Fast Maximum Power Point Tracking Research Articles

Intelligent algorithm-based maximum power point tracker for an off-grid photovoltaic-powered direct-current irrigation system

Abstract This research aims to enhance the performance of photovoltaic (PV) systems on a 2-fold basis. Firstly, it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking, ensuring optimal extraction of electrical power from PV arrays. Secondly, it proposes the use of 96-V, 2.98-kW direct-current (DC) water pumps for farm irrigation, aiming to improve efficiency, reduce cost and complexity, and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid. In this study, it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers, power passive filters and inverters, therefore simplifying the architecture of the system. The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink® simulation results, whereby the proposed algorithm attains a mean squared error of 6.5705 × 10–5 and a system efficiency approaching 99.8%, ensuring a steady voltage under varying load conditions.

Innovative metaheuristic algorithm with comparative analysis of MPPT for 5.5 kW floating photovoltaic system

Multiple localized maxima in power voltage features are triggered by non-uniform irradiance, which has a detrimental influence on photovoltaic (PV) system operation. Numerous control approaches for PV maximum power point tracking (MPPT) have lately been investigated. This paper proposes a metaheuristic algorithm to reach PV maximum power more efficiently based on the hybridization of Improved Grey Wolf Optimization (IGWO) and BAT algorithms, which are chosen for their reliability and fast MPPT for PV systems. However, they both encounter issues including delayed convergence and increased oscillations while tracking. To overcome these limitations, a newly Improved Grey Wolf BAT Optimization (IGWBO) is applied to specify the duty cycle parameter that is used in MPPT to enhance the performance of FPV systems under a variety of non-uniform weather scenarios. The results of implementing IGWBO-FPV are compared to those of IGWO-FPV, GWO-FPV, BAT-FPV, and FPV without any control algorithm. The findings revealed that the proposed IGWBO-FPV approach outperforms the annual generated power of the IGWO-FPV, GWO-FPV, BAT-FPV, and FPV without control by 5.64%, 10.58%, 17.54%, and 27.28% respectively. The optimal IGWBO-FPV enviro-economic evaluation indicates that 42.83 tons of CO2/year can be prevented from being released into the environment, with an LCOE of 0.052 $/kWh.

A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm

The design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.

Modeling and design of a photovoltaic‐fed single‐ended primary‐inductor converter for maximum power point tracking with current mode control

SummaryIn this work, the control of a Single‐Ended Primary‐Inductor Converter (SEPIC) supplied from a photovoltaic energy source and linked at its output to a DC current sink is addressed. The system is used for maximum power point tracking under varying environmental conditions. A strategy based on two loops of control is considered. A peak current mode controller is used for the inner loop, and a Proportional‐Integral (PI) controller is used for the outer voltage loop. Then, a comprehensive time‐domain and frequency‐domain modeling of the inner loop is theoretically addressed and then verified by the switched model of the system implemented in PSIM© software. The obtained results show that the system exhibits accurate tracking of the desired PV voltage while guaranteeing a fast maximum power point tracking under sudden changes in the environmental conditions. The mathematical modeling, design procedures, and the numerical simulations are validated using experimental results obtained from a laboratory prototype.

An Adaptive Perturb and Observe Algorithm With Enhanced Skipping Feature for Fast Global Maximum Power Point Tracking Under Partial Shading Conditions

Solar energy exposed its prominence to diminish the growing energy demands. But the formation of multiple peaks under partial shading conditions causes the conventional maximum power point tracking controllers to fail to track the global maximum power point (GMPP). Scanning the whole power-voltage curve to locate the GMPP takes extreme time and reduces algorithm effectiveness. Thus, the proposed method devotes the skipping and scanning mechanism incorporated with the perturb and observe (P&O) algorithm to reduce the scanning area drastically. The skipping mechanism skips over the scanning areas between successive peaks based on a specific formula that narrows down the scanning zone. Along with a buck-boost converter, the system includes a voltage control loop controlled by a proportional-integral controller. The proposed algorithm's effectiveness is tested in both simulation and experimental environments on discretized partial shading patterns while GMPP is deployed at different locations. According to the experimental outcomes, the proposed algorithm outperforms the modified maximum power trapezium, skipping fast GMPP, modified cuckoo search, and the conventional P&O algorithm with an average convergence time of 1.02 sec and average efficiency of 99.44 percent.

