作者：吴鹏1,2, 王鼎1, 马宇超1, 陈信华3, 沈金阳1, 苏畅宇1, 虞乐水1
作者单位：1. 常州大学机械与轨道交通学院, 江苏 常州 213164;
2. 上海交通大学电子信息与电气工程学院电气工程流动站, 上海 200030;
3. 溧阳市新力机械铸造有限公司, 江苏 溧阳 213300
关键词：风力发电;功率预测;局部最优;ABC-BP;算法模型
摘要：
稳定可控的风力发电系统是风力资源开发利用的核心，提出一种基于人工蜂群-神经网络算法（artificial bee colony-back propagation,ABC-BP）的风力发电机短期功率预测方法，进一步提高短期风电功率预测的准确性。针对现有短期功率预测方法中遇到的收敛速度慢、局部最优等缺陷，结合ABC人工蜂群算法，提出改进ABC-BP算法。在对其数学模型收敛性证明的基础上，采用实际风电数据进行仿真验证，并通过模拟风力发电平台，进行实验，实验结果表明，预测数据达到实验要求，所提出的改进算法模型是可行的。改进ABC-BP算法应用于风力发电机监测系统中，为降低风电系统运维成本、提高监测效率提供一种有效解决方案。
Short-term power prediction method for wind turbines based on ABC-BP
WU Peng1,2, WANG Ding1, MA Yuchao1, CHEN Xinhua3, SHEN Jinyang1, SU Changyu1, YU Leshui1
1. School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China;
2. Electrical Engineering Mobile Station, School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China;
3. Liyang Xinli Machinery Casting Co., Liyang 213300, China
Abstract: Stable and controllable wind power generation system is the core of wind resource development and utilization. Artificial bee colony-back propagation (ABC-BP) was proposed for short-term power prediction of wind turbine. It further improves the accuracy of short-term wind power forecast. The modified ABC-BP combined with artificial bee colony is presented for defects of slow convergence speed and local optimum in the existing short-term power prediction methods. The actual wind power data is used for simulation on the basis of the convergence proof of its mathematical model. Experimental analysis was carried out on the simulative wind power generation platform. Experimental results show that the predicted data meet the experimental requirements, and the proposed improved algorithm model is feasible. The improved ABC-BP algorithm is applied to the wind turbine monitoring system. It provides an effective solution for reducing the operation and maintenance cost of wind power system, and it can improve the monitoring efficiency.
Keywords: wind power generation;power prediction;local optimum;ABC-BP;algorithm model
2023, 49(6):82-91 收稿日期: 2022-03-02;收到修改稿日期: 2022-07-21
基金项目: 江苏省产学研合作项目（BY2021221）
作者简介: 吴鹏(1987-),男,江苏常州市人,讲师,博士,主要从事电力系统优化
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