Study On Mid-Long-term Forecasting And Operation Of Reservoirs Based On Intelligent Algorithms

Runoff forecasting and reservoir group is a key issue for optimal operation of hydropower systems. Accurate long-term runoff forecasting can provide a reliable guarantee for the draft of operation scheme. Timely and accurate mid-term forecasting can formulate storage and discharge plans of reservoirs for different weather types (such as typhoons, storms, etc.) and holiday special water needs, which has a very positive meaning for controlling flood and drought, as well as rational allocation of water resource. Mid-term operation combined forecasting which considers electricity demand during extreme weather (such as hot weather) and electricity demand differences between weekdays and holidays, clip peak fully rely on characteristics of hydropower plant for flexible operating and strong climbing ability, which can reserved for thermal power load as evenly as possible and ensure the safe and stable operation of the power system. Usually, hydrological forecasting and operation can’t be directly solved due to many considerations and complex targets, so analysis of the problem characteristics and high efficiency of practical methods for solving models are necessary. In recent years, intelligent algorithms out of the bondage of mechanism and causes need to be clear at traditional models, are developed unprecedented due to strong robustness, self-learning and non-differentiable. This paper analysis of features at mid-long-term runoff forecasting and operation, study the south hydroelectric system combined with intelligent algorithms. Details are as follows:(1) For SVM optimized slow and low accuracy in long-term runoff forecasting, using GA for parameters optimization, SVM is established based GA, the model used the first stage of cascade stations developed on Wujiang River, comparative analysis with the grid-search SVM. The results showed that the former are better than the latter, indicating that the use of GA for parameter optimization is reasonable and feasible.(2) Considering poor nonlinear processing capacity and adjusting the weights constantly in BP, GRNN model is proposed and be applied to day-runoff forecasting in Mianhuatan hydropower station. Compared with BP, GRNN has a better fitting and generation ability, which is an effective forecasting method can be used in mid-term runoff forecasting.(3) Building the maximum output peaking model makes sure power plant having a steady load. Studying the maximum output peaking model based on genetic algorithms, calculating to determine the order of cascade hydropower stations, coding sequence encodes each station level with real number, solve the model through genetic manipulation, selection, crossover and mutation.Finally giving peaking results, water level curve and generated output curve of main power plants in the basin. Examples show that this method can effectively contribute to arrange the process of hydropower stations to ensure that power plant has a smooth process of the remaining load.