Maximum Power Point Tracking Strategy Research Articles

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge–discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery’s SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

Research on intelligent auxiliary regulation technology of large power grid section based on artificial intelligence

In the modern era, large-scale renewable energy systems are integrated with advanced power systems and provide efficient operations. Also, optimized power systems require accurate energy generation and effective control systems to manage and ensure a stable power supply. Nevertheless, uncertainties are the intermittent balance of supply and high electricity demand. In addition, conventional power sources are not applicable for this challenging task and increase electricity costs. Therefore, an efficient Neuro Fuzzy Single phase Unified Power Quality Conditioner with Maximum Power Point Tracking (NF-SP UPQC -MPPT) strategy is developed for enhancing the grid-connected power systems. Here, Neuro-fuzzy logic is used as dynamic reactive power compensation in the grid. Also, this logic can efficiently handle the Energy storage system (ESS). Then SP UPQC was used to enhance power quality in electrical distribution systems. It combines both series and shunt compensators to mitigate various power quality issues such as voltage sags, harmonics, and unbalance. After that MPPT was utilized to extract the maximum power from the grid system. Model Predictive Control (MPC) controller to determine the overall stability and performance of the system. Moreover, the developed model was implemented on the MATLAB platform and performance is analyzed in terms of voltage deviation, grid current, reactive power fluctuations and Total Harmonic Distortion (THD).

MPPT efficiency enhancement of a grid connected solar PV system using Finite Control set model predictive controller

Maximum power point tracking (MPPT) is required to get the highest possible power generated from a photovoltaic (PV) cell. Numerous researchers have proposed different MPPT strategies to be able to collect maximum generated electricity from the photovoltaic cells.In this research paper, a MPPT model predictive control strategy for a grid-connected PV system is presented. Model predictive control (MPC) was used to develop and model the AC load energy tracking efficiency for the PV systems with a power rate of 20 kW at standard test conditions. For the purpose of obtaining the power tracking performance, a DC-DC boost converter, DC-AC two level three phase inverter, and control mechanism for a grid connected AC load system was examined and presented in this paper. To approximate the actual PV array properties, the PV model is used, and the MPPT approach is suggested as a way to regulate the DC-DC boost converter and get the most power possible from the PV array when compared to P&O and model predictive control system. A three-phase, two-level VSI is employed in this study that is controlled by a model predictive control system with SVPWM. The inverter's control structure is developed using a model predictive control system (inner loop current controller) with reference frame transformation (abc to dq) coordinates by utilizing PLL. The PLL is used to obtain critical information about the grid voltage. A RL filter is then used to lower the total harmonic distortion of the output and connect the inverter's output to the grid. The MATLAB R2019a environment is used to create the system model. The overall performance of the system for conventional perturb and observer is around 97.72%, while for Finite Control Set Model Predictive Controller is 99.80%, which is better than previous similar research with faster time response and less oscillation around maximum power point.

Improving Photovoltaic System Performance with Artificial Neural Network Control

Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness.

PERFORMANCE ANALYSIS OF P&O AND PSO MPPT ALGORITHMS FOR PV SYSTEMS UNDER PARTIAL SHADING

Photovoltaic (PV) modules are devices that transform photon energy into electrical energy. The output power of the PV modules is influenced by the intensity of solar radiation and the ambient temperature. Non-uniform shading can cause variations in the extent of sunlight absorbed by PV modules, resulting in a decrease in power output. Maximum power point tracking (MPPT) techniques are employed to optimize the power output of PV modules by operating them at their maximum power point (MPP). The main objective of the study is to investigate the performance analysis of Perturb and Observe (P&O) and Particle Swarm Optimization (PSO) MPPT strategies in uniform and partially shaded conditions with equally and unequally different irradiance differences. Simulation studies were conducted on the PV circuit model using Matlab/Simulink, and the results were evaluated. MPPT algorithms are compared based on their tracking efficiency and convergence speed when solar radiation conditions vary. The findings of the simulation indicate that the P&O is unable to determine global MPP and gets trapped in one of the local MPPs. However, the PSO is very effective in tracking MPP under different partial shading patterns with more than 96% tracking efficiency. In the first partial shading configuration where the sunlight intensity of the PV modules is uniformly distributed, the PSO technique has reduced steady-state oscillations around the MPP. However, the P&O technique demonstrates superior response time and convergence speed in comparison to the PSO technique.

