计及低频减载动作的最大暂态频率偏移快速估计

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。

Fast Estimation of Maximum Transient Frequency Deviation Considering Under-frequency Load Shedding

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.