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In general, a high voltage (HV) substation can be made up of multiple insulation subsystems: an air insulation subsystem (AIS), gas insulation subsystem (GIS), liquid insulation subsystem (power transformers), and solid insulation subsystem (power cables), all of them with their grounding structures interconnected and linked to the substation earth. Partial discharge (PD) pulses, which are generated in a HV apparatus belonging to a subsystem, travel through the grounding structures of the others. PD analyzers using high-frequency current transformer (HFCT) sensors, which are installed at the connections between the grounding structures, are sensitive to these traveling pulses. In a substation made up of an AIS, several non-critical PD sources can be detected, such as possible corona, air surface, or floating discharges. To perform the correct diagnosis, non-critical PD sources must be separated from critical PD sources related to insulation defects, such as a cavity in a solid dielectric material, mobile particles in SF6, or surface discharges in oil. Powerful diagnostic tools using PD clustering and phase-resolved PD (PRPD) pattern recognition have been developed to check the insulation condition of HV substations. However, a common issue is how to determine the subsystem in which a critical PD source is located when there are several PD sources, and a critical one is near the boundary between two HV subsystems, e.g., a cavity defect located between a cable end and a GIS. The traveling direction of the detected PD is valuable information to determine the subsystem in which the insulation defect is located. However, incorrect diagnostics are usually due to the constraints of PD measuring systems and inadequate PD diagnostic procedures. This paper presents a diagnostic procedure using an appropriate PD analyzer with multiple HFCT sensors to carry out efficient insulation condition diagnoses. This PD procedure has been developed on the basis of laboratory tests, transient signal modeling, and validation tests. The validation tests were carried out in a special test bench developed for the characterization of PD analyzers. To demonstrate the effectiveness of the procedure, a real case is also presented, where satisfactory results are shown.

This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal. Three clustering algorithms (K-means, Gaussian Mixture Model (GMM), and Mean-shift) and the Support Vector Machine (SVM) method were used for signal separation and classification. The proposed system effectively reduced high-frequency components up to 50 MHz, improved the signal-to-noise ratio, and effectively separated different sources of partial discharges without losing relevant information. An accuracy of up to 93% was achieved in classifying the partial discharge sources. The successful implementation of the signal conditioning system and the machine learning-based signal separation approach opens avenues for more economical, scalable, and reliable PD monitoring systems.

In this article, an extension of a conventional partial discharge (PD) approach called partial discharge echo (PDE), which is applied to different classes of electrical insulation systems of power devices, is presented. Currently, high-voltage (HV) electrical insulation is attributed not only to transmission and distribution grids but also to the industrial environment and emerging segments such as transportation electrification, i.e., electric vehicles, more-electric aircraft, and propulsion in maritime vehicles. This novel PDE methodology extends the conventional and established PD-based assessment, which is perceived to be one of the crucial indicators of HV electrical insulation integrity. PD echo may provide additional insight into the surface conditions and charge transport phenomena in a non-invasive way. It offers new diagnostic attributes that expand the evaluation of insulation conditions that are not possible by conventional PD measurements. The effects of partial discharge echo in various segments of insulation systems (such as cross-linked polyethylene [XLPE] power cable sections that contain defects and a twisted-pair helical coil that represents motor-winding insulation) are shown in this paper. The aim is to demonstrate the echo response on representative electrical insulating materials; for example, polyethylene, insulating paper, and Nomex. Comparisons of the PD echo decay times among various insulation systems are depicted, reflecting dielectric surface phenomena. The presented approach offers extended quantitative assessments of the conditions of HV electrical insulation, including its detection, measurement methodology, and interpretation.

In this paper, we present an unprecedented method based on Kohonen networks that is able to automatically recognize partial discharge (PD) classes from phase-resolved partial discharge (PRPD) diagrams with features of various simultaneous PD patterns. The PRPD diagrams were obtained from the stator windings of a real-world hydro-generator rotating machine. The proposed approach integrates classification probabilities into the Kohonen method, producing self-organizing probability maps (SOPMs). For building SOPMs, a group of PRPD diagrams, each containing a single PD pattern for training the Kohonen networks and single- and multiple-class-featured samples for obtaining final SOPMs, is used to calculate the probabilities of each Kohonen neuron to be associated with the various PD classes considered. At the end of this process, a self-organizing probability map is produced. Probabilities are calculated using distances, obtained in the space of features, between neurons and samples. The so-produced SOPM enables the effective classification of PRPD samples and provides the probability that a given PD sample is associated with a PD class. In this work, amplitude histograms are the features extracted from PRPDs maps. Our results demonstrate an average classification accuracy rate of approximately 90% for test samples.

