Partial Discharge Detection, Monitoring and Diagnosis

Dear Colleagues,

Condition monitoring (CM) of high-voltage (HV) insulation systems is essential for establishing a correct diagnosis regarding the health of these costly and safety-critical industrial assets, as well as for implementing practical condition-based-maintenance (CBM) regimes. The assets being monitored may include rotating machines, power transformers, HV cables and accessories, air-insulated-substations (AIS), gas-insulated-switchgear (GIS) and overhead lines. Recent advances have seen widespread development of contact and non-contact sensors for detecting and locating partial discharges and electrical arcs. These sensors play an important role in periodic testing, continuous monitoring or ‘fingerprinting’ of partial discharges (PDs) activity in insulation systems from HV equipment. Practical applications of inductive, acoustic, optical UHF and other RF techniques are leading to the development of new sensors and associated solutions for signal acquisition, processing, analysis and interpretation, which in turn require new approaches to decision making about the condition of assets being monitored.

The aim of this Special Issue is to report on recent advances relating to the following themes: (1) contact (inductive, HFCT, Rogowski coils, etc.) and non-contact sensors (RF, UHF, near field, electric, magnetic, acoustic, optical, etc.) used for detecting signals emitted by insulation defects either internally, or external to the equipment in question; (2) practical methods for integrating these sensors into real equipment for use in condition monitoring; (3) case studies and examples of implementation of the techniques in an industrial or laboratory setting; (4) sensor models to support the design process or for predicting their response (using data-driven modeling approaches, for example); (5) bridging the gap between condition monitoring research and subsequent decision making using these technologies, possibly in combination with other monitoring parameters, and (6) machine learning for smart detection, localization, and pattern recognition of PD pulses.

Prof. Dr. Ricardo Albarracín-Sánchez
Dr. Martin D. Judd
Prof. Dr. Guillermo Robles
Prof. Dr. Pavlos Lazaridis
Guest Editors

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• partial discharges
• condition monitoring
• diagnostic
• dielectrics
• inductive sensors
• optical sensors
• acoustic sensors
• UHF sensors
• IEC TS 62478:2016
• antennas
• electrical insulation
• partial discharge localization
• machine learning
• partial discharge signal processing