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基于超短期风电功率预测的混合储能控制策略研究 被引量:5

Research on hybrid energy storage control strategy based on ultra-short-term wind power prediction
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摘要 为了改善风机出力特性,提出了一种基于超短期风电功率预测的混合储能控制策略。首先,利用解析模态分解方法从风电信号中提取低频信号,采用了一种改进布谷鸟方法优化支持向量机的惩罚因子参数和核函数参数进行超短期功率预测;然后,对低频预测信号建立1 min时间尺度和30 min时间尺度的功率波动并网指标,判断是否触发蓄电池动作,若动作,采用AMD分解自适应调整低频预测信号的截止频率,直到满足并网要求,确定蓄电池补偿功率指令。最后根据蓄电池荷电状态和补偿功率指令自适应调节原始风电信号截止频率,高频信号通过模糊控制由超级电容器补偿。仿真算例表明,该方法可以有效平滑功率波动,减少蓄电池的循环次数,同时保证了蓄电池储能的平滑能力,避免过充过放,延长蓄电池的寿命。 An operation control strategy based on ultra-short-term wind power prediction for hybrid energy storage is proposed in order to improve the output characteristics of wind farm. Firstly, the low frequency signals are extracted from the wind signals by analytical mode decomposition (AMD) method, and the penalty parameter and kernel func- tion parameter of support vector machines (SVM) are found by using improved cuckoo search algorithms (ICSA) to predict the future wind power. Then, the power fluctuation index of the 1 rain time scale and the 30 rain time scale of low frequency predicted signal is established to judge whether the battery is triggered. If triggered, the cut-off frequen- cy of low frequency predicted signals are adjusted to meet the requirement of grid-connected and determine the instruc- tion of compensation power for battery. Finally, the cut-off frequency of original wind power is adjusted self-adaptively based on the state of charge (SOC) of battery and the instruction of compensation power of battery, and the high fre- quency component is compensated by super capacitor through fuzzy control. The simulation results show that the pro- posed strategy can smooth the fluctuation of wind power effectively, reduce the number of battery recycling greatly, ensure the smooth capacity of battery, avoid overcharging and over discharging, and extend the life of battery.
作者 李燕青 袁燕舞 郭通 王子睿 仝年 史依茗 Li Yanqing Yuan Yanwu Guo Tong Wang Zirui Tong Nian Shi Yiming(Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power University, Baoding 071003, Hebei, China)
出处 《电测与仪表》 北大核心 2017年第15期50-57,共8页 Electrical Measurement & Instrumentation
基金 国家自然科学基金资助项目(51177047)
关键词 混合储能 解析模态分解 改进布谷鸟 超短期功率预测 功率波动 自适应调节 hybrid energy storage, AMD, ICSA, ultra-short-term wind power prediction, power fluctuation, adjusted self-adaptively
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