This paper explains the hidden positive feedback in a two-stage fully differential amplifier through external feedback resistors and possible DC latch-up during the amplifier start-up. The biasing current selection among the cascade branches has been explained intuitively, with reference to previous literature. To avoid the latch-up problem, irrespective of the transistor bias currents, a novel hysteresis-based start-up circuit is proposed. An 87[ ]dB, 250[ ]MHz unity gain bandwidth amplifier has been developed in 65[ ]nm CMOS Technology and post-layout simulations demonstrate no start-up failures out of 1000 Monte-Carlo (6-Sigma) simulations. The circuit draws 126[ ][ ]A from a 1.2[ ]V supply and occupies the 2184[ ][ ]m² area.

The present article introduces the discrete grey wolf optimization algorithm (DGWOA), a novel variant of the grey wolf optimizer (GWO). DGWOA integrates discrete optimization techniques with explainable artificial intelligence (XAI) methodologies. This approach aims to overcome limitations associated with traditional fault diagnosis methods, such as limited accuracy in identifying complex patterns and low interpretability. Furthermore, it mitigates early convergence problems commonly encountered in optimization algorithms and enhances adaptability to discrete classification challenges. The DGWOA algorithm is designed to generate interpretable classification rules for fault detection through a stochastic search strategy. The explainability provided by the model not only enhances decision-making transparency but also improves diagnostic efficiency and predictive accuracy. The proposed algorithm was evaluated using a photovoltaic system dataset and benchmarked against established rule-based classifiers. DGWOA consistently achieved a classification accuracy of 99.48% and a precision of 100%, demonstrating its effectiveness in enhancing fault detection. Moreover, the interpretability of the generated classification rules contributes to the generation of outcomes that are both actionable and comprehensible to decision-makers.

This article deals with the denoising of signals from sensors (current and voltage) in a photovoltaic conversion chain using wavelet theory. The idea is to reduce the noise as much as possible without distorting or losing the original signal information. To see the reliability of this proposed method, a comparative study between our proposed method (WDD) and the classic low-pass filter method was presented. The contribution in this work is the improvement of the DC-DC boost converter via the use of wavelet theory, however, that’s why we set two objectives: the first is to obtain reliable control of the DC-DC boost converter via the proposed WDD and the second to increase the overall system efficiency. The experimental results show a very great improvement in the quality of the signals of the proposed method compared to the classic method of filtering by low-pass filter.

This paper proposes an adaptive vector control strategy for a photovoltaic (PV)-fed induction motor using a boost split-source inverter (BSSI). Unlike conventional PV conversion systems, the proposed topology combines voltage boosting and DC–AC conversion in a single stage, reducing component count, switching losses, and overall system complexity. In addition, the system operates without a maximum power point tracking (MPPT) algorithm, simplifying the control structure while maintaining stable operation under varying environmental conditions. The proposed control approach integrates sliding mode control (SMC) for robust DC-bus voltage regulation and an adaptive proportional–integral (API) speed controller based on Lie derivative theory for online tuning of controller gains according to the speed tracking error. An active DC-link protection mechanism is also introduced to prevent overvoltage during transient conditions. The main contribution of this paper lies in the combination of the BSSI topology with a hybrid adaptive control framework to improve robustness, dynamic performance, and system reliability under irradiance, temperature, and load variations. Simulation and experimental results demonstrate fast dynamic response, reduced speed and voltage oscillations, accurate speed tracking, and superior performance compared with conventional vector control methods.