Microgrid control systems are implemented using a hierarchical framework comprising primary, secondary and tertiary control levels. This paper proposes a distributed, time varying optimization method for the secondary control level of heterogeneous energy sources (photovoltaics and batteries) in a microgrid. The method addresses time-varying objectives and constraints while achieving frequency and voltage regulation and optimal power sharing. Its main advantages are: (i) provision of continuous-time optimal control inputs that adapt to fast changes in generation and load; (ii) seamless interaction with grid forming and grid following converters; (iii) adaptive real/reactive power sharing and battery state-of-charge balancing based on net power and state of charge; and (iv) implementation over a sparse neighbor-to-neighbor communication graph. Real-time validation on a modified IEEE 13- and 37- test feeder using RTDS with a server-side solver, hardware-in-the-loop, confirms feasibility. Compared with established distributed secondary control schemes, the proposed controller reduces frequency and voltage root-mean-square regulation errors, lowers the maximum observed voltage deviation under load or renewable disturbances, and achieves a lower cumulative operating cost while maintaining all battery state-of-charge trajectories balanced. Scalability and heterogeneity are further demonstrated on an islanded IEEE 37 bus case with mixed grid forming and grid following resources.

Voltage source converter (VSC) power balancing and voltage regulation are two critical and interrelated objectives for multi-terminal DC distribution networks (DCDNs). This paper proposes a distributed multi-objective control strategy for DCDNs based on linear quadratic (LQ) optimal control theory to achieve these objectives simultaneously. Unlike the conventional centralized control method, which is usually expensive in terms of communication and computation, our key technology is that the original nonlinear multi-objective control problem of DCDNs is converted into the LQ optimal control problem through the linear power flow sensitivity. A distributed optimal controller is presented based on an observer using the information of neighboring VSCs and is solved by the solution of an algebraic Riccati equation, which provides a faster convergence speed than conventional consensus-based algorithms. The proposed method enhances robustness by accounting for DCDN topology reconfigurations in the observer gain calculation. The proposed controller is tested on a ±10 kV four-terminal DCDN under communication failures, topology changes, and time delays. De tailed simulation studies and comparative analyses demonstrate the superior convergence performance and robustness of the proposed controller.

Design and performance evaluation of a predictive functional controller for automatic voltage regulator system

Improving the performance of the voltage regulation mechanism in a photovoltaic system by designing a fast and chattering-free control strategy

Human activity is intermittent, and social interaction changes over time. It embodies the time-varying nature of social networks. However, due to the complexity and dynamics of time-varying networks, the analysis opinion dynamics with decision-making over time-varying social networks is still a challenging problem. In this paper, we study the time-varying social network opinion dynamics under one-step ahead optimal decision-making mechanism. An explicit relationship between the supremum/infimum of the ultimate opinions, the maximum desired opinion and the maximum/minimum intensity of the influence of players is given. We provide the criteria for determining that individual achieves the desired opinion. Moreover, an explicit relationship between the ultimate opinions and the influence weight of the decision-making mechanism is presented. Besides, we employ our theory framework to analyze time-varying Friedkin-Johnsen opinion dynamics under one-step ahead optimal decision-making mechanism. Based on the real networks (Dolphin network and western US power grid) and the datasets, simulation experiments applying our theory illustrate that the dolphins achieve the desired performance by the keepers and the substations achieve the desired voltage regulation rates by the technicians.

The rapid growth of electric vehicles (EVs) has increased the demand for efficient and high-power quality battery charging systems. Conventional EV chargers typically use diode bridge rectifiers followed by DC–DC converters for AC–DC power conversion; however, these rectifier-based systems suffer from high conduction losses, poor power factor, increased total harmonic distortion (THD), and reduced overall efficiency. These drawbacks not only degrade charger performance but also introduce harmonics into the utility grid, resulting in additional losses and reduced reliability. To address these issues, this paper presents the design and analysis of a modified bridgeless AC–DC Landsman converter for EV charging applications. The proposed topology eliminates the conventional diode bridge rectifier, thereby reducing conduction losses and improving efficiency. The converter operates as a power factor correction (PFC) stage and provides a regulated DC output suitable for EV battery charging. The modified bridgeless configuration reduces the number of conducting devices in each switching cycle, which improves power quality and minimizes input current ripple. A PI controller is employed to regulate the DC-link voltage and maintain a constant output voltage. The proposed converter is modeled and simulated in MATLAB/Simulink using a 230 V single-phase AC input with a switching frequency of 20 kHz, and the output voltage is regulated to 48 V. Simulation results demonstrate improved power factor, reduced harmonic distortion, and stable output voltage. The input current waveform becomes nearly sinusoidal, and conduction losses are significantly reduced compared to conventional rectifier-based chargers. Therefore, the proposed modified bridgeless Landsman converter provides improved efficiency, reduced THD, better voltage regulation, and enhanced power quality, making it suitable for EV battery charging applications.

