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作者：王輝東，陳鋒（國網浙江杭州市余杭區供電有限公司，浙江 杭州 310007）

摘要：高壓開關柜發生局部放電時產生的超聲波信號中存在著大量的信息，局部放電作為開關柜絕緣故障的重要征兆及表現方式，其類型的識別對于開關柜絕緣狀態的評估具有重要的意義。為了準確地識別高壓開關柜局部放電類型，采用經驗模態分解（EMD）的方法對局放信號進行分解并提取能量信息，利用支持向量機（SVM）建立高壓開關柜局部放電信號分類模型。實驗結果驗證了上述方法的有效性。為了解決SVM核函數g和非負懲罰因子C主觀選取問題，運用灰狼算法（GWO）優化這兩個參數。研究結果表明，與SVM、PSO-SVM和GA-SVM相比，GWOSVM可有效提高開關柜局放信號分類精度。

關鍵詞：經驗模態分解；灰狼算法；支持向量機；分類識別；遺傳算法；粒子群算法

Abstract: There is a lot of information in ultrasonic signals generated when partial discharge occurs in high voltage switchgear. Partial discharge is an important sign and manifestation of insulation failure of switchgear. The identification of its type is of great significance for the assessment of insulation state of switchgear. In order to identify the partial discharge type of high voltage switchgear accurately, the empirical mode decomposition (EMD) method is used to decompose the local discharge signal and extract the energy information. A support vector machine (SVM) is used to establish the classification model of partial discharge signal of high voltage switchgear.Experimental results verify the effectiveness of the above methods. In order to solve the problem of subjective selection of SVM kernel function g and non-negative penalty factor C,the gray Wolf algorithm (GWO) was used to optimize these two parameters. Compared with SVM, PSO-SVM and GA-SVM,GWO-SVM can effectively improve the classification accuracy of switching cabinet signals.

Key words: Empirical modal decomposition; Gray wolf algorithm;Support vector machine; Classification and identification;Genetic algorithms; Particle swarm optimization

在線預覽：基于EMD分解和GWO-SVM的開關柜局放信號識別

摘自《自動化博覽》2019年12月刊