Maximum Power Point Tracking Research Articles

IoT Integration With CMPA‐PINN for Islanding Detection Through Microgrid Hierarchical Control

ABSTRACTInternet of Things (IoT) and cloud computing are becoming increasingly important in the solution of many industrial problems. Effective management of microgrid (MG) requires a strong and scalable information and communication technology (ICT) infrastructure. IoT devices with effective measurement and control capabilities have the potential to be very important in the MG environment. MG was run in both grid‐connected and island mode. This paper proposes to improve the MG hierarchical control with IoT using CMPA‐PINN techniques for islanding detection. The proposed hybrid method is the joint execution of both the Coronavirus Mask Protection Algorithm (CMPA) and physics‐informed neural networks (PINNs). Hence, it is named as CMPA‐PINN approach. The major goal of this proposed method is to reduce the deviation of voltage, frequency, and total harmonic distortion (THD). The proposed CMPA is used to optimize the traffic flow over a communication network, and the PINNs are used to predict the optimized traffic flow. By then, the MATLAB platform has adopted the proposed method, and the current process is used to compute its execution. The proposed technique outperforms all current systems, including maximum power point tracking (MPPT), multi‐agent reinforcement learning (MARL), and deep reinforcement learning (DRL). The proposed approach shows the THD is 2%, which is lower than other existing systems.

A high-speed MPPT based horse herd optimization algorithm with dynamic linear active disturbance rejection control for PV battery charging system

This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA’s reference voltage output. The LADRC, known for managing uncertainties and disturbances, improves the anti-interference capacity of the maximum power point tracking (MPPT) technique and speeds up the system’s response rate. Then, in comparison to the traditional method (perturb & observe; P&O) and metaheuristic algorithms (conventional particle swarm optimization; CPSO, grasshopper optimization; GHO, and deterministic PSO; DPSO) through DSEC, the simulations results demonstrate that the combination HHOA-LADRC can successfully track the global maximum peak (GMP) with less fluctuations and a quicker convergence time. Finally, the experimental investigation of the proposed HHOA-LADRC was accomplished with the NI PXIE-1071 Hardware-In-Loop (HIL) prototype. The output findings show that the effectiveness of the provided HHOA-LADRC may approach a value higher than 99%, showed a quicker rate of converging and less oscillations in power through the detection mechanism.

Evaluation of Photovoltaic Inverters According to Output Current Distortion in a Steady-State and Maximum Power Point Tracking

Evaluation of Photovoltaic Inverters According to Output Current Distortion in a Steady-State and Maximum Power Point Tracking

Stable and Efficient Perovskite Photovoltaics via a Three‐In‐One Passivating Approach by Aminoacetonitrile Hydrochloride

AbstractReducing defect density is of significant importance for enhancing the power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs). While most previous outstanding studies have focused on individual layers within the perovskite device structure. Herein, a three‐in‐one strategy using the aminoacetonitrile hydrochloride (AmiHCl) molecule to reduce the defects in the bulk and surface of perovskite. The results of the study found that the AmiHCl bottom modification can decrease the number of buried interface holes, doping into bulk perovskite can modulate crystallization via a strong interaction between AmiHCl and perovskite components, and the upper interface modification can inhibit the formation of vacancies by creating hydrogen bonds with A‐site cations. This approach yields PSCs with an efficiency of 25.90% and a high fill factor (FF) of 88.54%. Additionally, the modified PSCs show significantly enhanced operational stability, with the PCE retaining more than 90.0% of the initial value after 1350 h of maximum power point tracking.

Design of Non-isolated DC–DC Converters for Maximum Power Point Tracking in Stand-Alone Photovoltaic System

Design of Non-isolated DC–DC Converters for Maximum Power Point Tracking in Stand-Alone Photovoltaic System

An Approach Based on Golden Eagle Optimization Algorithm for Maximum Power Point Tracking of PV Panel Under Partial Shading Conditions

This study introduces an innovative Maximum Power Point Tracking (MPPT) technique utilizing the Golden Eagle Optimization (GEO) method, specifically designed to enhance the efficiency of photovoltaic (PV) systems under partial shading conditions. Unlike traditional MPPT approaches that struggle with local peaks in power-voltage curves caused by shading, the GEO method leverages the hunting behavior-inspired algorithm to accurately locate the global maximum power point (GMPP). The effectiveness of the GEO MPPT technique is demonstrated through extensive simulations across three diverse case scenarios, each representing different partial shading patterns. In all scenarios, the GEO method outperforms conventional MPPT techniques, showcasing its adaptability and superior performance in challenging conditions. The successful implementation of GEO MPPT leads to substantial improvements in PV panel energy extraction efficiency, even when faced with the complexities of partial shading. This research contributes significantly to the advancement of solar PV systems, enhancing their reliability and performance in real-world environments. By mitigating the impact of partial shading, this work promotes the wider adoption of solar energy as a viable and sustainable power solution.

