Search Results (808)

To address the critical issue of low reliability caused by fault impacts in large-scale wind farms transmitting power over long distances via flexible DC transmission systems, this study proposes a collaborative solution. First, a new protection scheme integrating variable quantity differential protection, steady-state quantity differential protection and zero-sequence differential protection is proposed. By establishing a refined model of a wind farm with a flexible DC system, the adaptability of the differential protection for the outgoing lines is checked. Simulation results show that the sensitivity of metallic faults within the protection zone is better than 3.0, and the protection reliably remains inactive for faults outside the protection zone. Second, an innovative fault ride-through strategy combining self-regulating resistor circuits with wind farm MPPT load reduction is proposed. During faults on the receiving grid, the DC voltage fluctuation is controlled within 1.05 p.u. through graded switching of resistor modules and dynamic power regulation. This solution offers both rapid response and smooth fault ride-through characteristics, significantly improving the feasibility and economic viability of wind farm integration via flexible DC transmission. Full article

The integration of large-scale photovoltaic (PV) systems requires advanced converter architectures capable of ensuring both high efficiency and fast dynamic response. Leveraging the inherent modularity and low harmonic distortion of Modular Multilevel Converters (MMCs), this paper presents a novel control and modulation framework for grid-connected PV applications. The key innovation lies in the implementation of distributed, string-level Maximum Power Point Tracking (MPPT), enabling optimal energy extraction even under non-uniform (shaded) irradiance conditions. The proposed method operates within a dual time-scale control architecture: an outer Perturb and Observe (P&O) loop assigns independent power references, while the inner modulation stage employs an innovative switching strategy that activates only one module per sampling period. Unlike conventional MPPT-based schemes, where submodules are driven by voltage references, the proposed approach directly regulates the power of each MMC submodule, eliminating the need for PV-side current measurement. Full article

The escalating demand for efficient energy harvesting in CubeSat missions necessitates advanced maximum power point tracking (MPPT) techniques. This work presents a comprehensive time-domain analysis and simulation of three MPPT algorithms: perturb and observe (PO), particle swarm optimization (PSO), and a novel hybrid PO-PSO method, tailored explicitly for CubeSat photovoltaic (PV) solar modules. Utilizing MATLAB R2025a/Simulink, a detailed model of a PV module based on the Azur Space 3G30C datasheet and a DC-DC boost converter was developed. The conventional PO MPPT, while simple, demonstrated limitations in tracking the global maximum power point (GMPP) under rapidly changing temperature conditions and exhibited significant oscillations around the GMPP. The PSO algorithm, known for its global search capabilities, was investigated to mitigate these shortcomings. This research introduces a hybrid PO-PSO MPPT technique that synergistically combines the low computational complexity of PO with the robust global optimization of PSO. Time-domain simulation results demonstrate that the proposed hybrid PO-PSO MPPT significantly reduces oscillations around the GMPP, enhances tracking accuracy under varying temperature conditions, and stabilizes output parameters more effectively than standalone PO or PSO methods. These findings validate the hybrid approach as a superior and reliable solution for optimizing power generation in constrained CubeSat applications. Full article

Maritime power systems must reduce fuel use and emissions while improving resilience. We study a shipboard PV–battery subsystem interfaced with a DC–DC converter running maximum power point tracking (MPPT) and curve-scanning GMPPT to manage partial shading. Dynamic average-value models capture irradiance steps and show GMPPT sustains operation near the global MPP without local peak trapping. We compare converter options—conventional single-port stages, high-gain bidirectional dual-PWM converters, and three-level three-port topologies—provide sizing rules for passives, and note soft-switching in order to limit loss. A Fourier framework links the switching ripple to power quality metrics: as irradiance falls, the current THD rises while the PCC voltage distortion remains constant on a stiff bus. We make the loss relation explicit via $I_{rms}^{2}R$ scaling with THDi and propose a simple reactive power policy, assigning VAR ranges to active power bins. For AC-coupled cases, a hybrid EMT plus transient stability workflow estimates ride-through margins and critical clearing times, providing a practical path from modeling to monitoring. Full article

This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 battery bank with a Battery Management System, an embedded controller with IoT connectivity, and DC/DC and DC/AC converters. The PV panel serves as the primary energy source, with the MPPT controller optimizing battery charging, while the DC/DC and DC/AC converters supply power to the connected electrical devices. The article includes a case study of a developed platform for powering an information and advertising system. The system features a predictive energy management algorithm, which optimizes the appliance operation based on daily solar irradiance forecasts and real-time battery State-of-Charge monitoring. The IoT-enabled controller obtains solar irradiance forecasts from an online meteorological service via API calls and uses these data to estimate energy availability for the next day. Using this prediction, the system schedules and prioritizes the operations of connected electrical devices dynamically to optimize the performance and prevent critical battery discharge. The IoT-based controller is equipped with both Wi-Fi and an LTE modem, enabling communication with online services via wireless or cellular networks. Full article

