Research Of The Reactive Power Optimization Of Oil Field Regional Energy-saving Based On Chaos Simulated Annealing Algorithm

In recent years, energy-saving technology has been paid to more and more attention by the government. This research is also focused on increasingly by scholars and experts from home and abroad. In the oil distribution network, due to the particularity of its own load, reactive power compensation is seriously unreasonable. Power loss is also extremely serious, and power quality is poor, resulting in a lot of waste. Through the distribution network reactive power optimization and reasonable reactive power configuration, voltage quality can be improved, meanwhile loss is reduced. Thus, reactive power optimization is an effective method to solve this problem. Therefore, it is very significant that effective and practical methods of reactive power optimization are studied.Combined with actual XinSi substation 110kV distribution network, the current grid structure,compensation status and load conditions of Shengli oilfield distribution power grid is analyzed and the specific method of distribution network reactive power configuration is proposed. After the comparison of the common power flow methods, the Newton - Raphson method is used as power flow calculation of the subject.After power flow analysis, the reactive power optimization algorithms are studied in this paper. Based on previous studies, reactive power optimization model of distribution power grid is established,and chaotic simulated annealing algorithm is used as the optimization algorithm, which is the combination of chaos optimization algorithm and simulated annealing algorithm. The ergodicity of chaos is applied to determine the initial temperature of simulated annealing algorithm, and disturbance criteria are established by chaotic perturbation. Then simulated annealing algorithm is used to achieve the global optimal or near optimal, which is programmed by VC + +. The data of reactive power optimization shows this algorithm has better convergence, higher precision, more accurate results, so it's very reasonable and effective for this subject.