GIS内多绝缘缺陷产生混合局部放电信号的分离研究
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摘要
气体绝缘组合电器(Gas Isolated Switchgear,简称GIS)以低故障率和长检修周期保障现代电网的安全稳定运行。局部放电(Partial Discharge,简称PD)是GIS内潜在绝缘缺陷故障的早期表现形式,外置超高频(Ultra-high Frequency,简称UHF)PD信号辨识已经发展成为GIS内绝缘缺陷故障辨识最直接最有效的手段。GIS内的特殊空间结构和复杂的电气元器件布局以及多态PD随机性的产生机理可能导致GIS内多绝缘缺陷状态。本文摈弃GIS内只有单一绝缘缺陷诱发PD信号的认知,首次从多绝缘缺陷研究GIS内的绝缘缺陷故障。
多绝缘缺陷必然诱发UHF混合PD信号,直接通过混合PD信号获取GIS内绝缘缺陷信息(绝缘缺陷个数和类型)是极具挑战的前沿性课题。盲源分离(Blind Source Separation,简称BSS)理论以不直接关心源信号的信息特征及混合过程,仅通过对检测混合信号分离就能获取源信号信息。这个间接获取源信号信息的“黑匣子”特点与通过外置UHF混合PD信号直接获取GIS内绝缘缺陷信息具有相同目的,因此,本文首次提出采用BSS理论对GIS内多绝缘缺陷诱发的外置UHF混合PD信号进行分离研究。
对受干扰PD信号直接降噪,会因过度降噪或降噪不够以及降噪方法的选择不当,达不到理想的降噪效果,以造成PD信号波形不应有的畸变乃至特征信息丢失。针对PD信号直接降噪的这种盲目性,本文首次提出对PD信号的受干扰程度先进行定量评估后再决定降噪以及降噪深度选择的思想,并提出采用基于二阶统计量的信噪比估计理论以及构建适合PD信号的信噪比估计规则,对实测UHF PD信号的受干扰程度进行定量评价。
在GIS内多绝缘缺陷状态下,各类绝缘缺陷对应的物理源位置以及PD信号传播通道具有不确定性,难以用确定的数学模型描述GIS内PD信号的混合过程。作为混合PD信号分离的理论分析基础,提出采用简化的线性瞬时混合模型和线性卷积混合模型分别定性描述GIS内PD信号的混合过程,并针对不同混合模型提出对应的混合信号分离算法。在实验室GIS模型内构造四类典型绝缘缺陷对应的物理模型,实测四类UHF单一PD信号,两两组合,构造四组模拟混合PD信号;同样,在试验GIS模型内构造四组对应的多绝缘缺陷,获取实测UHF混合PD信号。分别对模拟和实测混合PD信号进行分离和对比分析研究。
对线性瞬时混合模型,提出采用基于二阶统计量的盲辨识分离算法(SOBI)及其改进的权值调整分离算法(WASOBI),对模拟和实测混合PD信号进行分离;利用评价参数分别对混合PD信号的分离效果进行定量评价,比较算法改进前后分离性能优劣;同时,定量比较分离评价参数,以确定影响混合PD信号分离的关键因素:构成GIS内多绝缘缺陷的绝缘缺陷类型、绝缘缺陷间相对距离及混合过程。
对线性卷积混合模型,针对实测混合PD信号的长时非平稳特征,借助加窗傅里叶变换将混合PD信号转换为在短时条件下平稳的频域混合PD信号,并采用解相关算法进行分离;利用频域分离信号包络线间的相关性对分离信号进行准确重构,再通过傅里叶反变换获得分离的时域单一PD信号;采用模拟和实测混合PD信号分别进行分离和对比分析,研究实测混合信号的卷积混合模型构成特征以及验证GIS内单一PD信号的卷积混合模型假设,为GIS内多绝缘缺陷的深入研究奠定理论基础。
Gas isolated switchgear (GIS) with low failure rate and long inspection span serves the safe and stable operation of modern power grid. Partial discharge (PD) is a common precursor to the potential insulation defects in GIS cylinder and the identification of external ultra-high frequency (UHF) PD signal has developed to be an most immediate and effective approach to insulation defects inside GIS. It is possible that the special space architecture in GIS and PD mechanism together results in the multiple insulation defects in GIS cylinder. This paper gets rid of the unsound knowledge that the only single insulation defect excites single PD signal and considers the status on multiple insulation defects in GIS.
