The paper discusses the challenges in controlling a two-stage photovoltaic (PV)-based shunt active power filter (SAPF) under distorted grid conditions and stochastic PV system behavior. To address this, the paper proposes an adaptive proportional integral (API) regulator to regulate the DC-link voltage, which adapts its parameters based on the DC-link error voltage to provide low bandwidth for disturbance rejection and high bandwidth for dynamic performance enhancement. The paper also proposes an estimation method for the reference current using the sliding discrete Fourier transform and the instantaneous symmetrical component theory, which is suitable for non-ideal grid or load conditions. The accuracy of the proposed reference current estimation method is analyzed using time- and frequency-domain analysis. The performance of the PV-SAPF system with the proposed control algorithms is illustrated through simulation and real-time hardware validation under various grid conditions. Despite distorted grid conditions, the total harmonic distortion of the grid current is substantially within IEEE Std 519-2014. The proposed API regulator and reference current estimation method show promising results in improving the performance of the PV-SAPF system in the presence of internal and external disturbances.

In the literature, all investigations dealing with regulator design in the AVR loop observe the AVR system as a single input single output (SISO) system, where the input is the generator reference voltage, while the output is the generator voltage. Besides, the regulator parameters are determined by analyzing the terminal generator voltage response for a step change from zero to the rated value of the generator voltage reference. Unlike literature approaches, in this study, tuning of the AVR controllers is conducted while modeling the AVR system as a double input single output (DISO) system, where the inputs are the setpoint of the generator voltage and the step disturbance on the excitation voltage, while the output is the generator voltage. The transfer functions of the generator voltage dependence on the generator voltage reference value and the excitation voltage change were derived in the developed DISO-AVR model. A novel objective function for estimating DISO-AVR regulator parameters is proposed. Also, a novel metaheuristic algorithm named hybrid simulated annealing and gorilla troops optimization is employed to solve the optimization problem. Many literature approaches are compared using different regulator structures and practical limitations. Furthermore, the experimental results of 120 MVA synchronous generators in HPP Piva (Montenegro) are presented to show the drawbacks of the literature approaches that observe generator setpoint voltage change from zero to the rated value. Based on the presented results, the proposed procedure is efficient and strongly applicable in practice.

The rapid expansion of extended electric vehicle (xEV) adoption necessitates optimizing energy storage systems (ESS) management for enhanced performance, longevity, and reliability. However, traditional ESS management approaches struggle with real-time state estimation, energy optimization, and predictive maintenance, leading to inefficiencies in battery utilization and sustainability. This paper addresses these challenges by proposing an advanced ESS framework that integrates digital twin (DT) technology with the twin-delayed deep deterministic policy gradient (TD3) algorithm, a state-of-the-art reinforcement learning method derived from the deep deterministic policy gradient (DDPG). This integration enables precise real-time estimation of critical ESS states, including state of charge (SoC), state of health (SoH), state of energy (SoE), and remaining useful life (RUL), thereby enhancing predictive maintenance and operational efficiency. The proposed framework facilitates proactive battery health monitoring, generates early warnings for potential failures, and enables intelligent battery swapping via DT-driven ESS control. By dynamically adapting ESS control strategies, the TD3 algorithm optimizes energy distribution, reduces energy consumption, and improves overall vehicle performance. With a prediction accuracy of 99.8% for SoC, SoH, and SoE, the proposed approach effectively addresses key challenges in xEV ESS management like cell balancing, dynamic charging rate adjustment, battery swapping decisions, and energy optimization, contributing to a more sustainable and efficient EV ecosystem.

A constant worldwide growing load stress over a power system compelled the practice of a reactive power injection to ensure an efficient power network. For this purpose, multiple technologies exist in the knowledge market out of which this paper emphasizes the usage of the flexible alternating current transmission system (FACTS) and presents a comparative study of the static var compensator (SVC) with the static synchronous compensator (STATCOM), inducted in a real electric substation. The aim is to improve the power factor (PF) and power quality and to encounter reliably extreme conditions. A 220 kV electric substation was opted for the analysis, and both the static and dynamic conditions were observed with the help of a power system analysis tool termed PowerFactory-DIgSILENT. Multiple aspects were investigated via software simulations to assess the performance of the aforementioned FACTS devices, such as the voltage profile evaluation via the load flow analysis method (LFA), the harmonic response via the power quality and harmonic analysis tool, and the short-circuit response via the RMS simulation tool. The outcomes were verified and compared with permissible values included in the universal standards, such as IEC and IEEE. The superiority of the STATCOM over the SVC was proven in light of the simulative results.

In this research, a novel optimized Proportional-Derivative with Proportional-Integral-Derivative-Acceleration (PD-PIDA) driven static synchronous compensator (STATCOM) is proposed with the goal of improving the frequency performance of power systems through the manipulation of reactive power. When there is a variation in load or when there is a failure of generation, the presented controller is able to maintain the frequency of the system within the allowable limits (±2% as per IEC 60034–1 standard). A recently developed metaheuristic optimization strategy known as the Artificial Rabbits Optimizer, or ARO, is used to do the fine-tuning the PD-PIDA controller’s gain settings. In addition to this, the functionality of the ARO-based PD-PIDA-controlled STATCOM is evaluated in the context of two IEEE standard systems: the two-area four-machine system (Kundur system) and the New England IEEE 39-bus system. According to the outcomes, the ARO-based PD-PIDA controller can give a superior frequency response than the PIDA controller whose parameters are optimized by Marine Predator Algorithm (MPA) that was published in the prior literature. This was determined by comparing the two controllers. In addition, the proposed controller’s resiliency is tested by integrating case studies with wind generation. This helps to ensure that the controller is fit for purpose. In every single one of the examples that were researched, the application of the ARO-based PD-PIDA-controlled STATCOM resulted in a remarkable boost in the frequency performance. Additionally, the outputs are introduced in the form of time domain simulations that were carried out with MATLAB/SIMULINK.

A fuzzy cascaded PI−PD (FCPIPD) controller is proposed in this paper to optimize load frequency control (LFC) in the linked electrical network. The FCPIPD controller is composed of fuzzy logic, proportional integral, and proportional derivative with filtered derivative mode controllers. Utilizing renewable energy sources (RESs), a dual-area hybrid AC/DC electrical network is used, and the FCPIPD controller gains are designed via secretary bird optimization algorithm (SBOA) with aid of a novel objective function. Unlike the conventional objective functions, the proposed objective function is able to specify the desired LFCs response. Under different load disturbance situations, a comparison study is conducted to compare the performance of the SBOA-based FCPIPD controller with the one-to-one (OOBO)-based FCPIPD controller and the earlier LFC controllers published in the literature. The simulation’s outcomes demonstrate that the SBOA-FCPIPD controller outperforms the existing LFC controllers. For instance, in the case of variable load change and variable RESs profile, the SBOA-FCPIPD controller has the best integral time absolute error (ITAE) value. The SBOA-FCPIPD controller’s ITAE value is 0.5101, while sine cosine adopted an improved equilibrium optimization algorithm-based adaptive type 2 fuzzy PID controller and obtained 4.3142. Furthermore, the work is expanded to include electric vehicle (EV), high voltage direct current (HVDC), generation rate constraint (GRC), governor dead band (GDB), and communication time delay (CTD). The result showed that the SBOA-FCPIPD controller performs well when these components are equipped to the system with/without reset its gains. Also, the work is expanded to include a four-area microgrid system (MGS), and the SBOA-FCPIPD controller excelled the SBOA-CPIPD and SBOAPID controllers. Finally, the SBOA-FCPIPD controller showed its superiority against various controllers for the two-area conventionally linked electrical network.