Maximum Power Point Tracking Research Articles

Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.

Design and tests of a marine current turbine in low flow velocity

Marine current energy is recognized a promising energy source due to its inherent stability and high predictability. In the past decade, several milestones have been achieved in marine current turbine (MCT) research such as MeyGen and O2. However, compared with high-velocity currents, low velocity currents are more commonly distributed worldwide, which presents new challenges. The demand for low starting velocity and high efficiency necessitates some novel structure in the design of blade, power transmission, etc. To address these issues, taking a 50W MCT as research object, this paper firstly presents the MCT design of a new fan-shaped rotor configuration, a compact non-contact magnetic coupling, a low inertial power train and hollow-cup DC generator for easier starting. Furthermore, to realize the maximum power point tracking (MPPT) and sustainable power supply, the electrical system and its control strategy were designed. The hydrodynamic performance of the fan-shaped rotor was simulated and analyzed using CFD, and a simulation model of the electrical and control systems was constructed and tested to verify the performance. Finally, the tank and sea tests were respectively conducted to validate the performance of proposed MCT. The results indicate that the proposed MCT is capable of starting at velocities around 0.2 m/s and achieving a power coefficient (Cp) exceeding 0.4, making it well-suited for providing a continuous and stable power supply in areas with low flow velocities.

Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode

This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing online adaptive FD. The goal of this study is to present an artificial intelligence (AI) technique for detecting seven faults: inverter fault, feedback sensor fault, grid anomaly, nonhomogeneous partial shading, open circuit in PV array, MPPT controller fault, and boost converter controller fault. In this work, it was found that the application of Extreme Learning Machine (ELM) plays an important role in fault detection and localization. Nine (9) statistical features and eight (8) wavelet packet parameters are extracted from the data based on multiple default values. These features were used as an input vector to train and test the ELM and determine whether the system is operating under normal conditions or is faulty. The BDE feature selection algorithm is adopted to optimize the seven-fault classification procedure to reduce the number of features. The results showed that the Extreme Learning Machine (ELM), based on statistical parameters followed by BDE, can detect faults with high accuracy (98.3%) compared to a case without optimization.

PV Based Single Stage Cascaded Seven Level Modular Multilevel Converter with ZSV Control for Grid Interfaced System

Objectives: Climatic perturbation and environmental factors affect solar power generation. When these solar panels are connected to the grid it causes power quality issues such as voltage harmonics, voltage distortion, etc. These problems are addressed in this paper using a distributed MPPT algorithm. Methods: Multilevel inverters have made remarkable contributions to renewable energy, high voltage, and other high-power applications. Hence, a single-stage Cascaded Seven-Level Modular Multilevel Converter (CSLMMC) is used to address the issues. Zero Sequence Voltage (ZSV) control is proposed for producing balanced power during varying irradiance conditions. The proposed method is simulated using MATLAB SIMULINK software. Partial shading is conquered by using distributed MPPT. Findings: The simulation of the proposed system is performed for a three-phase system. The three-phase Cascaded Modular Multilevel Converter is connected to the grid through two loads which are controlled by a Zero Sequence Voltage Controller. Power quality parameters such as current, harmonics, and power are analyzed for the proposed system. Novelty: THD analysis for a five-level cascaded H bridge converter and the proposed systems are compared and presented. Keywords: Photo voltaic, Cascaded seven-level modular multilevel converter, Zero sequence control, Maximum power point tracking, Voltage Source Converter

Implementation of Solar PV Protection System in Indonesia: A Review

Conventional energy sources will run out if used continuously and damage the environment. Therefore, it is necessary to develop other energy sources that are safe, environmentally friendly, and inexhaustible known as renewable energy sources to meet energy needs in Indonesia. This article presents a review that discusses the protection of solar panels and PLTS. Solar panels are vulnerable to lightning strikes and other electrical disturbances, so an effective protection system is needed. This study uses a qualitative method through a literature study, reviewing various protection methods such as conventional and electrostatic lightning rods, good grounding systems, and overcurrent detection devices such as Solar Charge Controller (SCC) and Maximum Power Point Tracking (MPPT). In addition, battery protection with Automatic Transfer Switch (ATS) and Low Voltage Disconnected (LVD) is also discussed. Using Arduino Uno, ESP 32, and PLC, automation approaches offer real-time monitoring and control solutions for solar power plant performance. Innovations in the lightning protection zone concept and minimal quantity measurement schemes enable more cost-effective design of protection systems without reducing the level of protection. The results show that these various protection methods can provide effective and efficient protection for solar power plants, enabling optimization of system function and safety. In conclusion, the protection systems reviewed in this study offer a sustainable solution to Indonesia's energy challenges by utilizing the maximum potential of solar energy. The potential for further research is presented in this article to develop more efficient and effective protection methods.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