Energetic, environmental and economic (3E) analysis of a CSA algorithm optimized floatovoltaic system integrated with heat sink: Modeling with experimental validation

The irradiance's nonuniformity and elevated working temperature are the main harmful parameters that influence photovoltaic productivity. Hence, systematic research was conducted to establish an effective freestanding floating photovoltaic (FPV) system using a passive cooling system comprised of a finned heat sink to regulate the operating temperature in conjunction with a maximum power point tracking (MPPT) based in Cuckoo Search algorithm (CSA) to maximize the productivity of the FPV module. In this work, U-shaped fins are integrated into the rear of the (F-FPV) module through experimentally validated work at Port Said, Egypt, in an outdoor condition. Consequently, an energetic, environmental, and economic (3E) comprehensive evaluation has been performed to assist the overall performance of the proposed strategy. The results revealed that the finned F-FPV module's working temperature dropped to 45.27 °C, resulting in a 7.77 % temperature drop compared to the conventional FPV module. It has been concluded that utilizing both finned heat sink and CSA boosts the F-FPV-CSA power productivity by 14.92 %, which shortened the payback period to 10 years with a reduction of 44.5 % when compared to the conventional FPV system. The combination of cooling and CSA MPPT strategies help in reducing levelized cost of electricity by 21.54 % to 0.015 $/kWh.

A new real-time maximum power point tracking scheme for PV-BASED microgrid STABILITY using online DEEP ridge extreme learning machine algorithm

A new deep representation-based Maximum Power Point Tracking (MPPT) controller is proposed in this paper for accurate Control references calculation in Photovoltaic (PV) based Micro Grid (MG) operation. The deep representation is obtained by two-step estimation: Data dimension reduction and MPPT Tracker towards optimal computation. The considered deep learning architecture is targeted for N number (large scale) of PV-based DGs, connected locally in the distribution system (DC link extended to AC utility). The collected data of solar irradiation (in W/m2) and PV panel temperature (in oC) profiles of local DGs are subjected to data dimensions using Extreme Learning Machine (ELM) based on Moore-Penrose inverse technique. The compressed represented PV-DG data is further communicated to the Tertiary Control side MPPT Tracker, where Ridge Regression-based ELM is presented for estimating Maximum Power Point Power (PMPP) and Voltage (VMPP) values for kth instant. The initial randomness present in the proposed Deep Representation based Ridge Regression Extreme Learning Machine (DR-RRELM) is further minimized by adopting Huber's characteristic distribution-based likelihood estimator. The proposed MPPT scheme is effectively implemented for accurate control reference in DC-DC and DC-AC converters in the MG. The proposed controller is also suitable for stability improvement at point of common coupling (PCC). Three different case studies such as past data verification, stability analysis under various operating conditions, irradiant change and source power variation. The efficacy of the proposed deep representation-based MPPT scheme is evidenced in MATLAB-based simulation. The proposed technique provides better tracking ability, faster learning and effective reference generation. The case study with irradiation change is validated in TMS320C6713 (32-bit) based Hardware-in-Loop (HIL) validation.

Design and implementation of 2 KW OFF-GRID photovoltaic systems with maximum power point tracking using PSO algorithm operating under partial shading conditions

This paper presents the design and implementation of a metaheuristic control method for maximum power point tracking (MPPT) methodology in photovoltaic systems under partial shading conditions. To attain maximum efficiency, photovoltaic (PV) panels should operate at their maximum power point (MPP). Therefore, to maximize the power from the PV system, an MPP tracker is inserted between the PV arrays and the load to ensure that the operating point of the system is adjusted to be placed on the MPP. When the radiation distribution over PV panels is uniform, many traditional MPPT techniques can effectively monitor MPP. However, when the PV arrays are partially shaded, more MPPs are displayed, which usually results in the inability to find the overall MPP. To overcome this difficulty, this proposed work presents a particle swarm optimization algorithm-based MPPT scheme for PV systems. This PSO heuristic algorithm-based technique not only ensures the ability to find the global MPP but also gives a simpler control scheme. The feasibility of this proposed method is verified by various partial shading conditions using Matlab. The proposed method is implemented with a 2KW PV system and the power output is compared with the existing system.

