Search Results (199)

In this paper, a robust optimized controller is implemented in the photovoltaic generator system (PVGS). The PVGS is composed of individual photovoltaic (PV) cells, which convert solar energy to electrical energy. To optimize the efficiency of the PVGS under variable solar irradiance and temperatures, a maximum power point tracking (MPPT) controller is necessary. Additionally, the PVGS output voltage is typically low for many applications. To achieve the MPPT and to gain the output voltage, an increasing boost converter (IBC) is employed. Then, two issues should be considered in MPPT. At first, a smooth control signal for adjusting the duty cycle of the IBC is important. Another critical issue is the PVGS and IBC unknown sections, i.e., the total system uncertainty. Therefore, to address the system uncertainties and to regulate the smooth duty cycle of the converter, a robust dynamic sliding mode control (DSMC) is proposed. In DSMC, a low-pass integrator is placed before the system to suppress chattering and to produce a smooth actuator signal. However, this integrator increases the system states, and hence, a sliding mode observer (SMO) is proposed to estimate this additional state. The stability of the proposed control scheme is demonstrated using the Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed method and provide a reliable comparison, conventional sliding mode control (CSMC) with the same proposed SMO is also implemented. Full article

The amount of energy consumed worldwide is raising at a startling rate. This has led to a global energy crisis and a hike in fuel prices and has caused environmental jeopardy. Renewable energy resources offer a promising solution to the above situation. Solar energy is examined to be the most liberal source of renewable energy. The efficiency of solar PV cells show nonlinear characteristics and deliver poor performance. Consequently, it is imperative to use the maximum power point tracking (MPPT) technique to extract the optimum amount of energy from photovoltaic (PV) cells. Perturb and Observe (P&O) and Incremental Conductance (INC) are examples of MPPT algorithms. The performance of MPPT schemes below varying climatic ambience should be predominantly considered. The workings of these schemes under various load conditions becomes critical to analyze. This work deals with this issue and compares the conventional P&O MPPT and INC MPPT schemes for various solar irradiation and load conditions and designing solar panels optimized for maximum power generation. The designed MPPT scheme is carried out in the control circuit of a boost converter, evaluating and designing a converter to convert solar panel DC power into grid-compatible AC power. By analyzing different methods for managing and tracking PV power, this method proves to be fast and gives better results under changes in solar insolation. Full article

This paper aims to maximize the performance of photovoltaic generators under varying atmospheric conditions by employing an improved variable-step current perturbation Perturb and Observe (IVSCP-PO) MPPT controller. The proposed approach overcomes the limitations of traditional controllers and significantly enhances tracking efficiency. The IVSCP-PO controller locates the maximum power point (MPP) using current perturbation instead of voltage perturbation and employs a variable step iteration based on input variables such as power, voltage, and current for better adjustment of the boost converter’s duty ratio. Comprehensive simulations demonstrate the tracking effectiveness of the IVSCP-PO approach under varied and severe temperature and solar intensity conditions. The results indicate that the IVSCP-PO controller outperforms traditional and recently published methods by avoiding drift and oscillation and minimizing power loss. This translates to maximized static and dynamic tracking efficiencies, reaching 99.99% and 99.98%, respectively. Additionally, the IVSCP-PO controller boasts a record-breaking average tracking time of just 0.002 s, a substantial improvement over traditional and improved PO methods ranging from 0.036 to 0.6 s. To further validate these results, experiments were conducted using the dSPACE 1104 board, demonstrating the superior accuracy and effectiveness of the approach and providing a promising solution to optimize the performance of photovoltaic panels. Full article

This paper presents a real-time remote-control platform for small wind turbines (SWTs) equipped with a permanent magnet synchronous generator (PMSG). The proposed system integrates a DC–DC boost converter controlled by an Arduino^® microcontroller, a Raspberry Pi^® hosting a WebSocket server, and a desktop application developed using MATLAB^® App Designer (version R2024b). The platform enables seamless remote monitoring and control by allowing upper layers to select the turbine’s operating mode—either Maximum Power Point Tracking (MPPT) or Power Curtailment—based on real-time wind speed data transmitted via the WebSocket protocol. The communication architecture follows the IEC 61400-25 standard for wind power system communication, ensuring reliable and standardized data exchange. Experimental results demonstrate high accuracy in controlling the turbine’s operating points. The platform offers a user-friendly interface for real-time decision-making while ensuring robust and efficient system performance. This study highlights the potential of combining open-source hardware and software technologies to optimize SWT operations and improve their integration into distributed renewable energy systems. The proposed solution addresses the growing demand for cost-effective, flexible, and remote-control technologies in small-scale renewable energy applications. Full article

