本文選題：風電機組 + 風電場數(shù)據(jù)采集與監(jiān)控系統(tǒng)�。� 參考：《電力自動化設(shè)備》2017年08期

【摘要】：為提高風電機組的停運預警能力,基于風電場數(shù)據(jù)采集與監(jiān)控(SCADA)系統(tǒng)數(shù)據(jù)提出了一種風電機組狀態(tài)參數(shù)的異常辨識方法。對參數(shù)進行劃分,針對與環(huán)境因素密切相關(guān)的狀態(tài)參數(shù),采用神經(jīng)網(wǎng)絡(luò)建立了狀態(tài)參數(shù)預測模型。采用本機組近期SCADA樣本、本機組歷史樣本和其他機組近期樣本分別作為預測模型的訓練數(shù)據(jù),對比分析了基于3類樣本建立的模型的預測精度。采用平均絕對誤差對基于本機組歷史樣本和其他機組近期樣本建立的預測模型進行選擇。定義了異常程度指標量化預測殘差的異常程度。為了提高異常辨識的精度,采用模糊綜合評判對篩選出的預測模型的異常辨識結(jié)果進行融合。最后,以國內(nèi)某風場的1.5 MW風電機組為例進行了異常分析,并與傳統(tǒng)的風電機組狀態(tài)參數(shù)異常檢測方法進行了對比,實例分析結(jié)果表明所提出的異常辨識方法具有更高的準確性。
[Abstract]:In order to improve the early warning ability of wind turbine outage, an abnormal identification method of wind turbine state parameters is proposed based on the data of wind farm data acquisition and monitoring system (SCADAA). According to the state parameters which are closely related to environmental factors, the prediction model of state parameters is established by neural network. Using the recent SCADA sample of the unit, the historical sample of the unit and the recent sample of other units as the training data of the prediction model, the prediction accuracy of the model based on the three kinds of samples is compared and analyzed. The prediction model based on the historical samples of the unit and the recent samples of other units is selected by using the mean absolute error. Anomaly degree index is defined to quantitatively predict the anomaly degree of residual error. In order to improve the accuracy of anomaly identification, fuzzy comprehensive evaluation is used to fuse the results of anomaly identification of the selected prediction model. Finally, the anomalous analysis of 1.5 MW wind turbine in a domestic wind field is carried out and compared with the traditional method of detecting abnormal state parameters of wind turbine. The analysis results show that the proposed anomaly identification method is more accurate.
【作者單位】： 國網(wǎng)河南省電力公司電力科學研究院;重慶大學輸配電裝備及系統(tǒng)安全與新技術(shù)國家重點實驗室;
【基金】：國家電網(wǎng)公司重大科技專項(智能變電站母線及智能組件可靠性研究)~~
【分類號】：TM315

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本文編號：1808260