Space-Time Characteristics Analysis,Evaluation And Forecast Of Reactive Power Load In Power Grid

Reactive power is relativant to power loss of grids, voltage and its stability in power system. Compared with real power, researches on reactive power is realatively centralized, which rarely involve characteristic analysis, modelling and forecast. While characteristic analysis of reactive power load may help evaluate the state of reactive power in certain area and the forcast of reactive power load can provide more accurate data for reactive power compensation. Therefore, space-time characteristic analysis, evaluation, and the forecast of reactive power load became the topics of this paper, which are practically and theoretially valuable.Firstly, several types of reactive power load and their equivalent mathematic models are put forward. The relationship between reactive power load and time and the relationship between reactive power load and space are discussed. Then, A series of parameters and evaluation indicators are concluded and proposed systematically to discribe the state of reactive power load in a certain area. Take 500 k V grids in a certain province and 220 k V grids in a certain city as cases, the indicators proposed are used separately in the perspective of time and space to analyze the reactive power load of the grids. The case studies show that the indicators proposed are very useful to describe the state of reactive power in a certain area. And they also show that the values of the indicators is very stable in different years in certain grids which are in the same operation mode.Secondly, the accuracy of characteristic analysis and the forecast of reactive power load are both dependent on historical data seriously. However, bad data exsit widely in supervisory control and data acquisition system, which often lead to inaccuracy. A method for identifying the bad data among the historical data of power is proposed based on power balance and error analysis. The study case shows that the mothed proposed can identify the bad data effectively.Finally, a mid-term forecast for reactive power load based on the stability of reactive power load indicators and a short-term forecast for reactive power load of substation busbar based on seasonal ARIMA model are put forward, which are proved to be effective in the case study.