Partial discharge denoising for power cables

Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising.Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising

Enhanced partial discharge signal denoising using dispersion entropy optimized variational mode decomposition

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter [Formula: see text] for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals

A Research on Dimension Reduction Method of Time Series Based on Trend Division

The characteristics of high dimension, complexity and multi granularity of financial time series make it difficult to deal with effectively. In order to solve the problem that the commonly used dimensionality reduction methods cannot reduce the dimensionality of time series with different granularity at the same time, in this paper, a method for dimensionality reduction of time series based on trend division is proposed. This method extracts the extreme value points of time series, identifies the important points in time series quickly and accurately, and compresses them. Experimental results show that, compared with the discrete Fourier transform and wavelet transform, the proposed method can effectively process data of different granularity and different trends on the basis of fully preserving the original information of time series. Moreover, the time complexity is low, the operation is easy, and the proposed method can provide decision support for high-frequency stock trading at the actual level

Robust Andrew's sine estimate adaptive filtering

The Andrew's sine function is a robust estimator, which has been used in outlier rejection and robust statistics. However, the performance of such estimator does not receive attention in the field of adaptive filtering techniques. Two Andrew's sine estimator (ASE)-based robust adaptive filtering algorithms are proposed in this brief. Specifically, to achieve improved performance and reduced computational complexity, the iterative Wiener filter (IWF) is an attractive choice. A novel IWF based on ASE (IWF-ASE) is proposed for impulsive noises. To further reduce the computational complexity, the leading dichotomous coordinate descent (DCD) algorithm is combined with the ASE, developing DCD-ASE algorithm. Simulations on system identification demonstrate that the proposed algorithms can achieve smaller misalignment as compared to the conventional IWF, recursive maximum correntropy criterion (RMCC), and DCD-RMCC algorithms in impulsive noise. Furthermore, the proposed algorithms exhibit improved performance in partial discharge (PD) denoising.Comment: 5 pages, 5 figure

Examination on the Denoising Methods for Electrical and Acoustic Emission Partial Discharge Signals in Oil

Partial discharge (PD) measurements either through electrical or acoustic emission approaches can be subjected to noises that arise from different sources. In this study, the examination on the denoising methods for electrical and acoustic emission PD signal is carried out. The PD was produced through needle-plane electrodes configuration. Once the voltage reached to 30 kV, the electrical and acoustic emission PD signals were recorded and additive white Gaussian noise (AWGN) was introduced. These signals were then denoised using moving average (MA), finite impulse response (FIR) low/high-pass filters, and discrete wavelet transform (DWT) methods. The denoising methods were evaluated through ratio to noise level (RNL), normalized root mean square error (NRMSE) and normalized correlation coefficient (NCC). In addition, the computation times for all denoising methods were also recorded. Based on RNL, NRMSE and NCC indexes, the performances of the denoising methods were analyzed through normalization based on the coefficient of variation (). Based on the current study, it is found that DWT performs well to denoise the electrical PD signal based on the RNL and NRMSE index while MA has a good denoising NCC and computation time index for acoustic emission PD signal

An Automatic Tool for Partial Discharge De-noising via Short Time Fourier Transform and Matrix Factorization

This paper develops a fully automatic tool for the denoising of partial discharge (PD) signals occurring in electrical power networks and recorded in on-site measurements. The proposed method is based on the spectral decomposition of the PD measured signal via the joint application of the short-time Fourier transform and the singular value decomposition. The estimated noiseless signal is reconstructed via a clever selection of the dominant contributions, which allows us to filter out the different spurious components, including the white noise and the discrete spectrum noise. The method offers a viable solution which can be easily integrated within the measurement apparatus, with unavoidable beneficial effects in the detection of important parameters of the signal for PD localization. The performance of the proposed tool is first demonstrated on a synthetic test signal and then it is applied to real measured data. A cross comparison of the proposed method and other state-of-the-art alternatives is included in the study

Identification and Location of PD Defects in Medium voltage Underground Power Cables Using High Frequency Current Transformer

Fault location is an important diagnostic task in condition monitoring of underground medium voltage cables. Available solutions are well capable of determining the location of a single partial discharge (PD) defect on a cable section. In case several PD defects are active simultaneously along a cable section, the interpretation of the measured data becomes complex to identify the presence of more than one PD sources. In this paper, experimental investigation of two PD defects/sources at different locations on a medium voltage (MV) cable section is presented. A high frequency current transformer is used for single end PD measurements. Time domain reflectometry-based in-depth study of the reflected pulses provides the most valuable information to identify the presence of PD sources which further leads to the location of the individual PD sources. In this paper, the proposed solution is presented for two PD sources, however, the same methodology can be extended to locate multiple PD sources on the cable.fi=vertaisarvioitu|en=peerReviewed

