电容型设备绝缘在线监测与智能化故障诊断研究
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摘要
随着电力工业的迅速发展,电网规模不断扩大,结构日益复杂,由电气设备故障引发的电力系统事故所造成的危害也越来越严重。因此开展对电容型设备这种重要的输变电设备的绝缘在线监测与故障诊断任务对于全面实现状态维修具有十分重要的现实意义,但是目前离用户期望的稳定性和准确性还有相当距离。本文在总结和借鉴国内外相关电气设备绝缘在线监测与故障诊断技术的基础上,从信号频率提取、介质损耗测量方法以及绝缘状态与故障诊断三个方面进行了深入研究和探讨。
在分析介质损耗角正切测量中DFT方法存在的频谱泄漏和栅栏效应的基础上,本文提出了基于DFT的自适应整周期采样算法,以信号相邻两周期的相角差为控制条件,在现有采样信号波形上提取信号周期(频率),可以消除由非整周期截断带来的严重干扰介质损耗角测量准确性的DFT分析误差,同时还可以通过反馈控制回路调整硬件采样频率。推导了在基于DFT的自适应整周期采样算法误差对介质损耗角测量影响不大条件下的采样频率,并进行了数值仿真。仿真结果表明该方法具有很好的自适应性和灵活性。
针对监测现场干扰频率计算准确性的主要因素-高斯白噪声,本文引入了具有时频域特性的小波去噪方法作为采样信号的前置滤波环节,全面分析了基小波函数、分解层次等去噪因子对频率计算的影响,在此基础上,提出了在信号状态评估下基于最优小波去噪的频率提取算法,并采用BP神经网络实现了从信号状态到最优小波去噪模式的识别。本文详细介绍了自适应噪声抵消技术,为克服参考信号与被抵消信号的非线性相关问题,采用BP神经网络构建其滤波器,可以通过在线训练的方式学习现场噪声传输特性,并以此提出了基于BP神经网络非线性自适应滤波的频率提取算法。仿真结果表明两种方法均能有效提取信号频率,计算误差小于0.01Hz。本文还对比分析了小波去噪和自适应噪声抵消两种方法的使用特点,找出了其现场适用条件。
本文提出了具有同时性的综合相对测量法,以连接到同一母线下同相多台设备绝缘的泄漏电流信号互为基准进行比较,提取相对介质损耗角,不需要PT低压侧电压。该方法从所有纳入测量范围设备的全局衡量单个设备的绝缘状况,消除相似性干扰,提高监测数据的稳定性,考虑了多台设备特征参数的同时测量,避免由突发性瞬态干扰导致的测量背景不一致,并建设性的提出了沿绝缘出厂老化曲线线性部分对称变换的修正措施,削弱由介质损耗角曲线局部逆反性变化引起的相对介质损耗角振荡。现场应用表明该方法能较好的满足在线监测的需要。
本文系统总结和阐述了模糊集理论和神经网络的诊断原理、方法,将综合相对法获得的相对介质损耗角以及测量的电容作为特征输入,以实践经验为指导提出了基于模糊规则库推理的绝缘状态诊断模型,并将其诊断结果应用于绝缘状态变化趋势的研究,提出了基于模糊模式识别的趋势诊断模型。本文还在提炼的样本数据基础上建立了基于BP神经网络的绝缘状态分类器和基于PNN神经网络的绝缘故障主原因诊断模型,特别是这种绝缘故障主原因诊断模型可以与上述模糊诊断模型结合起来完成从绝缘状态到绝缘故障类型的识别。本文进一步探讨了将神经网络引入模糊诊断的方法。仿真研究表明上述模型都取得了较为理想的结果,具有一定的实用价值。
With the rapid development of power industry, the structure of power grid continues toexpand, and has become increasingly complicated. However, power system accident causedby electrical equipment fault load to more and more serious loss. Capacitive equipment isimportant power transmission and transformation unit, so the on-line monitoring and faultdiagnosis of their insulation is of great practical for realization of state maintenance. On thebasis of summarizing the electrical equipment insulation on-line monitoring and faultdiagnosis technology at home and abroad, stability and accuracy of monitoring is in-depthresearched and discussed in three aspects of signal frequency extraction, dielectric lossmeasurement, insulation state and fault diagnosis.
Based on analysis of spectrum leakage and picket fence effect in DFT method, aself-adaptive complete period sampling algorithm based on DFT is put forward to reduceserious phase error caused by incorrect truncation of sampled signal period. The phaseangle difference of two adjacent signal periods is treated as loop control condition in thismethod. When it is less than pre-established value, the period is found. If the phase angledifference is not less than the pre-established value beyond number of cycles, procedurewill sample again under normal sampling frequency or changed one by hardwareadjustment, and calculate signal frequency again. Sampling frequency is also deduced oncondition that algorithm error has little effect on the dielectric loss angle measurement. Thesimulation results show that this method has better adaptability and flexibility.