Study on impact of photovoltaic power tracking modes on photovoltaic-photothermal performance of PV-PCM-Trombe wall system

Due to the lack of research on the impact of photovoltaic (PV) power tracking methods on the performance of Building-Integrated Photovoltaic-Thermal (BIPV/T) systems, this study comparatively analyzes the photovoltaic-photothermal performance of the PV-PCM-Trombe Wall system operating in Pulse Width Modulation (PWM) and Maximum Power Point Tracking (MPPT) modes during winter passive heating conditions. Additionally, the study investigates the influence of these modes on the thermal characteristics of buildings. The results indicate that the MPPT strategy achieved a PV efficiency of 15.50 %, while the PWM strategy resulted in a significant reduction to 12.72 % (a decrease of 2.78 % compared to MPPT). Importantly, both strategies effectively utilized solar energy for passive space heating without noticeable differences in photothermal performance. Furthermore, the study observed that the upper part of the PCM plate reached higher peak temperatures earlier in the vertical dimension. Experiment A exhibited peak temperatures ranging from 33.57 °C to 34.76 °C, while Experiment B had a wider range of 27.99 °C to 36.10 °C. Minimal temperature variations were observed across the PCM plate in the thickness direction, measuring only 0.60 °C in Experiment A and 0.70 °C in Experiment B. These research findings provide valuable insights into the roles and impacts of different PV power tracking strategies in PV-PCM-Trombe wall systems.

A comparison of FLC MPPT techniques and HC MPPT techniques for photovoltaic systems

This study aims to examine the behaviour of various maximum power point tracking (MPPT) methods used with PV systems. The methodologies of hill climbing (HC) and fuzzy logic controller (FLC) are assessed in this paper. PV module and DC/DC boost converter models were simulated using PSIM and Simulink software with various MPPT strategies. The PSIM and Simulink software programmes were founder throughout the FLC MPPT method’s development. To employ each software for specific system components, the co-simulation was conducted. The effectiveness of the various MPPT approaches was assessed in response to rapidly varying weather conditions. The results show that for most of the tested MPPT approaches’ usual working ranges, FLC outperforms the HC MPPT approach in transient behavior and constant.

Critical analysis for evaluating maximum power point tracking strategy in photovoltaics and fuel cells using key performance metrics

This study performs detailed analyses to investigate and justify the utilization of maximum power point tracking (MPPT) algorithm in photovoltaics (PV) and fuel cells. Two devices are parallelly analyzed to establish a comparative and contrasting study on the performance of the two technologies at MPP regime for a given operational condition. Direct methanol fuel cell (DMFC) is chosen as a representative of the hydrogen fuel cells family. The operational variables affecting the electrical power in the devices, including temperature (for both devices), solar irradiance (for PV cells), and methanol concentration (for DMFC) are discussed, along with their similarities and differences. Voltage losses are addressed to gain insights into the electrical quality of each device at MPP regimes. Due to their pivotal role, fuel availability and cost, energy losses, overall efficiency, and cathode flooding are selected as crucial performance metrics to compare the two technologies at MPP. The results demonstrate that PV cells have a good voltage quality at MPP with an average retention of 87% from open-circuit voltage VOC, whereas DMFC performance deteriorates at MPP, resulting in a significant decline with a loss of 61% from VOC . Consistent with voltage loss results, fill factor analysis at MPP zones lead to the same conclusion wherein photovoltaics offer high fill factor of approximately 82%, whereas DMFC has lesser values that are below 40%. The maximum overall efficiency of PV cells is attained at the MPP zones, whereas optimal efficiencies do not match the MPP loci in fuel cells. Simulations show that DMFC is extremely prone to cathode flooding at MPP zones, where approximately 90% of pore spaces in the liquid distribution layer are occupied with water. Conversely, PV cells are completely free from such issues. Some recommendations for further advancement and investigation on the use of MPPT in both technologies are provided.

