Very Short-Term PV Power Forecasting Model Based on Kalman Filter Algorithm And BP Neutral Network
Wang Yu1, Su Shi2, Yan Yuting2
1. Graduate workstation of Yunnan Power Grid Co, Kunming 650217； 2. Electric Power Research Institute ,Yunnan Electric Power Research Institute (Group) Co., Ltd, Kunming 650217
Abstract：With the expanding of the capacity of photovoltaic power plants, uncertainty and volatility of the photovoltaic power generation will generate a negative impact on the safe operation of Power Grid. Therefore, To improve the PV power power forecasting accuracy and use the forecasting results will provide certain ancillary basis for Power Grid to operate and control PV power plants, which helps a lot for the grid stable operation. However, to be the most important impact factor of PV generation, irradiance is greatly influenced by cloud cover and changes randomly, especially when cloudy, changes rapidly and severely, which brings difficulty to the very short-term PV power forecasting. For this reason, this paper proposes a very short-term PV power forecasting model based on kalman filter algorithm and back propagation (BP) neutral network. This model applies extraterrestrial radiation and kalman filter algorithm to estimate irradiance, uses continuous prediction method to predict temperature and humidity, then input these three to BP neutral network to forecast PV power for the next 15 minutes. At last, apply actual historical data of three forecast days to test and verify effectiveness and feasibility of the proposed model.
王雨,苏适,严玉廷. 基于Kalman滤波和BP神经网络的光伏超短期功率预测模型[J]. 电气技术, 2014, 15(01): 42-46.
Wang Yu, Su Shi, Yan Yuting. Very Short-Term PV Power Forecasting Model Based on Kalman Filter Algorithm And BP Neutral Network. Electrical Engineering, 2014, 15(01): 42-46.
[1] 孙锐, 汤义勤, 马红伟,等. 一种实用的超短期光伏发电预测方法[J].技术与应用, 2013(1): 77-80. [2] 李建红,陈国平,葛鹏江,等.基于相似日理论的光伏发电系统输出功率预测[J].华东电力,2012,40(1)：153-157. [3] 王晓兰,葛鹏江.基于相似日和径向基函数神经网络的光伏阵列输出功率预测[J].电力自动化设备,2013,33(1):100-103. [4] 代倩,段善旭,蔡涛,等. 基于天气类型聚类识别的光伏系统短期无辐照度发电预测模型研究[J]. 中国电机工程学报, 2011, 31(34)：28-35. [5] 陈昌松,段善旭,蔡涛,等. 基于模糊识别的光伏发电短期预测系统[J].电工技术学报,2011,26(7)：83-89. [6] HAMMER A, HEINEMANN D, LORENZ E, et al. Short-term forecasting of solar radiation: a statistical approach using satellite data[J].Solar Energy,1999, 67(1-3): 139-150. [7] CHOWDHURY B H, RAHMAN S. Forecasting sub-hourly solar irradiance for prediction of photovoltaic output[C].IEEE Photovoltaic Specialists Conference, 19th, New Orleans. LA, May 4-8,1987, Proceedings (A88- 34226 13-44). New York,Institute of Electrical and Electronics Engineers,Inc., 1987:171-176. [8] HASSANZADEH M, ETEZADI-AMOLI M, FADALI M S, et al. Practical approach for sub-hourly and hourly prediction of PV power output[C]//North American Power Symposium (NAPS), Arlington,TX, 2010:1-5. [9] 韩力群.人工神经网络教程[M].北京:北京邮电大学出版社,2006. [10] 杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社,2001. [11] DUFFIE J A, BECKMAN W A. Solar engineering of thermal processes[M].New York:John Wiley&Sons, 1991:47- 141. [12] 谢开,汪峰,于尔铿,等.应用Kalman滤波方法的超短期负荷预报[J].中国电机工程学报,1996,16(4):245-249.
2014, Vol. 15 
