I am very glad to inform that paper submissions are open up to 31/12/2021 to the special issue named “Frontiers in Partial Discharge Detection, Monitoring and Diagnosis” in which I am Guest Editor for Sensors MDPI, journal ranked in the Q1 (JCR ranking).
In this link: https://www.mdpi.com/journal/sensors/special_issues/PDD2021 you can find additional information.
Special Issue Information
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
Below, are previous special issues in which I served as Guest Editor that have additional papers related with partial discharges, high voltage, condition monitoring and electrical insulation diagnosis, which could be of your interest:
Special Issue in Machines: Machinery Condition Monitoring and Industrial Analytics
Special Issue in Sensors: UHF and RF Sensor Technology for Partial Discharge Detection
Special Issue in Sensors: Acoustic, UHF and RF Sensor Technology for Partial Discharge Detection