Signal denoising techniques for partial discharge measurements
01 Dec 2005-IEEE Transactions on Dielectrics and Electrical Insulation (Institute of Electrical and Electronics Engineers)-Vol. 12, Iss: 6, pp 1182-1191
TL;DR: In this article, the denoising of PD signals caused by corona discharges is investigated and employed on simulated as well as real PD data, and several techniques are investigated.
Abstract: One of the major challenges of on-site partial discharge (PD) measurements is the recovery of PD signals from a noisy environment. The different sources of noise include thermal or resistor noise added by the measuring circuit, and high-frequency sinusoidal signals that electromagnetically couple from radio broadcasts and/or carrier wave communications. Sophisticated methods are required to detect PD signals correctly. Fortunately, advances in analog-to-digital conversion (ADC) technology, and recent developments in digital signal processing (DSP) enable easy extraction of PD signals. This paper deals with the denoising of PD signals caused by corona discharges. Several techniques are investigated and employed on simulated as well as real PD data.
Citations
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TL;DR: In this paper, the authors present a literature survey to access the state-of-the-art development in partial discharge classification, which varies greatly in terms of classification techniques used, choice of feature extraction, denoising method, training process, artificial defects created for training purposes and performance assessment.
143 citations
TL;DR: The most common insulating gas used in GIS is Sulfur hexafluoride (SF 6 ) gas, which is widely used as an effective electrical insulation as well as an arc-quenching medium.
Abstract: Power utilities are struggling to reduce power failure incidents in substations and their components to operate more reliably and economically [1]. Many power failures are produced directly or indirectly because of the insulation system of utility components [2], [3]. The selection of the insulation should ensure power plant operational continuity along with completely resolving or significantly limiting the actual power system's failures [4]. Gas insulated substations (GIS) have the best insulation performance which ensures achieving minimum failure incidents, although at high installation cost. The most common insulating gas used in GIS is Sulfur hexafluoride (SF 6 ) gas, which is widely used as an effective electrical insulation as well as an arc-quenching medium [5]. Basic GIS and gas insulated transmission lines (GITL or GIL) consist of a conductor supported by solid insulators inside an enclosure filled with SF 6 gas or its mixture [6].
84 citations
TL;DR: Wavelet-based denoising with a new histogram-based threshold function and selection rule is proposed, and two signal-to-noise ratio (SNR) estimation techniques are derived to fit with actual PD signals corrupted with real noise.
Abstract: Online condition assessment of the power system devices and apparatus is considered vital for robust operation, where partial discharge (PD) detection is employed as a diagnosis tool. PD measurements, however, are corrupted with different types of noises such as white noise, random noise, and discrete spectral interferences. Hence, the denoising of such corrupted PD signals remains a challenging problem in PD signal detection and classification. The challenge lies in removing these noises from the online PD signal measurements effectively, while retaining its discriminant features and characteristics. In this paper, wavelet-based denoising with a new histogram-based threshold function and selection rule is proposed. The proposed threshold estimation technique obtains two different threshold values for each wavelet sub-band and uses a prodigious thresholding function that conserves the original signal energy. Moreover, two signal-to-noise ratio (SNR) estimation techniques are derived to fit with actual PD signals corrupted with real noise. The proposed technique is applied on different acoustic and current measured PD signals to examine its performance under different noisy environments. The simulation results confirm the merits of the proposed denoising technique compared with other existing wavelet-based techniques by measuring four evaluation metrics: 1) SNR; 2) cross-correlation coefficient; 3) mean square error; and 4) reduction in noise level.
75 citations
Cites background from "Signal denoising techniques for par..."
...In [18], a comparative study is conducted on the denoising of PD signals caused by corona discharges, where several techniques are investigated and compared with each other....
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TL;DR: This paper provides case studies to demonstrate the effectiveness of the proposed framework and techniques for power transformer asset management and the hardware and software platform for implementing the proposed intelligent framework will also be presented.
Abstract: Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers' conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, extracting representative characteristics from raw data, and identifying types of faults in transformers. This paper provides case studies to demonstrate the effectiveness of the proposed framework and techniques for power transformer asset management. The hardware and software platform for implementing the proposed intelligent framework will also be presented in this paper.
70 citations
TL;DR: The proposed technique enhances the WMRA by decomposing the noisy data into different resolution levels while sliding it into Kaiser's window and using only the maximum expansion coefficients at each resolution level in de-noising and measuring the extracted PD signal.
