本文選題：優(yōu)化 + 電流��； 參考：《農(nóng)業(yè)工程學(xué)報(bào)》2014年23期

【摘要】：針對(duì)如何從低壓電網(wǎng)總泄漏電流中檢測(cè)出生物體觸電電流信號(hào)的難題,提出了一種基于網(wǎng)格搜索和交叉驗(yàn)證的最小二乘支持向量機(jī)的觸電電流信號(hào)檢測(cè)方法。首先在剩余電流動(dòng)作保護(hù)裝置觸電物理試驗(yàn)系統(tǒng)平臺(tái)上通過(guò)故障錄波器獲得生物體在3個(gè)典型時(shí)刻(電源電壓最大時(shí)刻、電源電壓過(guò)零時(shí)刻及電源電壓任意時(shí)刻)發(fā)生觸電過(guò)程的總泄漏電流和觸電電流波形,并截取觸電前1個(gè)周期和觸電后3個(gè)周期共800個(gè)采樣點(diǎn)的信號(hào)數(shù)據(jù)作為觸電試驗(yàn)樣本數(shù)據(jù);然后將觸電試驗(yàn)樣本數(shù)據(jù)進(jìn)行濾波預(yù)處理,預(yù)處理后的多個(gè)樣本采樣點(diǎn)的總泄漏電流組合成特征向量輸入最小二乘支持向量機(jī)(least square-support vector machine,LS-SVM),相應(yīng)樣本采樣點(diǎn)的觸電電流作為其輸出,并通過(guò)網(wǎng)格搜索與交叉驗(yàn)證相結(jié)合的方法來(lái)優(yōu)化最小二乘支持向量機(jī)參數(shù),利用輸出最優(yōu)參數(shù)組合對(duì)觸電電流與總泄漏電流的關(guān)系進(jìn)行訓(xùn)練,從而建立了觸電電流的檢測(cè)模型;最后利用該方法對(duì)10組測(cè)試樣本數(shù)據(jù)進(jìn)行了檢測(cè),檢測(cè)結(jié)果為:當(dāng)訓(xùn)練樣本數(shù)據(jù)為20組時(shí),檢測(cè)均方誤差為14.0040,當(dāng)訓(xùn)練樣本數(shù)據(jù)為40組時(shí),檢測(cè)均方誤差為11.7469,當(dāng)訓(xùn)練試驗(yàn)數(shù)據(jù)為65組時(shí),檢測(cè)均方誤差為11.1849。與徑向基(radial basis function,RBF)神經(jīng)網(wǎng)絡(luò)方法相比,最小二乘支持向量機(jī)方法比徑向基神經(jīng)網(wǎng)絡(luò)方法檢測(cè)均方誤差分別低3.7272、1.9132、0.1556,從而可較準(zhǔn)確地從總泄漏電流中檢測(cè)出生物體觸電電流信號(hào),為開(kāi)發(fā)新一代基于生物體觸電電流分量而動(dòng)作的自適應(yīng)型剩余電流保護(hù)裝置提供理論依據(jù)。
[Abstract]:In order to solve the problem of how to detect the biological electric current signal from the total leakage current of the low-voltage power network, a method of detecting the electric shock current signal based on the least square support vector machine (LS-SVM) based on grid search and cross-validation is proposed. First, on the platform of electroshock physical test system platform of residual current action protection device, through fault recorder, the organism is obtained at three typical times (maximum power supply voltage, The total leakage current and the electric shock current waveform of the electric shock process occur at the zero-crossing time of the power supply voltage and at any time of the power supply voltage. The signal data of 800 sampling points in the first cycle and three cycles after the electric shock are taken as the sample data of the electric shock test, and then the sample data of the electric shock test are filtered and preprocessed. After preprocessing, the total leakage current of several sample sampling points is combined into eigenvector input least squares support vector machine (LS-SVM), and the electric shock current of the corresponding sample sampling point is taken as its output. The parameters of least squares support vector machine are optimized by the combination of grid search and cross validation, and the relationship between the total leakage current and the electric shock current is trained by the optimal output parameter combination, and the detection model of the electric shock current is established. Finally, the method is used to detect 10 groups of test data. The results are as follows: when the training sample data is 20 groups, the mean square error of detection is 14.0040, and when the training sample data is 40 groups, the detection mean square error is 14.0040 when the training sample data is 40 groups. The mean square error was 11.7469, and the mean square error was 11.1849 when the training data was 65 groups. Compared with radial basis function RBF-based neural network method, least square support vector machine method is lower than radial basis function neural network method in measuring mean square error of 3.7272 (1.9132) and 0.1556, respectively, which can accurately detect the electrical current signal of organism from total leakage current, and the mean square error of the least square support vector machine method is lower than that of the radial basis function neural network method, and the mean square error is lower than that of the radial basis function neural network method, respectively. It provides a theoretical basis for the development of a new generation of adaptive residual current protection devices based on the components of biological shock current.
【作者單位】： 中國(guó)農(nóng)業(yè)大學(xué)信息與電氣工程學(xué)院;黑龍江八一農(nóng)墾大學(xué)信息技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(51177165) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助(2013YJ008)
【分類號(hào)】：TM774

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