Maximum Power Point Tracking Research Articles

Design and development of different adaptive MPPT controllers for renewable energy systems: a comprehensive analysis

As of now, all over the world is focusing on the Electric Vehicle (EV) technology because its features are low environmental pollution, less maitainence cost required, high robustness, and good dynamic response. Also, the EVs work continuously until the input fuel is provided to the fuel stack. Here, a Proton Exchange Membrane Fuel Cell (PEMFC) is used as an input source to the electric vehicle system because of its merits fast startup, and quick response. However, the PEMFC gives nonlinear voltage versus current characteristics. As a result, the extraction of maximum power from the fuel stack is very difficult. The main aim of this work is to study different Maximum Power Point Tracking Techniques (MPPT) for the DC-DC converter-fed PEMFC system. The studied MPPT controllers are Adjusted Step Value of Perturb & Observe (ASV with P&O), Adaptive Step Size with Incremental Conductance (ASS with IC), Radial Basis Functional Network (RBFN), Incremental Step-Fuzzy Logic Controller (IS with FLC), Continuous Step Variation based Particle Swarm Optimization (CSV with PSO), and Adaptive Step Value-Cuckoo Search Algorithm (ASV with CSA). The selected MPPT controllers’ comprehensive study has been in terms of maximum power extraction, tracking speed of the MPP, settling time of the fuel stack output voltage, oscillations across the MPP, and design complexity. From the comprehensive performance results of the hybrid MPPT controllers, the ASV with CSA technique gives superior performance when equated to the other MPPT controllers.

Deep reinforcement learning using deep-Q-network for Global Maximum Power Point tracking: Design and experiments in real photovoltaic systems

This paper presents a methodology for integrating Deep Reinforcement Learning (DRL) using a Deep-Q-Network (DQN) agent into real-time experiments to achieve the Global Maximum Power Point (GMPP) of Photovoltaic (PV) systems under various environmental conditions. Conventional methods, such as the Perturb and Observe (P&O) algorithm, often become stuck at the Local Maximum Power Point (LMPP) and fail to reach the GMPP under Partial Shading Conditions (PSC). The main contribution of this work is the experimental validation of the DQN agent's implementation in a synchronous DC-DC Buck converter (step-down converter) un-der both uniform and PSC conditions. Additionally, we establish a testing pipeline for DRL models. The DQN agent's performance is evaluated alongside the P&O algorithm. Results consistently indicate the DQN agent's superiority over the P&O algorithm in all simulated scenarios. Although this trend does not entirely replicate in real-world test setups, significant results are observed. Specifically, in PSC sce-narios where the P&O algorithm becomes trapped at an LMPP, the DQN algorithm extracts up to 63.5 % more power than the P&O algorithm. An open repository is available, containing PCB schematic designs and layouts, along with the code used for model training and deployment.

A Graded Redox Interfacial Modifier for High-Performance Perovskite Solar Cells.

Perovskite solar cells have emerged as a potential competitor to the silicon photovoltaic technology. The most representative perovskite cells employ SnO2 and spiro-OMeTAD as the charge-transport materials. Despite their high efficiencies, perovskite cells with such a configuration show unsatisfactory lifespan, normally attributed to the instability of perovskites and spiro-OMeTAD. Limited attention was paid to the influence of SnO2, an inorganic material, on device stability. Here we show that improving SnO2 with a redox interfacial modifier, cobalt hexammine sulfamate, simultaneously enhances the power-conversion efficiency (PCE) and stability of the perovskite solar cells. Redox reactions between the bivalent cobalt complexes and oxygen lead to the formation of a graded distribution of trivalent and bivalent cobalt complexes across the surface and bulk regions of the SnO2. The trivalent cobalt complex at the top surface of SnO2 raises the concentration of (SO3NH2)- which passivates uncoordinated Pb2+ and relieves tensile stress, facilitating the formation of perovskite with improved crystallinity. Our approach enables perovskite cells with PCEs of up to 24.91%. The devices retained 93.8% of their initial PCEs after 1000 hours of continuous operation under maximum power point tracking. These findings showcase the potential of cobalt complexes as redox interfacial modifiers for high-performance perovskite photovoltaics.