Enhancing photovoltaic system modeling and control under partial and complex shading conditions using a robust hybrid DE-FFNN MPPT strategy

Improving efficiency, reliability, and extending the life of Photovoltaic (PV) systems are some essential benefits of developing an effective Maximum Power Point Tracking (MPPT) control system. In this study, an intelligent MPPT approach based on the Differential Evolution (DE) algorithm, and an adaptive Feed-Forward Neural Network (FFNN) is developed to address various issues of the most commonly used MPPT algorithms that affect the performance of PV systems including, power generation efficiency, tracking time, and instabilities around the Global Optima (GO). Consequently, in order to determine the suitable hyper-parameters’ values for the optimization and training procedure, the suggested hybrid technique is evaluated in the first part of this study for a variety of the FFNN configuration’s possibilities. Afterwards, the best obtained neural model is implemented as an MPPT controller, and a performance appraisal has been made between the DE algorithm based Feed-Forward Neural Network (DE-FFNN) and the most efficient utilized MPPT techniques. The observed results in the optimization and training phases show that our proposed strategy with 11 neurons in the hidden layer presents the most optimal neural model with a Mean Square Error (MSE) of 1.4924 in 139.8613 s in the optimization phase, and 2.57e−14 in the training phase. Subsequently, the created DE-FFNN model based-MPPT with the proposed configuration illustrates excellent performance compared to the applied MPPT techniques with a tracking speed between 0.0237 and 0.0106 s, a remarkable reduction in instability, and a power efficiency that can go up to 99.9971% in four different irradiance scenarios.

Low-Cost Maximum Power Point Tracking Strategy and Protection Circuit Applied to an Ayanz Wind Turbine with Screw Blades

This paper provides three different research contributions applied to a Wind Turbine patented in 1606 by the inventor Jerónimo de Ayanz y Beaumont. The windmill under study is the Ayanz Wind Turbine with screw blades. The first contribution consists of an experimental characterization of the Ayanz Wind Turbine, incorporating the enclosure proposed at the patent and showing that the efficiency of the wind turbine is increased between 70% and 90% due to the enclosure being employed. As not many details about the shape of the screw blades are provided at the patent, in this article the nowadays well-studied and commercially available Archimedes Spiral Wind Turbine blade is utilized. It has been observed that by using an enclosure with a cylindrical shape, not only the efficiency of the wind turbine is increased, but the visual impact is reduced as seeing the blades rotating is avoided, which is a very important fact for many potential individual users of this wind turbine. In addition, it also enables the use of a protective mesh for birds, almost totally reducing the probability of bird deaths. The second contribution consists in a simple and low-cost Maximum Power Point Tracking (MPPT) strategy for the wind turbine, which only uses an AC three-phase impedance to capture the maximum energy from the wind, enabling to eliminate the DC-DC converter and microprocessor employed typically for this purpose. Due to this, the cost, complexity, failure rate, and power losses of the electronic power circuit are reduced which is very welcomed for small-scale wind turbines. Finally, the last contribution is a protection electronic circuit that fulfills several objectives: to brake the wind turbine under high winds and to disconnect and protect it when over-currents occur and when the voltage range of the batteries connected to the wind turbine is outside their safety range.

Multi-Timescale Lookup Table Based Maximum Power Point Tracking of an Inverse-Pendulum Wave Energy Converter: Power Assessments and Sensitivity Study

A novel, inverse-pendulum wave energy converter (NIPWEC) is a device that can achieve natural period control via a mass-position-adjusting mechanism and a moveable internal mass. Although the energy capture capacity of a NIPWEC has already been proven, it is still meaningful to research how to effectively control the NIPWEC in real time for maximum wave energy absorption in irregular waves. This paper proposes a multi-timescale lookup table based maximum power point tracking (MLTB MPPT) strategy for the NIPWEC. The MLTB MPPT strategy was implemented to achieve a theoretical “optimal phase” and “optimal amplitude” by adjusting both the position of the internal mass and linear power take-off (PTO) damping. It consists of two core parts, i.e., internal mass position adjustment based on a 1D resonance position table and PTO damping tuning based on a 2D optimal PTO damping table. Furthermore, power assessments and sensitivity study were conducted for eight irregular-wave sea states with diverse wave spectra. The results show that energy period resonance and the lookup table based PTO damping tuning have the highest possibility of obtaining the maximum mean time-averaged absorbed power. Additionally, both of them are robust to parameter variations. In the next step, the tracking performance of the MLTB MPPT strategy in terms of changing sea states will be studied in-depth.

