Power Point Research Articles

Deep Symbolic Regression-Based MPPT Control for a Standalone DC Microgrid System

Enhancing the performance of maximum power point tracking (MPPT) methods is essential for optimizing the operation of solar systems under any weather condition. This improvement is crucial because MPPT methods serve as the controllers for power converters, directly influencing the ability to maintain a steady and efficient energy output from solar installations. Therefore, we propose a deep symbolic regression (DSR)-based MPPT method in standalone direct current microgrids, which is particularly useful under drastically shifted meteorological conditions. This study aimed to improve the significant performance parameters of the MPPT approach, such as the rate of convergence during transient periods, tracking accuracy, steady-state stability, and overshoot/undershoot reduction, creating more reliable and efficient solar microgrid systems. To evaluate the proposed method, performance comparisons were made with those of existing advanced algorithms, namely particle swarm optimization-, artificial neural network-, and adaptive neuro-fuzzy inference system-based MPPT methods. The comparative results demonstrate a significant improvement with the proposed method. Moreover, the simple mathematical expression obtained by the DSR process minimizes the computational complexity of the control.

Multi‐source PV‐battery DC microgrid operation mode and power allocation strategy based on two layer fuzzy controller

AbstractThe conventional DC bus signaling (DBS) coordination control strategy for islanded DC microgrids (IDCMGs) faces challenges in coordinating multiple distributed generators (DGs) and fails to effectively incorporate the state of charge (SOC) information of the energy storage system, reducing system flexibility. In this article, a two‐layer fuzzy control‐based coordination strategy is proposed for multi‐PV islanded DC microgrids. The first layer fuzzy logic controller (FLC) quantifies and selects the optimal system operating mode, adaptively adjusting the number of PV units operating in maximum power point tracking (MPPT) mode to manage system power surplus or deficit, thereby simplifying system design and enhancing flexibility. The second layer FLC adaptively adjusts the output of distributed energy sources based on SOC and current system conditions to better align the energy storage system's output with overall system operation, resulting in at least a 4% improvement in SOC level and effectively preventing overcharging or over‐discharging issues seen in traditional control. Additionally, for PV units operating in droop mode, the droop coefficient is recalculated based on their maximum generation capacity under changing external conditions, thereby achieving more efficient power distribution and preventing system instability caused by power exceeding the limits of individual PV units. Finally, the effectiveness of the proposed control strategy is validated through RT‐lab hardware‐in‐the‐loop (HIL) simulations.

A Robust Salp Swarm Algorithm for Photovoltaic Maximum Power Point Tracking Under Partial Shading Conditions

Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. To address these challenges, this paper introduces a hybrid algorithm that integrates an improved salp swarm algorithm (SSA) with the perturb and observe (P&O) method. Initially, the SSA is augmented with a dynamic spiral evolution mechanism and a Lévy flight strategy, expanding the search space and bolstering global search capabilities, which in turn enhances the tracking precision. Subsequently, the application of a Gaussian operator for distribution calculations allows for the adaptive adjustment of step sizes in each iteration, quickening convergence and diminishing power oscillations. Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.

Deep learning‐based barrier‐function super‐twisting sliding mode control for integrating renewables in smart grid

AbstractThis study presents a two‐step controller design for integrating wind and photovoltaic energy systems into the smart grid. In the first stage, an enhanced deep neural network (DNN), optimised by particle swarm optimisation and a genetic algorithm, is employed to generate maximum power point (MPP) targets for both wind and solar systems. The DNN incorporates advanced features like hyperparameter tuning, softmax attention, dropout, and early stopping to improve prediction accuracy and prevent overfitting. In the second stage, a barrier‐function‐based adaptive super‐twisting sliding mode controller is developed to track the MPP and maintain stable DC bus voltage. This controller effectively reduces chattering and operates without requiring detailed knowledge of disturbance limits. The proposed design aims to maximise power extraction, ensure DC bus voltage stability, and enable smooth power delivery to the grid. MATLAB simulations and real‐time hardware testing validate the controller's performance, demonstrating significant improvements over traditional methods.