A Graded Redox Interfacial Modifier for High‐Performance Perovskite Solar Cells

Perovskite solar cells have emerged as a potential competitor to the silicon photovoltaic technology. The most representative perovskite cells employ SnO2 and spiro‐OMeTAD as the charge‐transport materials. Despite their high efficiencies, perovskite cells with such a configuration show unsatisfactory lifespan, normally attributed to the instability of perovskites and spiro‐OMeTAD. Limited attention was paid to the influence of SnO2, an inorganic material, on device stability. Here we show that improving SnO2 with a redox interfacial modifier, cobalt hexammine sulfamate, simultaneously enhances the power‐conversion efficiency (PCE) and stability of the perovskite solar cells. Redox reactions between the bivalent cobalt complexes and oxygen lead to the formation of a graded distribution of trivalent and bivalent cobalt complexes across the surface and bulk regions of the SnO2. The trivalent cobalt complex at the top surface of SnO2 raises the concentration of (SO3NH2)‐ which passivates uncoordinated Pb2+ and relieves tensile stress, facilitating the formation of perovskite with improved crystallinity. Our approach enables perovskite cells with PCEs of up to 24.91%. The devices retained 93.8% of their initial PCEs after 1000 hours of continuous operation under maximum power point tracking. These findings showcase the potential of cobalt complexes as redox interfacial modifiers for high‐performance perovskite photovoltaics.

Comprehensive assessment of voltage and current source PV‐based modular multilevel converters

AbstractRecently, modular multilevel converters (MMCs) gained popularity for the grid integration of photovoltaic (PV), due to their many advantages, including low total harmonic distortion, high control flexibility, and distributed maximum power point (MPP) tracking capability. Two distinguished families of MMCs exist: voltage source and current source. Voltage source MMCs are mostly studied, but current source MMCs offer advantages under certain operating conditions. This article compares voltage and current source MMCs, for PV integration. To this aim, several key indicators are identified: number of components, energy stored in passive elements, semiconductor power rating, and the number of MPP trackers. The results of the analysis, performed in MATLAB©, show that for a fixed number of output voltage levels, power rating, and switching frequency, voltage source MMCs have simpler control and higher number of MPP trackers. In contrast, current source MMCs minimize the semiconductor power rating, the number of components and the energy stored in passive elements. Regarding efficiency, in the analyzed case study, voltage source MMCs perform better under both homogeneous and non‐homogenous irradiance conditions. This article provides a tool to select the optimal solution based on the required target (e.g. efficiency, energy storage etc.), given the specific characteristics of the application.

Power and efficiency enhancement of solar photovoltaic power plants through grouped string voltage balancing approach

Solar photovoltaic (PV) power plants’ performance is severely impacted by multi-level irradiances or partial shading, leading to power losses and voltage instability. Also, partial shading adds further complexity to the maximum power point tracking algorithms by introducing numerous peaks in the power curves, resulting in additional losses. Numerous solutions are presented to deal with shading losses, and dynamic reconfiguration is the most effective; however, higher switch count and complex architecture make it impractical in real-world implementation. Hence, this study proposes a low-complexity architecture based on the grouped string voltage balancing approach. This approach utilizes a voltage balancing converter connected to groups of strings to enhance the power output of the array of PV plants, maintain overall system voltage stability, and eliminate the possibility of multiple peaks formation in the power curves. The effectiveness of the proposed approach is tested under numerous static and dynamic partial shadings and analyzed using power curves, power output, losses, efficiencies, and voltage stability. The validation is done by comparing the proposed approach with conventional and advanced architectures for a 32.5 kW system. The results show that the proposed method requires a 50 % reduced switch count than existing techniques, achieves 99.54 % efficiency, and maintains an average voltage stability of 0.01.

