本文選題：風(fēng)電機(jī)組 + 風(fēng)電場(chǎng)數(shù)據(jù)采集與監(jiān)控系統(tǒng)��； 參考：《電力自動(dòng)化設(shè)備》2017年08期

【摘要】：為提高風(fēng)電機(jī)組的停運(yùn)預(yù)警能力,基于風(fēng)電場(chǎng)數(shù)據(jù)采集與監(jiān)控(SCADA)系統(tǒng)數(shù)據(jù)提出了一種風(fēng)電機(jī)組狀態(tài)參數(shù)的異常辨識(shí)方法。對(duì)參數(shù)進(jìn)行劃分,針對(duì)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(yù)測(cè)模型。采用本機(jī)組近期SCADA樣本、本機(jī)組歷史樣本和其他機(jī)組近期樣本分別作為預(yù)測(cè)模型的訓(xùn)練數(shù)據(jù),對(duì)比分析了基于3類樣本建立的模型的預(yù)測(cè)精度。采用平均絕對(duì)誤差對(duì)基于本機(jī)組歷史樣本和其他機(jī)組近期樣本建立的預(yù)測(cè)模型進(jìn)行選擇。定義了異常程度指標(biāo)量化預(yù)測(cè)殘差的異常程度。為了提高異常辨識(shí)的精度,采用模糊綜合評(píng)判對(duì)篩選出的預(yù)測(cè)模型的異常辨識(shí)結(jié)果進(jìn)行融合。最后,以國(guó)內(nèi)某風(fēng)場(chǎng)的1.5 MW風(fēng)電機(jī)組為例進(jìn)行了異常分析,并與傳統(tǒng)的風(fēng)電機(jī)組狀態(tài)參數(shù)異常檢測(cè)方法進(jìn)行了對(duì)比,實(shí)例分析結(jié)果表明所提出的異常辨識(shí)方法具有更高的準(zhǔn)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(guó)網(wǎng)河南省電力公司電力科學(xué)研究院;重慶大學(xué)輸配電裝備及系統(tǒng)安全與新技術(shù)國(guó)家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家電網(wǎng)公司重大科技專項(xiàng)(智能變電站母線及智能組件可靠性研究)~~
【分類號(hào)】：TM315

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 李輝;胡姚剛;李洋;楊東;梁媛媛;歐陽海黎;蘭涌森;;大功率并網(wǎng)風(fēng)電機(jī)組狀態(tài)監(jiān)測(cè)與故障診斷研究綜述[J];電力自動(dòng)化設(shè)備;2016年01期

【共引文獻(xiàn)】

相關(guān)期刊論文 前9條

1 孫鵬;李劍;寇曉適;呂中賓;姚德貴;王吉;王磊磊;滕衛(wèi)軍;;采用預(yù)測(cè)模型與模糊理論的風(fēng)電機(jī)組狀態(tài)參數(shù)異常辨識(shí)方法[J];電力自動(dòng)化設(shè)備;2017年08期

2 袁h驕，

本文編號(hào)：1808260