油中悬移微粒产生的局部放电特性与特征提取研究
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摘要
随着我国智能电网建设规模的迅速扩大，对电力系统运行安全性和可靠性提出了更高的要求。大型电力变压器作为输变电装备中最重要和最昂贵设备之一，其安全可靠运行对整个电网安全起着至关重要的作用。局部放电（PD）是电力变压器绝缘劣化的重要原因，又是其重要征兆。因此对变压器内部PD进行在线监测能够及时有效地发现变压器内部绝缘的固有缺陷和因长期运行老化导致的局部隐患，判断绝缘劣化的程度，避免发生突发性绝缘故障，对电力变压器的安全可靠运行具有十分重要的意义。
本文结合实际电力变压器中出现的悬移微粒缺陷，设计了能够模拟变压器油中悬移微粒局部放电的试验装置，在实验室采用超高频法进行检测，获取了大量的试验样本，研究了悬移微粒产生的PD特性及影响因素；针对PD信号中无法有效去除的随机脉冲干扰导致放电图像染噪的问题，研究了适用于PD图像的Contourlet变换去噪算法，通过仿真实验验证了Contourlet变换抑制PD图像白噪声的能力；研究基于脉冲耦合神经网络（PCNN）的特征提取算法，试验结果验证了PD图像去噪的必要性和有效性，将提取的放电特征应用于悬移微粒缺陷PD模式识别，丰富了局部放电模式识别中关于特征提取的理论和方法。取得的创新性成果有：
①根据油浸式电力变压器中存在的悬移微粒缺陷模型的特点，设计了能够模拟电力变压器油中悬移微粒缺陷放电的试验装置，首次构造出了用于试验研究的三种变压器油中悬移微粒缺陷物理模型，获取大量人工试验数据。
②首次研究了油中悬移微粒PD特性和影响因素，外施电压的增大和油中悬移微粒含量的增加使得放电发展更为剧烈，油流速度的增加有利于提高悬移微粒缺陷的绝缘强度，存在一个合适的温度区间使得悬移微粒缺陷局部放电发展的严重程度降到最低。因此可以通过试验及理论分析找出实际运行中的每一台变压器的合适的运行温度区间和流速区间，改善油道内部的电场分布，进而最大程度的减轻局部放电的危害。
③针对变压器PD检测时存在的随机性脉冲干扰无法有效去除导致的PD图像染噪问题，首次提出采用Contourlet变换进行抑噪处理，对影响Contourlet变换去噪中的噪声水平、分解层数、分解方向数和不同PD类型等因素的研究发现，噪声水平和分解层数对PD图像信息的提取有较大影响，采用Contourlet变换实现的算法能够准确提取出PD三维图谱分布信息，有效地抑制PD图像中的白噪声。
④首次提出一种基于PCNN输出序列的熵值为特征量的局部放电特征提取方法，PD图像经PCNN处理输出熵序列在一定范围内具有平移、缩放不变性；提出的β取值准则能够使得熵序列较快收敛，从而在很大程度上减少熵序列的长度，提高识别的速度；对去噪处理前后的PD染噪图像提取PCNN输出的熵序列特征，发现被噪声污染后的图像输出熵序列与其他类型PD图像的输出熵序列间的均方误差很大，与自己所属的同类放电模式的输出熵序列均方差很小，有利于PD图像的准确分类，同时证明了Contourlet变换用于PD图像去噪的有效性和必要性。
With the rapidly expanding of the smart grid construction, higher requirements onthe power system security and reliability have been proposed. Large power transformersis the most important and expensive equipment of the power transmission devices, itssafe and reliable operation plays a vital role for the entire power grid. Partial discharge(PD) is one of the main reasons for causing internal insulation deterioration of largetransformers, online monitoring of PD can promptly and accurately determine the statusof the transformer internal insulation to prevent power transformer accidents and ensurepower system security and stability, therefore, it has an important and practical value.
