基于分段改进S变换和随机森林的复合电能质量扰动识别方法

各类分布式设备和智能设备接入电力系统,使得电力系统对电能的波动越来越敏感,这导致对电能质量扰动(PQD)的识别和处理变得越来越重要。通过将分段改进S变换(SMST)和随机森林(RF)算法相结合,提出了一种用于复杂噪声环境下PQD识别的新方法。首先,基于检测误差和峰度对SMST的不同频段进行分别调参,并使用SMST提取待检测信号的75种时频特征,构成原始特征集。然后,改进分类回归树(CART)的节点分裂过程,加入了离散值处理策略并使用Gini指数的下降作为新的节点分裂规则。同时,在下次节点分裂前,将基尼指数下降值为零的特征从特征集中删除。最后,使用改进的CART算法构建了RF分类器并对复合PQD信号进行分类。实验证明,在不同的信噪比条件下,新方法均能有效识别多数单一PQD信号和常见的双重复合PQD信号。虽然新方法在运行效率方面仍有一定的改进空间,但其在不同层面上的改进均能有效提升PQD识别精度,且平均分类精度明显高于各类传统PQD识别方法。

Composite power quality disturbance recognition based on segmented modified S-transform and random forest

Various types of distributed equipment and intelligent equipment are connected to the power system. It makes the power system more and more sensitive to power fluctuations, which has led to the identification and processing of Power Quality Disturbances (PQD) become increasingly important. By combining the Segmented Modified S-Transform (SMST) and the Random Forest (RF) algorithm, a new method for PQD identification under complex noise conditions is proposed. Firstly, different frequency bands of SMST are tuned based on various detection errors and kurtosis, and 75 time-frequency features are extracted from the signal using SMST to form the original feature set. Then, the node splitting process of Classification Regression Tree (CART) is improved. The discrete value processing strategy is added and the drop of Gini index is used as the new node splitting rule. Moreover, before the next node splitting, the feature whose Gini index drops to zero is removed. Finally, RF classifier is constructed with modified CART algorithm and used to classify the complex PQD signals. Experiments show that under the condition of different SNR, the new method can effectively identify most single PQD signals and common dual-compounded PQD signals. Although the new method still has some room for improvement in terms of efficiency, its improvement at different aspects can effectively benefit the accuracy of PQD recognition, and its average classification accuracy is significantly higher than traditional PQD recognition methods based on S-transform. This work is supported by National Natural Science Foundation of China (No. 61603212 and No. 51407104).