Fault Detection and Diagnosis of Electrical Power System Equipments

Dear Colleagues,

Developing fault detection and diagnosis algorithms for electrical power equipment is necessary to improve the reliability and efficiency of electrical power systems. Different converter types enable the connections between renewable energy systems (RESs) and AC or DC grids. This complexity requires the monitoring of different electrical power systems, including RESs, which are essential due to their rapid expansion for different purposes and applications. The monitoring of electrical power equipment is vital to detect and diagnose faults that might affect the operating state or performance of a power system. Methods for electrical power system equipment monitoring include HVDC and HVAC transmission systems, hybrid AC and DC distribution systems, RES fault-detection systems, classification and locations, and transformer fault detection and diagnosis based on DGA. The main drawbacks of most recent works regarding the fault detection and diagnosis of electrical power system components are that the suggested models require large case studies and long periods of training and testing. Therefore, simple detection and artificial and machine learning approaches for fault detection and diagnosis are crucial to facilitating the detection process without the requirement for complicated processes or classification methods.

The efficient monitoring of fault-detection systems reduces overall system cost, system discontinuity, and hardware-based redundancy realization. Additionally, annual maintenance plans and consequent costs can be optimized. Topics of interest for this Special Issue include (with emphasis on electrical power equipment), but are not limited to:

• Electrical power equipment monitoring;
• Condition monitoring;
• Data-driven approaches, including machine learning methods;
• Electrical power devices;
• Fault analysis;
• Fault detection and diagnosis;
• Fault ride through;
• Incipient faults;
• Online and offline condition monitoring techniques;
• Signal-based approaches for feature extraction.

Prof. Dr. Ibrahim B.M. Taha
Prof. Dr. Nagy Elkalashy
Dr. Hossam A. Abd El-Ghany
Guest Editors

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