The widespread usages of sensitive equipment such as computers, controllers and microelectronic devices have placed immense burden on the grid operators to deliver high quality electrical power to their customers. To achieve this end, the power quality disturbances (PQD) within the network need to be minimized. In this paper, a method to enhance the performance of the multiclass support vector machine (MSVM) classifier using the modified cuckoo search (MCS) is proposed. The wavelet packet transform is used to extract the crucial features from the PQD waveforms; these features are utilized as the input data to the classifier. In order to achieve high accuracy, robustness and speed, the MCS optimizes the number of selected features, as well as the penalty factor and slack variable of the MSVM. The proposed combinatorial algorithm (MCS-MSVM) is tested using 31 categories of PQD events; the hypothetical data for these events are generated by the IEEE 1159 Standard parametric equations. From simulation, the classification accuracy is recorded to be 100% under the no-noise condition, while at the signal-to-noise ratios (SNR) of 10, 20, 30 and 40 dB, the accuracies are 98.40, 98.54, 99.14 and 99.64%, respectively. Moreover, the comparative assessment concludes that the proposed method is superior to other heuristics-MSVM classification methods, namely the GA, PSO, differential evolution, harmony search and the conventional cuckoo search. The practical performance of the MCS-MSVM classifier is validated using real-time PQD data of a typical 11-kV underground distribution network, obtained from a particular electrical utility operator. For benchmarking, comparisons are made to 17 most recent PQD classification techniques published in literature. It is found that the proposed method exhibits the highest accuracies and the lowest computation times under ideal and noisy environments.

计算机、控制器和微电子设备等敏感设备的广泛使用给电网运营商带来了巨大的负担，需要为其客户提供高质量的电力。为实现这一目标，网络内的电能质量扰动 (PQD) 需要最小化。在本文中，提出了一种使用改进的布谷鸟搜索 (MCS) 来提高多类支持向量机 (MSVM) 分类器性能的方法。小波包变换用于从 PQD 波形中提取关键特征；这些特征被用作分类器的输入数据。为了实现高精度、鲁棒性和速度，MCS 优化了所选特征的数量，以及 MSVM 的惩罚因子和松弛变量。所提出的组合算法 (MCS-MSVM) 使用 31 类 PQD 事件进行了测试；这些事件的假设数据由 IEEE 1159 标准参数方程生成。从仿真来看，在无噪声条件下分类准确率为 100%，而在信噪比 (SNR) 为 10、20、30 和 40 dB 时，准确率分别为 98.40、98.54、99.14 和分别为 99.64%。此外，比较评估得出的结论是，所提出的方法优于其他启发式-MSVM 分类方法，即遗传算法、粒子群算法、差分进化、和声搜索和传统的布谷鸟搜索。MCS-MSVM 分类器的实际性能使用从特定电力运营商处获得的典型 11 kV 地下配电网络的实时 PQD 数据进行验证。对于基准测试，将与文献中发表的 17 种最新 PQD 分类技术进行比较。发现所提出的方法在理想和嘈杂的环境下表现出最高的准确性和最低的计算时间。