The integration of photovoltaic (PV) systems into global energy production is rapidly expanding. However, achieving maximum power extraction remains a significant challenge due to the nonlinear electrical characteristics of PV modules, which are highly sensitive to environmental variations such as temperature fluctuations and irradiance changes. This study presents a structured design, testing, and quasi-experimental validation methodology for robust Maximum Power Point Tracking (MPPT) control in PV systems. We propose two advanced AI-based nonlinear control strategies: an Adaptive Neuro-Fuzzy Inference System combined with Fast Terminal Synergetic Control (ANFIS-FTSC) for a boost converter and ANFIS with Backstepping (ANFIS-BS) for a Single-Ended Primary Inductor Converter (SEPIC), both of which have demonstrated tracking efficiencies exceeding 99.6%. To evaluate real-time performance, a Processor-in-the-Loop (PIL) validation is conducted using an ARM-based STM32F407VG microcontroller. The methodology adheres to a Model-Based Design (MBD) framework, ensuring systematic development, implementation, and verification of the MPPT algorithms in an embedded environment. Experimental results demonstrate that the proposed controllers achieve high efficiency, rapid convergence, and robust maximum power point tracking under varying operating conditions. The successful PIL-based validation confirms the feasibility of these intelligent control techniques for real-world deployment in PV energy systems, paving the way for more efficient and adaptive renewable energy solutions. Full article

The power output of a photovoltaic (PV) system is inherently dependent on climatic factors. To maximize the energy harvested from PV arrays, maximum power point tracking (MPPT) algorithms are employed. These algorithms dynamically adjust the operating point of the system to extract the maximum available power. However, under partial shading conditions (PSCs), conventional MPPT algorithms often fail to locate the global maximum power point, leading to suboptimal power extraction. In this study, a robust MPPT technique based on sliding mode control (SMC) is proposed to enhance tracking efficiency and optimize power extraction from PV arrays under PSC. Particle swarm optimization (PSO) is incorporated into the MPPT framework, enabling the dynamic tuning of SMC parameters for improved adaptability and performance. The proposed SMC structure is designed to regulate the duty cycle of a boost converter, ensuring effective power conversion. The system is simulated in Matlab/Simulink for various PSCs. The simulation results demonstrate that the PSO-tuned SMC methodology exhibits superior tracking performance, enabling the PV system to rapidly and accurately converge to the true MPP under varying weather and shading scenarios. The findings indicate that the proposed technique enhances the efficiency and reliability of PV energy harvesting in PSCs. Full article

A grid-tied inverter needs excellent maximum power point tracking (MPPT) topology to extract the maximum energy from PV panels regarding energy creation. An efficient MPPT ensures that grid codes are met, maintains power quality and system reliability, minimizes power losses, and suppresses rapid response to power fluctuations due to solar irradiance. Moreover, appropriate MPPT enhances economic returns by increasing energy royalties and ensures high power quality with reduced harmonic distortion. For these reasons, an improved hybrid MPPT technique for a grid-tied solar system is presented based on particle swarm optimization (PSO) and grey wolf optimizer (GWO-PSO) to achieve these objectives. The proposed method is tested under MATLAB/Simulink 2024a for a 100 kW PV array connected with a boost converter to link with a voltage source converter (VSC). The simulation results show that the proposed GWO-PSO can reduce the overshoot on rise time along with settling time, meaning less time is wasted within the grid power system. Moreover, the suggested method is compared with PSO, GWO, and horse herd optimization (HHO) under different weather conditions. The results show that the other algorithms respond more slowly and exhibit higher overshoot, which can be counterproductive. These comparisons validate the proposed method as more accurate, demonstrating that it can enhance the real power quality that is transferred to the grid. Full article

Photovoltaic energy is increasingly used in irrigation processes, particularly in arid regions, to pump water from rivers to fields. Rising oil prices, global warming, and the limited availability of fossil fuels have increased the need for alternative energy sources. This study focuses on the design and implementation of a transformerless single-phase photovoltaic system that powers a single-phase induction motor to drive a centrifugal water pump. The methodology aims to achieve the best system performance. A DC–DC boost converter maximizes the output voltage by utilizing maximum power point tracking (MPPT) and extracting the maximum power from the photovoltaic (PV) array. A bidirectional buck-boost converter charges the battery from the DC bus and discharges the battery voltage to the DC bus for loads. The DC voltage is then converted to AC output voltage using a single-phase inverter, which supplies power to the single-phase induction motor driver (IMD). The voltage/frequency (V/f) scaler control is used for a single-phase induction motor. The system employs scalar motor control to achieve the maximum motor speed required to operate the centrifugal water pump efficiently. All results and simulations are carried out in MATLAB/Simulink R2019a version and are compared for different motor and PV parameters numerically. Full article

