本文關(guān)鍵詞：人工神經(jīng)網(wǎng)絡(luò)和信息融合技術(shù)在變壓器狀態(tài)評估中的應(yīng)用，由筆耕文化傳播整理發(fā)布。

人工神經(jīng)網(wǎng)絡(luò)和信息融合技術(shù)在變壓器狀態(tài)評估中的應(yīng)用

Application of Artificial Neural Network and Information Fusion Technology in Power Transformer Condition Assessment

[1] [2] [3] [4] [5] [6]

RUAN Ling, XIE Qijia, GAO Shengyou, NIE Dexin, LU Wenhua, ZHANG Hailong （1. State Grid Key Laboratory of On-site Test Technology on High Voltage Power Apparatus, State Grid

[1]國網(wǎng)湖北省電力公司電力科學(xué)研究院國家電網(wǎng)公司高壓電氣設(shè)備現(xiàn)場試驗教術(shù)重點實驗室,武漢430077; [2]清華大學(xué)電機工程與應(yīng)用電子技術(shù)系電力系統(tǒng)及發(fā)電設(shè)備控制和仿真國家重點實驗室,北京100084; [3]國網(wǎng)電力科學(xué)研究院,武漢430074

文章摘要：為滿足電力系統(tǒng)對變壓器資產(chǎn)管理和風(fēng)險評估的需求，提出了一種基于人工神經(jīng)網(wǎng)絡(luò)和信息融合技術(shù)的變壓器狀態(tài)評估方法。以預(yù)防性試驗數(shù)據(jù)和在線監(jiān)測數(shù)據(jù)為例，選擇具有代表意義的信息量作為開展評估的靜態(tài)狀態(tài)量，，除此之外還選取部分靜態(tài)狀態(tài)量的變化趨勢作為開展評估的漸變狀態(tài)量，采用非線性指標(biāo)評價函數(shù)對狀態(tài)量進行歸一化處理，綜合應(yīng)用人工神經(jīng)網(wǎng)絡(luò)（朋州）和Dempster-Shafer（D．s）證據(jù)理論構(gòu)建多信息融合的變壓器狀態(tài)評估模型。通過對某臺500kV變壓器數(shù)據(jù)的實例分析，驗證了該評估模型應(yīng)用于變壓器狀態(tài)評估中的有效性。該方法將在線監(jiān)測數(shù)據(jù)與部分參數(shù)的變化趨勢緊密結(jié)合，有助于提高變壓器狀態(tài)評估的時效性和準(zhǔn)確性。

Abstr：To meet the needs of assets management and risk assessment for power transformers in power systems, we proposed a condition assessment method of power transformer based on artificial neural network and information fusion technology. Taking preventative test parameters and on-line monitoring parameters as the example, we chose some repre- sentative part of them as static condition parameters, and chose the variation trends of parts of the static condition parameters as trend condition parameters. We normalized these condition parameters using a nonlinear index evaluation function, and established a model of multi-information fusion transformer condition assessment based on the artificial neuron network （ANN） and Dempster-Shafer （D-S） evidence theory. Moreover, we analyzed data of an example from a 500 kV power transformer, and the results verified the effectiveness of the proposed model. It is concluded that combining on-line monitoring parameters and their variation trends, the proposed method is helpful to improving the accuracy and timeliness of transformer condition assessment.

文章關(guān)鍵詞：

Keyword：:transformer condition assessment multi-information fusion D-S evidence theory artificial neural network trend analysis nonlinear index evaluate function

課題項目：國家電網(wǎng)公司科技項目（SGl0028）;國網(wǎng)湖北省電力公司科技項目（201110101）.

  本文關(guān)鍵詞：人工神經(jīng)網(wǎng)絡(luò)和信息融合技術(shù)在變壓器狀態(tài)評估中的應(yīng)用，由筆耕文化傳播整理發(fā)布。