【摘要】：抑制現(xiàn)場噪聲干擾、有效提取信號特征是局部放電信號檢測和分析的關(guān)鍵。給出了利用Fisher可分離度選擇具有最強類可分離度的雙譜作為信號的特征參數(shù),并利用特征參數(shù)訓(xùn)練徑向基神經(jīng)網(wǎng)絡(luò)來判斷信號的類型的算法。通過混有高斯白噪聲的電磁波仿真軟件得到的模擬不同局部放電源輻射的電磁波信號,利用該算法進行信號分離,驗證了該算法的有效性。最后在變電站現(xiàn)場未知局部放電源的情況下,對采集到的局部放電輻射電磁波信號利用該算法進行處理得到信號類型數(shù),并訓(xùn)練用于信號分離的徑向基神經(jīng)網(wǎng)絡(luò)�；诂F(xiàn)場實測信號分離結(jié)果,并結(jié)合基于時延序列的局部放電源定位結(jié)果驗證了該算法在變電站現(xiàn)場干擾情況下分離多局部放電源的有效性。
[Abstract]:The key of PD signal detection and analysis is to suppress the field noise interference and extract the signal features effectively. This paper presents an algorithm to select bispectrum with the strongest separability by using Fisher separability as the characteristic parameter of the signal and to train the radial basis function neural network to judge the type of the signal by using the characteristic parameter. By mixing the electromagnetic wave simulation software with Gao Si white noise, the electromagnetic wave signals of different local discharge power sources are simulated. The algorithm is used to separate the signals, and the validity of the algorithm is verified. Finally, in the case of unknown partial discharge power supply in substation, the acquired partial discharge electromagnetic wave signal is processed by this algorithm to obtain the number of signal types, and the radial basis function neural network for signal separation is trained. Based on the field measured signal separation results and the local discharge location results based on time-delay sequence, the effectiveness of the proposed algorithm for separating multi-local discharge power sources in substation field interference is verified.
【作者單位】： 上海交通大學(xué)電氣工程系;國網(wǎng)山東省電力公司聊城供電公司;
【基金】：國家863高技術(shù)基金項目(SS2012AA050803)~~
【分類號】：TM855

【參考文獻】

相關(guān)期刊論文 前7條

1 李劍;王小維;金卓睿;孫才新;程昌奎;;變壓器局部放電超高頻信號多尺度網(wǎng)格維數(shù)的提取與識別[J];電網(wǎng)技術(shù);2010年02期

2 弓艷朋;劉有為;吳立遠;;采用分形和支持向量機的氣體絕緣組合電器局部放電類型識別[J];電網(wǎng)技術(shù);2011年03期

3 王彩雄;唐志國;常文治;鄭書生;李成榕;;局部放電超高頻檢測抗干擾與多源放電信號分離方法[J];電網(wǎng)技術(shù);2012年03期

4 張曉星;王震;唐炬;劉蕾;魏燕;;氣體絕緣變壓器局部放電超高頻在線監(jiān)測系統(tǒng)[J];高電壓技術(shù);2010年07期

5 侯慧娟;盛戈v，

本文編號：2242642