Fuzzypulse Solar Vista: Merging Advanced Fuzzy Logic with Perturb & Observe for Optimal Solar Tracking

Abstract—This paper presents a comparative study between the traditional Perturb and Observation (P&O) Maximum Power Point Tracking (MPPT) method and its enhancement through the integration of fuzzy logic control. The primary objective is to mitigate the shortcomings associated with the P&O method, particularly its tendency to induce output oscillations. By in- corporating fuzzy logic control into the P&O algorithm, the proposed method aims to alleviate these oscillations and enhance overall MPPT performance. The effectiveness of the combined approach is evaluated through extensive simulations, whereinthe results demonstrate a reduction in output oscillations and improved tracking efficiency. Moreover, the application scope of the system is extended by integrating a boost converter and a H bridge inverter on the load side, thereby enhancing its versatility and practical utility. Through comprehensive analysis and simulation validation, this study offers valuable insights into the potential synergies between conventional MPPT methods and advanced control techniques, paving the way for more robust and efficient renewable energy systems. Index Terms—MPPT Controller, P&O, Fuzzy Logic, Boost Con verter

LSTM-Based MPPT Algorithm for Efficient Energy Harvesting of a Solar PV System Under Different Operating Conditions

Solar energy is one of the most favorable renewable energy sources and has undergone significant development in the past few years. This paper investigates a novel concept of harvesting the maximum power of a photovoltaic (PV) system using a long-short term memory (LSTM) to forecast the irradiance value and a feedforward neural network (FNN) to predict the maximum power point (MPP) voltage. This study paves a way to mitigate avoidable inefficiencies that hinder the optimal performance of a PV system, due to the intermittent nature of solar energy. MATLAB/Simulink software platform was used to validate the proposed algorithm with real irradiance data from different geographical and weather conditions. Furthermore, the maximum power point tracking (MPPT) algorithm was implemented in a laboratory setup. The simulation results portray the superiority of the proposed method in terms of tracking performance and dynamic response through a comprehensive case study conducted with five other state-of-the-art MPPT methods selected from conventional, AI based, and bio-inspired MPPT categories. In addition to that, faster response time and lesser oscillations around the MPP were observed, even during volatile weather conditions and partial shading.

Methylthio Substituent in SAM Constructing Regulatory Bridge with Photovoltaic Perovskites.

Inverted (p-i-n) perovskite solar cells (PSCs) have experienced remarkable advancements in recent years, which is largely attributed to the development of novel hole-transport layer (HTL) self-assembled monolayer (SAM) materials. Methoxy (MeO-) groups are typically introduced into SAM materials to enhance their wettability and effectively passivate the perovskite buried interface. However, MeO-based SAM materials exhibit a mismatch in highest occupied molecular orbital (HOMO) levels with perovskite layer due to the strong electron-donating capability of methoxy group. In this work, we introduced a methylthio (MeS-) substituent that is superior to methoxy as a highly versatile self-assembled molecular design strategy. As a soft base, sulfur atom forms a stronger Pb-S bond than oxygen. Additionally, within the CbzPh series of SAM materials, MeS-CbzPh demonstrates a more optimal HOMO level and enhanced hole transport properties. Consequently, the MeS-CbzPh HTL based device achieved an impressive power conversion efficiency (PCE) of 26.01 % and demonstrated high stability, retaining 93.3 % efficiency after 1000 hours of maximum power point tracking (MPPT). Moreover, in comparison with the commonly used 4PACz-based SAM molecular series, MeS-4PACz also exhibited the best performance among its peers. Our work provides valuable insights for the molecular design of SAM materials, offering a highly versatile functional substituent group.

Spatial Conformation Engineering of Aromatic Ketones for Highly Efficient and Stable Perovskite Solar Cells.