Design and implementation of Hybrid Renewable energy (PV/Wind/Diesel/Battery) Microgrids for rural areas.

This study examines the variation in sensitivity of a microgrid system comprised of photovoltaics, wind turbines, diesel engines, and batteries. The primary objective is to increase our knowledge of renewable energy resources (RERs) and their technical and economic factors in the context of the conceptual design of a microgrid system. The investigation employs Typhoon HIL software for simulation and testing, concentrating on hybrid PV/Wind/Diesel/Battery systems and devising a perturb & observe (P&O) maximum power point tracking (MPPT) strategy. Additionally, the study investigates the Optimal Power Controlling MPPT technique and the development and implementation of hybrid renewable energy resources (HRES). The Typhoon HIL system is utilized in the power, automotive, and aerospace industries, among others, to simulate and test control systems in real-time. This study presents a control strategy for a microgrid system that combines renewable energy sources such as solar and wind power with reserve power options such as diesel generators and batteries. The coordinated control technique is implemented by employing a centralized control method, effectively managing the flow of electricity from diverse distributed energy resources (DER) and ensuring the microgrid's stability. The findings indicated that the coordinated control method and dynamic models could be utilized to design and optimize microgrid systems. Future research can concentrate on refining the accuracy of the models and verifying the proposed coordinated control method in microgrid systems that operate in the real world.

A Unified Active Frequency Regulating and Maximum Power Point Tracking Strategy for Photovoltaic Sources

In order to optimize the extraction of solar energy, photovoltaic sources are commonly operated under the control of the so-called maximum power point (MPPT) strategy. However, as the rate of PV installations increases explosively, traditional MPPT algorithms may cause problems such as frequency deviation and power fluctuations, making system frequency stability a challenge due to the inherent intermittent and stochastic nature of PVs. Consequently, in order to reduce the investment and maintenance costs of storage systems, innovative control is expected for PV sources to provide ancillary services for the system, especially for weak systems such as microgrids. In this paper, a novel active power control (APC) strategy, based on characteristic curve fitting, is proposed to flexibly regulate the PV output power. The transient process performance and robustness of the system are improved with the proposed APC strategy. In conjunction, an f–P droop mechanism is designed to provide a frequency regulating (FR) service for the AC microgrid. The comprehensive control strategy unifies the FR function with the traditional MPPT function in a single control structure, allowing the PV source to operate either in the MPPT mode when the system frequency is nominal or in FR mode when the frequency exceeds it. The transition between MPPT and FR is autonomous and fully decentralized, which improves the PV generation efficiency as well as ensuring generation fairness among different parallel PV sources. Importantly, the proposed control strategy does not require any internal bundled energy within the PV generation system to achieve FR capability, but it effectively collaborates with the system-level energy storage system, thus reducing the necessary battery capacity. A detailed dynamic model of a PV generation system is constructed to validate the feasibility and effectiveness of the proposed control strategy.

A Hybrid Particle Swarm Optimization with Butterfly Optimization Algorithm Based Maximum Power Point Tracking for Photovoltaic Array under Partial Shading Conditions

The key objective of this paper is to develop a photovoltaic (PV) maximum power point tracking (MPPT) algorithm based on particle swarm optimization–butterfly optimization algorithm (PSO-BOA) that is adapted for partial shading conditions (PSCs). Generally, conventional MPPT techniques are often unable to accurately locate the global maximum power point (GMPP) generated by partial shading in PV systems. As a result, a significant decrease in power output occurs. The traditional particle swarm optimization (PSO) algorithm traps the local maxima point easily, while the butterfly optimization algorithm (BOA) has slow convergence speed and large oscillations during its use in research. To address the limitations of the aforementioned PSO and BOA algorithms, the MPPT strategy of PV systems combining PSO-BOA is presented, which can ameliorate the efficiency and accuracy in PSCs. In this paper, the control parameter of sensory modality in the BOA can be acquired based on logistic mapping, and the self-adaptive adjustment of the inertial weight of the PSO algorithm is designed. According to the simulation findings, the suggested method is more suitable than PSO and BOA with respect to intricate shading-induced variations in irradiance and changes in external temperatures. The average tracking time is less than 0.5 s, and the tracking accuracy is not less than 99.94%. Especially under sudden variations in irradiance and temperature conditions, the tracking time of the PSO-BOA algorithm is only 49.70% of that of the PSO algorithm and 55.63% of that of the BOA. Therefore, the MPPT method presented has the ability to improve the oscillations and result in less convergence speed, which in turn accurately tracks the GMPP.