Enhanced MPPT in Permanent Magnet Direct-Drive Wind Power Systems via Improved Sliding Mode Control

Addressing the challenges of significant speed overshoot, stability issues, and system oscillations associated with the sliding mode control (SMC) strategy in maximum power point tracking (MPPT) for permanent magnet synchronous wind power systems, this paper introduces a fuzzy sliding mode control (FSMC) method employing an innovative exponential convergence law. By incorporating a velocity adjustment function into the traditional exponential convergence law, a novel convergence law was designed to mitigate oscillations during the sliding phase and expedite the convergence rate. Additionally, a fuzzy controller was developed to implement a fuzzy adaptive SMC strategy, optimizing the MPPT for permanent magnet synchronous wind power generation systems. Simulation results indicated that this approach offered a faster response and superior interference rejection capabilities, compared to conventional and modified SMC strategies. The improved FSMC strategy demonstrated a swift, dynamic response and excellent steady-state performance, improving the efficiency of MPPT, thus confirming the effectiveness of the proposed method.

Extending Shelf-Life of Two-Step Method Precursor Solutions through Targeted Regulation for Highly Efficient and Reproducible Perovskite Solar Cells.

Precursor solution aging process can cause significant influence on the photovoltaic performance of perovskite solar cells (PVSCs). Notably, we first observe that the aging phenomenon is more severe in the precursor of two-step sequential method compared to that in one-step method due to that the protic solvent isopropanol facilitates amine-cation side reaction and iodide ions oxidation. Herein, we report a novel approach for selectively stabilizing both organic amine salt and lead iodide (PbI2) precursor solutions in two-step method. The introduction of benzene-1,3-dithiol into organic amine salt solution can mitigate amine-cation side reactions due to the formation of an acidic and reducing environment. Simultaneously, decamethylferrocene (FcMe10/FcMe+ 10) pair can act as a redox shuttle in PbI2 solution to concurrently oxidize Pb0 and reduce I2 in cyclic manner. Consequently, the PVSCs device fabricated from ameliorative precursor solutions demonstrates superior power conversion efficiency of 25.31 %, retaining 95 % of its efficiency after 21 days of solution aging. Moreover, the unencapsulated devices maintain 85 % of primitive efficiency for 1500 h at maximum power point tracking under continuous illumination. This work establishes a fundamental guidance and scientific direction for the stabilization of two-step perovskite precursor solutions.

Design and implementation of Type-3 intuitionistic fuzzy logic MPPT controller for PV solar system: Comparative study

The performance of a photovoltaic (PV) solar system is affected by partial shading conditions (PSC) and environmental conditions, such as solar irradiance and ambient temperature, which vary throughout the day. This results in variations in the maximum power point (MPP) on the solar PV output characteristic curve. Therefore, various classical MPP tracking (MPPT) techniques have been used to track the MPP and extract maximum power from PV systems. However, these techniques have drawbacks such as lower stability, increased oscillation around the steady state, and slower convergence to the MPP. To overcome this problem, the newly proposed interval Type-3 intuitionistic fuzzy logic (T3IFL) controller has been proposed. The T3IFL MPPT controller combines the uncertainty of Type-3 fuzzy logic (T3FL) controller with intuitionistic concepts. The T3IFL controller is more accurate and offers faster convergence to the MPP under changing climatic and steady-state conditions than classical techniques and T3FL controller. The T3IFL algorithm provides better performance with excellent MPP tracking by controlling the duty cycle of the DC-DC buck converter. Four cases studied were investigated: uniform radiation conditions, a step change in solar radiation with constant temperature, replacing the battery load with the ohmic load with constant radiation and temperature, and partial shading conditions. Experimental validation of the T3IFL was performed on a DC-DC buck converter using real-time hardware-in-the-loop (HIL). Finally, the simulation and experimental results with comparative studies verified the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with better response.

