计及频率偏移分布与惩罚代价的最大频率偏移预测方法

现有基于数据驱动的电力系统最大频率偏移预测方法忽略了样本分布不均匀对最大频率偏移预测的影响,其预测精度仍有进一步提升空间.为此,文中提出一种计及频率偏移分布与惩罚代价的最大频率偏移预测方法.采用多源信息融合方法从系统运行信息中提取关键特征子集,避免维度爆炸问题.然后,构建级联轻梯度提升机,并在其损失函数中加入惩罚敏感机制,以使级联轻梯度提升机训练时根据频率偏移样本的分布概率及保守预测的惩罚代价自动修正样本损失值.基于IEEE 118节点标准系统的仿真结果表明,所提方法具有优异的预测精度和抗噪性能.

Maximum Frequency Deviation Prediction Method Considering Frequency Deviation Distribution and Penalty Cost

Current data-driven maximum frequency deviation prediction methods for power systems ignore the effect of uneven sample distribution on the maximum frequency deviation, and its prediction accuracy can be further improved. To this end, this paper proposes a maximum frequency deviation prediction method considering the frequency deviation distribution and penalty cost. To avoid the dimensional explosion problem, the multi-source information fusion method is used to extract key input feature subsets from power system operation information. Then, a cascaded light gradient boosting machine (CasLightGBM) is constructed, and a punishment sensitivity mechanism is embedded into its loss function, which helps CasLightGBM automatically correct the sample loss value in training according to the probability distribution of frequency deviation samples and the penalty cost of conservative prediction. Simulation results based on the IEEE 118-bus test system show that the proposed method has excellent prediction accuracy and anti-noise performance.