Enhancing MPPT Performance in Partially Shaded PV Systems under Sensor Malfunctioning with Fuzzy Control

The shift towards sustainable energy sources is gaining momentum due to their environmental cleanliness, abundant availability, and eco-friendly characteristics. Solar energy, specifically harnessed through photovoltaic (PV) systems, emerges as a clean, abundant, and environmentally friendly alternative. However, the efficacy of PV systems is subjective depending on two critical factors: irradiance and temperature. To optimize power output, maximum power point tracking (MPPT) strategies are essential, allowing operation at the system’s optimal point. In the presence of partial shading, the power–voltage curve exhibits multiple peaks, yet only one global maximum power point (GMPP) can be identified. Existing algorithms for GMPP tracking often encounter challenges, including overshooting during transient periods and chattering during steady states. This study proposes the utilization of fuzzy sliding mode controllers (FSMC) and fuzzy proportional-integral (FPI) control to enhance global MPPT reference tracking under partial shading conditions. Additionally, the system’s performance is evaluated considering potential sensor malfunctions. The proposed techniques ensure precise tracking of the reference voltage and maximum power in partial shading scenarios, facilitating rapid convergence, improved system stability during transitions, and reduced chattering during steady states. The usefulness of the proposed scheme is confirmed through the use of performance indices. FSMC has the lowest integral absolute error (IAE) of 946.94, followed closely by FPI (947.21), in comparison to the sliding mode controller (SMC) (1241.6) and perturb and observe (P&O) (2433.1). Similarly, in integral time absolute error (ITAE), FSMC (56.84) and FPI (57.06) excel over SMC (91.03) and P&O (635.50).

Optimization of Image Processing Based MPPT Algorithm Using FSO Algorithm

An image processing-based Firebug Swarm Optimization (FSO) algorithm is developed in this article to detect maximum power point tracking (MPPT) under shadowing conditions on PV arrays. After capturing an image and applying image processing techniques, the proposed method can detect the shading portion of photovoltaic array (PV). The image processing results can then be used to modify the current rating in power equations. The Firebug swarm optimization (FSO) algorithm is metaheuristic algorithm that identifies and controls switching configurations under partial shading patterns by detecting the maximum global peak point (GPP) of output power generated by multiple configurations. This research is proposed with an image processing edge detection algorithm called the Otsu-Canny algorithm for detecting the partial shade on PV array. The FSO algorithm is used to rearrange the shaded photovoltaic array to significantly reduce the power and efficiency losses. MATLAB simulations were carried out using a 280 W PV panel to verify the efficiency of the proposed FSO MPPT technique with an image processing-based algorithm. The proposed FSO MPPT scheme’s performance is compared to current cutting-edge MPPT methods. Under partial shading conditions, the proposed algorithm successfully detected the maximum peak point of a photovoltaic array, increasing power output and efficiency.

An MPPT Strategy for Wind Turbines Combining Feedback Linearization and Model Predictive Control

This paper proposes a model predictive controller (MPC) design based on the optimal tip-speed ratio method for maximum power point tracking (MPPT) of a direct-driven permanent magnet synchronous generator (D-PMSG)-based wind energy conversion system (WECS). To eliminate system nonlinearity and time-varying characteristics, a control variable was added at the wind turbine and the system model was feedback-linearized to create a linear time-invariant system, reducing the computational burden of the MPC and improving system performance. MATLAB/Simulink simulations were performed and the results show that the linearized system has high fidelity. Compared to traditional MPC that use an operating point to linearize the system, it has better adaptability to turbulent wind speeds, improving the stability and rapidity of the system.