光伏发电有功功率预测及其在电网频率控制中的应用

针对光伏发电有功功率预测准确率低的问题，提出了基于马尔科夫修正的小波神经网络光伏发电有功功率预测算法。将历史数据分为晴朗、多云等天气类型，分别进行训练，建立网络模型，再根据预测日天气预报采用不同的网络模型预测。光伏的大规模并网将会对电力系统的有功平衡造成很大的扰动，调度部门借助光伏发电有功功率的准确预测进而对自动发电控制进行超前控制可以有效降低频率的波动，减轻自动发电控制机组的调节压力。实际算例对算法进行了验证，并仿真了有功扰动下的频率恢复特性，表明了基于光伏发电有功功率预测的自动发电控制超前控制方法的有效性。

Prediction of photovoltaic power and its application in power grid frequency control

For the problem of low accuracy of photovoltaic power low prediction, this paper puts forward the algorithm of forecasting active power of wavelet neural network based on Markov modification. This paper divides the historical data into sunny, cloudy weather types, trains them respectively, and establishes network model, and then according to the prediction of daily weather forecast, uses different model prediction algorithms. Large-scale grid-connected photovoltaic power will cause great disturbance on the balance of power system, so the accurate prediction of dispatching department by photovoltaic power and advanced control on the automatic generation control can effectively reduce the frequency fluctuation and reduce the regulating pressure of automatic generation control unit. Practical examples are presented to validate the algorithm and frequency recovery characteristics under active disturbance are simulated, which show that ahead control of automatic generation control based on photovoltaic power generation prediction is effective.