本文選題：全壽命狀態(tài)評價 + 檢修消缺��； 參考：《電力系統(tǒng)自動化》2016年06期

【摘要】：電網(wǎng)企業(yè)記錄了大量故障與缺陷中文文本,這些文本蘊藏了豐富的設備健康信息。但迄今為止,鮮有電力領(lǐng)域的文本信息挖掘技術(shù)研究。以斷路器全壽命狀態(tài)評價為應用研究背景,探索了電網(wǎng)中文文本挖掘方法。首先,根據(jù)斷路器狀態(tài)評價的研究現(xiàn)狀,提出了構(gòu)建文本挖掘與全壽命狀態(tài)評價模型的關(guān)鍵問題。然后,構(gòu)建了包含文本挖掘信息的全壽命狀態(tài)評價模型,通過基于隱馬爾可夫法(HMM)的文本預處理與向量化、自主區(qū)間搜索k最近鄰(KNN)算法的文本分類和比率型狀態(tài)信息融合模型完成了斷路器全壽命健康狀態(tài)指數(shù)的展示。最后,采用某電網(wǎng)公司實際缺陷文本構(gòu)建算例。算例表明,文本挖掘技術(shù)實現(xiàn)了相似缺陷的相關(guān)性學習,比率型信息融合模型能更全面真實地展示健康狀態(tài)評價的歷史流。
[Abstract]:Power grid enterprises record a large number of fault and defect Chinese text, which contains a wealth of equipment health information.But so far, there are few research on text information mining technology in power field.Based on the life state evaluation of circuit breakers, a Chinese text mining method is explored.Firstly, according to the status quo of circuit breaker status evaluation, the key problems of constructing text mining and life state evaluation model are put forward.Then, a whole life state evaluation model including text mining information is constructed, and text preprocessing and vectorization based on Hidden Markov method (HMMM) is proposed.The text classification and ratio state information fusion model of autonomous interval search k nearest neighbor KNN algorithm are used to display the lifetime health state index of circuit breakers.Finally, the actual defect text of a power grid company is used to construct an example.Numerical examples show that text mining technology can realize the correlation learning of similar defects, and the ratio information fusion model can show the historical flow of health status evaluation more comprehensively and truly.
【作者單位】： 浙江大學電氣工程學院;國網(wǎng)金華供電公司;國網(wǎng)浙江省電力公司電力科學研究院;
【基金】：國家電網(wǎng)公司科技項目~~
【分類號】：TP391.1

【參考文獻】

相關(guān)期刊論文 前1條

1 嚴英杰;盛戈v，

本文編號：1741516