Structurally Compact Penta(N,N-diphenylamino)corannulene as Dopant-free Hole Transport Materials for Stable and Efficient Perovskite Solar Cells.

Hole transport materials (HTMs) are essential for improving the stability and efficiency of perovskite solar cells (PSCs). In this study, we have designed and synthesized a novel organic small molecule HTM, cor-(DPA)5, characterized by a bowl-shaped core with symmetric five diphenylamine groups. Compared to already-known HTMs, the bowl-shaped and relatively compact structure of cor-(DPA)5 facilitates intermolecular π-π interactions, promotes film formations, and enhances charge transport. Consequently, the cor-[DPA(2)]5 HTM exhibits high charge mobility, exceptional hydrophobicity, and a significantly elevated glass transition temperature. Superior to previously reported HTMs such as spiro-OMeTAD and cor-OMePTPA, our newly synthesized cor-(DPA)5 HTM is free from any ionic dopants. As a result, the dopant-free cor-[DPA(2)]5-based PSC demonstrates an impressive efficiency of 24.01%, and exhibits outstanding operational stability. It retains 96% after continuous exposure to 1 sun irradiation for 800 hours under MPP (maximum power point) tracking in ambient air. These findings present a structurally compact novel HTM and exemplify a new approach to the molecular design of HTM for the development of stable and effective PSCs.

Structurally Compact Penta(N,N‐diphenylamino)corannulene as Dopant‐free Hole Transport Materials for Stable and Efficient Perovskite Solar Cells

Hole transport materials (HTMs) are essential for improving the stability and efficiency of perovskite solar cells (PSCs). In this study, we have designed and synthesized a novel organic small molecule HTM, cor‐(DPA)5, characterized by a bowl‐shaped core with symmetric five diphenylamine groups. Compared to already‐known HTMs, the bowl‐shaped and relatively compact structure of cor‐(DPA)5 facilitates intermolecular π–π interactions, promotes film formations, and enhances charge transport. Consequently, the cor‐[DPA(2)]5 HTM exhibits high charge mobility, exceptional hydrophobicity, and a significantly elevated glass transition temperature. Superior to previously reported HTMs such as spiro‐OMeTAD and cor‐OMePTPA, our newly synthesized cor‐(DPA)5 HTM is free from any ionic dopants. As a result, the dopant‐free cor‐[DPA(2)]5‐based PSC demonstrates an impressive efficiency of 24.01%, and exhibits outstanding operational stability. It retains 96% after continuous exposure to 1 sun irradiation for 800 hours under MPP (maximum power point) tracking in ambient air. These findings present a structurally compact novel HTM and exemplify a new approach to the molecular design of HTM for the development of stable and effective PSCs.

Improving the efficiency of a non-ideal grid coupled to a photovoltaic system with a shunt active power filter using a self-tuning filter and a predictive current controller