A solar-powered, internet of things (IoT)-controlled water irrigation system supported by rainfall forecasts utilizing aerosols: a review

Abstract Efficient water management is crucial in modern agriculture, especially in regions facing water scarcity. Traditional irrigation systems often result in water wastage, which challenges sustainability goals. This paper presents a comprehensive review of a novel Internet of Things (IoT)-based smart irrigation system with rainfall prediction based on pollutant concentration designed to optimize water usage through real-time environmental monitoring and promote sustainable agriculture through the integration of photovoltaic (PV) power. The system's components, including 160 Wp semi-crystalline PV panels, ESP8266 Wi-Fi module, Arduino Uno, Raspberry Pi, Partial Maximum Power Point Tracking (MPPT) control methods, and a reversible pump, were selected based on a comprehensive literature review, establishing the foundation for the proposed system’s design. The review extensively covers previous PV-irrigation integration systems, their performance in varied environments, and the cost–benefit analysis with special reference to Indian government subsidies for solar adoption in agriculture. In addition, various IoT-based irrigation systems and communication technologies such as Wi-Fi, LoRa, and Zigbee were reviewed, ensuring that the selected components represent the most efficient and secure combination for field deployment. The key novelty of this system is the rainfall prediction methodology, which focuses on pollutant concentration (PM10, PM2.5, SO₂, NO₂, CO, NH3, Cr and Pb) rather than the traditional aerosol size used in previous studies. This approach leverages the unique environmental characteristics of the chosen location, providing more accurate predictions of rainfall, which can be used to dynamically adjust irrigation schedules, reducing water waste. Sample data of pollutant levels and weather conditions from the area are provided as a demonstration. The paper also reviews security concerns in IoT systems, highlighting potential vulnerabilities and mitigation strategies to ensure robust system operation. By combining IoT, PV, and predictive weather analytics, the proposed system offers significant advantages in water and energy management, with the MPPT controller maximizing solar energy efficiency. The review presents a detailed flow diagram of the system, showcasing components chosen through a thorough literature survey. It also examines portable PV-pumping systems, recommending the proposed design for effective use in both small- and medium-scale farms. This paper provides a comprehensive analysis of sustainable irrigation technologies, highlighting their potential to significantly improve agricultural water management practices.

A novel method of MPPT of photovoltaic power generation system based on MSINGO

Abstract The speed and accuracy of Maximum Power Point Tracking (MPPT) have a significant impact on photovoltaic power generation. In this paper, a novel method is proposed for fast and accurate MPPT of photovoltaic power generation systems. First, the Northern Goshawk Optimization algorithm (NGO) is introduced in the MPPT. Second, a multi strategy is applied to improve the NGO, and MSINGO is proposed. In the MSINGO algorithm, the optimal individual leadership strategy enhances the optimization accuracy and convergence speed, the enhanced Levy flight strategy leads it to escape from the local optimum, and the pinhole imaging learning strategy guides the population towards the global optimum. Finally, MSINGO was applied to MPPT, and a simulation analysis of a single photovoltaic module and photovoltaic array under different working conditions was conducted and compared with other intelligent optimization algorithms. The results show that the proposed method can achieve MPPT with minimal time consumption and better tracking efficiency.

Multiconnected Interleaved Boost Converter for Hybrid Energy System

Concern over the condition of our decaying planet is making environmentally friendly solutions more popular than before. A novel front- end rectifier stage layout for a hybrid solar/fuel system is presented in this research. Depending on the energy sources' availability, this arrangement enables the two sources to serve the load either independently or concurrently. This interleaved boost converter's intrinsic feature is that it can filter out high frequency harmonics without the need for extra input filters. The longevity, heating problems, and efficiency of the converter are all negatively impacted by harmonic content. Additionally, Maximum Power Point Tracking (MPPT) enables the fused multi-input rectifier stage to maximize solar power when it is available. For the PV system, the conventional perturb and observe approach will be applied. To demonstrate the advantages of the suggested circuit, simulation results are provided. This technique can be used for both online and offline applications.