This study introduces a multi-objective electro-thermal optimization method for a 500 W synchronous buck converter with a 48 V input, 12 V output, and 200 kHz switching frequency. The converter is modeled in MATLAB/Simulink using a detailed representation that includes practical parasitic elements, temperature-dependent characteristics, and the coupling between electrical losses and heat generation. The design task is formulated as a constrained multi-objective optimization problem targeting higher efficiency, lower total loss, reduced junction temperature, and improved power density while satisfying voltage regulation and ripple limits. The NSGA-II algorithm is applied to obtain Pareto-optimal solutions. To reduce computational cost, Gaussian Process Regression is incorporated within the optimization loop. Compared with a conventional sequential design approach, the optimized converter increases full-load efficiency from 94.3% to 97.6%, reduces total loss by 60.5%, lowers peak junction temperature by 25.1%, and improves power density by 43.9%. The results show that simultaneous electrical and thermal evaluation inside the optimization process produces converter designs that better reflect practical operating conditions and realistic performance trade-offs.

DoS-resilient simultaneous voltage and frequency control in wind-integrated power system

In recent years, the rapid expansion of electric vehicle (EV) charging infrastructure and the increasing penetration of renewable energy sources require highly efficient and dynamically robust power electronic interfaces. In photovoltaic (PV)-assisted EV charging stations and DC microgrids, bidirectional DC-DC converters (BDCs) are essential for managing power flow between PV arrays, battery energy storage systems, and the DC bus supplying EV chargers. This paper presents a novel voltage and current control design for a BDC operating in a PV-powered DC microgrid oriented to EV charging applications. Following a detailed mathematical model of the converter, a digital current controller and a predictive voltage regulator were developed using Model-Based Predictive Control (MBPC). The proposed cascade control structure enables accurate DC bus voltage regulation and seamless bidirectional power flow under dynamic load variations representative of EV charging and discharging scenarios. The control scheme was evaluated in MATLAB/SIMULINK® and experimentally validated through Field-Programmable Gate Array (FPGA)-based test benches using an OPAL-RT real-time (RT) simulator, integrating the RT-LAB and RT-eFPGAsim environments. The predictive controller achieved precise regulation in both buck and boost modes, reaching efficiencies of 97.07% and 98.57%, respectively. The results demonstrate that integrating MBPC with RT validation provides high performance, fast dynamic response, and computational efficiency, making the proposed approach suitable for renewable-integrated EV charging stations and next-generation DC microgrid-based mobility systems.

Reliable communication in extreme cold and high-altitude military environments is frequently compromised due to accelerated voltage degradation in lithium-ion batteries that power portable soldier communication devices. Reduced electrochemical performance at low temperatures results in rapid voltage decline and unexpected system shutdowns during mission-critical operations. This paper proposes an intelligent, energy-aware backup communication system that integrates real-time battery condition monitoring with adaptive auxiliary power support. A lightweight AI-driven voltage assessment algorithm, implemented on an ESP32 microcontroller, continuously analyses discharge characteristics to identify early indicators of abnormal battery deterioration. To enhance operational robustness, mechanical energy generated from soldier movement is harvested using piezoelectric transducers. The harvested energy is converted into electrical power through rectification and voltage regulation stages, and subsequently stored in a supercapacitor governed by a dedicated charge management unit to prevent overcharging. Upon detection of critical voltage instability, a MOSFET-based automatic switching mechanism seamlessly transfers the load to the backup energy source. This ensures the transmission of short emergency voice messages and distress alerts even in the event of primary battery failure. The proposed system significantly improves battlefield communication reliability while preserving energy efficiency and maintaining a compact, field-deployable design suitable for harsh environmental conditions.

Toward adaptive control power sharing and bus voltage regulation for DC microgrids.

Abstract The control and power production of a Doubly Fed Induction Generator (DFIG) wind turbine are highly dependent on grid parameters. To reach the maximum power point (MPPT) and hence to maximize the power output from the wind energy system, the MPPT algorithms used are generally dependent on the grid voltage measurements. Therefore, any deviation of the grid voltage from its rated value can reduce both the power production efficiency and the reactive power performance of the wind turbine. The main sources of these deviations are voltage unbalance and grid disturbances. Deep investigations of a year of data from the Nouadhibou wind farm located in Mauritania, have shown that DFIG wind turbines are very sensitive to the operating conditions defined by the grid code. This grid code specifies two operation modes: unlimited temporal operation and limited operation. In many situations, the wind farm exceeds the allowed time for limited operation, leading to grid disconnection. This paper proposes a new reactive power control strategy to reduce DFIG’s sensitivity to grid voltage variations. The proposed control compares the measured Phase-Locked Loop (PLL) signal with the reference voltage. Based on this comparison, the controller chooses the suitable combination of capacitors to generate or absorb the required reactive power for voltage regulation. This method allows the wind turbine to remain in unlimited operation mode, preventing unwanted disconnections. Furthermore, it reduces reactive power exchange between the DFIG and the grid while keeping the Point of Common Coupling (PCC) voltage within optimal limits.

