As the world's demand for electricity is rising with a growing emphasis on environmental sustainability, the need for efficient renewable energy solutions becomes increasingly critical. Wind power, which comprises 26% of renewable resources, is essential in this transition. Nevertheless, the performance of wind farms (WFs) can be adversely affected by uncertainties in wind speed. In response to this challenge, we introduce a novel four-degree-of-freedom (4DoF)-based fractional-order cascade control approach for WFs based on doubly fed induction generators (DFIGs) to enhance the efficiency and robustness of wind energy conversion systems (WECSs). The presented control method leverages the flexibility and disturbance-reduction capabilities of fractional-order proportional- integral-derivative and tilt- integral-derivative controllers in a 4DoF framework called 4DoF-IHYB. Then, the 4DoF-IHYB controller is cascaded with a fractional-order tilt-derivative controller to mitigate the impact of input noises and disturbances. Furthermore, a deep deterministic policy gradient (DDPG) method is utilized to optimize the controller's parameters and improve the control system's efficiency in the face of uncertainties stemming from volatile environmental conditions. DDPG is an algorithm based on deep reinforcement learning that integrates the advantages of both deep learning and policy gradient methods. The proposed control technique's effectiveness is assessed in a case study of a prominent wind energy facility in New Zealand subject to various operating conditions. Moreover, the presented control method's efficiency is compared with control methods available in the literature. The simulation results disclose that the proposed control method provides much better dynamic stability for the practical case study than the other methods.

This article introduces a novel technique known as bidirectional thyristor capacitor designed to interrupt dc currents effectively and mitigate power surges in low-voltage (LV) solid-state dc circuit breakers (SS-DCCB). The method employs parallel snubber capacitors to absorb switching effects, subsequently releasing stored energy during the subsequent switch operation. The model incorporates considerations for both line and load inductances, offering a realistic portrayal of a dc system and ensuring authentic protective measures. To validate the efficacy of this approach, practical results from the system are cross-referenced with simulation outputs, validating the credibility of the research findings. Additionally, an ARM microcontroller is programmed to control the sequence of actions among the active SS switches, optimizing their performance. The proposed LV SS-DCCB operates at a voltage level of 48 VDC and nominal current of 8 A. However, the design is scalable and can be extended to accommodate higher voltage and current ranges.

This research introduces the Bidirectional Passive Technique (BPT), a novel topology for low-voltage solid-state circuit breakers (SSCBs) that is based on the RCD model. BPT achieves fast switching, minimizes surge voltage across the main switch, and reduces circuit complexity by limiting active components to the primary switch. The study details the working principles, operating modes, and design procedure, supported by key equations for time intervals, voltages, and currents. Validation through MATLAB simulations and practical tests demonstrates rapid interruption within 40 μsec, ensuring effective protection for loads and DC microgrid (DC-MG) systems. The proposed model targets low-voltage systems (48 V) with a 10 A threshold current for the Future Architecture of Network Project, demonstrating the high effectiveness of the BPT in DC microgrid protection..

As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable formulations, energy-consistent representations, and modular plug-and-play scalability. However, the practical deployment of PH-based stability analysis remains hindered by the absence of reliable, high-fidelity parameter identification methods that rely on sensor measurements to capture system dynamics while remaining compatible with PH model structures. This paper addresses that gap by proposing a comprehensive three-stage data-driven identification framework for PH modeling of synchronous generators—the central dynamic component of any power system. While the IEEE Standard 115 provides established procedures for transient parameter identification, it exhibits fundamental limitations when applied to PH modeling, including single-scenario identifiability constraints, noise-sensitive derivative-based formulations that amplify sensor measurement errors, and the inability to decouple generator-internal damping from grid contributions. The proposed framework resolves these limitations through multi-scenario excitation using sensor-acquired voltage and current signals, derivative-free state consistency optimization, and physics-based regularization that enforces PH structure preservation. Complete identification of eight key parameters (H, D, $X_d$, $X_q$, $X_d^{\prime }$, $X_q^{\prime }$, $T_{do}^{\prime }$, $T_{qo}^{\prime }$) is achieved with errors ranging from 1.26% to 9.10%, and validation confirms RMS rotor angle errors below 1.2° and speed errors below 0.15%, demonstrating suitability for transient stability analysis, passivity-based control design, and oscillation damping assessment.