In this paper, based on the actual suspended movable particles defects in powertransformers, designed a set of PD test platform that can simulate suspended movableparticles defect, used the ultra high frequency method for signal testing. For the randompulse disturbance can not be effectively suppressed, leading to the PD image added withwhite noise, developed Contourlet transform denoising algorithm, the simulationexperiments prove its effectiveness. Pulse coupled neural networks (PCNN) featureextraction algorithm is studied, the results proved the necessity and effectiveness of thePD image denoising, the extracted discharge characteristics are used in PD patternrecognition, enriched the theory of feature extraction methods in PD pattern recognition.The main achievements are as follows:
According to the characteristics of suspended movable particles defect existing inoil-immersed power transformer, a set of PD test platform that can simulate suspendedmovable particles defect is designed, three typical particles defects are used in PDmeasurement to obtain a large number of artificial test data, a systematic analysis for thePD signals waveform characteristics in different experimental conditions are studied.
First studied the PD characteristics of suspended movable particles defect and itsinfluencing factors, the increase of applied voltage and suspended particles content willlead to a more intense discharge development, while the the increase in the oil flowvelocity will help improve the insulation strength, there is a suitable temperature rangethat makes development of partial discharge severity to a minimum. Experimental andtheoretical analysis should be carried to identify the appropriate range of operatingtemperature and flow velocity range for each transformer, to improve the internalelectric field distribution of the oil duct, thus make the maximum extent to reduce the hazards of partial discharge.
For the random pulse disturbance resulting the PD image dye-noise problem, firstproposed Contourlet transform noise suppression processing. Based on analyzing themain factor influenced the effect of Contourlet transform denoising, which are noiselevel, the decomposition layer, the decomposition of direction number and the differentPD types, it can be seen that noise level and the decomposition layer has great influence,revealing that Contourlet transform algorithm can correctly extract the distributioninformation from PD three-dimensional image, effectively suppress the white noise inPD image.
First proposed a PD feature extraction method based on the PCNN output entropysequence, the PCNN output entropy sequence in a certain range has the character oftranslation and scaling invariance. The proposed criteria for β value makes theentropy sequence faster convergence and thus greatly reduce the length of entropysequence, improving the speed of the identification. Extracting the PCNN outputentropy sequence from the dye-noise PD images and denoised PD images, find that theoutput entropy sequence from the dye-noise PD image has huge mean square error withthe other types PD defects, while it is smaller in the similar discharge patterns, helpfulfor the accurate classification of the PD image and prove the effectiveness and necessityfor PD image denoising by Contourlet transform.
本文结合实际电力变压器中出现的悬移微粒缺陷，设计了能够模拟变压器油中悬移微粒局部放电的试验装置，在实验室采用超高频法进行检测，获取了大量的试验样本，研究了悬移微粒产生的PD特性及影响因素；针对PD信号中无法有效去除的随机脉冲干扰导致放电图像染噪的问题，研究了适用于PD图像的Contourlet变换去噪算法，通过仿真实验验证了Contourlet变换抑制PD图像白噪声的能力；研究基于脉冲耦合神经网络（PCNN）的特征提取算法，试验结果验证了PD图像去噪的必要性和有效性，将提取的放电特征应用于悬移微粒缺陷PD模式识别，丰富了局部放电模式识别中关于特征提取的理论和方法。取得的创新性成果有：
①根据油浸式电力变压器中存在的悬移微粒缺陷模型的特点，设计了能够模拟电力变压器油中悬移微粒缺陷放电的试验装置，首次构造出了用于试验研究的三种变压器油中悬移微粒缺陷物理模型，获取大量人工试验数据。
②首次研究了油中悬移微粒PD特性和影响因素，外施电压的增大和油中悬移微粒含量的增加使得放电发展更为剧烈，油流速度的增加有利于提高悬移微粒缺陷的绝缘强度，存在一个合适的温度区间使得悬移微粒缺陷局部放电发展的严重程度降到最低。因此可以通过试验及理论分析找出实际运行中的每一台变压器的合适的运行温度区间和流速区间，改善油道内部的电场分布，进而最大程度的减轻局部放电的危害。
③针对变压器PD检测时存在的随机性脉冲干扰无法有效去除导致的PD图像染噪问题，首次提出采用Contourlet变换进行抑噪处理，对影响Contourlet变换去噪中的噪声水平、分解层数、分解方向数和不同PD类型等因素的研究发现，噪声水平和分解层数对PD图像信息的提取有较大影响，采用Contourlet变换实现的算法能够准确提取出PD三维图谱分布信息，有效地抑制PD图像中的白噪声。