The increasing emphasis on sustainable and decentralized energy has elevated microgrids as a central element of modern power systems. By integrating renewable energy sources, advanced energy storage technologies, and intelligent control strategies, microgrids enhance efficiency, stability, and flexibility and play a vital role in creating resilient and adaptable energy networks. This review provides a comprehensive analysis of Energy Management Systems (EMSs) in microgrids, distinguishing between planning-oriented tools for techno-economic evaluation and control-oriented platforms for real-time operation and optimization. Hierarchical control architectures spanning primary, secondary, and tertiary levels are examined, highlighting their roles in frequency and voltage regulation, load sharing, and economic dispatch. Optimization techniques for EMSs are analyzed across deterministic, stochastic, metaheuristic, and artificial intelligence/machine learning methods, addressing objectives, constraints, uncertainties, and multi-timeframe decision-making. AI-based methods, including supervised learning, deep learning, and reinforcement learning, are highlighted for their ability to enhance predictive control, system autonomy, and operational efficiency, despite their computational demands. Future trends emphasize AI-based predictive control, deep learning for energy forecasting, multi-microgrid coordination, hybrid energy storage management, and cybersecurity enhancements. Overall, an intelligent EMS, combined with innovative technologies, is critical for developing resilient, scalable, and sustainable microgrid solutions that meet the evolving demands of modern energy systems.

ABSTRACT: Agriculture in developing countries faces challenges such as labor shortage, energy inefficiency, and lack of automation. This project presents the design and implementation of the Solar Based Multifunctional Agricultural Machine using Zigbee is designed to perform multiple farming operations such as ploughing, sowing, grass cutting, and water sprinkling with reduced human effort and improved efficiency. The system uses solar energy as the main power source, making it eco-friendly and cost-effective for agricultural applications. An Arduino Uno is used as the main controller to manage all operations, while Zigbee communication enables wireless control of the machine through a laptop. DC motors are used for robot movement, servo motors perform ploughing and sowing, and relay modules control the grass cutter and water sprinkler. The LM2596 voltage regulator provides stable power supply to all components. This system helps in saving time, reducing labor, and improving agricultural productivity by integrating renewable energy and wireless communication technology, making it suitable for modern smart farming applications. Keywords: Solar energy, Zigbee wireless communication, smart farming, agricultural robot, smart farming, Arduino UNO, wireless control.

Abstract The growing use of solar photovoltaic systems needs dependable multiport DC-DC converters that can perform at high levels. The performance of converters declines when semiconductor components experience open-circuit or short-circuit faults because these faults require immediate detection to prevent total system failure. A unified system for current control and fault detection based on diode current as a shared state element for output control and fault detection has been developed in this research paper for solar-powered multiport DC-DC converters. The predictive control law establishes duty cycle estimation through charge-second balance principles, which allow voltage regulation to function properly during standard operations without needing extra sensor equipment. The analysis through frequency-domain methods shows the closed-loop system maintains stable operation because it runs with a 1.318 kHz bandwidth and positive phase margin, which allows for quick system response and protection against disturbances. The diagnostic algorithm proposed in this study measures diode current during multiple switching periods to investigate conduction properties, which help identify normal functioning versus OC and SC faults. The experimental tests on a laboratory prototype running at 20 kHz show that the system can detect faults within a time period shorter than one-fourth of the switching cycle, which enables quick fault management and improved system security. The proposed method achieves multiple benefits by lowering both sensor requirements and system processing demands while providing quick fault detection, accurate fault identification, and consistent output control for renewable energy systems and critical power electronic systems. The effectiveness of the proposed method is validated through both simulation and experiment using a 200 W laboratory prototype, where accurate fault detection and control performance are achieved.