Discrimination of PD Signal using Wavelet Transform for Insulation Diagnosis of GIS under HVDC

중전기 산업에서 부분방전의 검출 및 분석 기술은 전력설비의 상태진단 및 자산관리를 위한 가장 효과적인 방법으로 간주되어 왔다. 그러나 검출의 감도 및 정확도는 현장 노이즈에 영향을 받아 위험도 평가, 결함 판별 또는 위치 추정의 오류를 유발한다. 교류전압에서 부분방전 신호의 노이즈 제거는 활발히 연구되었지만, 최근 이슈가 되고 있는 HVDC에서 관련 연구는 미흡한 실정이다. HVDC 기술이 급속히 발전되면서 관련 전력설비 진단을 위하여, HVDC에서 부분방전 신호의 노이즈를 제거할 필요가 있다. 이들 배경으로 본 논문에서는 HVDC 가스절연구조에서 절연진단의 감도 및 정확도를 향상할 목적으로 웨이블릿 변환을 이용하여 부분방전 신호를 식별하였다. 직류에서 부분방전 신호를 발생하기 위하여 실험계를 구축하였다. HVDC는 몰드변압기, 고압 다이오드 및 커패시터로 구성된 정류회로로 발생시켰다. 가스절연구조에서 발생하는 절연결함을 모의하기 위하여 도체돌출, 외함돌출, 자유입자 및 절연물 크랙 4종의 전극계를 제작하였다. 전극계는 SF6 가스를 0.5MPa로 충진하였으며, 차폐함을 사용하여 외부 노이즈의 영향을 최소화하였다. 4종의 모의결함에서 부분방전 단일펄스를 검출하여 HVDC에서 부분방전을 분석하기 위한 웨이블릿 변환 기술을 최적화하였다. 상관계수 및 동적시간워핑 법을 이용하여 부분방전 펄스와 다양한 모웨이블릿의 유사성을 비교하였다. 결과로부터 동적시간워핑 법에 의해 선정된 모웨이블릿 bior2.6이 HVDC에서 부분방전 신호 분석에 가장 적합하였다. 최적의 문턱함수 및 문턱값을 선정하기 위하여 감쇠 지수 펄스 및 감쇠 진동 펄스를 모의하였으며, 신호-잡음비, 상관계수, 크기 변화를 비교한 결과, 중간 문턱함수-자동 문턱값이 최적의 조합으로 선정되었다. 실제 부분방전 분석 및 평가 시 단일 펄스가 아닌 펄스 시퀀스가 사용되기 때문에, 최적화된 웨이블릿 변환 기술을 이용하여 모의결함으로부터 검출된 부분방전 신호를 식별하였으며, 그 효과를 고역 통과 필터와 비교하였다. 결과로부터, 부분방전 신호 식별 시 고역통과필터에 비해 웨이블리 기술이 잡음 감소와 상관계수가 높게, 크기 변화가 낮게 나타났다. 뿐만 아니라 웨이블릿 방법은 배경 잡음, 진폭 변조 전파 장해, 비정현 잡음 및 스위칭 임펄스로 간섭된 부분방전 신호를 식별하는 데 효과적이었다. 본 논문에서 제안한 웨이블릿 변환 기술은 현장의 노이즈로부터 부분방전 신호를 성공적으로 식별하였다. 향후 HVDC에서 가스절연구조의 부분방전 검출 및 분석에 적용될 것으로 예상되며, 부분방전 검출, 위험도 평가, 결함 판별 및 위치 측정의 정확도가 향상될 수 있을 것으로 기대된다.Contents ⅰ Lists of Figures and Tables ⅲ Abstract ⅵ Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Dissertation Outline 5 Chapter 2 Partial Discharge Review 7 2.1 Mechanism and Recurrence 7 2.2 Detection and Measurement 12 2.3 Analysis Methods 23 Chapter 3 Experiment and Optimization 45 3.1 Experimental Setup 45 3.2 Optimization of Wavelet Transform 49 Chapter 4 Discrimination of PD Sequences 66 4.1 DEP-type Pulse Sequence 70 4.2 DOP-type Pulse Sequence 79 Chapter 5 Conclusions 89Docto