Abstract: In extracting partial discharge (PD) signals embedded in excessive noise, the need for an online and automated tool becomes a crucial necessity One of the recent approaches that have gained some acceptance within the research arena is the Wavelet multi- resolution analysis (WMRA) However selecting an accurate mother wavelet, defining dynamic threshold values and identifying the resolution levels to be considered in the PD extraction from the noise are still challenging tasks This paper proposes a novel wavelet-based technique for extracting PD signals embedded in high noise levels The proposed technique enhances the WMRA by decomposing the noisy data into different resolution levels while sliding it into Kaiser's window Only the maximum expansion coefficients at each resolution level are used in de-noising and measuring the extracted PD signal A small set of coefficients is used in the monitoring process without assigning threshold values or performing signal reconstruction The proposed monitoring technique has been applied to a laboratory data as well as to a simulated PD pulses embedded in a collected laboratory noise
69 citations
References
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TL;DR: An algorithm to estimate a signal from its modified short-time Fourier transform (STFT) by minimizing the mean squared error between the STFT of the estimated signal and the modified STFT magnitude is presented.
Abstract: In this paper, we present an algorithm to estimate a signal from its modified short-time Fourier transform (STFT). This algorithm is computationally simple and is obtained by minimizing the mean squared error between the STFT of the estimated signal and the modified STFT. Using this algorithm, we also develop an iterative algorithm to estimate a signal from its modified STFT magnitude. The iterative algorithm is shown to decrease, in each iteration, the mean squared error between the STFT magnitude of the estimated signal and the modified STFT magnitude. The major computation involved in the iterative algorithm is the discrete Fourier transform (DFT) computation, and the algorithm appears to be real-time implementable with current hardware technology. The algorithm developed in this paper has been applied to the time-scale modification of speech. The resulting system generates very high-quality speech, and appears to be better in performance than any existing method.
1,899 citations
TL;DR: A tutorial review of both linear and quadratic representations is given, and examples of the application of these representations to typical problems encountered in time-varying signal processing are provided.
Abstract: A tutorial review of both linear and quadratic representations is given. The linear representations discussed are the short-time Fourier transform and the wavelet transform. The discussion of quadratic representations concentrates on the Wigner distribution, the ambiguity function, smoothed versions of the Wigner distribution, and various classes of quadratic time-frequency representations. Examples of the application of these representations to typical problems encountered in time-varying signal processing are provided. >
1,587 citations
Book•
01 Jan 1987
TL;DR: Introduction to Digital Filters Properties of Finite Impulse-Response Filters Design of Linear-Phase Finite Filters Minimum Phase and Complex Approximation and Comparison of Filtering Alternatives Appendix Index.
Abstract: Introduction to Digital Filters Properties of Finite Impulse-Response Filters Design of Linear-Phase Finite Filters Minimum Phase and Complex Approximation Implementation of Finite Impulse-Response Filters Properties of Infinite Impulse-Response Filters Design of Infinite Impulse-Response Filters Implementation of Infinite-Response Filters Comparison of Filtering Alternatives Appendix Index
908 citations
TL;DR: A signal synthesis algorithm that works directly with the real-valued high-resolution WD will be derived and examples of how this WD synthesis procedure can be used to perform time-varying filtering operations or signal separation will be given.
Abstract: The short-time Fourier transform (STFT), the ambiguity function (AF), and the Wigner distribution (WD) are mixed time-frequency signal representations that use Fourier transform techniques to map a one-dimensional function of time into a two-dimensional function of time and frequency. These mixed time-frequency mappings have been used to analyze the local frequency characteristics of a variety of signals and systems. Although much work has also been done to develop STFT and AF synthesis algorithms that can be used to implement a variety of time-varying signal processing operations, no such synthesis techniques have thus far been developed for the WD. In this paper, a signal synthesis algorithm that works directly with the real-valued high-resolution WD will be derived. Examples of how this WD synthesis procedure can be used to perform time-varying filtering operations or signal separation will be given.
278 citations
TL;DR: Two simple methods for retrieving a single sinusoid corrupted with noise are proposed, based on the lattice form realization of an adaptive infinite-impulse-response (IIR) notch filter, which have considerable potential in adaptive notch filter applications, especially when the input signal-to-noise ratio is low.
Abstract: Two simple methods for retrieving a single sinusoid corrupted with noise are proposed. They are based on the lattice form realization of an adaptive infinite-impulse-response (IIR) notch filter. The IIR filter is a cascade of second-order all-pole and all-zero filters, and the coefficients of the finite-impulse-response (FIR) section are adapted. The proposed algorithms keep the poles of the filter inside the unit circle. The computer simulation results show that the algorithms have considerable potential in adaptive notch filter applications, especially when the input signal-to-noise ratio is low. >
111 citations