It is sure that multiple insulation defects ignite mixing PD signals. Directly through these mixing signals to acquire the information on insulation defects in GIS is extremely of challenging and front. Blind source separation (BSS) theory disconsiders the information from source signals and mixing process to acquire the knowledge of source signals with the help of separating several mixing signals. The characteristic of“black box”indirectly for PD knowledge is extremely similar to that of obtaining the information (number and type) on multiple insulation defects in GIS from external UHF mixing PD signals. Therefore, this paper introduces the BSS theory into research on multiple insulation defects in GIS for the first time.
Directly denoising PD signal probably makes the expected denoising effects unsatisfying due to over-denoising or under-denoising and inappropriate denoising method, and even causes the unexpected distortion of PD waveform and the loss of its characteristic information. Hence, this idea on firstly evaluating the interference degree of PD signal in the quantitative and then denoising it is proposed in this paper and the rule is built for the signal-to-noise ratio (SNR) estimation suitable for PD signal to quantitatitively evaluate the interference degree on actual PD signal on the basis of 2- order statistic SNR estimation theory.
Due to uncertain positions of insulation defects under the status of multiple insulation defects in GIS, it is hard to build up an accurate mathematical model for mixing process of PD signals in GIS cylinder. For theoretical analysis to the separation of mixing PD signals, the simplified linear & instantaneous mixing model and linear & convolutive mixing model are employed, respectively. Furthermore, different BSS algorithms are described for different models. In experimental GIS model, four physical models for typical insulation defects are constructed for actual PD source signals. Two of them for a group, 4 groups of simulating mixing PD signals are designed. Similarly, 4 groups of models of multiple insulation defects are arranged in this GIS for actual mixing UHF PD signals. Both of them are for the separation and comparative analysis of mixing PD signals.
2-oder statistic blind identification separation algorithm SOBI and its weights-adjusted algorithm WASOBI for linear & instantaneous mixing model are employed to separate these mixing PD signals including simulating and real ones. Their separation effects are quantitatively evaluated by several evaluation parameters so that the performances for SOBI and WASOBI are compared. On the other hand, critical influence factors on separation effect are discussed, including the types of insulation defects, the distance between two insulation defects and the mixing process in GIS.
For linear & convolutive mixing model, actual mixing PD signals with long time and non- stationary character are partitioned into a series of short time and stationary mixing PD signals in frequency domain space by virtue of windowed Fourier transform. Correlation decomposition algorithm is used to separate these subsets of statinary PD signals. Taking advantage of the correlation of envelopes of these separated PD signals, they are accurately reconstructed and then are transformed into single PD signals in time domain space by inverse Fourier transform. Mixing PD signals from simulative mixing process and GIS model are employed to demosnstrate separation experiment and comparative analysises are done. The characteristic of linear & convolutive mixing model for actual PD signals are stuyed and then the hypothesis of convolutive mixing process on actual GIS inside is validted, which paves a way to the further research on multiple insulation defects in GIS.
多绝缘缺陷必然诱发UHF混合PD信号,直接通过混合PD信号获取GIS内绝缘缺陷信息(绝缘缺陷个数和类型)是极具挑战的前沿性课题。盲源分离(Blind Source Separation,简称BSS)理论以不直接关心源信号的信息特征及混合过程,仅通过对检测混合信号分离就能获取源信号信息。这个间接获取源信号信息的“黑匣子”特点与通过外置UHF混合PD信号直接获取GIS内绝缘缺陷信息具有相同目的,因此,本文首次提出采用BSS理论对GIS内多绝缘缺陷诱发的外置UHF混合PD信号进行分离研究。
对受干扰PD信号直接降噪,会因过度降噪或降噪不够以及降噪方法的选择不当,达不到理想的降噪效果,以造成PD信号波形不应有的畸变乃至特征信息丢失。针对PD信号直接降噪的这种盲目性,本文首次提出对PD信号的受干扰程度先进行定量评估后再决定降噪以及降噪深度选择的思想,并提出采用基于二阶统计量的信噪比估计理论以及构建适合PD信号的信噪比估计规则,对实测UHF PD信号的受干扰程度进行定量评价。
在GIS内多绝缘缺陷状态下,各类绝缘缺陷对应的物理源位置以及PD信号传播通道具有不确定性,难以用确定的数学模型描述GIS内PD信号的混合过程。作为混合PD信号分离的理论分析基础,提出采用简化的线性瞬时混合模型和线性卷积混合模型分别定性描述GIS内PD信号的混合过程,并针对不同混合模型提出对应的混合信号分离算法。在实验室GIS模型内构造四类典型绝缘缺陷对应的物理模型,实测四类UHF单一PD信号,两两组合,构造四组模拟混合PD信号;同样,在试验GIS模型内构造四组对应的多绝缘缺陷,获取实测UHF混合PD信号。分别对模拟和实测混合PD信号进行分离和对比分析研究。
对线性瞬时混合模型,提出采用基于二阶统计量的盲辨识分离算法(SOBI)及其改进的权值调整分离算法(WASOBI),对模拟和实测混合PD信号进行分离;利用评价参数分别对混合PD信号的分离效果进行定量评价,比较算法改进前后分离性能优劣;同时,定量比较分离评价参数,以确定影响混合PD信号分离的关键因素:构成GIS内多绝缘缺陷的绝缘缺陷类型、绝缘缺陷间相对距离及混合过程。
