摘要
气体绝缘组合电器(Gas Insulated Substation,简称GIS)具有占地面积小、运行安全可靠、维护工作量少、检修周期长等一系列优点,在电力系统得到了广泛的应用。然而,GIS内部不可避免的绝缘缺陷会逐步扩展并导致故障发生,最常见的电气故障特征是在绝缘完全击穿或闪络前发生局部放电(Partial Discharge,简称PD)。研究表明,采用超高频(Ultra-high frequency,简称UHF)法对GIS PD信号检测,可以避开常规电气测试方法中难以避开的电晕放电等干扰,从而有效发现GIS内部存在的绝缘缺陷。GIS中常见的绝缘缺陷有金属突出物缺陷、自由金属微粒缺陷、绝缘子金属污染物缺陷、气隙缺陷等。因此,针对GIS内不同绝缘缺陷的PD所激发的超高频电磁波,深入研究GIS内UHF PD信号特征,分别建立相应的数学模型并进行模式识别的研究,对于认识GIS中放电信号的传输特性、指导GIS放电检测、掌握GIS的绝缘状况和缺陷类型,以及指导GIS的检修工作和保证GIS的安全可靠运行有着十分重要的意义。
为此,本文在分析国内外GIS局部放电检测、数学模型构建和模式识别研究状况的基础上,深入研究了不同绝缘缺陷UHF PD信号波形特征,首次构建了GIS内UHF PD信号数学模型,并提出了一种适合UHF PD信号模式识别的新方法。其主要工作有:
①采用UHF法对GIS模拟装置内所设计的4种绝缘缺陷物理模型进行了大量的PD试验,获取了4种典型GIS缺陷的物理模型分别在不同尺寸规格下、置于不同位置处、在不同电压等级下的UHF PD信号波形,对GIS内不同绝缘缺陷UHF PD信号特征做了深入研究和系统分析;
②针对GIS内4种典型绝缘缺陷UHF PD信号特征,构建了GIS的UHF PD信号数学模型,给出了数学模型的参数值,并从拟合误差、能量分布和时频联合分析的角度证明了数学模型构建的正确性,最后将构建的数学模型应用于PD信号复小波去噪的理论仿真研究;
③根据二元树复小波变换优良平移时不变特性的特点,提出了一种基于二元树复小波变换的时频特征空间构造方法,通过伸缩和平移等运算功能对UHF PD信号进行多尺度细化分析,综合选用了UHF PD信号在各频带投影序列的能量、在各个尺度下的极大值和统计参量,完备地描述了UHF PD信号的特性,构造了完整的UHF PD信号特征空间,并根据已建立的GIS内4种典型缺陷UHF PD数学模型,对特征子集进行验证,进一步证明了所构造特征空间的正确性;
④深入研究流形学习中的LLE算法、ISOMAP算法、Laplacian Eigenmap算法、KPCA等,提出了基于流形学习的UHF PD特征空间降维方法,可保持降低维数空间后的原有信号特性最大限度不变,并使降维处理后的UHF PD信号能量特征子集、极大值特征子集、统计特征子集作为组合神经网络分类器成员分类器的输入量,识别效果达到最优。
Gas Insulated Substation (GIS) has developed very quickly and has been in use all over the world, its compact size, high reliability, low maintenance have made it an attractive option in many circumstance. However, operation experiences show that intrinsic defects in GIS still cause accidents though its high reliability. It is well known that insulation breakdown is often preceded by Partial discharge (PD) activities. GIS internal defects can be discovered in time with the interferences such as corona discharge from the power system using Ultra-high frequency (UHF) method. Several types of defects occur in GIS, such as protrusion fixed to the conductor, free particles, surface contamination on the insulator and electrode gap defects and so on. Therefore, the analysis of UHF PD pulse shape, establishment of UHF PD mathematical models and pattern recognition of UHF PD are very important and necessary to realize UHF PD transmission, detect UHF PD signals, estimate GIS insulation condition and types of defects and guide its maintenance.
In this paper, UHF PD signals of typical insulated defects in GIS are deeply studied based on analyzing researches about PD detection in GIS, establishment of mathematical model and its pattern recognition home and abroad; secondly, mathematical models are established, analyzed and justified; lastly, a new method of PD pattern recognition are introduced. The main achievements are as follows,
①A plenty of PD data are sampled with artificial physical models of PD defect in laboratory in different condition, including different defect size or shape, different detector location, different voltage level and so on, and the same time, UHF PD signals are analyzed roundly;
②Mathematical models based on these defects are established, and the principles and methods of establishment are summarized. Furthermore, the parameters of mathematical models are given and the validity of PD mathematical model is proved by fitting error, power spectrum and time-frequency analysis. In the end, applications of the UHF PD mathematical models and complex wavelet transform for extracting the relevant signals from a white noise background are illustrated.
③A new method of feature extraction for UHF PD signals based on the dual-tree complex wavelet transform is proposed with its shift invariance. Furthermore, feature space of UHF PD signals are constructed by energy values, maximum of module and statistical parameters, which describes the characters of UHF signals very well. At last, the validity of feature space has been proved by UHF PD mathematical models in the simulation.
④A new method of dimension reduction for feature space of UHF PD signals based on manifold learning is proposed, and LLE algorithm, ISOMAP algorithm, Laplacian Eigenmap algorithm and KPCA condense data from the high-dimensional space to the low-dimensional space and maintain the structure of the original sample patterns greatly. The results show that the UHF PD pattern recognition is satisfied when the sub-space features of energy, maximum values and statistical parameters are used as the input of the combined NN.
引文
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