Model-Free Speed Control for Pumping Kite Generator Systems Based on Nonlinear Hyperbolic Tangent Tracking Differentiator

This paper investigates the emerging field of grid-connected wind-powered pumping kite generator system (PKGS), focusing on the challenges associated with the generator/motor speed control. Conventional use of proportional–integral (PI) controllers faces difficulties in meeting requirements for dynamic response, tracking performance, stability, and disturbance rejection encountered in this technology, notably the periodical variation in the rotational speed reference in maximum power point tracking in generation phases and the dynamic response for the step reference in transient ones. To overcome these limitations, a model-free controller (MFC) approach is introduced, also known as intelligent PID controllers. Unlike traditional methods, MFC does not rely on a control model of the system and adapts to uncertainties and disturbances through online estimation based on the system’s input–output behavior. To further improve the control performances, a tracking differentiator based on a nonlinear hyperbolic tangent function is integrated in the MFC. The effectiveness of the proposed strategy is proved through simulations in MATLAB/Simulink. The results highlight the superior performances of the proposed MFC approach in terms of speed control accuracy, response time, and robustness.

Tuning Isomerism Effect in Organic Bulk Additives Enables Efficient and Stable Perovskite Solar Cells

Organic additives with multiple functional groups have shown great promise in improving the performance and stability of perovskite solar cells. The functional groups can passivate undercoordinated ions to reduce nonradiative recombination losses. However, how these groups synergistically affect the enhancement beyond passivation is still unclear. Specifically, isomeric molecules with different substitution patterns or molecular shapes remain elusive in designing new organic additives. Here, we report two isomeric carbazolyl bisphosphonate additives, 2,7-CzBP and 3,6-CzBP. The isomerism effect on passivation and charge transport process was studied. The two molecules have similar passivation effects through multiple interactions, e.g., P = O···Pb, P = O···H-N and N-H···I. 2,7-CzBP can further bridge the perovskite crystallites to facilitates charge transport. Power conversion efficiencies (PCEs) of 25.88% and 21.04% were achieved for 0.09 cm2 devices and 14 cm2 modules after 2,7-CzBP treatment, respectively. The devices exhibited enhanced operational stability maintaining 95% of initial PCE after 1000h of continuous maximum power point tracking. This study of isomerism effect hints at the importance of tuning substitution positions and molecular shapes for organic additives, which paves the way for innovation of next-generation multifunctional aromatic additives.

Improved Efficiency and Stability of Perovskite Solar Cells Through Long-Chain Phenylammonium Additives.

The addition of organic cationic iodides to form low-dimensional perovskite is an essential strategy for defect passivation in perovskite solar cells (PSCs). Specially, the 2D/3D perovskite structure can combine the stability of 2D perovskite and the high charge transport performance of 3D perovskite. Here, we introduced phenylammonium hydroiodide salts with different alkyl chain lengths into PSCs precursor solution to research the influence on formation of perovskite thin films and the photovoltaic performance of PSCs. As a result, the champion power conversion efficiency (PCE) of PSCs was increased to 22.68% by incorporation of the organic cation phenylpropyl ammonium iodide (PPAI) with the best alkyl chain length. We further fabricated the perovskite solar modules (PSMs) with an aperture area of 21 cm2, achieving an impressive efficiency of 21.65% with excellent stability that maintaining 89% of its initial efficiency after 1000 h of maximum power point tracking.

An Enhanced Solar Battery Charger Using a DC-DC Single-Ended Primary-Inductor Converter and Fuzzy Logic-Based Control for Off-Grid Photovoltaic Applications

