This paper introduces a novel direct control quasi-Z-source inverter (qZSI) topology as a viable alternative to conventional two-stage converters in photovoltaic (PV) systems. The proposed control strategy effectively merges duty cycle and modulation index within a space vector pulse width modulation (SVPWM) framework for both DC and AC control. To assess the system’s performance under diverse weather conditions, the INC and P&O maximum power point tracking maximum power point tracking (MPPT) algorithms are employed. Rigorous simulations conducted using MATLAB/Simulink demonstrate the proposed method’s ability to achieve multi-objective optimization of PV systems, enhancing overall system efficiency and reliability.

This paper presents a comprehensive review of the fault detection and diagnosis (FDD) techniques that exist for wind turbine systems, which are classified into three categories: model-based methods, signal-based methods, and knowledge-based methods. The analysis of the reviewed papers shows that the performance of the diagnosis process is the highest in the case of advanced AI models, especially deep learning hybrid models. CNN-LSTM with attention mechanism achieves an accuracy of 95% for converter fault detection, one-dimensional CNN based on acoustic vibrations achieves an accuracy of 96.8%, Bayesian-GAN-LSTM achieves an accuracy of 97.6% for bearing fault diagnosis, and Graph LSTM achieves an accuracy of 95.2% for multi-component fault diagnosis. The accuracy of the tree-based random forest models for gearbox temperature-based classification is 86% while the enhanced random forest models on SCADA derived features is 99.72%. The multi-scale dense fusion networks and synchronized wavelet transform-CNN models achieve gearbox fault and planetary gearbox fault accuracy larger than 98%. Even with these developments, there are some key challenges: the lack of standardized benchmarking procedures, low model generalizability across a range of operating conditions, and computational limitations in real-time applications. The findings have led to this review concluding that data-driven and hybrid approaches are the most promising diagnostic capabilities that may be integrated into wind turbine condition monitoring systems, and that future research directions to make these systems more robust, more interpretable, and more deployable in practice include physics-informed learning, federated learning architectures, multi-modal data fusion, and explainable AI systems.