Review on Partial Discharge Diagnostic Techniques for High Voltage Equipment in Power Systems

Hussain, G. Amjad; Hassan, Waqar; Mahmood, Farhan; Shafiq, Muhammad; Rehman, Habibur; Kay, John

doi:10.1109/access.2023.3279355

Cited by 33 publications

(12 citation statements)

References 120 publications

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“…Cables, transformers, and switchgear are examples of MV components where PD can damage the insulation materials. From gas-insulated to oil-insulated to solid-insulated equipment, PD can happen anywhere if there is an insulation defect or accelerated aging [13]. The most Energies 2024, 17, 1142 4 of 31 critical components of the power system are the power cables, which need to be extremely reliable to prevent revenue losses from early failure.…”

Section: Condition Monitoring and Pd Signalsmentioning

confidence: 99%

“…Numerous PD signal parameters (key parameters) that show details regarding the severity of the PD and the insulation state of MV equipment can be used to evaluate PD characteristics. PD inception voltage, cumulative energy function, accumulated apparent charge, average discharge current, discharge power, signal frequency, and quadratic rate are considered as the key parameters [13].…”

Section: Condition Monitoring and Pd Signalsmentioning

confidence: 99%

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A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Kumar,

Shafiq,

Kauhaniemi

et al. 2024

Energies

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Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

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Section: Condition Monitoring and Pd Signalsmentioning

confidence: 99%

Section: Condition Monitoring and Pd Signalsmentioning

confidence: 99%

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Kumar,

Shafiq,

Kauhaniemi

et al. 2024

Energies

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“…The measurement and detection of partial discharges (PDs) are invaluable tools for the evaluation of insulation quality, generating improvements in both maintenance practices and risk management in energy devices. A literature review on current online PD monitoring techniques used for different high-voltage electrical components in power electrical systems is presented in [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…In [ 1 ], a detailed review and analysis of online monitoring of PDs in order to reduce breakdowns in electrical power systems and the advances in this area are presented. An intelligent framework based on wireless sensors is also proposed to improve the performance of power systems.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

Monzón-Verona,

González-Domínguez,

García-Alonso

2024

Sensors

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In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils.

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“…Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. defect types in high-voltage electrical power systems [1][2][3]. According to the IEC 60270 standard [4], PD signifies an electrical pulse discharge that partially bridges the insulation.…”

Section: Introductionmentioning

confidence: 99%

Study of partial discharges measurement cycles effect on defect recognition for underground cable joints

Chang,

Boyanapalli

2024

Meas. Sci. Technol.

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This study investigates the influence of various partial discharge (PD) measurement durations on defect recognition in high voltage power cable joints, aiming to address a crucial challenge in the field of insulation assessment. The main objective is to determine the optimal measurement cycle duration for accurate defect recognition, thereby enhancing the reliability of PD-based diagnostic techniques. Totally 14 cable joints, each containing three different types of prefabricated artificial defects, were analyzed across five different measurement cycle durations: 40, 80, 120, 200, and 1200 cycles. Subsequently, Convolutional Neural Networks (CNNs) were employed for defect recognition analysis. The results reveal a significant impact of measurement cycle duration on defect recognition accuracy. Particularly, a CNN based on 200 measurement cycles demonstrates superior performance compared to models with fewer cycles, achieving a total defect recognition accuracy of 100%. This finding underscores the importance of sufficient measurement cycles for obtaining comprehensive PRPD patterns and accurate defect classification. Furthermore, the study highlights the significance of setting a threshold value to mitigate false conditions in defect type recognition, offering valuable insights for practical applications in power system maintenance and diagnostics. Overall, this research contributes to advancing the understanding of PD-based insulation assessment techniques and provides practical recommendations for optimizing measurement cycle duration to enhance defect recognition accuracy in high voltage power cable joints.

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Review on Partial Discharge Diagnostic Techniques for High Voltage Equipment in Power Systems

Cited by 33 publications

References 120 publications

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

Study of partial discharges measurement cycles effect on defect recognition for underground cable joints

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