Fault Detection in Power Electronics

Dear Colleagues,

Numerous dynamic systems in engineering are critical, where having reliability, availability, and safety is very important. A fault or malfunction in these kinds of systems can lead to high economic losses or even put lives at risk. Fault detection and fault diagnosis emerged near the beginning of the last century, and since then have been applied in several fields, such as, for instance, in the aerospace industry, automotive systems, steam turbines, pumps, pipelines, chemical processes, heat exchangers, industrial robots, electrical machines, motor drives, power electronics converters, photovoltaic systems, wind turbines, mechanical systems, and distribution grids.

In the literature, several methods of fault detection and fault diagnosis have been reported for different kinds of systems. They are generally classified into two main groups: model-based methods, and signal-based methods. Among the model-based methods, one can mention the state-space models, the state observers and parameter estimators, Kalman filter, trend checking, residual generation, and adaptive methods. Concerning the signal-based methods, we can find a vast amount of signal processing methods such as, estimation of mean and variance, filtering, wavelet transform, fast Fourier transform, Huang Hilbert transform, rule-based expert systems, statistical methods, among many others. Methods for fault-classification have also been reported, using artificial intelligence, fuzzy logic, artificial neural networks, support vector machines, Bayesian, geometric classifiers, and decision trees, and so on.

A field that has become important for fault diagnosis is power electronics converters. Converters are in several systems an essential stage to achieve the objective of power and energy delivery. Alongside the electrolytic capacitors, semiconductor devices are the components most prone to failure inside power electronics converters. The uninterrupted and correct operation of the power converters involved in some applications has become crucial. The possibility of performing a predictive or a fast corrective maintenance can guarantee better usage and less interruptions in the system, optimizing power production. Indeed, one way to accomplish it is by adopting and integrating fault diagnosis methods into these systems. In some structures, fault-tolerant control, redundancy and circuit reconfiguration can also be applied to achieve an uninterruptible and more reliable operation. Fault detection, fault-tolerance, and reconfiguration schemes for power converters continue to be an open research field.

This Special Issue welcomes submissions of recent research work on this widespread field. The call is open to a broad range of applications where power electronics is used as a stage for the dynamic system.

Recommended topics include, but are not limited to, the following:

• Fault detection, fault diagnosis, and classification methods for power converters
• Artificial intelligence and machine learning fault diagnosis methods for power converters
• Fault detection and diagnosis in converters for wind turbines
• Fault detection and diagnosis in converters for photovoltaic systems
• Fault detection in photovoltaic panel arrays
• Fault detection, fault tolerance, and reconfiguration for multilevel converters
• Fault detection for converters of automotive systems
• Fault detection and fault tolerance for DC-DC, DC-AC, and AC-DC, matrix, NPC, and dual active bridge converters
• Fault detection in electrical drives, electric machines, and power transformers
• Fault detection in converters for power quality improvement

Prof. Dr. Mario Arturo González García
Prof. Dr. Ricardo Álvarez Salas
Dr. Alessandro Lo Schiavo
Guest Editors

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• power electronics converters
• fault detection
• fault diagnosis
• fault tolerant control
• fault classification
• reconfiguration
• PV systems
• wind turbines
• multilevel converters
• electric machines
• automotive systems