基于无迹卡尔曼滤波神经网络的光伏发电预测
发布时间：2019-06-29 22:36
【摘要】：针对光伏发电系统在不同天气状况下发电功率预测精度不高的问题,在分析传统方法的基础上,提出一种无迹卡尔曼滤波神经网络光伏发电预测方法。该方法利用无迹卡尔曼滤波实时更新神经网络模型的权重,以直流电压和电流作为系统的输入,以有功功率和无功功率作为系统的输出,分别建立两个独立的双输入单输出功率预测模型。实验结果表明:所提出的方法对有功功率和无功功率的预测精度分别为97.3%和94.2%,并且对天气具有良好的鲁棒性。
[Abstract]:In order to solve the problem that the prediction accuracy of photovoltaic power generation system under different weather conditions is not high, based on the analysis of the traditional method, an unscented Kalman filter neural network photovoltaic power generation prediction method is proposed. In this method, the unscented Kalman filter is used to update the weight of the neural network model in real time, DC voltage and current are used as the inputs of the system, and the active power and reactive power are taken as the outputs of the system. Two independent double input and single output power prediction models are established respectively. The experimental results show that the prediction accuracy of the proposed method for active power and reactive power is 97.3% and 94.2% respectively, and it is robust to weather.
【作者单位】： 国网青海省电力公司电力科学研究院(青海省光伏发电并网技术重点实验室);重庆大学输配电装备及系统安全与新技术国家重点实验室;
【基金】：青海省光伏发电并网技术重点实验室项目(2014-Z-Y34A)~~
【分类号】：TM615;TP183
本文编号：2508174
[Abstract]:In order to solve the problem that the prediction accuracy of photovoltaic power generation system under different weather conditions is not high, based on the analysis of the traditional method, an unscented Kalman filter neural network photovoltaic power generation prediction method is proposed. In this method, the unscented Kalman filter is used to update the weight of the neural network model in real time, DC voltage and current are used as the inputs of the system, and the active power and reactive power are taken as the outputs of the system. Two independent double input and single output power prediction models are established respectively. The experimental results show that the prediction accuracy of the proposed method for active power and reactive power is 97.3% and 94.2% respectively, and it is robust to weather.
【作者单位】： 国网青海省电力公司电力科学研究院(青海省光伏发电并网技术重点实验室);重庆大学输配电装备及系统安全与新技术国家重点实验室;
【基金】：青海省光伏发电并网技术重点实验室项目(2014-Z-Y34A)~~
【分类号】：TM615;TP183
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