Battery charging systems are crucial for energy storage in off-grid photovoltaic (PV) installations. Since the power generated by a PV panel is conditioned by climatic conditions and load characteristics, a maximum power point tracking (MPPT) technique is required to maximize PV power and accelerate battery charging. On the other hand, a battery must be carefully charged, ensuring that its charging current and voltage limits are not exceeded, thereby preventing premature degradation. However, the voltage generated by the PV panel during MPPT operation fluctuates, which can harm the battery, particularly during periods of intense radiation when overvoltages are likely to occur. To address these issues, the design and construction of an enhanced solar battery charger utilizing a single-ended primary-inductor converter (SEPIC) and soft computing (SC)-based control is presented. A control strategy is employed that integrates voltage stabilization and MPPT functions through two dedicated fuzzy logic controllers (FLCs), which manage battery charging using a three-mode scheme: MPPT, Absorption, and Float. This approach optimizes available PV power while guaranteeing fast and safe battery charging. The developed charger leverages the SEPIC’s notable features for PV applications, including a wide input voltage range, minimal input current ripple, and an easy-to-drive switch. Moreover, unlike most PV charger control strategies in the literature that combine improved traditional MPPT methods with classical proportional integral (PI)-based control loops, the proposed control adopts a fully SC-based strategy, effectively addressing common drawbacks of conventional methods, such as slowness and inaccuracy during sudden atmospheric fluctuations. Simulations in MATLAB/Simulink compared the FLCs’ performance with conventional methods (P&O, IncCond, and PID). Additionally, a low-power hardware prototype using an Arduino Due microcontroller was built to evaluate the battery charger’s behavior under real weather conditions. The simulated and experimental results both demonstrate the robustness and effectiveness of the solar charger.

Spatially Isomeric Fulleropyrrolidines Enable Controlled Stacking of Perovskite Colloids for High-Performance Tin-Based Perovskite Solar Cells.

The advancement of tin-based perovskite solar cells (TPSCs) has been severely hindered by the poor controllability of perovskite crystal growth and the energy level mismatch between the perovskite and fullerene-based electron transport layer (ETL). Here, we synthesized three cis-configured pyridyl-substituted fulleropyrrolidines (PPF), specifically 2-pyridyl (PPF2), 3-pyridyl (PPF3), and 4-pyridyl (PPF4), and utilized them as precursor additives to regulate the crystallization kinetics during film formation. The spatial distance between the two pyridine groups in PPF2, PPF3, and PPF4 increases sequentially, enabling PPF4 to interact with more perovskite colloidal particles. These interactions effectively enlarge the precursor colloid size and decelerate the crystallization rate of the perovskite, resulting in high-quality PPF4-based perovskite films with reduced defect density and lower exciton binding energy. Additionally, we incorporated a well-defined fullerene bis-adduct, C60BB, as an interlayer between the perovskite and PCBM layers to optimize energy level alignment. Through the synergistic effects of PPF4 and C60BB, our champion device achieved an efficiency of 16.05 % (certified: 15.86 %), surpassing the 16 % efficiency bottleneck and setting a new benchmark for TPSCs. Moreover, the devices exhibited outstanding stability, retaining 99 % of their initial efficiency after 600 hours of maximum power point tracking under 1 sun condition.

Type-2 fuzzy logic controller-based maximum power point tracking for photovoltaic system

Introduction. Photovoltaic (PV) systems play a crucial role in converting solar energy into electricity, but their efficiency is highly influenced by environmental factors such as irradiance and temperature. To optimize power output, Maximum Power Point Tracking (MPPT) techniques are used. This paper introduces a novel approach utilizing a Type-2 Fuzzy Logic Controller (T2FLC) for MPPT in PV systems. The novelty of the proposed work lies in the development of a T2FLC that offers enhanced adaptability by managing a higher degree of uncertainty, we introduce an original method that calculates the error between the output voltage and a dynamically derived reference voltage, which is obtained using a mathematical equation. This reference voltage adjusts in real-time based on changes in environmental conditions, allowing for more precise and stable MPPT performance. The purpose of this paper is to design and validate the effectiveness of a T2FLC-based MPPT technique for PV systems. This approach seeks to enhance power extraction efficiency in response to dynamic environmental factors such as changing irradiance and temperature. The methods used in this study involve the implementation of T2FLC to adjust the duty cycle of a DC-DC converter for continuous and precise MPPT. The system was simulated under various environmental conditions, comparing the performance of T2FLC against T1FLC. The results show that the T2FLC MPPT system significantly outperforms traditional methods in terms of tracking speed, stability, and power efficiency. T2FLC demonstrated faster convergence to the MPP, reduced oscillations, and higher accuracy in rapidly changing environmental conditions. The findings of this study confirm the practical value of T2FLC logic in improving the efficiency and stability of PV systems, making it a promising solution for enhancing renewable energy technologies. References 33, tables 4, figures 10.

Robust MPPT Control of PMSG Wind Turbines Using Sliding Mode Control

Wind energy is becoming an increasingly promising technology and a more significant player in energy production. However, due to the nonlinear nature of the system and the impact of external conditions, an effective control strategy is crucial. In this paper, we employ Sliding Mode Control as a Maximum Power Point Tracker (MPPT) on a Buck-Boost converter to connect the DC load to the wind turbine generator. All system components are modeled and simulated using MATLAB/Simulink, and the results demonstrate the promising potential of the SMC method.