对线性卷积混合模型,针对实测混合PD信号的长时非平稳特征,借助加窗傅里叶变换将混合PD信号转换为在短时条件下平稳的频域混合PD信号,并采用解相关算法进行分离;利用频域分离信号包络线间的相关性对分离信号进行准确重构,再通过傅里叶反变换获得分离的时域单一PD信号;采用模拟和实测混合PD信号分别进行分离和对比分析,研究实测混合信号的卷积混合模型构成特征以及验证GIS内单一PD信号的卷积混合模型假设,为GIS内多绝缘缺陷的深入研究奠定理论基础。
Gas isolated switchgear (GIS) with low failure rate and long inspection span serves the safe and stable operation of modern power grid. Partial discharge (PD) is a common precursor to the potential insulation defects in GIS cylinder and the identification of external ultra-high frequency (UHF) PD signal has developed to be an most immediate and effective approach to insulation defects inside GIS. It is possible that the special space architecture in GIS and PD mechanism together results in the multiple insulation defects in GIS cylinder. This paper gets rid of the unsound knowledge that the only single insulation defect excites single PD signal and considers the status on multiple insulation defects in GIS.
It is sure that multiple insulation defects ignite mixing PD signals. Directly through these mixing signals to acquire the information on insulation defects in GIS is extremely of challenging and front. Blind source separation (BSS) theory disconsiders the information from source signals and mixing process to acquire the knowledge of source signals with the help of separating several mixing signals. The characteristic of“black box”indirectly for PD knowledge is extremely similar to that of obtaining the information (number and type) on multiple insulation defects in GIS from external UHF mixing PD signals. Therefore, this paper introduces the BSS theory into research on multiple insulation defects in GIS for the first time.
Directly denoising PD signal probably makes the expected denoising effects unsatisfying due to over-denoising or under-denoising and inappropriate denoising method, and even causes the unexpected distortion of PD waveform and the loss of its characteristic information. Hence, this idea on firstly evaluating the interference degree of PD signal in the quantitative and then denoising it is proposed in this paper and the rule is built for the signal-to-noise ratio (SNR) estimation suitable for PD signal to quantitatitively evaluate the interference degree on actual PD signal on the basis of 2- order statistic SNR estimation theory.
Due to uncertain positions of insulation defects under the status of multiple insulation defects in GIS, it is hard to build up an accurate mathematical model for mixing process of PD signals in GIS cylinder. For theoretical analysis to the separation of mixing PD signals, the simplified linear & instantaneous mixing model and linear & convolutive mixing model are employed, respectively. Furthermore, different BSS algorithms are described for different models. In experimental GIS model, four physical models for typical insulation defects are constructed for actual PD source signals. Two of them for a group, 4 groups of simulating mixing PD signals are designed. Similarly, 4 groups of models of multiple insulation defects are arranged in this GIS for actual mixing UHF PD signals. Both of them are for the separation and comparative analysis of mixing PD signals.
2-oder statistic blind identification separation algorithm SOBI and its weights-adjusted algorithm WASOBI for linear & instantaneous mixing model are employed to separate these mixing PD signals including simulating and real ones. Their separation effects are quantitatively evaluated by several evaluation parameters so that the performances for SOBI and WASOBI are compared. On the other hand, critical influence factors on separation effect are discussed, including the types of insulation defects, the distance between two insulation defects and the mixing process in GIS.
For linear & convolutive mixing model, actual mixing PD signals with long time and non- stationary character are partitioned into a series of short time and stationary mixing PD signals in frequency domain space by virtue of windowed Fourier transform. Correlation decomposition algorithm is used to separate these subsets of statinary PD signals. Taking advantage of the correlation of envelopes of these separated PD signals, they are accurately reconstructed and then are transformed into single PD signals in time domain space by inverse Fourier transform. Mixing PD signals from simulative mixing process and GIS model are employed to demosnstrate separation experiment and comparative analysises are done. The characteristic of linear & convolutive mixing model for actual PD signals are stuyed and then the hypothesis of convolutive mixing process on actual GIS inside is validted, which paves a way to the further research on multiple insulation defects in GIS.
引文
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