文章摘要
李常刚,李华瑞,刘玉田,等.计及低频减载动作的最大暂态频率偏移快速估计[J].电力系统自动化,2019,43(12):27-35. DOI: 10.7500/AEPS20180630003.
LI Changgang,LI Huarui,LIU Yutian, et al.Fast Estimation of Maximum Transient Frequency Deviation Considering Under-frequency Load Shedding[J].Automation of Electric Power Systems,2019,43(12):27-35. DOI: 10.7500/AEPS20180630003.
计及低频减载动作的最大暂态频率偏移快速估计
Fast Estimation of Maximum Transient Frequency Deviation Considering Under-frequency Load Shedding
DOI:10.7500/AEPS20180630003
关键词: 电力系统  频率偏移  低频减载  支持向量机  集成学习  特征降维
KeyWords: power system  frequency deviation  under-frequency load shedding(UFLS)  support vector machine  ensemble learning  feature dimension reduction
上网日期:2019-04-11
基金项目:国家重点研发计划资助项目(2017YFB0902600);山东大学青年学者未来计划(2018WLJH31)
作者单位E-mail
李常刚 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061 lichgang@sdu.edu.cn 
李华瑞 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061  
刘玉田 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061  
吴海伟 国网江苏省电力有限公司, 江苏省南京市 210024  
徐春雷 国网江苏省电力有限公司, 江苏省南京市 210024  
摘要:
      随着大容量远距离高压直流输电工程建设和大规模可再生能源的接入,受端电网频率安全风险增大。针对大容量直流闭锁等可能触发低频减载的严重扰动,文中提出基于机器学习的电力系统最大暂态频率偏移快速估计方法。将问题分解为低频减载响应判断和最大频率偏移估计两个子问题,通过子模型交替求解估计最大暂态频率偏移;基于支持向量回归方法构建最大频率偏移估计子模型,以支持向量机为个体学习器构建基于Bagging集成学习的低频减载响应判断子模型;以运行方式信息和扰动信息为输入,采用ReliefF算法和主成分分析法对输入特征进行选择和提取,降低模型复杂度。以某多直流馈入受端系统为例构建最大暂态频率偏移估计模型,验证所提方法的准确性和适应性。
Abstract:
      With integration of high voltage direct current(HVDC)transmission with large capacity and long distance and large scale of renewable generation, the risk of frequency security at the receiving-end of power systems is rising. Aiming at severe disturbances such as large-scale HVDC blocking which may trigger under-frequency load shedding(UFLS), a method for fast estimation of maximum transient frequency deviation is proposed based on machine learning. The problem is decomposed into two sub-problems i. e. UFLS response judgment and maximum frequency deviation estimation, respectively. The maximum transient frequency deviation is estimated by solving the sub-models alternately. Support vector regression method is used to establish the sub-model of maximum frequency deviation estimation, the Bagging ensemble learning method based on support vector machine is used to establish the sub-model of UFLS response judgement. Operation condition and disturbance information are regarded as inputs. ReliefF method and principal component analysis are introduced to select and extract input features to reduce the model complexity. A receiving-end power system with multiple HVDC links is taken as an example to build a maximum transient frequency deviation model and verify the accuracy and adaptability of the proposed method.
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