The increasing utilization of renewable energy sources in low-inertia power systems demands advanced control strategies for grid-forming inverters (GFMs). Conventional Model Predictive Control (MPC) methods, which depend on static models and predefined boundaries, often struggle to preserve frequency stability in dynamic grid conditions. This research presents an Adaptive Model Predictive Control (AMPC) framework to enhance GFM performance in Virtual Synchronous Machine (VSM) mode, ensuring robust frequency stability under uncertainties. The primary issue addressed is the inefficiency of traditional MPC in adapting to dynamic grid conditions. To resolve this, the AMPC framework combines offline reinforcement learning for parameter tuning with online MPC using soft constraints. The offline phase employs a novel Hybrid Crayfish Optimization and Self-Adaptive Differential Evolution Algorithm (COA-jDE) to minimize the cost function \(U_{offline}\), deriving optimal control parameters (Q, R) before real-time deployment. This process, termed cost function minimization using COA-jDE in a reinforcement learning framework, enhances GFM performance by adaptively adjusting virtual inertia and damping. Simulations on a 16MW wind-powered DFIG microgrid demonstrate that AMPC outperforms traditional MPC and VSM methods during grid disturbances, symmetrical faults, islanding, and load shifts. Furthermore, AMPC is computationally efficient compared to conventional reinforcement learning techniques, as adaptation is restricted to offline tuning. The framework not only improves compliance with grid codes (e.g., GC0137, IEEE 1547) but also provides a flexible, resilient control strategy for modern low-inertia grids.

The increase in wind power‐based microgrids emphasizes the importance of addressing stability challenges during low‐voltage ride‐through (LVRT) events in weak AC grid‐connected doubly fed induction generator systems. Compliance with grid standards, notably LVRT capabilities, is critical as wind power plants integrate increasingly into power systems, raising concerns about generation loss and post‐fault oscillations in microgrids. Previously, researchers have utilized techniques like fuzzy logic, ant colony, and genetic algorithms for static synchronous compensator (STATCOM) tuning to enhance microgrid stability during fault scenarios. This study uses the grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), and a novel hybrid GOA‐PSO. On the main grid, the power system is subject to both symmetrical and asymmetrical faults. The proposed novel technique aims to improve LVRT, minimize generation loss during faults, and reduce after‐fault oscillations by optimizing reactive power flow between the point of common coupling and the microgrid while adhering to the LVRT grid code. MATLAB/Simulink is utilized to evaluate the LVRT performance of a 16 MW DFIG‐based microgrid operating in grid‐connected mode. The performance of the GOA‐PSO‐tuned STATCOM is evaluated by comparing it with conventional, PSO, and GOA‐tuned STATCOM in three fault scenarios. The comparison shows that GOA‐PSO‐tuned STATCOM improves grid stability and reliability.

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources.

The increasing integration of inverter-interfaced renewable energy resources, particularly doubly fed induction generators based wind turbines, has considerably reduced system inertia within microgrids. This reduction poses challenges for frequency reserve management and grid code compliance, especially in weak alternating current grids. Furthermore, generation losses and post-fault oscillations due to grid disturbances have raised significant concerns about microgrid stability. To address these challenges, a fast frequency response (FFR) is essential. This study introduces a novel Modified Grasshopper Optimization Algorithm (MGOA) to optimize the proportional-derivative with proportional-integral derivative-acceleration (PD-PIDA) controller, which dynamically adjusts the reference voltages of the static synchronous compensator (STATCOM), providing precise reactive power support and frequency stabilization. The MGOA combines the strengths of the Grasshopper Optimization Algorithm (GOA) and the Differential Evolution algorithm to achieve two critical objectives: mitigating frequency deviations and minimizing direct current-link voltage fluctuations, ensuring enhanced stability and FFR. Simulation results validate the efficacy of the proposed approach, showing significant improvements in frequency stabilization under various scenarios: during a sudden wind speed increase (60.08 Hz compared to 60.23 Hz), a 25% microgrid load increase (59.94 Hz compared to 59.78 Hz), the disconnection of microgrid units (59.904 Hz compared to 59.78 Hz), and a three-phase fault (59.77 Hz compared to 57.77 Hz) compared to the absence of STATCOM. These findings highlight the resilience and exceptional FFR capabilities of the MGOA-tuned PD-PIDA controller while ensuring compliance with North American Electric Reliability Corporation's PRC-024 standard. The proposed method outperforms recent optimization techniques, offering a highly effective solution for enhancing microgrid stability.

The main drive system of the rolling mill often experiences torsional vibrations, which severely affect product quality, precision, and the service life of the transmission equipment. This paper investigates the torsional vibration suppression problem in the main drive system of the rolling mill, considering actuator faults, nonlinear friction, nonlinear damping, and model uncertainties. Based on the high-order fully actuated (HOFA) system approach, the main drive system of the rolling mill is transformed into a rolling mill main drive fully actuated system (RMMDFAS). Adaptive neural networks are introduced to address unknown uncertainties, and a continuous differentiable Gaussian error function is used to handle actuator faults. An adaptive neural network fault-tolerant control law for motor torque is proposed. The stability of the designed main drive torsional vibration system is rigorously proven, while maintaining the performance of the transformed states. Finally, the effectiveness and superiority of the proposed algorithm are verified through simulations.

The incorporation of high-penetration Variable Renewable Energy (VRE) sources results in considerable intermittency and inertia reduction, hence undermining the effectiveness of traditional Load Frequency Control (LFC). This research offers a novel framework based on a Double-Layer Fractional-Order Cascaded Controller (DL-FOCC) to achieve strong, flexible, and computationally efficient frequency regulation. The DL-FOCC's hierarchical cascaded architecture separates setpoint governance from dynamic disturbance rejection. It also has a real-time adaptive mechanism that changes its fractional-orders based on the Area Control Error. A new algorithm called Fractal-Firefly-Whale Optimization (F-FWO) is also proposed to optimally tune the parameters of this complex controller. The suggested F-FWO-DL-FOCC architecture is thoroughly tested using detailed MATLAB/Simulink simulations on a genuine dual-area hybrid power system subjected to strong and simultaneous disturbances. Comparative analyses against ten state-of-the-art controllers confirm the framework's superiority, demonstrating substantial improvements in key performance metrics such as the Integral of Time-weighted Absolute Error and transient frequency deviations. Furthermore, the F-FWO method itself cuts computing costs by about 30%, highlighting the framework's practicality for real-time applications. This work successfully connects the need for good performance in VRE-rich contexts with the need for efficient computing for practical LFC implementation.