The power transformer is one of the most crucial pieces of high-voltage equipment in the power system, and its stable operation is crucial to the reliability of power transmission. Partial discharge (PD) is a key factor leading to the degradation and failure of the insulation performance of power transformers. Therefore, online monitoring of partial discharge can not only obtain real-time information on the operating status of the equipment but also effectively predict the remaining service life of the transformer. Meanwhile, accurate localization of partial discharge sources can assist maintenance personnel in developing more precise and efficient maintenance plans, ensuring the stable operation of the power system. Dual partial discharge sources in transformer oil represent a more complex fault type, and piezoelectric transducers installed outside the transformer oil tank often fail to accurately capture such discharge waveforms. Additionally, the sensitivity of the built-in F-P sensors can decrease when installed deep within the oil tank due to the influence of oil pressure on its sensing diaphragm, resulting in an inability to accurately detect dual partial discharge sources in transformer oil. To address the impact of oil pressure on sensor sensitivity and achieve the detection of dual partial discharge sources under high-voltage conditions in transformers, this paper proposes an optical fiber ultrasonic sensor with a pressure-balancing structure. This sensor can adapt to changes in oil pressure environments inside transformers, has strong electromagnetic interference resistance, and can be installed deep within the oil tank to detect dual partial discharge sources. In this study, a dual PD detection system based on this sensor array is developed, employing a cross-positioning algorithm to achieve detection and localization of dual partial discharge sources in transformer oil. When applied to a 35 kV single-phase transformer for dual partial discharge source detection in different regions, the sensor array exhibits good sensitivity under high oil pressure conditions, enabling the detection and localization of dual partial discharge sources in oil and winding interturn without obstruction. For fault regions with obstructions, such as within the oil channel of the transformer winding, the sensor exhibits the capability to detect the discharge waveform stemming from dual partial discharge sources. Overall, the sensor demonstrates good sensitivity and directional clarity, providing effective detection of dual PD sources generated inside transformers.

Classification of the sources of partial discharges has been a standard procedure to assess the status of insulation in high voltage systems. One of the challenges while classifying these sources is the decision on the distinct properties of each one, often requiring the skills of trained human experts. Machine learning offers a solution to this problem by allowing to train models based on extracted features. The performance of such algorithms heavily depends on the choice of features. This can be overcome by using deep learning where feature extraction is done automatically by the algorithm, and the input to such an algorithm is the raw input data. In this work, an enhanced convolutional neural network is proposed that is capable of classifying single sources as well as multiple sources of partial discharges without introducing multiple sources in the training phase. The training is done by using only single-source phase-resolved partial discharge (PRPD) patterns, while testing is performed on both single and multi-source PRPD patterns. The proposed model is compared with single-branch CNN architecture. The average percentage improvements of the proposed architecture for single-source PDs and multi-source PDs are 99.6% and 96.7% respectively, compared to 96.2% and 77.3% for that of the traditional single-branch CNN architecture.

Partial discharge (PD) detection can be considered one of the most useful tools for assessing the insulation conditions of the power apparatus in high-voltage systems. Under AC conditions, this analysis is widely employed in online and offline tests, such as type tests or commissioning, and can be carried out by applying the phase-resolved PD (PRPD) method, since the patterns can give information about the defect classification. Under DC voltages, the classic pattern recognition method cannot be performed, and the measurements show complexities related to the nature of the phenomena. For this reason, to date, a standard for PD measurements under DC does not exist. In previous papers, a new method for PD detection under DC stress voltages has been proposed by the authors. It is based on the application of a direct current periodic (DCP) waveform useful in obtaining PRPD patterns. The dependence of partial discharge inception voltage (PDIV) and PD repetition rate (PDRR) on the δ shape parameter of the DCP for different materials, as well as the capability to recognize different discharge phenomena, provided valid indications on the behavior of PD in the transition from AC to DC. The aim of this paper is to evaluate the time dependence of PD occurring in a dielectric by applying the DCP waveform. In our previous studies, the investigations were focused on the PD behavior under different values of the DC voltage periodic part. In another work, the DCP waveform with both positive and negative polarity was applied to several dielectric materials. In the proposed work, instead, the DCP waveform is applied for a long time in order to observe its effect on the PD behavior for 72 h. In this way, due to the space charge accumulation phenomenon, the aging effect, also due to the space charge accumulation phenomenon, is evaluated. The methodological approach was to acquire PRPD patterns over time and evaluate their trends in comparison with the sinusoidal case. The experimental results show that, with a DCP waveform having δ = 0.6, the aging effect similar to that provided by pure DC stress is observed, while the acquired PRPD patterns are easily interpretated, as in the AC case.

