S. Sriram

Bio: S. Sriram is an academic researcher. The author has contributed to research in topics: Signal & Partial discharge. The author has an hindex of 1, co-authored 1 publications receiving 134 citations.

Signal denoising techniques for partial discharge measurements

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.

Partial discharge detection and diagnosis in gas insulated switchgear: State of the art

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].

Wavelet Transform With Histogram-Based Threshold Estimation for Online Partial Discharge Signal Denoising

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.

Smart Transformer for Smart Grid—Intelligent Framework and Techniques for Power Transformer Asset Management

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.

On-line detection and measurement of partial discharge signals in a noisy environment

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