Partial discharge (PD) localization employing acoustic emission technique is commonly done by estimating the time difference of arrival (TDOA) between signals captured at multiple acoustic sensors placed on the walls of the transformer tank. The localization accuracy of PD sources depends on the accuracy with which the TDOA is estimated. Hence it is important to accurately estimate the TDOA as far as possible. This paper presents a novel approach for the estimation of TDOA which is based on the source-filter model of acoustic theory. The TDOA is estimated by extraction of the excitation source signal from the acoustic signals in the form of an estimation of the PD pulse. The PD pulse serves as the excitation source signal for the acoustic detection system, whereas the acoustic path through the transformer tank and oil constitutes the physical system, which when excited by the PD pulse, gives rise to the acoustic pressure waves. The source-filter model extracts an estimation of the excitation source (PD pulse) by isolating it from the acoustic response of the tank-oil system. The extracted PD pulse information gives a sharp estimate of the instant of appearance of the PD pulse at each sensor. Hence, the TDOA between any two sensors determined from the cross-correlation function between the PD pulse estimates at the respective sensors gives a high estimation accuracy.

Partial discharge (PD) detection is a fundamental tool for the design and diagnosis of insulation systems, particularly organic materials that must operate throughout their life without PD. While the methods for PD measurements under sinusoidal supply voltage are well established, the same does not hold under repetitive-pulse voltage waveforms, such as those provided by inverters. Electromagnetic noise generated by electronic switch commutation in inverters can overlap with PD pulses, making it difficult to measure, separate from noise and identify PD. This is particularly more cumbersome when fast rise-time switches are employed. To address these issues, this work investigates methods to detect PD pulses on enameled wires under repetitive impulsive voltages with different rise-times of 60 ns, 500 ns and 1000 ns, proposing techniques which allows separation of PD from noise even when they overlap in time and frequency domain. This work employs a time-domain approach using standard deviation curve acquired from a set of measurements to discriminate PD from repetitive commutation disturbance. These techniques can be implemented to achieve unsupervised noise rejection, which would be an important goal for both off-line and on-line PD testing. The proposed approach is validated successfully through tests carried out on a low-voltage motor.

Partial Discharge (PD) is one of the most critical electrical phenomena affecting the life of electrical equipment. Repetitive PD leading to arcing faults is a recurrent problem in many air insulated systems, such as high voltage air-insulated switchgears. Hence, detection and localization of PD inside such electrical equipment is necessary for early prevention of impending failure. Keeping this in mind, this paper proposes a methodology to localize single and multiple PD sources employing two recent developments in signal processing and machine learning techniques. Optical sensors have been used to record the PD data inside a cubical steel box. A cylindrical barrier has been inserted inside this box to emulate geometrical structures inside Switchgears. Mathematical Morphology aided feature extraction has been employed to extract important features from the PD signal. Sparse Representation classification has been employed to classify the extracted features and to identify the type and location of PD source. The results show that this methodology gives very high classification accuracy corresponding to both single and multiple PD sources.

Condition assessment of Medium Voltage switchgears requires effective, but inexpensive solutions to become a common practice. On the other hand, failure of a switchgear component can cause cascade effects and outage of other apparatus, in addition to significant economic losses due, e.g., to lack of energy availability. A major diagnostic property to evaluate switchgear health and reliability is partial discharges, being a direct cause of failure of organic insulation systems. A new approach to automatic, self-assessment of switchgear reliability based on partial discharge, PD, acquisition and processing, as well as a new type of PD sensor solution are presented in this paper. The sensor is based on the capacitive divider existing in most bushings to indicate the presence or absence of voltage. The innovative automatic software is able to provide automatic noise and PD separation, noise rejection and type of PD-source identification. The embedded bushing sensor has been tested on switchgears in the presence of defective components as cable terminations and bushings. Results show that the sensor, coupled with the automatic software, is able to provide good sensitivity compared to other sensors, and good capability to locate the cabinet and identify the type of source generating partial discharges.

Condition monitoring and maintenance in rotating machine are generally managed through expert evaluation of the diagnostic properties and relevant harmfulness of the underlying aging mechanisms. In order to prompt broad diffusion of condition monitoring systems, especially in large assets and in the rising field of electrification transport, however, ways to get rid of the time-consuming and expensive support of experts must be prompted. A straightforward solution is transiting towards automatic and unsupervised diagnostics and condition assessment methodologies. This paper proposes and applies algorithms for the automatic detection of partial discharges, which is the property most often associated to the fastest accelerated aging mechanisms in electrical insulation, including noise rejection, identification of the type of partial discharge sources and estimation of an health condition index in rotating machines fed by AC sinusoidal voltage. The goal is to obtain a self-assessment of the health condition by each rotating machine of an asset, which can thus interact with the asset or maintenance manager when needed, that is, when reliability is at risk.