④首次提出一种基于PCNN输出序列的熵值为特征量的局部放电特征提取方法，PD图像经PCNN处理输出熵序列在一定范围内具有平移、缩放不变性；提出的β取值准则能够使得熵序列较快收敛，从而在很大程度上减少熵序列的长度，提高识别的速度；对去噪处理前后的PD染噪图像提取PCNN输出的熵序列特征，发现被噪声污染后的图像输出熵序列与其他类型PD图像的输出熵序列间的均方误差很大，与自己所属的同类放电模式的输出熵序列均方差很小，有利于PD图像的准确分类，同时证明了Contourlet变换用于PD图像去噪的有效性和必要性。
With the rapidly expanding of the smart grid construction, higher requirements onthe power system security and reliability have been proposed. Large power transformersis the most important and expensive equipment of the power transmission devices, itssafe and reliable operation plays a vital role for the entire power grid. Partial discharge(PD) is one of the main reasons for causing internal insulation deterioration of largetransformers, online monitoring of PD can promptly and accurately determine the statusof the transformer internal insulation to prevent power transformer accidents and ensurepower system security and stability, therefore, it has an important and practical value.
In this paper, based on the actual suspended movable particles defects in powertransformers, designed a set of PD test platform that can simulate suspended movableparticles defect, used the ultra high frequency method for signal testing. For the randompulse disturbance can not be effectively suppressed, leading to the PD image added withwhite noise, developed Contourlet transform denoising algorithm, the simulationexperiments prove its effectiveness. Pulse coupled neural networks (PCNN) featureextraction algorithm is studied, the results proved the necessity and effectiveness of thePD image denoising, the extracted discharge characteristics are used in PD patternrecognition, enriched the theory of feature extraction methods in PD pattern recognition.The main achievements are as follows:
According to the characteristics of suspended movable particles defect existing inoil-immersed power transformer, a set of PD test platform that can simulate suspendedmovable particles defect is designed, three typical particles defects are used in PDmeasurement to obtain a large number of artificial test data, a systematic analysis for thePD signals waveform characteristics in different experimental conditions are studied.
First studied the PD characteristics of suspended movable particles defect and itsinfluencing factors, the increase of applied voltage and suspended particles content willlead to a more intense discharge development, while the the increase in the oil flowvelocity will help improve the insulation strength, there is a suitable temperature rangethat makes development of partial discharge severity to a minimum. Experimental andtheoretical analysis should be carried to identify the appropriate range of operatingtemperature and flow velocity range for each transformer, to improve the internalelectric field distribution of the oil duct, thus make the maximum extent to reduce the hazards of partial discharge.
For the random pulse disturbance resulting the PD image dye-noise problem, firstproposed Contourlet transform noise suppression processing. Based on analyzing themain factor influenced the effect of Contourlet transform denoising, which are noiselevel, the decomposition layer, the decomposition of direction number and the differentPD types, it can be seen that noise level and the decomposition layer has great influence,revealing that Contourlet transform algorithm can correctly extract the distributioninformation from PD three-dimensional image, effectively suppress the white noise inPD image.
First proposed a PD feature extraction method based on the PCNN output entropysequence, the PCNN output entropy sequence in a certain range has the character oftranslation and scaling invariance. The proposed criteria for β value makes theentropy sequence faster convergence and thus greatly reduce the length of entropysequence, improving the speed of the identification. Extracting the PCNN outputentropy sequence from the dye-noise PD images and denoised PD images, find that theoutput entropy sequence from the dye-noise PD image has huge mean square error withthe other types PD defects, while it is smaller in the similar discharge patterns, helpfulfor the accurate classification of the PD image and prove the effectiveness and necessityfor PD image denoising by Contourlet transform.
引文
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