Chemically‐Modified 2D Covalent Organic Framework as an HTL Dopant for High‐Performance, Stable, and Sustainable Perovskite Solar Cells and Modules

AbstractDespite significant advances in perovskite solar cells (PeSCs), the operational instability and susceptibility to Pb leakage of PeSCs severely limit their widespread application. To address these issues, this study investigates the effect of doping the Spiro‐OMeTAD hole‐transporting layer (HTL) with a chemically‐modified 2D conjugated covalent organic framework (Tp‐Azo‐COF) on the photovoltaic performance and stability of PeSCs. Enriched with abundant carbonyl (C═O) groups and azo (N═N) nodes, Tp‐Azo‐COF has excellent chelation and adsorption capabilities, and experimental results confirm that Tp‐Azo‐COF effectively decreases Pb leakage and Li‐ion migration, improving the environmental safety and operational stability of PeSCs. The optimized PeSCs (0.12 cm2) exhibit an efficiency of 24.25%, a new benchmark for COF‐modified devices, and maintain robust performance in large‐area modules (18 cm2) with an efficiency of 21.96%. Under accelerated aging tests, including continuous light irradiation at maximum power point tracking for 980 h, the module demonstrated exceptional durability, with near‐100% efficiency retention. The COF doping strategy developed in this study significantly enhances operational stability and minimizes Pb leakage in PeSCs, paving the way for the sustainable, large‐scale deployment of perovskite photovoltaics.

Solar PV system with maximum power tracking

The paper investigates the state-of-the-art architecture of photovoltaic systems (PVS) by evaluating the performance of the developed maximum power point tracking (MPPT) algorithm of fuzzy particle swarm optimization (FPSO) for temperate continental latitudes. The material of the paper gives an evaluation of traditional MPPT algorithms in relation to the state-of-the-art FPSO algorithm. The study summarizes the problems of On-Grid PV systems and analyzes methods of solving them by combining them with hydrogen technologies. The paper summaries the experience of observations of climatic factors and solar resources on the example of the Russian Federation. Thus, in 2023, the average winter temperature exceeded the norm by 4.7 °C, and the average summer temperature exceeded the norm by 4.9 °C compared to 2022. The paper analyses the level of insolation, which increased by 0.03 kWh/m2 by regions of Russia for the period 2022–2023. The experimental part of the work was carried out at the solar power plant (SPP) «Kalmykskaya» with coordinates 53.422832 north latitude and 55.266895 east longitude. The watt-volt field characteristic of photovoltaic modules (PVM) connected to the inverter was obtained experimentally. When compared, the correlation coefficient between the experimental power (P) and voltage (U) of the panels was found to be higher than that of the ideal panels, 0.933 versus 0.914. The correlation coefficient between the ideal P(U) function and the experimental one is (−0.475). The data for calculating the coefficient of performance (COP) of the implemented MPPT algorithm was also obtained experimentally, which was about 98.7%. The reliability of the data used in the calculations was confirmed by two independent means of measurement, the difference of the obtained results was less than 1%. In the last part of the experiment of this study, the dependence of insolation in a given geographical point on the generated PV field power was evaluated. The correlation coefficient was 0.47, while the inverter output voltage was maintained in the nominal range of (600 ± 20%)V. Thus, the authors of the study experimentally proved the efficiency of using MPPT on FPSO in temperate continental climate with duration of sunshine T = 1850 h per year. The effectiveness of the FPSO algorithm under the conditions of inverter distance from the common point of the DC switching cabinet (DСCC) has been confirmed. The effectiveness of the MPPT algorithm based on FPSO under conditions of partial shading, increased cloud cover and increased air temperature is concluded. Using the description of the current architecture of On-Grid SPP, the authors draw attention to the impossibility of operation of such systems without the presence of voltage in the reference network. In addition, the impossibility of the system operation at the value of PVM power over 1500 kW and at the voltage of panels less than 900 V is noted. To modernize the existing PVS architecture, for the first time, the use of a DCCC and an inverter with implemented MPPT on the FPSO in combination with a hydrogen (H2) production unit and nickel-hydrogen (Ni–H2) batteries is proposed. The researchers propose that when the PVM field voltage is below 900 V and when the PVM field power exceeds 1500 kW, energy can be diverted to H2 generation or to charge Ni–H2 batteries via a DCCC controller. Such an architecture will improve the continuity and efficiency of the PVS, reduce the carbon footprint, and allow the PVS to be used as an industrial uninterruptible power supply (UPS). The authors see the lack of standardization of implemented projects based on the ESG principle as the main problem of alternative energy development.

