基于時(shí)序建模的光纖電流互感器隨機(jī)噪聲卡爾曼濾波方法
發(fā)布時(shí)間:2018-04-29 07:24
本文選題:隨機(jī)噪聲 + 測(cè)量精確度; 參考:《電機(jī)與控制學(xué)報(bào)》2017年04期
【摘要】:針對(duì)光纖電流互感器(FOCT)隨機(jī)噪聲特性及其對(duì)繼電保護(hù)、電能計(jì)量等間隔層設(shè)備的影響,建立FOCT隨機(jī)誤差的時(shí)序模型,并采用濾波方法有效提高了FOCT測(cè)量精確度。首先,預(yù)處理和統(tǒng)計(jì)檢驗(yàn)FOCT原始數(shù)據(jù),獲取數(shù)據(jù)隨機(jī)特征;根據(jù)赤池信息準(zhǔn)則(AIC)準(zhǔn)則選擇時(shí)間序列模型的階次,求出模型系數(shù)建立FOCT隨機(jī)誤差的ARMA(2,1)模型,并檢驗(yàn)其適用性;采用卡爾曼濾波方法對(duì)FOCT輸出數(shù)據(jù)進(jìn)行濾波處理?偡讲罘治鼋Y(jié)果表明:建立的FOCT時(shí)序模型經(jīng)卡爾曼濾波后,隨機(jī)噪聲幅值明顯減小,方差值降低了兩個(gè)數(shù)量級(jí),各項(xiàng)隨機(jī)噪聲的誤差系數(shù)均下降一個(gè)數(shù)量級(jí),采用的時(shí)序建模和卡爾曼濾波方法能有效減小FOCT的隨機(jī)噪聲,提高電流信息的測(cè)量精確度。
[Abstract]:Aiming at the random noise characteristics of optical fiber current transformer (FOCTT) and its influence on relay protection, electric energy metering and other spacer equipment, the time series model of FOCT random error is established, and the accuracy of FOCT measurement is improved effectively by using filtering method. Firstly, we preprocess and statistically test the original FOCT data to obtain the random characteristics of the data, select the order of the time series model according to the red pool information criterion and calculate the model coefficients to establish the ARMA-2Q1) model of the FOCT random error, and test its applicability. Kalman filtering method is used to filter the FOCT output data. The results of total variance analysis show that the amplitude of random noise is obviously reduced, the square difference is reduced by two orders of magnitude, and the error coefficient of each random noise is reduced by one order of magnitude after Kalman filter. The time series modeling and Kalman filtering method can effectively reduce the random noise of FOCT and improve the measurement accuracy of current information.
【作者單位】: 云南電網(wǎng)有限責(zé)任公司電力科研究院;中國(guó)南方電網(wǎng)公司電能計(jì)量重點(diǎn)實(shí)驗(yàn)室;東南大學(xué)儀器科學(xué)與工程學(xué)院;
【基金】:南方電網(wǎng)科技項(xiàng)目(YNKJ0000124) 云南電網(wǎng)科技項(xiàng)目(HLZB20150738)
【分類號(hào)】:TM452.94
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本文編號(hào):1818977
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