Efficient MPPT for BLDCM-Driven PV Pumping System Based on Ripple Correlation Control

A fast and parameter-intensive maximum power point tracking (MPPT) technique is presented for a photovoltaic (PV) water pumping system, driven by the Brushless DC motor (BLDCM). The technique is based on the application of “ripple correlation control” (RCC). As an advantage, rather than artificial signal injection, the proposed technique employs the inherent perturbations introduced due to phase commutation and non-sinusoidal back-EMF of BLDCM to determine the maximum power points of the PV system. The technique can be simply employed for different configurations of BLDCM based PV water pumping systems. Experiments are performed to demonstrate the effectiveness of the proposed technique.

Experimental Validation of a Fast-Tracking FOCV-MPPT Circuit for a Wave Energy Converter Embedded into an Oceanic Drifter

Wave Energy Converters (WECs) are an ideal solution for expanding the autonomy of surface sensor platforms such as oceanic drifters. To extract the maximum amount of energy from these fast-varying sources, a fast maximum power point tracking (MPPT) technique is required. Previous studies have examined power management units (PMU) with fast MPPT circuits, but none of them have demonstrated their feasibility in a real-world scenario. In this study, the performance of a fast-tracking fractional open circuit voltage (FOCV)-MPPT circuit (sampling period TMPPT of 48 ms) is compared with a commercial slow-tracking PMU (TMPPT of 16 s) in a monitored sea area while using a small-scale, pendulum-type WEC. A specific low-power relaxation oscillator circuit is designed to control the fast MPPT circuit. The results demonstrate that by speeding up the sampling frequency of the MPPT circuit, the harvested energy can be increased by a factor of three.

Whale optimization algorithm based MPPT control of a fuel cell system

Renewable energy sources have provided a great contribution to global energy demand; However, their intermittent characteristics can cause sustainability and efficiency problems. To handle these, alternative systems are utilized. Among these, proton exchange membrane fuel cells (PEMFCs) stand out with their longer lifecycle, efficient, and cost-effective features. However, their performance depends on operating conditions such as temperature, gas pressure, and membrane water content. These nonlinear features require instant and proper control for maximizing efficiency and longer working life. In this study, a whale optimization algorithm (WOA) based maximum power point tracking (MPPT) controller is utilized for a PEMFC system. To validate the proposed controller, the PEMFC system has been analyzed under changing conditions in the MATLAB/Simulink environment. The proposed method has been compared with the other MPPT methods. The results indicate that the proposed controller can provide accurate and fast MPPT performance, less power fluctuations, and higher production efficiency.

Maximum power point tracker using an intelligent sliding mode controller of a photovoltaic system

<span lang="EN-US">The operating performance of a PV module/array is extremely reliant on the weather (temperature/irradiation) and non-linear. Thus, to ensure that the PV array produces the maximum possible power at any time and regardless of the external conditions, maximum power point tracking (MPPT) techniques are required. The solution suggested in this paper involves taking into account two cascaded controllers as follows; the incremental conductance (INC) controller, which is intended to provide a reference proportional to the PV array's optimal power P<sub>MPP</sub>, and the sliding mode control (SMC), which is in charge of controlling the GPV voltage. The strategy of the SMC is to design a sliding surface that defines the operating point. The SMC combined with the INC aims to achieve fast MPPT action on PV systems using cascade control. The proposed controller is robust to changing weather conditions. In order to evaluate what is done, the results are compared with the INC+PI controller. When an abrupt change occurs, the SMC has a low transient and arrives to equilibrium sooner than the INC+PI controller. the results are presented by the PSIM software, and demonstrate the SMC controller's performance while confirming that the new approach has increased both production and energy efficiency.</span>

Data-driven green energy extraction: Machine learning-based MPPT control with efficient fault detection method for the hybrid PV-TEG system

