首页 > 过刊浏览>2021年第40卷第3期 >42-50. DOI:10.12158/j.2096-3203.2021.03.007
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西南电网异步联网后的负荷预测及频率波动抑制
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中图分类号:

TM712

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国家自然科学基金资助项目（51707166）


Load forecasting and frequency fluctuation suppression under asynchronous operation of Southwest Power Grid
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    摘要:

    负荷波动是导致频率不稳定的重要因素，而短期负荷预测可以预估系统有功出力，有效抑制频率波动。针对西南电网异步互联后的频率波动问题，文中首先对西南地区负荷波动幅值的概率分布进行分析，并将负荷波动概率纳入负荷预测过程，提高预测精度。然后，基于西南电网的负荷概率分布特点，提出融合反向传播（BP）神经网络和支持向量回归（SVR）的新型混合算法，采用BP神经网络对负荷水平进行评估，利用所得负荷水平作为SVR训练集的选取依据，进而预测当日负荷。最后，基于实际负荷数据进行频率仿真，并与传统方法的预测结果进行对比，验证了所提模型负荷预测精度及频率波动抑制效果的优越性。

    Abstract:

    Load fluctuation is an important factor leading to frequency instability. Short-term load forecasting can predict the active power output of the system and suppress the frequency fluctuation effectively. In order to solve the problem of frequency fluctuation after asynchronous connection of Southwest Power Grid, the probability distribution of load fluctuation amplitude in Southwest China is firstly analyzed. Secondly, the load fluctuation probability is incorporated into load forecasting process to improve the forecasting accuracy. Then, based on the load probability distribution characteristics of Southwest Power Grid, a new hybrid algorithm combining back propagation (BP) neural network and support vector regression (SVR) is proposed. BP neural network is used to evaluate the load level, and the load level obtained is used as the selection basis of SVR algorithm training set to predict the load results of the day. Finally, based on the actual load data, the frequency simulation is carried out. The prediction results are compared with the traditional prediction methods without considering the load probability distribution characteristics, which verifies the superiority of the proposed model in prediction accuracy and effectiveness.

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罗卫华,余锐,兰强,吴京驰,王民昆,肖嵩.西南电网异步联网后的负荷预测及频率波动抑制[J].电力工程技术,2021,40(3):42-50

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  • 收稿日期:2020-12-13
  • 最后修改日期:2021-01-29
  • 录用日期:2020-11-09
  • 在线发布日期: 2021-06-11
  • 出版日期: 2021-05-28
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