Suzhou Electric Appliance Research Institute
期刊號(hào): CN32-1800/TM| ISSN1007-3175

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一種基于SAE-RF算法的配電變壓器故障診斷方法

來源:電工電氣發(fā)布時(shí)間:2021-02-23 09:23 瀏覽次數(shù):755

一種基于SAE-RF算法的配電變壓器故障診斷方法

陳錦鋒1,張軍財(cái)1,盧思佳2,高偉2,范賢盛1,陳致遠(yuǎn)3
(1 國網(wǎng)福建南平供電公司,福建 南平 353000;2 福州大學(xué) 電氣工程與自動(dòng)化學(xué)院,福建 福州 350108;
3 上海宏力達(dá)信息技術(shù)股份有限公司,上海 200030)
 
    摘 要:為有效解決配電變壓器故障診斷中面臨的數(shù)據(jù)特征人工提取、機(jī)器學(xué)習(xí)調(diào)參困難等問題,提出了一種基于堆棧自編碼器(SAE)和隨機(jī)森林(RF)組合的配電變壓器故障診斷方法。建立SAE配電變壓器故障特征自動(dòng)挖掘模型,利用大量的無標(biāo)簽數(shù)據(jù)對(duì)SAE模型中的每一個(gè)自編碼器進(jìn)行逐層無監(jiān)督訓(xùn)練,通過貝葉斯優(yōu)化算法自動(dòng)選擇模型的最優(yōu)參數(shù);通過有標(biāo)簽數(shù)據(jù)對(duì)模型參數(shù)進(jìn)行有監(jiān)督細(xì)調(diào),挖掘出能夠代表各種故障本質(zhì)屬性的特征量;創(chuàng)建一個(gè)RF分類器對(duì)故障類型進(jìn)行辨識(shí),調(diào)參過程同樣實(shí)現(xiàn)參數(shù)的自動(dòng)尋優(yōu)。試驗(yàn)結(jié)果表明,所提方法對(duì)配電變壓器故障診斷準(zhǔn)確率達(dá)96.67%,顯著優(yōu)于單獨(dú)使用SAE和RF的分類結(jié)果。
    關(guān)鍵詞:配電變壓器;故障診斷;堆棧自編碼器;隨機(jī)森林;貝葉斯優(yōu)化
    中圖分類號(hào):TM407;TM421     文獻(xiàn)標(biāo)識(shí)碼:A     文章編號(hào):1007-3175(2021)02-0017-07
 
A Novel Fault Diagnosis Method for Distribution Transformer Via Automatic
Feature Mining and Automatic Parameter Optimization
 
CHEN Jin-feng1, ZHANG Jun-cai1, LU Si-jia2, GAO Wei2, FAN Xian-sheng1, CHEN Zhi-yuan3
(1 State Grid Nanping Power Supply Company, Nanping 353000, China;
2 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China;
3 Shanghai Holystar Information Technology Co., Ltd, Shanghai 200030, China)
 
    Abstract: In order to effectively solve the problems of manual extraction of data features and difficulty of machine learning parameter adjustment in distribution transformer fault diagnosis, a fault diagnosis method for distribution transformer via the combination of stacked autoencoder (SAE) and random forest (RF) is proposed. First, a SAE model is established to realize automatic mining of distribution transformer fault characteristics, and a large number of unlabeled data is used to perform layer-by-layer unsupervised training of each auto-encoder in the model. After that, the optimal parameters of the model are automatically selected by Bayesian optimization algorithm. And then, fine-tune the model parameters through labeled data to mine features that can represent the essential attributes of various faults. Finally, an RF classifier is created to identify the fault type, and the parameter tuning process also realizes automatic parameter optimization. The test results show that the proposed method has an accuracy of 96.67% for distribution transformers fault diagnosis, which is significantly better than the results using SAE and RF alone.
    Key words: distribution transformer; fault diagnosis; stacked auto-encoder (SAE); random forest (RF); Bayesian optimization
 
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