The hybrid photovoltaic-thermoelectric generation system (PVTEG) gives two-fold benefits; firstly, it efficiently utilizes the available solar energy as it converts both solar irradiance and solar thermal energy into electricity, secondly, it enhances PV efficiency by reducing the PV module surface temperature. However, the efficiency of the hybrid PVTEG system is usually low because both PV and TEG are not highly efficient devices. Under changing environmental conditions, a well-designed maximum power point tracking (MPPT) controller can enhance the generation efficiency by 10%–15%. Therefore, this research explores the evolutionary Neural Network based MPPT control technique for the hybrid PVTEG systems. The snake optimizer optimally tuned weight and biases of the Multilayer perceptron neural network (MLPNN), which provides fast real-time global maxima (GM) tracking. Furthermore, to enhance the robustness of MPPT control, PID gains are tuned using the SO algorithm. Use of the snake optimizer based PID (SOPID) controller with the snake optimizer based neural network (SOANN) results in stable, accurate and fast MPPT under varying environmental conditions. The effectiveness of the proposed SOANN MPPT controller is validated by comparing it with PSOANN, RSANN and GWOANN. SOANN based MPPT controller provides very fast real-time global maxima (GM) tracking with negligible power oscillations. The SOANN controller extract optimal power with an average efficiency of 99.928% and tracking time of less than 5ms. Furthermore, an intelligent data driven based fault detection algorithm is proposed, which do not require any temperature or irradiance sensors reducing cost of the system. Comparative, simulation, quantitative, and statistical results second superior performance of SOANN controller in terms of efficiency, tracking time, stability and fault detection capability under various practical condition.

Application of a Novel Synergetic Control for Optimal Power Extraction of a Small-Scale Wind Generation System with Variable Loads and Wind Speeds

The synergetic control technique (SCT) has the solution for understanding the symmetry inherent in the non-linear properties of wind turbines (WTs); therefore, they achieve excellent performance and enhance the operation of the WT. Small-scale WTs are efficient and cost-effective; they are usually installed close to where the generated electricity is used. This technology is gaining popularity worldwide for off-grid electricity generation, such as in rural homes, farms, small factories, and commercial properties. To enhance the efficiency of the WT, it is vital to operate the WT at its maximum power. This work proposes an efficient and fast maximum power point tracking (MPPT) technique based on the SCT to eradicate the drawbacks of the conventional methods and enhance the operation of the WT at the MPP regardless of wind speed and load changes. The SCT has advantages, such as robustness, simplified design, fast response, no requirement for knowledge of WT characteristics, no need for wind sensors or intricate power electronics, and straightforward implementation. Furthermore, it improves speed convergence with minimal steady-state oscillations at the MPP. The investigated configuration involves a wind-driven permanent magnet synchronous generator (PMSG), uncontrolled rectifier, boost converter, and variable load. The two converters are used to integrate the PMSG with the load. Three scenarios (step changes in wind speed, stochastic changes in wind speed, and variable electrical load) are studied to assess the SCT. The results prove a high performance of the suggested MPPT control method for a fast convergence speed, boosted WT efficacy, low oscillation levels, and applicability under a variety of environmental situations. This work used the MATLAB/Simulink program and was then implemented on a dSPACE 1104 control board to assess the efficacy of the SCT. Furthermore, experimental validation on a 1 kW Darrieus-type WT driving a PMSG was performed.

An Improved Optimally Designed Fuzzy Logic-Based MPPT Method for Maximizing Energy Extraction of PEMFC in Green Buildings

Recently, the concept of green building has become popular, and various renewable energy systems have been integrated into green buildings. In particular, the application range of fuel cells (FCs) has become widespread due to the various government plans regarding green hydrogen energy systems. In particular, proton exchange membrane fuel cells (PEMFCs) have proven superiority over other existing FCs. However, the uniqueness of the operating maximum power point (MPP) of PEMFCs represents a critical issue for the PEMFC control systems. The perturb and observe, incremental conductance/resistance, and fuzzy logic control (FLC) represent the most used MPP tracking (MPPT) algorithms for PEMFC systems, among which the FLC-based MPPT methods have shown improved performance compared to the other methods. Therefore, this paper presents a modified FLC-based MPPT method for PEMFC systems in green building applications. The proposed method employs the rate of change of the power with current (dP/dI) instead of the previously used rate of change of power with voltage (dP/dV) in the literature. The employment of dP/dI in the proposed method enables the fast-tracking of the operating MPP with low transient oscillations and mitigated steady-state fluctuations. Additionally, the design process of the proposed controller is optimized using the enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE algorithm. The design optimization of the proposed method is advantageous for increasing the adaptiveness, robustness, and tracking of the MPP in all the operating scenarios. Moreover, the proposed MPPT controller can be generalized to other renewable energy and/or FCs applications. The proposed method is implemented using C-code with the PEMFC model and tested in various operating cases. The obtained results show the superiority and effectiveness of the proposed controller compared to the classical proportional-integral (PI) based dP/dI-based MPPT controller and the classical FLC-based MPPT controller. Moreover, the proposed controller achieves reduced output waveforms ripple, fast and accurate MPPT operation, and simple and low-cost implementation.