This paper introduces wavelet denoising method having time-frequency domain aspre-filter of sampling signal against the main factor interfering with calculation accuracy offrequency - Gaussian white noise on the monitoring scene. A comprehensive analysis ofinfluence of denoising factors, such as wavelet functions and level of decomposition, tofrequency calculation is done. On this basis, frequency algorithm based on optimal waveletdenoising under assessment of signal condition is presented, and pattern recognition fromsignal condition to optimal mode of wavelet denoising is realized by BP neural network.This paper also describes adaptive noise cancellation technology in detail. For overcomingthe non-linear-related problem between reference signal and noise, BP neural network isapplied to construct its filter that can learn transmission characteristics of on-site noise byreal-time BP neural network training. Therefore, frequency algorithm based on BP neural network non-linear adaptive filtering is proposed. Numerical simulation shows that twomethods can effectively extract signal frequency, and errors are lower than 0.01Hz. Thispaper also compares the wavelet denoising method and adaptive noise cancellation method,and acquires their applied conditions on the scene.
Synthetical relative method including synchronously measuring idea is proposed. Itselects the same phases of all measured equipments connected to same bus, and thencompares the phase angle difference between leakage currents of insulation of theseequipments, called as relative dielectric loss angle. Therefore, it needn’t regard voltage onsecondaryside of PT as reference voltage. The method incorporates all of equipments in themeasuring range as an organic whole, and considered the insulation condition of singleequipment from the global view, so it can effectively eliminate similar disturbance andimprove stabilityof the monitoring data, and because of synchronyof signal acquirement ofmultiple equipments, can avoid the inconsistent measurement background caused bysudden transient interference. The corrective measure on symmetrical transform along thelinear part of insulation factory aging curve is also put forward to weaken oscillation ofrelative dielectric loss angle led by the local converse changes of loss angle. Theapplication of the method on the scene is satisfied.
Fuzzy diagnosis theory is systematically analyzed in this paper. Regarding relativedielectric loss as feature space of input, diagnostic model based on the reasoning of fuzzyrules for the insulation state is proposed under the guidance of collection of practicalexperience, and its diagnostic results is further used for study of insulation state trend.Diagnostic model based on fuzzy pattern recognition for insulation state trend is built. Theneural network method has also been applied for diagnosis of insulation state and the mainreason of insulation fault. Accordingly, BP and PNN neural network model is established.The model of main insulation fault also can be combined with fuzzy diagnosis to realizecomplete recognition from insulation state to type of fault. The simulation results show thatthese models have better practical value. Integration of fuzzy theory and neural network indiagnosis is further explored.
在分析介质损耗角正切测量中DFT方法存在的频谱泄漏和栅栏效应的基础上,本文提出了基于DFT的自适应整周期采样算法,以信号相邻两周期的相角差为控制条件,在现有采样信号波形上提取信号周期(频率),可以消除由非整周期截断带来的严重干扰介质损耗角测量准确性的DFT分析误差,同时还可以通过反馈控制回路调整硬件采样频率。推导了在基于DFT的自适应整周期采样算法误差对介质损耗角测量影响不大条件下的采样频率,并进行了数值仿真。仿真结果表明该方法具有很好的自适应性和灵活性。
针对监测现场干扰频率计算准确性的主要因素-高斯白噪声,本文引入了具有时频域特性的小波去噪方法作为采样信号的前置滤波环节,全面分析了基小波函数、分解层次等去噪因子对频率计算的影响,在此基础上,提出了在信号状态评估下基于最优小波去噪的频率提取算法,并采用BP神经网络实现了从信号状态到最优小波去噪模式的识别。本文详细介绍了自适应噪声抵消技术,为克服参考信号与被抵消信号的非线性相关问题,采用BP神经网络构建其滤波器,可以通过在线训练的方式学习现场噪声传输特性,并以此提出了基于BP神经网络非线性自适应滤波的频率提取算法。仿真结果表明两种方法均能有效提取信号频率,计算误差小于0.01Hz。本文还对比分析了小波去噪和自适应噪声抵消两种方法的使用特点,找出了其现场适用条件。
本文提出了具有同时性的综合相对测量法,以连接到同一母线下同相多台设备绝缘的泄漏电流信号互为基准进行比较,提取相对介质损耗角,不需要PT低压侧电压。该方法从所有纳入测量范围设备的全局衡量单个设备的绝缘状况,消除相似性干扰,提高监测数据的稳定性,考虑了多台设备特征参数的同时测量,避免由突发性瞬态干扰导致的测量背景不一致,并建设性的提出了沿绝缘出厂老化曲线线性部分对称变换的修正措施,削弱由介质损耗角曲线局部逆反性变化引起的相对介质损耗角振荡。现场应用表明该方法能较好的满足在线监测的需要。
本文系统总结和阐述了模糊集理论和神经网络的诊断原理、方法,将综合相对法获得的相对介质损耗角以及测量的电容作为特征输入,以实践经验为指导提出了基于模糊规则库推理的绝缘状态诊断模型,并将其诊断结果应用于绝缘状态变化趋势的研究,提出了基于模糊模式识别的趋势诊断模型。本文还在提炼的样本数据基础上建立了基于BP神经网络的绝缘状态分类器和基于PNN神经网络的绝缘故障主原因诊断模型,特别是这种绝缘故障主原因诊断模型可以与上述模糊诊断模型结合起来完成从绝缘状态到绝缘故障类型的识别。本文进一步探讨了将神经网络引入模糊诊断的方法。仿真研究表明上述模型都取得了较为理想的结果,具有一定的实用价值。