Introduction. Recently, photovoltaic (PV) systems are increasingly favored for converting solar energy into electricity. PV power systems have successfully evolved from small, standalone installations to large-scale, grid-connected systems. When the nonlinear loads are connected to a grid-tied PV system, the power quality can deteriorate due to the active power supplied by the PV array, there’s a noticeable decline in the quality of power delivered to consumers. Its combination with the shunt active power filter (SAPF) enhances system efficiency. Consequently, this integrated system is adept at not only powering local loads but also at compensating for reactive power and filtering out harmonic currents from the main grid. The novelty of the work describes how an operation of a small scale PV system connected to the low voltage distribution system, and nonlinear load can be achieved, the investigation aims to analyze the system’s behavior and elucidate the advantages of employing various control algorithms. These proposed algorithms are designed to ensure a unity power factor for the utility grid while prioritizing high convergence speed and robustness against load power fluctuations across different levels of solar irradiation affecting the PV modules. The purpose of this work is to enhance the dynamic performance of the SAPF by cooperatively using a self-tuning filter (STF) based instantaneous active and reactive power method (PQ) with a novel predictive current control, enhance the system resilience, ensure optimal management of the total active power between the PV system, the electrical network and the non-linear load by integrating the functionalities of the SAPF under different levels of solar irradiation and maintain the DC-link capacitor voltage constant. Methods. A novel predictive current controller is designed to generate the switching signals piloted the three phase source voltage inverter, also a novel algorithm of instantaneous active and reactive power is developed, based on STF, to extract accurately the harmonic reference under non ideal grid voltage, also the perturb and observe algorithm is used to extract, under step change of solar irradiation, the maximum power point tracking of the PV module and the PI controller is used to maintain constant the DC-link capacitor voltage of the SAPF. Results. The efficacy of the proposed system is primarily centered on the grid side, and the performance evaluation of the control system is conducted using the STF based PQ algorithm and predictive current control. In addition, comprehensive testing encompasses all modes of operation, including scenarios involving distorted voltage sources, step changes in solar radiation, and variations in nonlinear loads. Results highlight superior performance in both transient and stable states, affirming the robustness and effectiveness of the proposed controllers. Practical value. The total harmonic distortion value of the grid current for all tests respects the IEEE Standard 519-1992. References 21, tables 7, figures 25.

Cross-Linkable Fullerene Electron Transport Layer with Internal Encapsulation Capability for Efficient and Stable Inverted Perovskite Solar Cells.

A stable and compact fullerene electron transport layer (ETL) is crucial for high-performance inverted perovskite solar cells (PSCs). However, traditional fullerene-based ETLs like C60 and PCBM are prone to aggregate under operational conditions, a challenge recently recognized by academic and industrial researchers. Here, we designed and synthesized a novel cross-linkable fullerene molecule, bis((3-methyloxetan-3-yl)methyl) malonate-C60 monoadduct (BCM), for use as an ETL in PSCs. Upon a low-temperature annealing at 100 °C, BCM undergoes in-situ cross-linking to form a robust cross-linked BCM (CBCM) film, which demonstrates excellent electron mobility and a suitable band structure for efficient PSCs. Our results show that PSCs incorporating CBCM-based ETL achieve an impressive efficiency of 25.89% (certified: 25.36%), significantly surpassing the 23.25% efficiency of PCBM-based devices. The intramolecular covalent interactions within CBCM films effectively prevent aggregation and enhance film compactness, creating an internal encapsulation layer that mitigates the decomposition and ion migration of perovskite components. Consequently, CBCM-based PSCs show exceptional stability, maintaining 97.8% of their initial efficiency after 1000 hours of maximum power point tracking, compared to only 78.6% retention in PCBM-based devices after less than 820 hours.

A robust sliding mode control strategy for DC voltage stabilization in solar powered electric vehicle charging station

ABSTRACT Integrating DC power sources and AC grid in an electric vehicle charging station through converters can introduce oscillations, potentially leading to system instability. This paper explored the utilization of a sliding mode controller in a bidirectional DC-DC buck-boost converter for DC bus voltage control within the charging station. This controller aims to promptly respond to power fluctuations, offering robustness against variations in system parameters, external disturbances, and uncertainties. Control strategies play a critical role in mitigating DC link voltage fluctuations and ensuring power stability. The system entails a photovoltaic array employing maximum power point tracking for optimal energy harnessing. In addition, a bidirectional buck-boost converter is employed to effectively manage battery charging in buck mode and discharging in boost mode. A comparative analysis between Proportional-Integral control and sliding mode control incorporating a washout filter was conducted. This assessment is simulated and validated using MATLAB/Simulink, and the Sim Power System toolbox. The proposed model offers a comprehensive solution for integrating solar power, battery storage, and grid-based charging by enhancing overall system efficiency and reliability.