Partial discharge inception modeling is a powerful tool for material investigation and insulation system design in order to achieve the objective of PD-free operation of insulation systems. Model validation, however, requires accurate and repeatable testing conditions, and the aim of this paper is to look at the influence of electrodes and electric field simulation on partial discharge inception model prediction accuracy. Awareness of the geometric electrode configuration is important to forecast both the typology of discharge and the corresponding partial discharge inception voltage value. It is shown, in fact, that inaccurate evaluation of electrode shape (e.g., its flat part and contour) might impact significantly on electric field estimation, the typology of incepted discharges, and, thus, on model accuracy, i.e., on partial discharge inception voltage prediction, which is the basis for the partial discharge-free design of insulating materials and systems. In particular, small electrode curvature radius variations do not significantly affect the PDIV value or PD typology identification. However, worsening electrode/insulation specimen contact can significantly impact PD inception and typology evaluation.

Phase-resolved partial discharge (PRPD) measurement has been used for decades as a method of monitoring defects in electrically insulating materials. More recently, it has seen a renewed interest in the context of flash sintering, a novel ceramic densification process where the sample to be densified is subjected to an electric field in addition to the usual application of heat. In the context of flash sintering, the monitoring of partial discharge (PD) activity has shown that this activity increases when approaching the onset of the thermal runaway phenomenon leading to the quick densification of the material, and is influenced by environmental factors such as relative humidity or pressure. A new microcontroller-based PRPD measurement system architecture has recently been proposed as a means to explore this PD activity in further details. While PDRD measurement is traditionally carried out by comparing the measured partial discharge pattern to the waveform of the voltage applied to the device under test (DUT), we show in this work that expanding this bespoke measurement system to be able to simultaneously monitor the waveform of the current going through the DUT allows for the collection of data related to the electrical power transferred to the DUT during the process that displays peculiar features. In the present work, the DUT consists of a classical needle-plane setup. As pressure decreases down from atmospheric levels, the threshold voltage leading up to the apparition of discharges decreases following a trend similar to the classical Paschen curve. Additionally, the nature of the discharge activity transitions from low-amplitude, rapid-firing tightly packed trains of pulses to high-amplitude, longer-lasting and more spread out pulses. Simultaneous measurement of the discharges, applied voltage and current going through the DUT shows that this second type of discharge activity can be synchronous with an asymmetric, distorted current waveform having the same period as the applied voltage, corresponding to a transfer of active electrical power into the DUT. Furthermore, the width of these current waveforms expands as the applied voltage is increased progressively starting from the threshold voltage for the activation of discharge activity, indicating that the rate of total power transferred in the DUT may be tuned using the amplitude of the applied voltage. External confirmation of a significant power transfer taking place in these conditions is obtained through the observation of damage inflicted on the DUT after a period of sustained discharge activity at low pressure.

Since partial discharges, PD, are a major accelerated degradation mechanism of organic electrical insulation systems, measuring partial discharge inception voltage, PDIV, of electrical asset components in aviation and aerospace is a fundamental tool to cope with life, safety, and reliability requirements. Partial discharge phenomenology and inception voltage depend on pressure, specifically, PDIV decreases with pressure. To avoid PD inception during aircraft or aerospace vehicle operation, the value of PDIV must be known at any pressure that electrical asset components will experience. However, a lack of experimental facilities adequate to test PD on real-size asset components might prevent from having PD-related information at pressures lower (or higher) than standard atmospheric pressure, SAP. This paper presents a heuristic approach, based on physics-derived PD field inception models, that allows the estimation of PDIV at low pressure to be carried out based on measurements made at SAP, when the typology of the defect causing partial discharge is known (from SAP PD measurements), but the precise type, size, and location of the PD-generating defect is unknown. It is shown that PDIV estimates obtained by the proposed models for internal and surface discharges are in good agreement with measured values in a range from SAP to 0.05 bar, testing simple insulation geometries but also real asset components, such as motors.