With the rapid development of power industry, the structure of power grid continues toexpand, and has become increasingly complicated. However, power system accident causedby electrical equipment fault load to more and more serious loss. Capacitive equipment isimportant power transmission and transformation unit, so the on-line monitoring and faultdiagnosis of their insulation is of great practical for realization of state maintenance. On thebasis of summarizing the electrical equipment insulation on-line monitoring and faultdiagnosis technology at home and abroad, stability and accuracy of monitoring is in-depthresearched and discussed in three aspects of signal frequency extraction, dielectric lossmeasurement, insulation state and fault diagnosis.
Based on analysis of spectrum leakage and picket fence effect in DFT method, aself-adaptive complete period sampling algorithm based on DFT is put forward to reduceserious phase error caused by incorrect truncation of sampled signal period. The phaseangle difference of two adjacent signal periods is treated as loop control condition in thismethod. When it is less than pre-established value, the period is found. If the phase angledifference is not less than the pre-established value beyond number of cycles, procedurewill sample again under normal sampling frequency or changed one by hardwareadjustment, and calculate signal frequency again. Sampling frequency is also deduced oncondition that algorithm error has little effect on the dielectric loss angle measurement. Thesimulation results show that this method has better adaptability and flexibility.
This paper introduces wavelet denoising method having time-frequency domain aspre-filter of sampling signal against the main factor interfering with calculation accuracy offrequency - Gaussian white noise on the monitoring scene. A comprehensive analysis ofinfluence of denoising factors, such as wavelet functions and level of decomposition, tofrequency calculation is done. On this basis, frequency algorithm based on optimal waveletdenoising under assessment of signal condition is presented, and pattern recognition fromsignal condition to optimal mode of wavelet denoising is realized by BP neural network.This paper also describes adaptive noise cancellation technology in detail. For overcomingthe non-linear-related problem between reference signal and noise, BP neural network isapplied to construct its filter that can learn transmission characteristics of on-site noise byreal-time BP neural network training. Therefore, frequency algorithm based on BP neural network non-linear adaptive filtering is proposed. Numerical simulation shows that twomethods can effectively extract signal frequency, and errors are lower than 0.01Hz. Thispaper also compares the wavelet denoising method and adaptive noise cancellation method,and acquires their applied conditions on the scene.
Synthetical relative method including synchronously measuring idea is proposed. Itselects the same phases of all measured equipments connected to same bus, and thencompares the phase angle difference between leakage currents of insulation of theseequipments, called as relative dielectric loss angle. Therefore, it needn’t regard voltage onsecondaryside of PT as reference voltage. The method incorporates all of equipments in themeasuring range as an organic whole, and considered the insulation condition of singleequipment from the global view, so it can effectively eliminate similar disturbance andimprove stabilityof the monitoring data, and because of synchronyof signal acquirement ofmultiple equipments, can avoid the inconsistent measurement background caused bysudden transient interference. The corrective measure on symmetrical transform along thelinear part of insulation factory aging curve is also put forward to weaken oscillation ofrelative dielectric loss angle led by the local converse changes of loss angle. Theapplication of the method on the scene is satisfied.
Fuzzy diagnosis theory is systematically analyzed in this paper. Regarding relativedielectric loss as feature space of input, diagnostic model based on the reasoning of fuzzyrules for the insulation state is proposed under the guidance of collection of practicalexperience, and its diagnostic results is further used for study of insulation state trend.Diagnostic model based on fuzzy pattern recognition for insulation state trend is built. Theneural network method has also been applied for diagnosis of insulation state and the mainreason of insulation fault. Accordingly, BP and PNN neural network model is established.The model of main insulation fault also can be combined with fuzzy diagnosis to realizecomplete recognition from insulation state to type of fault. The simulation results show thatthese models have better practical value. Integration of fuzzy theory and neural network indiagnosis is further explored.
引文
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