Maximum Power Extraction for PMVG-Based WECS Using Q-Learning MPPT Algorithm With Finite-Time Control Scheme

This study is concerned with the problem of maximum power extraction for a permanent magnet vernier generator (PMVG)-based wind energy conversion system (WECS) using the Q-learning maximum power point tracking (MPPT) algorithm with the finite-time control (FTC) scheme. To do this, the model-free sensorless reinforcement Q-learning algorithm-based MPPT method is firstly proposed. At this time, the reward and learning rate are updated by the Q-values for each state-action pair. Next, the proposed learning algorithm is used to construct an optimal speed–power curve for achieving the fast and steady MPPT operation for the WECS using the learned action values. Besides, the wind-speed change detection algorithm is added to the proposed method so that the PMVG-based WECS can work in various wind speed conditions. And then, the FTC method is proposed to track the reference speed of PMVG which support to achieving the maximum power extraction, and Lyapunov stability theory is derived to ensure the overall system's stability. Finally, the simulation and experimental results from the 5 kW PMVG-based WECS are presented to demonstrate the applicability and superiority of the proposed control method.

Design and Implementation of a New Fast and Efficient MPPT Controller under Different Solar Irradiance Conditions

The power-voltage (P-V) characteristic curve of solar photovoltaic (PV) systems operating in partial shading conditions (PSC) is nonlinear and has multiple local maximum peak power (LMPP) points, rendering many of the maximum power point tracking (MPPT) algorithms ineffective at locating global maximum peak power (GMPP) points. This work proposes a novel slime mould algorithm- (SMA-) based MPPT controller to utilise maximum peak power (MPP) from solar PV systems during uniform irradiance conditions (UC) and nonuniform irradiance conditions (NUC). On the basis of the MPP they tracked, tracking time, and power efficiency, MPPT controller performance is assessed through MATLAB simulations and implemented experimentally with dSPACE MicroLabBox under various irradiance conditions. The effective performance of the proposed controller is validated and demonstrated in comparison to existing popular MPPT controllers.

A 1.4mW to 119mW, Wide Output Power Range Energy Harvesting System With 2-D Fast MPPT Based on HC for 1k to 50k Illuminated Solar Cell

A photovoltaic (PV) energy harvesting system with 2-Dimensional Fast Maximum Power Point Tracking (2-D FMPPT), based on the hill climbing (HC) MPPT technique is proposed. The 3-step MPPT modes for the energy harvesting system are employed to obtain KVOC that is independent of environmental effects such as irradiance and radiated temperature. The system consists of a dynamic peak transducer power sensor (DPTPS), Coarse (1st) FMPPT mode, Ton Increment/Decrement Mode, Fine (2nd) mode, MPPT logics, and a controller. The system can handle a voltage range of 0.5 V to 2.4 V and a power range of 1.4 mW to 119 mW. A peak MPPT efficiency of 99.4% and over 96.1% MPPT efficiency in 1 k to 50 k illuminance are achieved. The proposed system is fabricated on 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$ </tex-math></inline-formula> CMOS process and occupies an active area of 1.4 mm2.

An Enhanced Control Strategy to Mitigate Grid Current Harmonics and Power Ripples of Grid-Tied PV System Without PLL Under Distorted Grid Voltages

This article proposes an enhanced integrated control strategy to mitigate grid current harmonics and power ripples for a three-phase grid-tied photovoltaic (GTPV) system without a phase-locked loop (PLL) unit. The proposed control strategy consists of an enhanced proportional resonant (EPR) current controller, which suppresses the harmonics of grid injected current under distorted grid voltage conditions. A phase compensated reference current generator (PCRCG) is integrated with the proposed EPR controller and used to eliminate active and reactive power oscillations under grid voltage sags and swells. The proposed integrated control strategy is implemented in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula> stationary reference frame. The synchronization task is included in the PCRCG and making the system PLL-less. Thus, the associated computational burden and implementation complexity are reduced. Furthermore, the frequency adaptability of the GTPV system is enhanced as compared with the PLL-based control strategy in the presence of grid frequency variations using the proposed control strategy. To ensure the fast maximum power point (MPP) tracking, a modified incremental conductance MPP tracking algorithm is implemented in integration with the proposed control strategy. The effectiveness of the proposed control strategy is validated using OPAL-RT real-time digital simulator and compared with the conventional PLL-based control strategy.

