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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.