Operating PV panels at the Maximum Power Point (MPP) is crucial for increasing efficiency and reducing the payback period of the system. However, the voltage and current characteristics of PV panels are nonlinear and depend on environmental conditions like temperature and irradiance. This paper presents a comparative analysis of Fuzzy Logic Control (FLC) and Model Predictive Control (MPC) for Maximum Power Point Tracking (MPPT) applied to a photovoltaic generation system. The study focuses on FLC due to its rapid response and robustness against circuit parameter variations. MPC, known for its predictive capabilities, is also investigated for comparison. A PI control strategy is employed to maintain the desired current and voltage during battery charging. The results show that, under standard test conditions (1000 W/m² irradiance and 25 °C temperature), the FLC-based MPPT achieved an average efficiency of 98.298%, with a response time of 12 ms. In comparison, the MPC-based MPPT achieved 96.598% efficiency and a 25 ms response time. During dynamic irradiance changes, FLC demonstrated faster adaptation with a peak tracking error of 2.398%, while MPC had a tracking error of 4.598%. These findings highlight the superior dynamic performance of FLC in real-time PV tracking and the stability of MPC for long-term optimization. Full article

The rapid rise in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) has opened the door for diverse potential applications in powering indoor Internet of Things (IoT) devices. An energy harvesting system (EHS) powered by a PSC module with a backup Li-ion battery, which stores excess power at moments of high irradiances and delivers the stored power to drive the load during operation scenarios with low irradiances, has been designed. A DC-DC boost converter is engaged to match the voltage of the PSC and Li-ion battery, and maximum power point tracking (MPPT) is achieved by a perturb and observe (P&O) algorithm, which perturbs the photovoltaic (PV) system by adjusting its operating voltage and observing the difference in the output power of the PSC. Furthermore, the charging and discharging rate of the battery storage is controlled by a DC-DC buck–boost bidirectional converter with the incorporation of a proportional–integral (PI) controller. The bidirectional DC-DC converter operates in a dual mode, achieved through the anti-parallel connection of a conventional buck and boost converter. The proposed EHS utilizes DC-DC converters, MPPT algorithms, and PI control schemes. Three different case scenarios are modeled to investigate the system’s behavior under varying irradiances of 200 W/m², 100 W/m², and 50 W/m². For all three cases with different irradiances, MPPT achieves tracking efficiencies of more than 95%. The laboratory-fabricated PSC operated at MPP can produce an output power ranging from 21.37 mW (50 W/m²) to 90.15 mW (200 W/m²). The range of the converter’s output power is between 5.117 mW and 63.78 mW. This power range can sufficiently meet the demands of modern low-energy IoT devices. Moreover, fully charged and fully discharged battery scenarios were simulated to study the performance of the system. Finally, the IoT load profile was simulated to confirm the potential of the proposed energy harvesting system in self-sustainable IoT applications. Upon review of the current literature, there are limited studies demonstrating a combination of EHS with PSCs as an indoor power source for IoT applications, along with a bidirectional DC-DC buck–boost converter to manage battery charging and discharging. The evaluation of the system performance presented in this work provides important guidance for the development and optimization of new-generation PV technologies like PSCs for practical indoor applications. Full article

The paper proposes an original single-phase transformerless three-level (S-PT) photovoltaic (PV) inverter in the cascade H bridge (CHB) configuration. The DC-link voltage of the inverter is created by two serial voltage sources with a voltage twice as low as the DC-link voltage. An appropriate V_CC DC-link voltage is generated by a two-phase DC-DC boost converter, fed from the string panel output at a level determined by the maximum power point tracking (MPPT) algorithm. Two symmetrical sources with V_CC/2 are formed by a divider of two series-connected capacitors of large and the same capacitance. The common mode (CM) voltage of the proposed inverter is constant, and the voltage stresses across all switches, diodes and gate drive circuits are half of the DC-link voltage. The principles of operation of the S-PT inverter, an implementation of a complete gate control system with galvanic isolation for all IGBTs, are also presented. The proposed inverter topologies have been implemented using high-speed IGBTs and simulated in PSPICE, as well as being experimentally validated. Full article

