The global shift towards decentralized energy systems, driven by the integration of distributed generation technologies and renewable energy sources, underscores the critical need for effective energy management strategies in microgrids. This study proposes a novel multi-objective optimization framework for grid-connected microgrids using quantum particle swarm optimization (QPSO) to address the dual challenges of minimizing operational costs and reducing environmental emissions. The microgrid configuration analyzed includes renewable energy sources like photovoltaic panels and wind turbines, along with conventional energy sources and battery storage. By incorporating quantum-inspired mechanics, QPSO overcomes limitations such as premature convergence and solution stagnation, often seen in traditional methods. Simulation results demonstrate that QPSO achieves a 9.67% reduction in operational costs, equating to savings of €158.87, and a 13.23% reduction in carbon emissions, lowering emissions to 513.70 kg of CO₂ equivalent in the economic scheduling scenario. In the environmentally constrained economic scheduling scenario, the method delivers a balanced solution with operational costs of €174.11 and emissions of 401.63 kg of CO₂. The algorithm’s performance is validated across various microgrid configurations, including standard low-voltage setups. These results highlight QPSO’s potential as an efficient tool for optimizing microgrid energy management, promoting both economic and environmental sustainability. This study provides a robust framework for achieving practical solutions in real-world applications, emphasizing the role of advanced optimization techniques in sustainable energy systems.

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Sliding mode control (SMC) of induction motor is a new concept in the current scenario, as it seeks to improve torque control accuracy and power steering efficiency through the use of pulse width modulation schemes. Furthermore, artificial neural network‐based sliding mode control is applied to a squirrel cage induction motor, which is used in the steering control of automobiles. The artificial neural network (ANN)‐based SMC is more popular due to its robustness and good stability in external parameter variation. Additionally, an SMC and an ANN‐based SMC are employed to compute the torque and flux, improving performance for power steering applications. The performance of the designed model is validated through MATLAB/Simulink and experimental models with different controllers under various operating conditions. The controller has been embedded into a TMS320F28335 controller, and performances have been evaluated. The performance analyses of induction motor using different controllers are performed. The transient performances of induction motor such as delay time, rise time, settling time, and steady‐state error are investigated. The proposed work is analysed by using a mathematical model and implemented in a test‐bench model for validation.

DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

This paper proposes a step-up 3-Ф switched-capacitor multilevel inverter topology with minimal switch count and voltage stresses. The proposed topology is designed to provide five distinct output voltage levels from a single isolated dc source, making it suitable for medium and low-voltage applications. Each leg of the proposed topology contains four switches, one power diode, and a capacitor. The switching signals are also generated using a staircase universal modulation method. As a result, the proposed topology will operate at both low and high switching frequencies. To highlight the proposed topology’s advantages, a comparison of three-phase topologies wasperformed in terms of the switching components, voltage stress, component count per level factor, and cost function withthe recent literature. The topology achieved an efficiency of about 96.7% with dynamic loading, and 75% of the switches experienced half of the peak output voltage (V_DC), whereas the remaining switches experienced peak output voltage (2V_DC) as voltage stress. The MATLAB/Simulink environment was used to simulate the proposed topology, and a laboratory prototype was also built to verify the inverter’s theoretical justifications and real-time performance.