Development of maximum power point tracking algorithm based on Improved Optimized Adaptive Differential Conductance Technique for renewable energy generation.

Maximum Power Point Tracking (MPPT) is a technique employed in photovoltaic (PV) systems to ensure that the modules transfer the maximum generated power to the load. An advanced algorithm, the Improved Optimized Adaptive Differential Conductance (IOADC), was developed by applying Kirchhoff's law within a single diode model framework. The algorithm's performance was evaluated under various solar irradiance levels of 500W/m2, 750W/m2, and 1000W/m2 at a constant temperature of 298K, analyzing its impact on power generation and transfer. Additionally, the performance was assessed at varying temperatures of 250K, 298K, and 350K under a constant irradiance of 1000W/m2 to examine its effect on the Module Saturation Current (MSC). The analysis revealed that the PV modules' impedance decreases with increasing irradiance, while the load's impedance remains largely unaffected which aligns with the PV applications. However, the implementation of the IOADC technique showed significant effectiveness. It was also noted that an increase in temperature raises the module saturation current, which in turn reduces the power output, and vice versa which also agrees with the PV application. Real-world application results indicated that at an irradiance of 750W/m2, the output power at the maximum power point (MPP) for the Optimized Adaptive Differential Conductance (OADC), Voltage Control Technique, and IOADC were 83.3346W, 86.9122W, and 100.1739W, respectively. The 100.1739W obtained from the IOADC technique showed a significant improvement. Through comprehensive comparative evaluation, analysis, and validation of the effects of varying temperature, irradiance, and MSC on output power, the developed IOADC model demonstrated a relative improvement of 15.82% in simulations and 20.21% in real-world conditions compared to the Voltage Control Technique and the OADC technique, respectively. Simulation validation and real-world application validation were performed using MATLAB 2020b. These validations confirmed the superior performance of the IOADC algorithm under varying conditions of temperature, irradiance, and module saturation current.

A comparative analysis of photovoltaic system using three algorithms: P&O, Hill Climbing and Fuzzy Logic

Photovoltaic solar energy plays a crucial role in our lives, as it provides electricity from the sun, a renewable source. The objective of this paper is to create a comprehensive model of a photovoltaic solar system, representing its various components, with the aim of maximizing power output using a control technique called MPPT (Maximum Power Point Tracking). Additionally, the work involves using algorithms to manage energy within this solar system to optimize power generation. We focus on the following algorithms: Perturb and Observe (A simple and highly responsive algorithm with a quick response that adjusts the voltage), Hill Climbing (It is a simple algorithm to implement and is effective for solving combinatorial problems by adjusting the duty cycle), and fuzzy logic (Using linguistic rules, it is possible to intelligently adjust the parameters, improving the connection between the photovoltaic generator and the load to ensure optimal operation). This work will be conducted using MATLAB/SIMULINK.

Energy Management System for Distributed Energy Resources using Blockchain Technology

: Power generation in today’s world is of utmost importance, due to which blockchain is used for the categorization and formation of decentralized structures. This paper has proposed decentralized energy generation using a nester, i.e., energy sharing without third-party intervention. Decentralized blockchain technology is applied to ensure power sharing between buyer and seller, and also to achieve efficient power transmission between prosumer and consumer. Energy management is associated with controlling and reducing energy consumption. Blockchain technology plays a major role in distributed power generation, for example, power-sharing (solar and wind energy), price fixation, energy transaction monitoring, and peer-to-peer power-sharing. These are operations performed by blockchain in renewable power generation. Solar power generation using blockchain technology can obtain an impact resting upon the power generation system. Distributed ledger is the key area of blockchain technology for recording and tracking each transaction in the distribution system to improve the efficiency of the overall transmission system. A smart contract is another important tool in the blockchain technology, which is issued to confirm an assent between buyer and seller before starting any energy transaction without external intervention and also to avoid time delay. Maximum power point tracking is conducted in PV cells using blockchain technology. Blockchain influences energy management systems to improve the utilization of energy, optimize energy usage, and also to reduce the cost.

Maximum Efficiency and Power Point Tracking System for Hybrid Compensated Wireless Charging of Electric Vehicle

Maximum Efficiency and Power Point Tracking System for Hybrid Compensated Wireless Charging of Electric Vehicle