Carbonyl-containing materials employed in state-of-the-art hybrid lead halide perovskite solar cells (PSCs) exhibit a strong structure-dependent electron donor effect that predominates in defect passivation. However, the impact of the molecular spatial conformation on the efficacy of carbonyl-containing passivators remains ambiguous, hindering the advancement of molecular design for passivating materials. Herein, we show that altering the spatial torsion angle of aromatic ketones from twisted to planar configurations, as seen in benzophenone (BP, 27.2°), anthrone (AR, 15.3°), and 9-fluorenone (FO, 0°), leads to a notable increment of the electron cloud density around the carbonyl group, thus improving the passivation ability for lead-based defects. Consequently, the PSC performance also relies on the torsion angle of the aromatic ketones, with the coplanar FO-based PSC achieving a highest power conversion efficiency (PCE) of 25.13% and retaining 92% of its initial efficiency after 1000 h of operation at the maximum power point under continuous 1-sun illumination (ISOS-L-1I). Moreover, a perovskite mini-module (14.0 cm2) containing FO exhibits a PCE of 20.19%. Our findings highlight the influence of molecular conformation on the passivation effect of the carbonyl group, offering deeper insight into the design and development of aromatic ketones as passivators for highly efficient and stable PSCs.

Performance Enhancement of Microgrid Systems Using Backstepping Control for Grid Side Converter and MPPT Optimization

Microgrid networks represent a crucial model in the field of power transmission and distribution, integrating renewable energy sources with modern control technologies. This research focuses on enhancing the performance of a microgrid system connected to the main grid through a three-phase converter controlled using Voltage Oriented Control (VOC). The microgrid comprises a storage element and a photovoltaic (PV) generation system. To improve system efficiency and power quality, backstepping (BS) controllers are implemented and compared with traditional control methods. Three control systems are evaluated: a conventional system using Perturb and Observe (P&O) algorithm for Maximum Power Point Tracking (MPPT) and Proportional-Integral (PI) controllers for grid-side converter (GSC) control, a hybrid system with BS-modified P&O (BS-P&O) for MPPT and PI controllers for GSC, and an advanced system employing BS-P&O for MPPT and BS controllers for GSC. The research demonstrates that BS controllers exhibit superior dynamic performance, contributing to improved regulation of DC-link voltage, active and reactive powers. Additionally, they achieve lower Total Harmonic Distortion (THD) values and increase the overall efficiency of the PV system. The proposed advanced control strategy shows particular effectiveness in tracking speed, disturbance rejection, and power quality enhancement. This study underscores the potential of nonlinear control techniques in optimizing microgrid operations and facilitating the integration of renewable energy sources into the main grid.

Evaluation and comparison of novel phase method against previous approaches for maximum power point tracking in single- and two-steps

Evaluation and comparison of novel phase method against previous approaches for maximum power point tracking in single- and two-steps

Empowering Presentations: Streamlining Content Generation through

Creating research paper presentation slides is often a labor-intensive process that tools like Open Office and Microsoft PowerPoint Office do not adequately streamline, as they offer templates but lack content selection capabilities. Academic presentations are meant to succinctly showcase research findings, using visuals to engage the audience and emphasize key points. To address this, we propose an automated system that generates presentation slides directly from PDF research papers, significantly reducing the time, effort, and cost involved. This system leverages the Bidirectional Encoder Representations from Transformers (BERT) model, developed by Google, To encapsulate the content of research papers. By using Python’s unpdfer tool, the system extracts text from PDFs, and then employs BERT to evaluate the significance of sentences and produce concise summaries.

A novel method for fault diagnosis in photovoltaic arrays used in distribution power systems

AbstractThis study addresses the critical issue of fault diagnosis in photovoltaic (PV) arrays, considering the increasing integration of distributed PV systems into power grids. The research employs a novel approach that combines artificial neural networks, specifically radial basis functions (RBFs), with machine learning techniques. The methodology involves training the RBF neural network using input features like voltage, current, temperature, and irradiance, derived from the PV array, to detect and classify various fault types. Notably, it comprehensively evaluates the accuracy of this approach, with a particular focus on detecting maximum power point tracking (MPPT) and mismatch faults. The findings reveal significant advantages, in which the proposed method outperforms existing techniques, achieving an approximately 20% increase in accuracy, with fault detection rates for specific faults ranging from 81.29 to 93.44%. Simulation results represent that by leveraging RBFs within neural networks, it offers improved fault detection and classification, making it a valuable advancement in the field of PV fault diagnosis.

