为提升风电爬坡事件的检测效果,针对含电池储能的风电场,提出基于改进天牛群优化旋转门算法的爬坡事件检测方法。首先,对天牛群搜索算法进行改进,利用其搜索旋转门算法的最优门宽,并据此提取风电功率的特征数据点;然后,对特征数据点进行处理以消除“凸起”现象,进而连接相邻两个数据点形成一个风电特征时段,将风电特征时段进行分类,结合爬坡方向对其进行编码,并依据编码结果进行合并;接着,结合给出的含电池储能风电场爬坡事件定义,对合并后的风电特征时段进行爬坡事件检测;最后,利用某风电场实际运行数据对检测方法进行仿真,并与多种爬坡事件检测方法进行对比,验证了所提方法的有效性。
Abstract
To improve the detection effect of wind power ramp event, a wind power ramp event detection method based on swing door trending optimized by improved beetle swarm optimization algorithm is proposed for wind farm with battery storage. Firstly, the beetle swarm optimization algorithm is improved and used to search the optimal door value of swing door trending. The wind power feature data points are extracted through the swing door trending with optimal door value. Then, the feature data points are processed to eliminate the‘bump’phenomenon. Two adjacent data points is connected to form a wind power feature period. The wind power feature periods are classified and coded in combination with the ramp direction. They are also combined according to the coding results. Next, the ramp definition of wind farm with battery storage is introduced and the ramp detection is performed based on the combined wind power feature periods. Finally, the actual wind power output of a wind farm is used to simulate the proposed detection method. The effectiveness of the proposed detection method is verified in comparison with the various ramp event detection algorithms.
关键词
风电场 /
电池储能 /
启发式算法 /
旋转门算法 /
爬坡事件检测
Key words
wind farm /
battery storage /
heuristic algorithms /
swing door trending algorithm /
ramp event detection
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基金
国家重点研发计划（2018YFE0122200）; 国家自然科学基金（52077078）; 中央高校基本科研业务费（2020MS090）
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