Magnetron sputtered nickel oxide with suppressed interfacial defect states for efficient inverted perovskite solar cells

Widely used spin-coated nickle oxide (NiOx) based perovskite solar cells often suffer from severe interfacial reactions between the NiOx and adjacent perovskite layers due to surface defect states, which inherently impair device performance in a long-term view, even with surface molecule passivation. In this study, we developed high-quality magnetron-sputtered NiOx thin films through detailed process optimization, and compared systematically sputtered and spin-coated NiOx thin film surfaces from materials to devices. These sputtered NiOx films exhibit improved crystallinity, smoother surfaces, and significantly reduced Ni3+ or Ni vacancies compared to their spin-coated counterparts. Consequently, the interface between the perovskite and sputtered NiOx film shows a substantially reduced density of defect states. Perovskite solar cells (PSCs) fabricated with our optimally sputtered NiOx films achieved a high power conversion efficiency (PCE) of up to 19.93% and demonstrated enhanced stability, maintaining 86.2% efficiency during 500 h of maximum power point tracking under one standard sun illumination. Moreover, with the surface modification using (4-(2,7-dibromo-9,9-dimethylacridin-10(9H)-yl)butyl)phosphonic acid (DMAcPA), the device PCE was further promoted to 23.07%, which is the highest value reported for sputtered NiOx based PSCs so far.

An enhanced maximum power point tracking and voltage control for proton exchange membrane fuel cell using predictive model control techniques

Proton Exchange Membrane Fuel Cells serve as sustainable devices for converting renewable energy sources into electricity, offering advantages such as rapid startup, high power density, and operation at low temperatures. They are highly efficient and environmentally friendly energy sources, yet their non-linear output characteristics under variable conditions present challenges in maximizing power output. Furthermore, they generates direct current, while many electrical devices rely on alternating current. This research addresses the necessity of enhancing the performance and efficiency through the development of an advanced maximum power point tracking technique and voltage control strategy. A modified finite-control-set model predictive control technique is proposed for both maximum power point tracking and voltage control. Specifically, a finite-control-set model predictive control technique approach is employed to modulate the switching signal for both the DC-DC boost converter and the DC-AC inverter. The DC-DC boost converter step up the fuel cell output voltage to the desired level, ensuring it reaches the maximum power point, and a DC-AC inverter to convert the direct current voltage to a pure sinusoidal alternating waveform. The investigation demonstrates the effectiveness of the proposed method in achieving its objectives. The proposed maximum power point tracking technique accuracy achieved the maximum power with a rate of 97 % with excellent respond time within 7 ms. For alternating current power, only less than 1.5 % of total harmonic distortion is recorded. The study evaluates the control scheme under robust operating conditions, demonstrating its effectiveness in optimizing PEMFC output and providing high-quality AC voltage.

Hybrid feedback control of PMSG-based WECS with multilevel flying-capacitor inverter enhanced by fractional order extremum seeking

This paper proposes an original hybrid control strategy for a three-phase multilevel flying capacitor inverter (FCI) used in wind energy conversion system (WECS). A state feedback law, computed using a Lyapunov function and an invariance principle, is designed to manage grid current control and capacitor voltage balancing, guaranteeing the asymptotic stability of the closed-loop system. Additionally, a fractional-order extremum seeking control (FOESC) algorithm for Maximum Power Point Tracking (MPPT) is introduced, which optimizes power capture by adjusting turbine speed from the DC side without requiring a detailed system model (model-free approach). The use of fractional-order calculus leads to improved convergence and more seamless performance while preserving the robust characteristics of the system. The proposed control strategies are validated through simulation, demonstrating improved transient response and stability under varying wind conditions and parameter uncertainties. These results highlight the potential of advanced control algorithms to enhance the efficiency and reliability of wind energy systems, contributing to global efforts to achieve sustainable energy goals.

