基于自组织竞争神经网络虚拟测风的分散式风电场超短期功率预测

张小贝; 李润; 王振福; 徐峰; 宋美洋

张小贝,李润,王振福,徐峰,宋美洋.基于自组织竞争神经网络虚拟测风的分散式风电场超短期功率预测[J].电测与仪表,2025,62(9):142-148.
Zhangxiaobei,Lirun,Wangzhenfu,xufeng,Songmeiyang.Ultra-short term power prediction of decentralized wind farm based on self-organizing competitive neural network virtual wind measurement[J].Electrical Measurement & Instrumentation,2025,62(9):142-148.

基于自组织竞争神经网络虚拟测风的分散式风电场超短期功率预测

Ultra-short term power prediction of decentralized wind farm based on self-organizing competitive neural network virtual wind measurement

DOI：10.19753/j.issn1001-1390.2025.09.016

中文关键词: 风电功率超短期预测虚拟测风技术自组织竞争神经网络广义误差分布

英文关键词:wind power, ultra-short-term forecast, virtual wind measurement technology, self-organizing competitive neural network, generalized error distribution

基金项目:国家自然科学(51877174)

作者中文名	作者英文名	单位
张小贝	Zhangxiaobei	华能新能源股份有限公司陕西分公司
李润	Lirun	北京东润环能科技股份有限公司
王振福	Wangzhenfu	华能新能源股份有限公司陕西分公司
徐峰	xufeng	华能新能源股份有限公司陕西分公司
宋美洋	Songmeiyang	北京东润环能科技股份有限公司

摘要点击次数: 71

中文摘要:

为充分降低分散式风电场超短期预测功率的误差，提出基于自组织竞争神经网络虚拟测风的分散式风电场超短期功率预测模型。从软硬件和数据流分析了分散式风电场功率预测系统架构，为模型建立提供基础。采用自组织竞争神经网络理论建立大型风电场的虚拟测风模型，以虚拟测风点测量数据，气温，天气类型，风向，湿度在内的预测特征集为输入数据，以超短期范围风机风速为输出数据构建网络，从而实现了基于若干个虚拟测风塔测量数据得到分散式风电场中不同风机的风速环境。进一步针对各个风机的参数数据进行广义误差分布最优化算法进行拟合，进而基于虚拟测风结果计算各个风机的超短期预测出力，通过某地区分散式风电场的超短期风电功率预测算例验证了所建立模型的有效性。

英文摘要:

In order to fully reduce the error of ultra-short term power prediction of decentralized wind farms, a model of ultra short-term power prediction of decentralized wind farms based on self-organizing competitive neural network virtual wind measurement is proposed. The architecture of decentralized wind farm power prediction system is analyzed from the aspects of software, hardware and data flow, which provides the basis for the establishment of the model. The self-organizing competitive neural network theory is used to establish the virtual wind measurement model of large-scale wind farm. The virtual wind measurement point measurement data, temperature, weather type, wind direction, humidity and other prediction feature sets are used as input data, and the ultra-short term fan wind speed is used as output data to build the network. Thus, the wind speed environment of different fans in the decentralized wind farm is obtained based on the measurement data of several virtual wind measurement towers. Furthermore, the generalized error distribution optimization algorithm is used to fit the parameter data of each fan, and then, the ultra-short term predicted output of each fan is calculated based on the virtual wind measurement results. The validity of the proposed model is verified by an ultra-short term wind power prediction example of a regional distributed wind farm.

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