This paper proposes a model predictive control (MPC)-based approach for optimizing the performance of a photovoltaic (PV) system. The proposed method employs finite voltage-set maximum power point tracking (FVS-MPPT), ensuring precise duty cycle adjustment for a boost converter in the PV system considering the environmental changes in irradiation and temperature. Additionally, MPC is implemented for the grid-side converter to determine the optimal switching vector, ensuring precise control of active power via reference d-axis current and the elimination of reactive power by setting the reference q-axis current to zero. This approach optimizes the converter’s performance, maintaining a stable DC-link voltage while ensuring efficient grid integration. To ensure proper synchronization with the grid, a phase-locked loop (PLL) is utilized to provide the necessary grid voltage angle for dq frame transformation. Simulation results highlight the efficiency of the proposed MPC strategy, with the PV-side converter showing a robust response by dynamically adjusting the duty cycle to maintain optimal performance under varying irradiation and temperature conditions. Furthermore, the grid-side converter ensures precise control of active power and eliminates reactive power, enhancing the overall system’s stability and efficiency during grid interactions. A functional comparison of simulation results between the conventional P&O algorithm and the FVS-MPPT approach is presented, demonstrating the enhanced performance of the proposed technique over the conventional method including the total harmonic distortion for both techniques. Full article

Photovoltaic (PV) energy has been a preferable choice with the rise in global energy demand, as it is a sustainable, efficient, and cost-effective source of energy. Optimizing the power generation is necessary to fully utilize the PV system. Harvesting more power uses cascading of impedance source converters taking input from low-voltage PV arrays which requires multiple maximum power point tracking (MPPT) controllers. To solve this problem, a three-level inverter topology with a proposed PV arrangement, offering higher voltage boosting and a smaller size with a lower cost suitable for low-voltage panels, is designed in this article. The design criteria for parameters are discussed with the help of the small signal analysis. In this paper, three PV arrays are used to harvest maximum energy, which require only one MPPT controller and employ an extended perturb and observe (P&O) algorithm, being faster, highly efficient, and reducing the computational burden of the controller. Moreover, a three maximum power points tracker algorithm, which perturbs one parameter and observes six variables, is designed for the selected converter topology. Finally, the designed 1.1 kVA grid-connected PV system was simulated in MATLAB (R2023a) which shows that the MPPT algorithm offers better dynamics and is highly efficient with a conversion efficiency of 99.2% during uniform irradiance and 97% efficiency during variable irradiance conditions. Full article

Numerous recent studies address the concept of energy harvesting from natural wind excitation vibration to piezoelectric surfaces, aerodynamic losses, and electromagnetic dampers. All these techniques require a connection to an energy-management circuit. However, the simulation model for energy conversion and management dedicated to this task has not yet been described. This paper presents a model-based simulation for an energy conversion system using piezoelectric energy-harvester system (PEHS) technology. A controlled pulse width modulation (PWM) rectifier, a closed-loop buck-boost converter, and a piezoelectric transducer comprise a dynamic mathematical model of a PEHS. The control blocks of the closed-loop buck-boost converter use the perturbation and observation (P&O) algorithm based on maximum power point tracking (MPPT), which adapts the operational voltage of the piezoelectric source to deliver the maximum power to load. A simulation program is employed to perform mathematical analysis on various wind vibration scenarios, piezoelectric sources without PWM converters, and piezoelectric vibration sources connected to a closed-loop P&O converter. The crucial results of this paper demonstrated that the proposed dynamic PEHS model effectively fed low-power electronic loads by directly adjusting the output voltage level to the set voltage, even under different vibration severity levels. As a result, the proposed PEHS dynamic model serves as a guideline for researchers in the development of self-powered sensors, which contributes to understanding sustainable energy alternatives. Full article

As electricity demand increases, especially in transportation, renewable sources such as solar energy become more important. The direct integration of solar energy in rail transportation mostly involves utilizing station roofs and track side spaces. This paper proposes a novel approach by proposing the integration of photovoltaic systems directly on the roofs of trains to generate clean electricity and reduce dependence on the main grid. Installing solar photovoltaic (PV) systems on train rooftops can reduce energy costs and emissions and develop a more sustainable and ecological rail transport system. This research focuses on the Milan Cadorna-Saronno railway line, examining the feasibility of installing PV panels onto train rooftops to generate power for the train’s internal consumption, including lighting and air conditioning. In addition, it is a solution to reduce the power absorbed by the train from the main supply. Simulations conducted using PVSOL software 2023 (R7) indicate that equipping a train roof with PV panels could supply up to almost 10% of the train’s auxiliary power needs, equating to over 600 MWh annually. Implementing the suggested system may also result in a decrease of more than 27 tons of CO₂ emissions per year for one train. To optimize the performance of PV systems and maximize power output, the gravitational search algorithm (GSA) as an evolutionary-based method is proposed alongside a DC/DC boost converter and its performance is compared with two other main maximum power point tracking (MPPT) methods of perturb and observe (PO), and incremental conductance (INC). The accuracy of the suggested algorithm was confirmed utilizing MATLAB SIMULINK R2023b, and the results were compared with those of the PO and INC algorithms. The findings indicate that the GSA performs better in terms of accuracy, while the PO and INC algorithms demonstrate greater robustness and dynamic response. Full article