Improvement of Power Production Efficiency Following the Application of the GD InC Maximum Power Point Tracking Method in Photovoltaic Systems

This paper proposes a new maximum power point tracking (MPPT) method based on machine learning with improved power production efficiency for application to photovoltaic (PV) systems. Power loss occurs in the incremental conductance (InC) method, depending on the size of the voltage step used to track the maximum power point. Additionally, the size of the voltage step must be specified by the initial user; however, an appropriate size cannot be determined in a rapidly changing environment. To solve this problem, this study presents a gradient descent InC (GD InC) method that optimizes the size of the voltage step by applying an optimization method based on machine learning. The effectiveness of the GD InC method was verified and the optimized size of the voltage step was confirmed to produce the largest amount of power. When the size of the voltage step was optimized, a maximum difference of 4.53% was observed compared with the case when the smallest amount of power was produced. The effectiveness of the GD InC method, which improved the efficiency of power production by optimizing the size of the voltage step, was verified. Power can be produced efficiently by applying the GD InC method to PV systems.

Efficient and Stable Inverted Perovskite Solar Cells Enabled by a Fullerene‐Based Hole Transport Molecule

AbstractDesigning an efficient modification molecule to mitigate non‐radiative recombination at the NiOx/perovskite interface and improve perovskite quality represents a challenging yet crucial endeavor for achieving high‐performance inverted perovskite solar cells (PSCs). Herein, we synthesized a novel fullerene‐based hole transport molecule, designated as FHTM, by integrating C60 with 12 carbazole‐based moieties, and applied it as a modification molecule at the NiOx/perovskite interface. The in situ self‐doping effect, triggered by electron transfer between carbazole‐based moiety and C60 within the FHTM molecule, along with the extended π conjugated moiety of carbazole groups, significantly enhances FHTM's hole mobility. Coupled with optimized energy level alignment and enhanced interface interactions, the FHTM significantly enhances hole extraction and transport in corresponding devices. Additionally, the introduced FHTM efficiently promotes homogeneous nucleation of perovskite, resulting in high‐quality perovskite films. These combined improvements led to the FHTM‐based PSCs yielding a champion efficiency of 25.58 % (Certified: 25.04 %), notably surpassing that of the control device (20.91 %). Furthermore, the unencapsulated device maintained 93 % of its initial efficiency after 1000 hours of maximum power point tracking under continuous one‐sun illumination. This study highlights the potential of functionalized fullerenes as hole transport materials, opening up new avenues for their application in the field of PSCs.

Efficient and Stable Perovskite Solar Cells and Modules Enabled by Tailoring Additive Distribution According to the Film Growth Dynamics

HighlightsTwo pre-crystallization processes of gas quenching and vacuum quenching lead to different order of crystallization dynamics within the perovskite thin film, resulting in the differences of additive distribution.A tailor designed 1,3-bis(4-methoxyphenyl)thiourea was utilized to improve the buried interface, leading to a certified efficiency of 23.75% for blade-coated perovskite solar cell.The perovskite solar module (aperture area: 60.84 cm2) demonstrates an efficiency of 20.18% with excellent operational stability (maximum power point tracking of T90 > 1000 h).

Efficient and cost-effective maximum power point tracking technique for solar photovoltaic systems with Li-ion battery charging

This paper presents an effective approach to achieve maximum power point tracking (MPPT) in photovoltaic (PV) systems for battery charging using a single-sensor incremental conductance (InC) method. The objective is to optimize the MPPT process while minimizing the number of sensors required. The suggested technique leverages the relationship between the PV module's output voltage and the duty cycle to automatically adjust and reach the MPP, resulting in optimal power generation. By eliminating the PV current sensor from the control circuit, the developed method reduces both the cost and size of the MPPT circuit. Compared to the conventional InC method, the developed approach demonstrates improved response speed and accuracy in steady-state operation, along with the ability to damp oscillations near the MPP. Extensive simulations using MATLAB/Simulink validate the performance of the developed technique across various environmental conditions. The results highlight the recommended method's realistic and effective MPP tracking capabilities, achieving higher efficiency (99.12 %) compared to the classical method (97.8 %) under high irradiance levels.

Cost-Effective Acetonitrile-Based Precursor Solution Mitigates Heterogeneous Nucleation for Efficient and Stable Perovskite Solar Cells.