Abstract This paper presents a hybrid Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) approach for optimal slip coordination and power management of converter-free grid-connected parallel induction generators (2.2 kW and 5.5 kW) in micro-hydro power plants. The methodology addresses multi-objective optimization of slip values for maximum power extraction, inrush current mitigation through intelligent synchronization, and power flow coordination without power electronic converters. The hybrid PSO-GWO achieves superior convergence compared to standalone PSO (18.3% faster), GWO (12.7% faster), and five recent hybrid methods (AGWOPSO, HFPSO, FFA, HCHOPSO, PSO-AHA), attaining 96.4% global optimum detection rate across 500 Monte Carlo simulations confirmed by the Wilcoxon rank-sum test ( $$p < 10^{-5}$$ for all comparisons). Experimental validation demonstrates 68.8% inrush current reduction (45.2 A to 14.1 A), 126% active power improvement through optimal slip matching (2328 W to 5262 W), and 74–79% cost reduction ($960 versus $3,650–$4,650). The optimization identifies the Pareto-optimal slip range of $$-1.8\%$$ to $$-1.0\%$$ with power factor between 0.85 and 0.92. A comprehensive distribution network model incorporating transformer and feeder impedances validates voltage regulation within ±4.2% at the point of common coupling, with uncertainty analysis confirming predictions within instrument accuracy. Robustness evaluation under four fault conditions (LL, LG, LLG, LLL) confirms stable recovery satisfying IEEE Standard 1547–2018 fault ride-through requirements. The proposed approach offers a computationally efficient and economically viable solution for rural electrification with total harmonic distortion below 4.2%.

Design, construction, and field evaluation of an intelligent solid-state voltage regulator for voltage profile improvement in low-voltage distribution networks.

DC microgrids have become a viable solution for modern power distribution systems because they offer better control, improved efficiency, and simpler integration with renewable energy sources and energy storage systems. However, the performance of low-voltage DC microgrids can suffer from stability issues related to unpredictable sensor faults, parameter uncertainty, and equipment failure. In recent years, disturbance-rejection methods and robust control methods have been effective in improving microgrid resilience during these situations. This paper proposes a decentralized sensor fault-tolerant control approach for an islanded low-voltage DC (LVDC) microgrid using the active disturbance rejection control (ADRC). The ADRC control preserves the DC grid stability in the presence of unknown and time variant sensor faults by estimating and compensating for lumped disturbances through an extended state observer without the need for fault detection or reconfiguration of the system. A thorough mathematical model and an analytical control formulation are provided and thoroughly examined through single, consecutive, and simultaneous sensor-fault scenarios. Time-domain nonlinear simulation studies on a multi-DG DC microgrid show that the proposed controller provides better voltage regulation, faster transient recovery, and better robustness compared to other proposed methods in the literature, such as the conventional autotune PI controllers and the attractive ellipsoidal-based methods. The simulation studies' results verified that the proposed ADRC scheme noticeably increases the reliability and resilience of the DC microgrid under realistic simulation conditions of sensor faults.

ABSTRACT Accurate tuning of PI controllers is essential for high-performance voltage regulation in DC–DC buck converters under varying operating conditions. In this work, a real-time Particle Swarm Optimisation (PSO)-based PI control strategy is implemented entirely on an STM32 microcontroller, executing both optimisation and control processes directly on embedded hardware. Unlike conventional offline tuning methods, the PI gains are computed in real time by minimising a composite fitness function incorporating RMSE, rise time, and overshoot. The buck converter is experimentally validated to provide a reliable hardware platform for performance assessment. Simulation results are benchmarked against Genetic Algorithm (GA), Educational Competition Optimiser (ECO), and Dragonfly Algorithm (DA)-based tuning approaches. The PSO-based method achieves superior performance, yielding a minimum fitness of 1.0244, a mean fitness of 1.0263, and a standard deviation of 6.84 × 10 − 4 , indicating high convergence reliability. It also attains the lowest error metrics (RMSE = 0.0020, MAE = 0.0019, relative error = 0.0186), outperforming GA and ECO while providing comparable accuracy to DA with significantly lower computational cost. Experimental results confirm fast transient response, negligible overshoot, and near-zero steady-state error, demonstrating the practicality and robustness of the proposed approach.

In three-phase PWM rectifiers, abrupt load changes and parameter variations challenge DC-bus voltage regulation and degrade the performance of conventional controllers. To ensure robust regulation under nonlinear and time-varying conditions, this study proposes a type-3 fuzzy logic controller (T3-FLC) for DC-bus voltage regulation. The T3-FLC enhances the conventional type-1 framework by employing a three-dimensional membership structure that captures both vertical and horizontal uncertainties in the fuzzy inference process. This structure improves adaptability and stability in the face of system disturbances. The proposed controller was compared with a conventional proportional-integral (PI) controller and a type-1 fuzzy logic controller (T1-FLC) under different operating conditions: constant reference, reference tracking, load variation, regenerative operation, and grid disturbances. Under reference tracking mode, it settles within approximately 12 ms for the largest reference step, with the overshoot kept below 0.3%, whereas the T1-FLC and PI controllers require noticeably longer settling times and exhibit higher overshoot. In regenerative operation, the T3-FLC maintains tight DC-bus regulation with recovery times of 10–12 ms and an overshoot of about 2.7%, outperforming the benchmark controllers. Power quality analysis further shows that the proposed controller maintains low input-current distortion, with THD approximately 5–13%, and a near-unity power factor across all scenarios. These results confirm the T3-FLC as an effective control strategy for power converters.