system currently to realize dynamic reactive power optimization in district power grid. In this paper, new resolutions have been put forward from several aspects, such as optimization method, load forecasting and dynamic optimization, and satisfied results are obtained. On the basis of utilizing the gradient concept in classical optimization method, the concept of evolution-direction is introduced into this field for the first time. An evolution-direction based mutation GA is put forward. This algorithm with short code and high precision overcomes the main shortcoming of slow convergence speed in GA. A grouped, integer and real number mixed coding method is applied in this algorithm. The mapped-method is adopted to calculate the fitness function. Compared with the other methods, mapped-method, which is very simple and easy, has no negative and provides the group a constant elective pressure. The optimal individual in every generation is more possible closer to global optimization than other individual. Based on this idea, the optimal individual in every generation is carried out particular mutation, thus the search efficiency is improved greatly. The global convergence character of Box algorithm is not better than GA. But it's simple and practical for Box algorithm to find optimum direction by comparing several points. It's worthy and convenience for GA to use the method. The Box algorithm is used to calculate evolution-direction of GA. The algorithm that combined GA and Box algorithm has the advantages of both GA and Box algorithm, and increased the convergence speed. Box algorithm is improved in accordance with two features of discrete variables in reactive power optimization and concave in feasible region. The center point and mapped point are judged whether they are feasible or not. The mapped point is turned into integer and its adjacent region is searched for better point. Such improvements make box algorithm more fit for the requirement of reactive power optimization. It should have precise bus load forecasting if we want to obtain dynamic reactive global optimum result in time axis. The weights of ANN are self-adapted according to the change of recent load so as to get higher forecasting precision and lower calculation quantity. It should do correlation analysis first and eliminate the input variable with small correlation coefficient. When the forecasting load is departure from the real load, it's necessary to utilize the known information of the past load to reforecast the future load in the remaining time of the day. Combination methods are utilized to reforecast the future load of the day so as to improve the real time reactive power optimization control. Dynamic reactive power optimization model is established in the paper. It is studied how to deal with the restriction of dynamic reactive power optimization. A new approach utilizing genetic algorithm to auto-separate load curve in segments is offered. This method lays a foundation for realization of dynamic reactive power optimization. Calculating quantity is reduced a lot by appropriate separations of load curve giving consideration to both power loss and action times of controlling machine. The difference between optimization control method in this paper and other methods is that the optimization results do not control the equipment directly. It is studied how to build appropriate VQ limits according to the calculation of reactive power optimization. When deciding VQ limits of VQC, the forecasting load of every period of time is defined as a fuzzy number, which turned exact by a certain believe level to calculate the range of reactive control. The soft structure of the reactive power control system is designed in this paper. The reactive power optimization program has been tested in IEEE 6 nodes system, IEEE 30 nodes and a district power net. The results are fairly satisfactory.
Keywords/Search Tags:Reactive power optimization, Genetic algorithm, Load forecasting, Artificial neural network
PDF Full Text Request
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