This paper presents a novel, robust, and reliable control strategy for renewable energy production systems, leveraging artificial neural networks (ANNs) to optimize performance and efficiency. Unlike conventional ANN approaches that rely on perturbation-based methods, we develop a fundamentally different ANN model incorporating equilibrium points (EPs) that achieve superior regulation of photovoltaic (PV) systems. The efficacy of the proposed approach is evaluated through comparative analysis against the conventional control strategy based on perturb and observe (MPPT/PO), demonstrating a 3.3% improvement in system efficiency (98.3% vs. 95%), a five times faster response time (6 s vs. 30 s), and six-fold reduction in voltage ripple (1% vs. 5.95%). A critical aspect of ANN-based controller design is the learning phase, which is addressed through the integration of deep reinforcement learning (DRL) for primary PV system control. Specifically, a hybrid control architecture combining the Artificial Neural Network based on Equilibrium Points (ANN/EP) model with DRL (ANN/PE-RL) is introduced, utilizing a synergistic integration of two reinforcement learning agents: Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG). The TD3-based hybrid approach achieves an average reward value of 434.78 compared to 422.767 for DDPG, representing a 2.84% performance improvement in tracking maximum power points under imbalanced conditions. This hybrid approach demonstrates significant potential for improving the overall performance of grid-connected PV systems, reducing energy losses from 1.95% to below 1%, offering a promising solution for advanced renewable energy management. Full article

This paper presents a three-phase powerline energy harvesting circuit with doubly regulated output voltages to power wireless sensors for the monitoring of railroad powerline status. Three ring-shaped silicon steel cores coupled to the three phases of a powerline convert the line current into three-phase voltages, which are applied to an energy harvesting circuit. The key parts of the circuit are a series three-phase voltage rectifier, a buck–boost converter operating in discontinuous conduction mode (DCM), and a microcontroller unit (MCU) for maximum power point tracking (MPPT). The MCU performs two-step MPPT, coarse and fine, for impedance matching based on the perturb and observe method. Two parallel voltage regulators deliver 5 V and 5.7 V regulated DC voltages to power a radio and a set of sensors, respectively. The energy harvesting circuit is prototyped using commercial-off-the-shelf (COTS) components on an FR4 PCB. The measured maximum efficiency is 84% for the three-phase voltage rectifier and 89% for the buck–boost converter under the powerline current ranging from 5 A to 20 A. Full article

This paper investigates the feasibility of vehicle-integrated photovoltaic (VIPV) systems for light vehicles by developing and simulating an intelligent solar integration design based on the Tesla Model 3. The proposed system incorporates roof and bonnet-mounted photovoltaic modules, each managed by independent buck converters employing maximum power point tracking (MPPT) for optimal energy extraction. A novel fuzzy logic controller was designed to dynamically allocate auxiliary battery charging between the traction battery and the solar subsystem, using real-time irradiance and state-of-charge (SOC) inputs. The system was implemented in MATLAB/Simulink with location-specific data for Melbourne, Australia. Simulation results demonstrate high converter efficiencies of 94–95%, stable MPPT convergence within 0.5 s and an estimated annual solar contribution of 930 kWh, confirming effective control and energy management under varying conditions. This work highlights the innovative application of adaptive fuzzy control and dual MPPT coordination within VIPV systems and provides a validated basis for future optimisation and real-world integration. Full article

The rural community of L’Anse au Loup in southern Labrador depends on a long-distance transmission link to Hydro-Québec for its electricity supply, with diesel generation as backup during outages. This dependence raises electricity costs, exposes the community to supply disruptions, and limits control over local energy security. This study evaluates the feasibility of a solar–wind hybrid energy system to reduce imported electricity and improve supply reliability. A detailed site assessment identified a 50-hectare area north of the community as suitable for system installation, offering adequate space and minimal land-use conflict. Using Hybrid Optimization of Multiple Energy Resources (HOMER Pro 3.18.3) software, the analysis modeled local load data, renewable resource profiles, and financial parameters to determine the optimal grid-connected configuration. The optimized design installs 19.25 MW of photovoltaic (PV) and 4.62 MW of wind capacity, supported by inverters and maximum power point tracking (MPPT) to ensure stable operation. Simulations show that the hybrid system supplies about 70% of annual demand, cuts greenhouse gas emissions by more than 95% compared with conventional generation, and lowers long-term energy costs. The results confirm that the proposed configuration can strengthen local energy security and provide a replicable framework for other remote and coastal communities in Newfoundland and Labrador pursuing decarbonization. Full article

