基于相空间重构和长短期记忆算法的电力系统无功负荷预测模型

为了优化无功控制策略，改善电压质量，减小网损，针对无功负荷的随机性与非线性，提出一种基于相空间重构和长短期记忆神经网络的无功负荷预测模型。利用C-C 法确定最优重构维数和延迟时间；通过计算最大Lyapunov指数说明无功负荷的混沌性；利用相空间重构技术将无功序列映射到高维空间，在高维空间利用长短期记忆神经网络进行预测；最后以海南省某地区的有功及无功负荷数据为例，通过Kolmogorov熵证实无功负荷的混沌程度大于有功负荷的混沌程度，算例验证了该方法的可行性，能提高无功负荷预测结果的准确度，有利于对电力系统无功进行更合理的调配和控制。

Reactive Load Forecasting Model Based on PSR-LSTM in Power System

To optimize reactive power control strategy, in allusion to randomness and nonlinearity of reactive load a reactive load forecasting model based on phase space reconstruction and long short term memory neural network was proposed to improve voltage quality and reduce network loss. Utilizing C-C method the optimal reconstruction dimension and latency time were determined and by means of calculating the maximal Lyapunov exponent the chaos characteristic of reactive load was illustrated. The phase space reconstruction technology was used to map the reactive power sequence to high-dimensional space and in high-dimensional space the long short-term memory (abbr. LSTM) neural network is utilized to perform the prediction. Finally, taking data of active and reactive power load of a certain region in Hainan province for example, by use of Kolmogorov entropy it was verified that the chaos degree of reactive power load was larger than the chaos degree of active power. The feasibility of the proposed method is verified by results of calculation example. Using the proposed method the accuracy of reactive load prediction may be improved and the dispatching and controlling of reactive power in the power system may be more rationally.