發(fā)布時(shí)間：2018-09-14 11:55

【摘要】：抑制現(xiàn)場(chǎng)噪聲干擾、有效提取信號(hào)特征是局部放電信號(hào)檢測(cè)和分析的關(guān)鍵。給出了利用Fisher可分離度選擇具有最強(qiáng)類可分離度的雙譜作為信號(hào)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(lái)判斷信號(hào)的類型的算法。通過(guò)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(hào),利用該算法進(jìn)行信號(hào)分離,驗(yàn)證了該算法的有效性。最后在變電站現(xiàn)場(chǎng)未知局部放電源的情況下,對(duì)采集到的局部放電輻射電磁波信號(hào)利用該算法進(jìn)行處理得到信號(hào)類型數(shù),并訓(xùn)練用于信號(hào)分離的徑向基神經(jīng)網(wǎng)絡(luò)。基于現(xiàn)場(chǎng)實(shí)測(cè)信號(hào)分離結(jié)果,并結(jié)合基于時(shí)延序列的局部放電源定位結(jié)果驗(yàn)證了該算法在變電站現(xiàn)場(chǎng)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(guó)網(wǎng)山東省電力公司聊城供電公司;
【基金】：國(guó)家863高技術(shù)基金項(xiàng)目(SS2012AA050803)~~
【分類號(hào)】：TM855

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