An Overview on Photovoltaic System

Photovoltaic (PV) cells are a renewable energy source that is safe for the environment; they capture and convert sunlight into electrical power using solar arrays or modules. The PV panels generate electrical energy from solar radiation, and in this PV panel-based energy conversion system, maximum power point tracking (MPPT) is a crucial component. Partial shading conditions result in a decrease in the PV systems' energy output, and this phenomenon is encountered in all types of PV systems. Hence, an in-depth description of the PV system is provided in this study.

Enhancing FAPbI3 Perovskite Solar Cell Performance and Stability Through Bespoke Graphene Quantum Dots

ABSTRACTA novel approach to enhancing the efficiency and long‐term stability of perovskite solar cells (PSCs) is presented through strategic interfacial modification using bespoke graphene quantum dots (GQDs). GQDs with controlled alkylamine chain lengths, such as butylamine (C4), octylamine (C8), and dodecylamine (C12), were customized to have the proper optical and electronic properties toward specific interfaces within the PSCs. The incorporation of C4‐GQDs significantly improved the energy level alignment and conductivity of the SnO2 electron transport layer (ETL), while C12‐GQDs effectively reduced trap density on the perovskite surface, leading to enhanced defect passivation. These modifications resulted in a substantial increase in power conversion efficiency of 24.41% in a unit cell and 18.91% in a mini‐module, respectively. Notably, the maximum power point tracked perovskite mini‐module retained 89% of its initial efficiency during 1000 h of continuous light soaking condition at 25°C under 35% relative humidity. This work highlights the potential of bespoke GQDs to advance both the performance and durability of PSCs, providing a scalable approach for future photovoltaic applications. image

Methylthio Substituent in SAM Constructing Regulatory Bridge with Photovoltaic Perovskites

Inverted (p‐i‐n) perovskite solar cells (PSCs) have experienced remarkable advancements in recent years, which is largely attributed to the development of novel hole‐transport layer (HTL) self‐assembled monolayer (SAM) materials. Methoxy (MeO‐) groups are typically introduced into SAM materials to enhance their wettability and effectively passivate the perovskite buried interface. However, MeO‐based SAM materials exhibit a mismatch in highest occupied molecular orbital (HOMO) levels with perovskite layer due to the strong electron‐donating capability of methoxy group. In this work, we introduced a methylthio (MeS‐) substituent that is superior to methoxy as a highly versatile self‐assembled molecular design strategy. As a soft base, sulfur atom forms a stronger Pb‐S bond than oxygen. Additionally, within the CbzPh series of SAM materials, MeS‐CbzPh demonstrates a more optimal HOMO level and enhanced hole transport properties. Consequently, the MeS‐CbzPh HTL based device achieved an impressive power conversion efficiency (PCE) of 26.01% and demonstrated high stability, retaining 93.3% efficiency after 1000 hours of maximum power point tracking (MPPT). Moreover, in comparison with the commonly used 4PACz‐based SAM molecular series, MeS‐4PACz also exhibited the best performance among its peers. Our work provides valuable insights for the molecular design of SAM materials, offering a highly versatile functional substituent group.

Automatic Step Size Selection of the PO MPPT Algorithm to Improve Wind Power Generation

Perturb and Observe (P&O) is a commonly used algorithm for Maximum Power Point Tracking (MPPT) in wind turbines. MPPT plays a critical role in enhancing wind turbine efficiency by dynamically adjusting operating parameters to adapt to fluctuating wind conditions. Although P&O is favored for its simplicity and adaptability, its performance is hindered by step size selection issues, which lead to inefficiency, oscillations, and slow convergence. To overcome these limitations, this research proposes a modified P&O algorithm that automates step size selection based on divided sectors of wind speed and normalized power in region two. Additionally, an integration of the pitch-angle control from region three was employed to maintain the optimal power output under variable wind conditions. The proposed approach reduces tracking time, minimizes perturbation errors, and ensures a stable power output. The proposed modifications enhance the efficiency and reliability of Wind Energy Conversion Systems (WECS) by addressing the shortcomings of the conventional P&O methods.

