Maximum Power Point Tracking Control Research Articles

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

Performance of wind turbine system based on a brushless doubly fed reluctance generator and a six pulse bridge cycloconverter

In this paper, a variable speed wind energy conversion system (WECS) made with a six pulse bridge cycloconverter and a brushless doubly fed reluctance generator (BDFRG) is investigated. The novelty of the proposed work is to use a brushless doubly fed reluctance generator and a six pulses cycloconverter for working in high power and for reducing the cost, also the proposed work use a maximum power point tracking algorithm to capture the optimal power available in the wind in order to increase the efficiency of the system. The power control of this system is applied, to capture the maximum power from the wind by using a maximum power point tracking (MPPT) control technique, to achieve the independent control of the active and reactive powers exchanged between the wind generator and the grid, also to test the possibility to operate in two quadrant modes (subsynchronous and supersynchronous modes). The dynamic modeling equations that describe the suggested system are presented here. The results of the simulation are shown in order to illustrate the performance and efficiency of this system.

Design and Analysis of a Triple-Input Three-Level PV Inverter with Minimized Number of MPPT Controllers

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.

Analysis and control of grid-interactive PV-fed BLDC water pumping system with optimized MPPT for DC-DC converter

In this study, a novel water pumping module fed by grid interactive Photo-Voltaic with a bidirectional Power Flow Control was proposed. In addition to improving the pumping system’s reliability, a water pump is powered by a brushless DC motor drive. This method enables the pump to work at its maximum capacity for the entirety of that day, regardless of the weather. The entire system becomes more reliable as a result of the motor’s increased use of photovoltaic (PV) generated power for pumping applications. Maximum Power Point Tracking (MPPT) controller incorporating Machine Learning algorithm drives bridgeless greater static gain DCDC converter to achieve higher power generation point and increment PV efficiency. The PV array’s operation would be managed using the ML back propagation technology to capture the most electricity under any ecological circumstance. A BLDC motor is fed by a Voltage Source Inverter (VSI) that includes a DC bus controlled in both directions by a unit vector template (UVT) approach incorporated in a single-phase voltage source converter (VSC). Additionally, utilizing a PI controller to manage the DC capacitor voltage in the UVT controller at a particular level is not appropriate for the increased PQ capabilities. However, due to tuning problems with the current controller, this controller is unpopular. The aforementioned problems are resolved by employing a unique intelligent-based fuzzy logic controller that achieves good performance features. In this technique, the function of a PV array at its Maximum Power Point (MPP), as well as power quality enhancements and a decrease in Total Harmonic Distortion (THD) of the grid are accomplished. The proposed PI controller attains a significant voltage THD of 3.736. The PI controller, on the other hand, managed to achieve a load voltage THD of 2.629%. The ANFIS method, whose value is 1.739%, is discovered to have a lower THD than all remotes with improved features, it lessens abrupt swings while maintaining steady DC-link voltage.

Photovoltaic Maximum Power Point Tracking Technology Based on Power Prediction Algorithm Combined with Variable Step Length Disturbance Observation Method

Under partial shading conditions, the system operating sequence of photovoltaic cells does not fall on a single characteristic curve, and the traditional maximum power point tracking (MPPT) control algorithm is prone to causing the system output power to oscillate around the maximum power point. In the case of multiple peaks, the traditional MPPT algorithm is prone to being trapped in a local optimum solution. This paper proposes an MPPT control algorithm based on the WOA-RF prediction algorithm combined with a variable step disturbance observation method. By establishing a photovoltaic system simulation model, the improved algorithm is verified through simulation. The improved MPPT control algorithm can adaptively adjust the step size under different conditions to ensure that the system can quickly and accurately track the maximum power point. At the same time, by optimizing the algorithm logic and parameter settings, it can effectively avoid problems such as local optimum solutions and instability in the system, and improve the system convergence speed and tracking accuracy.

A Maximum Power Point Tracking (MPPT) Strategy Based on Harris Hawk Optimization (HHO) Algorithm

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

A Communication‐Free Master–Slave Control of Cascaded‐Type DC Microgrids With Nondispatchable Generations

ABSTRACTThis paper addresses the challenge of integrating dispatchable and nondispatchable distributed generators (DGs) in cascaded‐type direct current (DC) microgrids and proposes a communication‐free master–slave control strategy. In the proposed method, all DGs are treated as voltage sources. A master dispatchable DG is primarily responsible for maintaining a constant DC voltage at the point of common coupling (PCC). The rest slave nondispatchable DGs adjust their output power based on a front maximum power point tracking (MPPT) controller. This communication‐free approach enhances system reliability and ease of implementation. Moreover, it ensures load voltage stability, providing a high‐quality power supply for users. Additionally, it enables nondispatchable DGs to achieve MPPT‐based output power and enables power curtailment operation. Subsequently, a small‐signal stability analysis confirms the efficacy of the proposed strategy. Finally, simulation and experiment results further validate its efficacy.

Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.

Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode

This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing online adaptive FD. The goal of this study is to present an artificial intelligence (AI) technique for detecting seven faults: inverter fault, feedback sensor fault, grid anomaly, nonhomogeneous partial shading, open circuit in PV array, MPPT controller fault, and boost converter controller fault. In this work, it was found that the application of Extreme Learning Machine (ELM) plays an important role in fault detection and localization. Nine (9) statistical features and eight (8) wavelet packet parameters are extracted from the data based on multiple default values. These features were used as an input vector to train and test the ELM and determine whether the system is operating under normal conditions or is faulty. The BDE feature selection algorithm is adopted to optimize the seven-fault classification procedure to reduce the number of features. The results showed that the Extreme Learning Machine (ELM), based on statistical parameters followed by BDE, can detect faults with high accuracy (98.3%) compared to a case without optimization.

Maximum power point tracking control of wind turbines based on speed hysteresis loop to reduce drive-train loads

By adding compensation torque based on the optimal torque, the torque compensation control method expands the unbalanced torque at varying wind speeds, thereby improving the maximum power point tracking (MPPT) performance of wind turbines (WT). However, while improving the wind energy capture efficiency, such methods will lead to drastic fluctuations of generator torque, resulting in significantly increased drive-train loads. To solve this problem, based on the amplitude-frequency characteristics analysis of the WT system transfer function, it is found rotor speed information can not only reflect the main trend of wind speed changes, but also has the characteristics of not being easily affected by the high-frequency component of wind speed. Therefore, setting compensation torque according to it can effectively alleviate the above phenomenon. On this basis, the MPPT control of WT based on speed hysteresis loop to reduce drive-train loads is proposed. The speed information is used to replace the torque information to set the compensation torque amplitude term, and the speed hysteresis loop is introduced to improve the design of the compensation torque symbol term, which can significantly decreased drive-train loads without compromising on power capture. Finally, the effectiveness of the proposed method is verified by the experiments.

Current-Sensorless Method for Photovoltaic System Using Capacitor Charging Characteristics

The installed capacity of photovoltaic (PV) systems has increased significantly over the past few decades, and related technologies have advanced significantly. The electrical characteristics of a PV system change nonlinearly based on irradiation and temperature, and the I–V characteristic curve, expressed in terms of the voltage and current, is used to verify these characteristics. The maximum power point tracking (MPPT) control method was applied to maximize the performance of the PV system. Voltage and current sensors are used to control the I–V characteristic curve and MPPT; however, current sensors have various disadvantages in terms of price and system configuration. Therefore, this study presents a method for calculating the current of a PV system using the charging characteristics of a capacitor. The method presented in this paper analyzes the I–V characteristic curve’s qualities through simulations and experiments under normal, shaded, and mismatched conditions of the PV module.

Comparative analysis of direct coupling and MPPT control in standalone PV systems for solar energy optimization to meet sustainable building energy demands

Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India. The computer center at the engineering college has an annual energy requirement of 260,552 kWh/Year. Consequently, the research focused on the planning and implementation of a standalone photovoltaic (SAPV) system, assessing it against the institution's total annual energy consumption. The performance ratio and losses of the SAPV system with both direct coupling and an MPPT charge controller was compared. This comparative analysis aimed to evaluate the efficacy of two solar photovoltaic control methods—SAPV direct coupling and Maximum Power Point Tracking control—in optimizing energy harvesting from solar panels.

Optimized Energy Management System for Wind Lens-Enhanced PMSG Utilizing Zeta Converter and Advanced MPPT Control Strategies

This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to capture wind energy by increasing the wind influx through the turbine. In the contemporary wind energy sector, PMSGs are extensively employed due to their superior performance characteristics. This study integrates a 1 kW PMSG system with a wind lens to optimize power extraction from the wind energy conversion system (WECS) under varying wind speeds. A comparative analysis of different control strategies for maximum power point tracking (MPPT) is conducted, including the incremental conductance (INC) method and the perturb and observe (P&O) method. The performance of the MPPT controller integrated with the wind lens-based PMSG system is assessed based on output DC voltage and power delivered to the load. To evaluate the overall effectiveness of these control strategies, both steady-state voltage and dynamic response under diverse wind conditions are examined. The system is modeled and simulated using the MATLAB R2023a/Simulink 9.1 software, and the simulation results are validated to demonstrate the efficacy of the proposed energy management system.

ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

African vulture optimized RNN algorithm maximum power point tracking (MPPT) controller for photovoltaic (PV) system

African vulture optimized RNN algorithm maximum power point tracking (MPPT) controller for photovoltaic (PV) system

Achieving Off-Grid Refrigeration in Remote Areas: A Solar-Powered Vapor Compression Refrigerator Prototype with PCM Integration

The availability of vaccines, medicines, and perishable goods in remote or off-grid areas remains a formidable challenge. Integrating solar photovoltaic (PV) systems with refrigeration technology has emerged as a promising solution to address this critical need. This paper presents an autonomous solar-powered refrigerator prototype for off-grid refrigeration in remote areas utilising renewable energy. The system comprises a 160 W photovoltaic module, a 12/24 V DC compressor refrigerator, a lead-acid battery, and a Maximum Power Point Tracking (MPPT) controller. Its main feature is complete autonomy from the electricity grid, thanks to its standalone configuration. An experimental campaign evaluated the system's behaviour in the laboratory for 24 hours at different set-point temperatures. A water-based Phase Change Material (PCM) was implemented to improve its autonomy in severe outdoor conditions. A further experimental campaign emulated the functioning of the prototype while managing the temperature of a sample solution whose melting temperature was equal to – 21°C to ensure its liquid state during the tests. The target range for preserving the sample was defined at ± 1°C. Several real conditions have been considered, such as higher cooling loads realised through 21 litres of additional thermal mass in the refrigerator and pick and place conditions for simulating the opening and closing of the door. The results demonstrate that the solar refrigerator prototype achieves complete autonomy from the electricity grid, paving the way for solutions for preserving perishable goods such as medicines and food in unelectrified areas.

Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.

Research on Hybrid Approach for Maximum Power Point Tracking of Photovoltaic Systems under Various Operating Conditions

Based on the characteristics of the whale optimization algorithm (WOA) and perturbation observation (P&O) method, this paper proposes a novel hybrid approach called the improved chaotic whale optimization combined with perturb and observe (ICWOA-P&O) method for maximum power point tracking (MPPT) control to solve the challenge of low efficiency in photovoltaic (PV) power generation under local shadows. First, the ICWOA is used for a global search to quickly locate the position of the maximum power point (MPP). Then, the P&O method is used for a fine-grained local search to quickly track the position of the global maximum power point (GMPP) with low oscillation. To ensure accuracy, the tracking performance of the ICWOA-P&O method is comprehensively compared with the WOA-P&O, WOA, and PSO models under four conditions: uniform irradiance, static local shading, dynamic shading, and sudden changes in irradiance and temperature. The simulation results verify that under the above four conditions, the ICWOA-P&O method can track the MPP continuously and stably and greatly improves the convergence time and accuracy. Compared with the other three methods, the ICWOA-P&O method can effectively obtain the fastest tracking speed (less than 0.1 s), the highest tracking accuracy (more than 99.97%), the smallest relative error (less than 0.03%), and the smallest oscillation fluctuation. Finally, this study integrated the ICWOA-P&O algorithm into the designed MPPT controller hardware and established a practical PV experimental platform based on the ICWOA-P&O control algorithm for practical tests.

ANFIS-RTSMC based MPPT for a hybrid PV-diesel-battery water pumping system

This paper introduces a novel hybrid method for achieving Maximum Power Point Tracking (MPPT) in a photovoltaic-diesel-battery hybrid water pumping system driven by an induction motor drive (IMD). The innovative MPPT control strategy uniquely combines the rapid response and minimal oscillations of the Adaptive Neuro Fuzzy Inference System with the high robustness of Robust Terminal Sliding Mode Control (RTSMC). This combination ensures unprecedented steady-state resilience to changes in solar irradiance, enhancing system stability across a variety of IMD speeds, and making the system robust to parameter variations. Unlike traditional approaches, this two-stage photovoltaic (PV) setup integrates a regulated boost converter for MPPT control and a three-phase inverter using Space Vector Pulse Width Modulation (SVPWM) to regulate the DC link voltage (Vdc). Extensive MATLAB Simulink simulations demonstrate the proposed method’s superior efficiency in power extraction and its resilience under varying operating conditions, showcasing its originality and effectiveness.

Bio-inspired MPPT and advanced control for grid-connected solar photovoltaic systems with flyback converter

ABSTRACT This paper presents an enhanced approach for grid-connected photovoltaic (PV) systems using a flyback converter and Sovereign Butterfly Optimization for advanced Maximum Power Point Tracking (MPPT). The focus is on improving energy extraction efficiency from PV panels by dynamically adjusting system parameters. A flyback converter ensures reliable grid integration, while the Butterfly Optimization Algorithm, inspired by butterfly foraging, provides real-time MPPT to rapidly and accurately track the maximum power point, even under varying environmental conditions. A novel voltage harmonic mitigation technique processes three-phase currents through dq0 frames for closed-loop control, reducing harmonics and optimising voltage precision. The DC/DC flyback converter stabilizes AC conversion, minimizing high-frequency harmonics. Additionally, a Subsystem Synchronization Control Strategy synchronizes active and non-active grid currents, improving system stability. Simulations show a 99.3% tracking accuracy, 2% power oscillation around the maximum power point, and a Total Harmonic Distortion (THD) of 1.78%, meeting IEC 61,727 standards.