A comparative study of maximum power point tracking techniques for a photovoltaic grid-connected system

Purpose. In recent years, the photovoltaic systems (PV) become popular due to several advantages among the renewable energy. Tracking maximum power point in PV systems is an important task and represents a challenging issue to increase their efficiency. Many different maximum power point tracking (MPPT) control methods have been proposed to adjust the peak power output and improve the generating efficiency of the PV system connected to the grid. Methods. This paper presents a Beta technique based MPPT controller to effectively track maximum power under all weather conditions. The effectiveness of this algorithm based MPPT is supplemented by a comparative study with incremental conductance (INC), particle swarm optimization (PSO), and fuzzy logic control (FLC). Results Faster MPPT, lower computational burden, and higher efficiency are the key contributions of the Beta based MPPT technique than the other three techniques.

An Improved and Fast MPPT Algorithm for PV Systems Under Partially Shaded Conditions

The use of solar power in some applications is challenging due to sudden changes in irradiance level and the fast occurrence of partial shading condition (PSC). For this reason, maximum power point tracking (MPPT) methods employed in photovoltaic (PV) applications allow for very fast detection and tracking of the maximum power point (MPP) during a partial shading condition. This fast reaction should minimize the power losses, have minimum steady-state oscillation and be accurate. In this paper, an analytical approach is proposed to track the real MPP in uniform irradiance condition (UIC) and PSC via the use of analytical formulas of the PV system behavior. The proposed method exhibits a rapid tracking speed, stable steady-state performance, and low sampling rate in all conditions. The characteristics and performance of the proposed algorithm under UIC and PSC are investigated and demonstrated through simulations and experimental tests.

Fast Two-Stage Global Maximum Power Point Tracking for Grid-Tied String PV Inverter Using Characteristics Mapping Principle

In principle, the location of global maximum power point (GMPP) of partially shaded photovoltaic (PV) array highly correlates with the shape of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P - V$ </tex-math></inline-formula> curve, but such correlation is complex and hard to be specified using the traditional mathematic expressions. This article, therefore, first explores the nonlinear characteristics of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P - V$ </tex-math></inline-formula> curve under partial shading conditions (PSCs) using an artificial neural network (ANN), which could help locate the GMPP after scanning several selected characteristic points. Then, the perturb and observe (P&O) algorithm will be further integrated to achieve fast GMPP tracking based on the obtained location of GMPP. Being different from the existing ANN-based GMPPT methods, meteorological information, such as irradiance and temperature, is not mandatory. In implementation, both PV array model construction and ANN training process are performed offline, which will not complicate the online GMPP tracking algorithm. Compared with several GMPPT algorithms in recent references, the proposed method achieves higher tracking speed and meanwhile guarantees a high tracking efficiency. The performance of the proposed method was verified in simulation and experiment, where it achieves an average tracking efficiency of 99.32% and an average tracking time of 0.8 s.

Dynamic Model and Intelligent Maximum Power Point Tracking Approach for Large‐Scale Building‐Integrated Photovoltaic System

Due to the complex surrounding environments, unpredictable occupants’ behaviors, and the photovoltaic (PV) module degradation after years of operation, the fast and accurate maximum power point tracking (MPPT) of building‐integrated photovoltaic (BIPV) system is very difficult. Herein, the MPPT of a huge BIPV system is modeled as a large‐scale global optimization (LSGO) problem, in which all the variables are proven to be separable. Then, a novel differential evolution (DE) algorithm with the “DE/Current‐to‐Tbest/Current‐to‐Gbest/1” mutation operation (DE‐TG1) is developed to optimize the aforementioned separable LSGO−MPPT model. In addition, the problem of PV module degradation and its effect on the LSGO‐MPPT model are analyzed in detail. To reduce the influence of PV degradation on MPPT accuracy, the dynamic LSGO−MPPT model is developed by introducing the annual drift rates into the proposed LSGO−MPPT model. Finally, the developed methodologies are comprehensively tested by simulation experiments. Experimental results show that the developed DE‐TG1 algorithm significantly outperforms the other state‐of‐the‐art algorithms on separable LSGO problems. In addition, result of the experiments also indicates that with the cooperation of the DE‐TG1 algorithm and the dynamic LSGO−MPPT model, the effective MPPT control of a large‐scale BIPV system can be obtained with accurate and robust performance.