A high effectiveness impact‐optimized piezoelectric energy harvesting interface system

AbstractThis paper presents a novel high‐performance impact‐optimized interface system for an impact‐driven piezoelectric energy harvester (PEH) which utilizes two independent parallel harvesting plans, that is, low‐efficiency self‐powered passive path and high‐efficiency active maximum power point tracking (MPPT)‐based path, for different situations based on the characteristics of the input excitation and stored energy content which are evaluated by the mode detection unit. It uses a synchronous electrical charge extraction‐based self‐powered passive circuit as a primary energy extraction strategy. Thus, the proposed structure is self‐sustained with cold start capability. When the determined prerequisites are met, the system switches to the secondary energy extraction strategy, that is, high‐efficiency MPPT‐based path, in which during the maximum power point sensing phase, the PEH is sensed without disconnecting it from the interface and during the maximum power point setting phase, a bidirectional DC/DC converter performs a fully bidirectional energy transfer, increasing the extraction efficiency. The proposed system is designed and simulated using standard 180 nm complementary metal‐oxide semiconductor (CMOS) technology. Post‐layout simulation results show that when the input energy content is around 50 µJ, the FoMMOPIR, periodic harvesting efficiency, shock harvesting efficiency, MPPT efficiency, and effectiveness of the proposed system are 505%, 65%, 80%, 70%, and 56%, respectively.

A Novel Hybrid Approach for Global Maximum Power Point Tracking in Standalone PV Systems

The efficiency of Photo-Voltaic (PV) systems is highly dependent on their ability to accurately track the Global Maximum Power Point (GMPP) under varying environmental conditions. Traditional Maximum Power Point Tracking (MPPT) methods often struggle with issues such as slow tracking speed, susceptibility to local maxima, and the need for complex parameter tuning, particularly in dynamically changing environments with Partial Shading Conditions (PSCs) and rapid irradiation changes. To address these challenges, this study introduces a hybrid approach that combines a modified Rao algorithm with the Perturb and Observe (P&O) method. The modified Rao algorithm was employed in the initial tracking stages to quickly locate the global vicinity, benefiting from its simplicity and the absence of algorithm-specific parameters, whereas the P&O method ensured precise tracking in the final stages. The performance of the proposed method was assessed on a PV array subjected to PSCs and compared with several well-known MPPT algorithms, such as Gray Wolf Optimization (GWO), JayaDE, and the Slime Mould Algorithm (SMO). The proposed approach was implemented and analyzed using the MATLAB/Simulink software.

A Novel EPSO Algorithm Based on Shifted Sigmoid Function Parameters for Maximizing the Energy Yield from Photovoltaic Arrays: An Experimental Investigation

This paper presents a new method for maximizing the energy production of photovoltaic (PV) arrays, utilizing the Enhanced Particle Swarm Optimization (EPSO) algorithm. In contrast with the classical PSO, the proposed EPSO algorithm employs shifted sigmoid functions in order to adapt the acceleration coefficients and the inertia weight instead of using constant coefficients. The EPSO algorithm proves its effectiveness even in challenging either condition, such as abrupt variations in solar irradiance or instances of partial shading conditions (PSCs). The proposed EPSO-based Maximum Power Point Tracking (MPPT) controller has undergone testing and a comparative analysis alongside well-known metaheuristic algorithms, which include the PSO, Bat Algorithm (BAT), Grasshopper Optimization Algorithm (GOA), and Grey Wolf Optimizer (GWO). The results obtained through simulations consistently demonstrate that the EPSO-based MPPT method surpasses other metaheuristic approaches in terms of energy yield and the speed at which it tracks the Maximum Power Point (MPP) under PSCs. In addition, the proposed EPSO algorithm demonstrates robustness, maintaining consistent tracking of the MPP during sudden solar radiation changes. Experimental validation on a real PV system affirms these findings, demonstrating that the EPSO algorithm surpasses its counterparts by achieving a high MPPT efficiency of approximately 99.19% in a fast-tracking time of 2.4 s while maintaining minimal power oscillations of around 2.04 %.

Development and evaluation of artificial intelligence based maximum power point tracking for photovoltaic systems across diverse weather conditions

Development and evaluation of artificial intelligence based maximum power point tracking for photovoltaic systems across diverse weather conditions