Isomeric Dimer Acceptors for Stable Organic Solar Cells with over 19 % Efficiency.

The strategy of isomerization is known for its simple yet effective role in optimizing molecular configuration and enhancing the power conversion efficiency (PCE) of organic solar cells (OSCs). However, the impact of isomerization on the design of dimer acceptors has been rarely investigated, and the relationship between the chemical structure and optoelectronic property remains unclear. In this study, we designed and synthesized two dimer acceptor isomers named D-TPh and D-TN, which differ in the positional arrangement of their end capping groups. Compared to D-TN, D-TPh exhibited enhanced backbone planarity, elevated lowest unoccupied molecular orbital energy level, and more ordered molecular stacking. Consequently, the OSC device based on PM6 : D-TPh achieved a PCE of 19.05 %, higher than that (PCE=18.42 %) of the device based on PM6 : D-TN. Large-area PM6 : D-TPh devices (1 cm2) yielded a PCE of 18.00 %. More importantly, the extrapolated T80 lifetime of the PM6 : D-TPh device is over 2800 h with MPP tracking under continuous one-sun illumination. These results suggest that isomerization strategy is an effective way to optimize the molecular configuration of dimer acceptors for the fabrication of high-efficiency and stable OSCs.

Sand cat swarm optimization based maximum power point tracking technique for photovoltaic system under partial shading conditions

Maximum power point tracking (MPPT) plays a crucial role in photovoltaic systems (PVS). In partial shading conditions (PSCs), the P-V characteristic curves of PVS exhibit multiple peaks. Traditional MPPT algorithms, like perturbation and observation (P&O), may fall into local maximum power points (LMPP) and fail to identify the global maximum power point (GMPP). To address the drawbacks of conventional optimal search algorithms, this paper introduces a bio-inspired approach named Sand Cat Swarm Optimization (SCSO) for maximizing the power output of individual PVS. The SCSO can mitigate the adverse effects of partial shadows on PVS performance by precisely identifying the GMPP. In comparison to other bio-inspired algorithms, SCSO exhibits lower complexity and higher efficiency by utilizing only one optimization parameter. SCSO’s performance is evaluated in four scenarios: uniform irradiance, complex partial shading conditions, step-varying stochastic irradiance, and gradual irradiance. A comparative analysis is conducted with Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Crow Search Algorithm (CSA), Slap-Swarm Optimization (SSA) and P&O, focusing on factors such as high efficiency, accuracy, convergence time, and implementation simplicity. Simulation results demonstrate that, on average, the tracking time improves by 19.91%, achieving an efficiency of over 98% while maximizing energy yield. Simultaneously, experimental results indicate that the SCSO is capable of tracking to a larger power output in a shorter time, with an average tracking efficiency improvement of 3.16%.

A Novel Strategy for Multitype Fault Diagnosis in Photovoltaic Systems Using Multiple Regression Analysis and Support Vector Machines

This study focuses on analyzing common fault types in photovoltaic (PV) modules, employing fault diagnosis methods based on machine learning technology to enhance the accuracy and efficiency of diagnosing faults in solar power systems. Initially, we collected relevant data from the solar power system and used data analysis techniques to identify system faults, designing a human-machine monitoring interface for practical application. Furthermore, the experimental results proved that the system could accurately identify eight major types of faults, including solar panel output circuits, energy storage batteries, maximum power point tracking (MPPT) controllers, inverters, dust accumulation, loosening of mounting rack screws, damage to the mounting rack foundation, and deformation of the mounting rack structure. Particularly in the detection of dust accumulation, we developed a new method of estimating power generation from multiple regression analysis (MRA), which closely aligns the estimated power output with the actual power output, highlighting the significant impact of dust accumulation on the efficiency of solar power systems. Next, by integrating voltmeters and support vector machines (SVM) into the solar PV array modules, we are able to quickly and accurately measure and locate short-circuit and open-circuit faults in bypass diodes. Ultimately, the proposed PV fault diagnosis strategy includes diagnostics for dust accumulation and mounting frame faults, making it particularly suitable for areas with severe air pollution and frequent earthquakes, providing a comprehensive fault diagnosis solution.