IoT (Internet of Things)-enabled solar irradiance sensors are evolving toward energy harvesting, interoperability, and open-source availability, yet current solutions remain either costly, closed, or limited in robustness. Based on a thorough literature review and identification of future trends, we propose an open-source smart wireless sensor that employs a small photovoltaic module simultaneously as sensing element and energy harvester. The device integrates an ESP32 microcontroller, precision ADC (Analog-to-Digital converter), and programmable load to sweep the PV (photovoltaic) I–V (Current–Voltage) curve and compute irradiance from electrical power and solar-cell temperature via a calibrated third-order polynomial. Supporting Modbus RTU (Remote Terminal Unit)/TCP (Transmission Control Protocol), MQTT (Message Queuing Telemetry Transport), and ZigBee, the sensor operates from batteries or supercapacitors through sleep–wake cycles. Validation against industrial irradiance meters across 0–1200 W/m² showed average errors below 5%, with deviations correlated to irradiance volatility and sampling cadence. All hardware, firmware, and data-processing tools are released as open source to enable reproducibility and distributed PV monitoring applications. Full article

Synthetic inertia (SI) enables wind turbine generators (WTGs) to support frequency stability by releasing stored kinetic energy during disturbances. Existing grid-code requirements, such as those of Hydro-Québec TransÉnergie (HQT) and ENTSO-E/Nord Pool, improve the first frequency nadir but often aggravate a second frequency dip (SFD) or risk rotor over-deceleration (OD) when the boost magnitude is large. This paper proposes an enhanced SI requirement that retains the stepwise boost-and-hold structure but replaces the time-based ramp-down with a rotor-speed-dependent recovery, followed by a smooth transition back to maximum power point tracking (MPPT). The proposed scheme was validated using an electromagnetic transient model of the Unison U151 wind turbine (4.569 MW, inertia constant 9.68 s), designed for Korea’s low-wind conditions. Five case studies at wind speeds of 5 and 7 m/s with varying boost levels confirmed that all methods yield identical first nadirs for a given boost, but only the proposed approach consistently maintained a higher second nadir, stabilized rotor dynamics, and prevented repeated dips. These results demonstrate that rotor-speed-dependent SI requirements, when combined with high-inertia turbines, can enhance frequency stability while protecting turbine operation, offering practical guidance for future grid-code revisions. Full article

A new energy storage unit, which is fed by a piezoelectric wind energy harvester, is explored. The outputs of a three-phase piezoelectric wind energy device have been initially recorded from the laboratory experiments. Following the records of voltage outputs, the power ranges of the device were measured at several hundred microwatts. The main issue of piezoelectric voltage generation is that voltage waveforms of piezoelectric materials have high total harmonic distortion (THD) with incredibly high subharmonics and superharmonics. Therefore, such a material reply causes a certain power loss at the output of the wind energy generator. In order to fix this problem, we propose a combination of a rectifier and a storage system, where they can operate compatibly under high THD rates (i.e., 125%). Due to high THD values, current–voltage characteristics are not linear-dependent; indeed, because of capacitive effect of the piezoelectric (i.e., lead zirconium titanite) material, harvested power from the material is reduced by nearly a factor of 20% in the output. That also negatively affects the storage on the Li-based battery. In order to compensate, the output waveform of the device, the waveforms, which are received from the energy-harvester device, are first rectified by a full-wave rectifier that has a maximum power point tracking (MPPT) unit. The SOC values prove that almost 40% of the charge is stored in 1.2 s under moderate wind speeds, such as 6.1 m/s. To conclude, a better harvesting performance has been obtained by storing the energy into the Li-ion battery under a current–voltage-controlled boost converter technique. Full article

The Organic Rankine Cycle (ORC) offers an efficient approach for harnessing low-grade thermal energy. However, ORC systems often struggle to achieve maximum output power when subject to fluctuations in sink and heat source temperatures. To address this challenge, this paper proposes a Maximum Power Point Tracking (MPPT) strategy based on the optimal evaporation pressure and superheat degree, enabling ORC systems to achieve maximum power output even under varying thermal conditions. First, a dynamic model of the ORC system is established, and the variations in key parameters under different expander and working fluid pump speeds are analyzed. Based on this analysis, the MPPT strategy is developed and its performance is verified through simulations under fluctuating sink and heat source temperatures. The results demonstrated that the ORC system must simultaneously adjust both the expander speed and the working fluid pump speed to maximize power output. Moreover, there exist optimal values of evaporation pressure and superheat degree that yield maximum system performance. Compared with the optimal evaporation pressure strategy, the proposed MPPT approach improves power generation by 14.15%. Full article

The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency. Full article

The main focus of this article is the simulation and analysis of the operating principles of selected maximum power point tracking (MPPT) algorithms for photovoltaic panels, as well as a comparison of various techniques used to address this challenge. The article discusses the underlying rationale for the necessity of identifying the operating point at which a photovoltaic panel delivers maximum power. In addition to the theoretical description, the algorithms were classified, and in the simulation section, the most popular and widely used MPPT algorithms were implemented and compared in the MATLAB 24.11 (R2024a) environment. The application of appropriate solutions among the modelled and tested algorithms enables improved efficiency of photovoltaic cells within panels or larger photovoltaic systems. Full article