This paper presents modeling, design and analysis of a bidirectional half-bridge DC/DC converter suitable for power electronic interface between the main energy storage system and the electric traction drive in hybrid electric vehicles. A hybrid energy storage system composed of a battery unit and an ultracapacitor pack is considered. A parallel dc-linked multi-input converter with a half-bridge bidirectional DC/DC cell topology is chosen to link the battery/ultracapacitor storage unit with the dc-link. The paper focuses on modeling the proposed converter for both dynamic and steady state analysis. Averaging and linearization techniques are applied to obtain the averaged state space models and small signal models of the converter in both boost and buck operation modes. A criterion for sizing the converter passive components based on the imposed design specifications and constraints is illustrated. Simulation results of the buck-boost converter during normal functioning and under faulty conditions are presented. In particular, short-circuit faults and open-circuit faults of diodes and transistors are analyzed.

Early and accurate fault detection and diagnosis for renewable energy systems can increase their safety and ensure the continuity of the service. In this paper, the authors are interested in presenting different methods of fault detection and diagnosis for industrial systems. Then, an overview of the diagnosis of wind turbine power generator, PV cell and PEM fuel cell is presented.

This review paper offers a comprehensive examination of the various types of faults that occur in inverters and the methods used for their identification. The introductory segment investigates the internal component failures of voltage-source inverters (VSIs), examining their failure rates and the consequent effects on the overall system performance. Subsequently, this paper classifies and clarifies the potential malfunctions in components and sensors, placing particular emphasis on their frequency of occurrence and the severity of their impact. The examination encompasses issues associated with transistors, including open circuits, short circuits, gate firing anomalies, as well as failures in capacitors, diodes, and sensors. Following this, the paper delivers a comparative assessment of fault diagnosis techniques pertinent to each type of component, appraised against specific criteria. The concluding section encapsulates the findings for each fault category, delineates the fault detection and diagnosis (FDD) methodologies, analyzes the outcomes, and provides recommendations for future scholarly investigation.

This paper presents a study of different converter topologies used in the design of hybrid electric vehicle drive trains with emphasis on the power electronic interface for a battery/ultracapacitor hybrid energy storage pack. Each of the topologies proposed in literature has its own advantages and disadvantages, thus choosing an appropriate topology depends greatly on the specified application, design and operation requirements. Several aspects have to be considered to obtain a reliable comparison among different power converter topologies.

Multi-Level Inverters (MLI) have become of great importance for electrical energy supplement to grids due to its modularity, lower Total Harmonic Distortion (THD) and decreased filter needs. However, although multilevel inverters achieve high voltage levels, increased number of power switches are required which make them more prone to breakdowns and faults. Among active devices failures, Open-Circuit (OC) faults are extensively explored in research studies. Hence, this paper presents a deep overview concerning methods based on Artificial Intelligence (AI) algorithms involved in diagnosis and localization of OC failure in different multilevel inverter architectures. Initially, two major classifications of fault diagnosis methods for OC switch failure are listed and clarified briefly. Then, AI algorithms are discussed thoroughly. Further, certain criteria with several standards are formulated to differentiate between strategies investigated in publications. Then, various implemented techniques in literature are widely overviewed and compared in an original table.

This work presents an algorithm to detect and diagnose faults in PhotoVoltaic (PV) systems based on the I-V curve analysis. Three types of faults are investigated: mismatch and shading faults, connectivity faults and short circuit faults. The PV system is modeled using MATLAB/Simulink to simulate the faulty I-V curve behavior for each fault. During each simulation, the I-V curve is examined and compared with that during normal operation (without faults), in order to identify and characterize the anomalies. The different faulty modes affect the I-V characteristics of the PV string in different ways, leaving distinct signatures during its operation. Four attributes are extracted allowing for classification of faults into five classes. For some classes involving more than one fault, further analysis and comparison is carried out to allow discrimination between them. Results show that the proposed technique has good performance in detecting faults even if they are not severe.