Design and simulation of zeta solar charge controller with artificial neural network MPPT

Fossil fuel are non-renewable energy sources with constrained reserves. This advocates renewable energy as a viable alternative for electricity generation. PV is a renewable energy source that harnesses solar energy. The current issue with PV is its low efficiency and elevated cost. Photovoltaic control devices and Maximum Power Point Tracking (MPPT) mimic human neural networks in processing multiple conditions and providing solutions from current reference data to optimise photovoltaic power. This technique is known as Artifical neural network (ANN). This method uses a zeta converter to adjust the photovoltaic voltage to supply specific loads, allowing ANN to optimize power as sunlight intensity changes. In this work, a charge controller for solar applications was designed using MPPT algorithms. This is responsible for overseeing the battery circuit and producing PWM signals to regulate zeta converter according to the battery voltage. The artificial neural network (ANN) received as inputs temperature and irradiation (G). Several simulations verified the efficiency of the suggested MPPT algorithm. The Zeta solar charge controller (ZSCC) device achieved up to 92% efficiency with low irradiance. The proposed ANN-based MPPT controller and perturb-observed Maximum Power Point Tracking (MPPT) algorithm were compared in different solar insolations. The simulation results show that MPPT-ANN can track power up to 2000 Watts for a 2000 WP photovoltaic in 0.056 seconds, with no power oscillations at the MPP.

Advanced Perturb and Observe Algorithm for Maximum Power Point Tracking in Photovoltaic Systems with Adaptive Step Size

Maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) systems to optimize the power output from the solar panels. Among the various MPPT algorithms, the perturb and observe (P&O) algorithm is a popular choice due to its simplicity and effectiveness. However, the basic P&O algorithm has some limitations such as oscillations and steady-state error under rapidly changing irradiance conditions. The enhanced algorithm includes a modified perturbation step and a dynamic step size adjustment scheme. This reduces the oscillations and improves the tracking accuracy. In the dynamic step size adjustment scheme, the step size is adjusted based on the rate of change of the PV output power. This improves the tracking performance under rapidly changing irradiance conditions. In order to prove the performance of the designed control algorithm, we will test it under simple climatic conditions of fixed temperature (30°C) and variable irradiation in the form of steps (500W/m² and 2000w/m²) and see the system response. The performance of the enhanced P&O algorithm has been evaluated using MATLAB simulations.

Next-Generation MPPT: neural network-driven optimization for superior solar performance

This paper presents a novel smart Maximum Power Point Tracking (MPPT) method based on a Neural Network Controller (NNC) to enhance solar power efficiency and mitigate significant oscillations. Traditional MPPT techniques, including perturbation and observation, fixed and variable incremental conductance methods, and fractional open circuit voltage, often suffer from slow response to sudden environmental changes (temperature and irradiation), inaccurate tracking, lack of prediction abilities, and complex structures. These drawbacks lead to power loss, reduced effectiveness of photovoltaic (PV) systems and raised broadband oscillations. To address these issues, we propose an intelligent MPPT strategy utilizing NNC. The NNC inputs are the error and derived error (E&ΔE) between the reference and actual inputs, with the duty cycle (D) as the target output. The proposed algorithm demonstrates an efficiency of approximately 0.75% compared to 0.63% for the P&O method. Simulation and experimental validation results confirm the NNC strategy's effectiveness, robustness, and efficiency, providing a thorough system analysis.