• Energy Research

This paper describes active fault-tolerant control systems for a high-performance induction-motor drive that propels an electrical vehicle (EV) or a hybrid one (HEV). The proposed systems adaptively reorganize themselves in the event of sensor loss or sensor recovery to sustain the best control performance, given the complement of remaining sensors. Moreover, the developed systems take into account the controller-transition smoothness, in terms of speed and torque transients. The two proposed fault-tolerant control strategies have been simulated on a 4-kW induction-motor drive, and speed and torque responses have been carried to evaluate the consistency and the performance of the proposed approaches. Simulation results, in terms of speed and torque responses, show the global effectiveness of the proposed approaches, particularly the one based on modern and intelligent control techniques in terms of speed and torque smoothness

Abstract Today, grid-connected photovoltaic systems have gained widespread penetration among renewable energy systems. For low power applications, a single-phase inverter with less power converter is a good compromise for high efficiency. The control must make it possible to extract the maximum power from the photovoltaic modules, ensure good dynamic performance for active and reactive power injection, ensure power quality, and reject disturbances and parameter mismatch. Besides, the controllers of the grid and PV sides should be coordinated. In this study, a fast terminal sliding mode control combined with Direct Power Control is proposed. Thanks to the two-cascaded control loops, simulations and experimental results with a 1 kW test bench have proven the proposal’s effectiveness in terms of dynamic performances and robustness to irradiance variations. Comparison with deadbeat-Direct Power Control, predictive control, and power hysteresis control shows that our proposal leads to lower Total Harmonic Distortion (3,5%) for the electrical grid’s current and lower time response (one-tenth of half the grid cycle).

This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault detection approach is extended to form a new monitoring index based on Hotelling’s T 2 , Q and a CVR-based monitoring index, T d . A CVR-based contribution plot approach is also proposed based on Q and T d statistics. Two performance metrics: (1) false alarm rate and (2) missed detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor.

Abstract The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented.

To enable health monitoring and fault diagnosis of PV modules using current-voltage characteristics (I–V curves), it is generally necessary to correct the I–V curves measured under different environmental conditions to the standard condition. The most common correction methods are those from IEC 60891: 2021 standard. However, these methods can introduce significant errors when dealing with degraded PV modules due to the inability to account for changes in resistance. To address this, we propose an improved I–V curve procedure, denoted Pdynamic, which considers different types of degradation by dynamically deriving the correction coefficients from the measured I–V curves. To evaluate the performance, we simulate I–V curves across a wide range of irradiance and temperature for the healthy and degraded module, where the degradation involves increased series resistance, decreased shunt resistance, or both. The results reveal that Pdynamic can produce corrected I–V curves closer to the reference ones than Procedures 1, 2, and 4 of the IEC 60891:2021 standard. Moreover, Pdynamic exhibits resilience to both seasonal fluctuations and varying levels of degradation. These results highlight Pdynamic as a promising and robust I–V curve correction method, particularly for degraded PV modules. A Python-based open-source tool for this procedure is also available at https://github.com/DuraMAT/IVcorrection.