Solution-processing is the primary method for fabricating high-efficiency perovskite solar cells (PSCs), where solvent choice critically influences film formation and quality. Although additives can optimize film formation dynamics by balancing nucleation and growth of perovskite, they can also induce heterogeneous nucleation due to competitive coordination and varying crystallization kinetics, leading to compositional heterogeneity and structural disorders. Herein, a perovskite precursor solution is developed using acetonitrile (ACN) as a weak coordination host solvent instead of the traditional N,N-dimethylformamide (DMF). The ACN-based perovskite precursor solution reduces heterogeneous nucleation typically caused by the competitive coordination effect of DMF, and cuts costs by half compared to DMF-based precursor solutions. This approach promotes a single crystallization pathway via a dimethyl sulfoxide-solvated intermediate phase to α-FAPbI3, which extends the anti-solvent operation window, and enhances the crystallinity of perovskite films, and reduces defect states. The power conversion efficiencies (PCE) of 23.62% and 20.13% is achieved for the PSC and minimodule, respectively. The PSC retains over 97% of its initial efficiency after 800h of continuous illumination under maximum power point tracking (MPPT). These findings provide valuable insights into solvent interactions in perovskite film formation and offer a cost-effective strategy for improving the device's performance and stability.

Dynamic Wireless Charging of Electric Vehicles Using PV Units in Highways

Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This paper presents a comprehensive approach to address the challenges of wireless power transfer (WPT) for EVs by optimizing coupling frequency and coil design to enhance efficiency while minimizing electromagnetic interference (EMI) and heat generation. A novel coil design and adaptive hardware are proposed to improve power transfer efficiency (PTE) by defining the optimal magnetic resonant coupling WPT and mitigating coil misalignment, which is considered a significant barrier to the widespread adoption of WPT for EVs. A new methodology for designing and arranging roadside lanes and facilities for dynamic wireless charging (DWC) of EVs is introduced. This includes the optimization of transmitter coils (TCs), receiving coils (RCs), compensation circuits, and high-frequency inverters/converters using the partial differential equation toolbox (pdetool). The integration of wireless charging systems with smart grid technology is explored to enhance energy distribution and reduce peak load issues. The paper proposes a DWC system with multiple segmented transmitters integrated with adaptive renewable photovoltaic (PV) units and a battery system using the utility main grid as a backup. The design process includes the determination of the required PV array capacity, station battery sizing, and inverters/converters to ensure maximum power point tracking (MPPT). To validate the proposed system, it was tested in two scenarios: charging a single EV at different speeds and simultaneously charging two EVs over a 1 km stretch with a 50 kW system, achieving a total range of 500 km. Experimental validation was performed through real-time simulation and hardware tests using an OPAL-RT platform, demonstrating a power transfer efficiency of 90.7%, thus confirming the scalability and feasibility of the system for future EV infrastructure.

A modified perturb and observe MPPT algorithm with zero steady state oscillation for single-phase grid-connected transformerless solar inverter

ABSTRACT This paper proposes a modified perturb and observe (P&O) algorithm to improve the performance of maximum power point tracking in a grid-connected solar inverter. The proposed method eliminates the steady state oscillations in photovoltaic panel voltage under stable irradiance conditions there by enhancing its performance. It also retains the simplicity of conventional P&O algorithm and introduces only one additional step of checking the quantum of power change to bypass the perturbations. A cascaded multi-loop control structure is adopted to achieve the objectives and retain the ability to respond to changing environmental conditions. The proposed and conventional P&O algorithms along with a designed 1 kW single-phase grid-connected transformerless solar inverter are simulated in PSIM to perform a comparative study. Furthermore, the proposed algorithm is validated experimentally on the developed solar inverter prototype.

Research on Decoupling Duty Cycle Optimization Control Method of a Multiport Converter for Dual-Port Direct Drive Wave Power Generation System

Dual-port direct drive wave energy power generation systems (DP-DDWEPGS) have received widespread attention due to their smooth and zero-free output power, compared to single-port direct drive wave energy power generation systems (SP-DDWEPGS) which have the disadvantage of large out-put power fluctuations. To further enhance the performance of the DP-DDWEPGS, optimal power capture control is proposed to achieve maximum power point tracking. Meanwhile, a multiport converter is applied to the DP-DDWEPGS to solve the problem caused by an excessive number of switching devices in the overall system converter. The multiport converter fulfills all the functional requirements of the DP-DDWEPGS while reducing the number of switching devices. However, switch multiplexing of the multiport converter also introduces coupling relationships between each port and the wave force exhibits time-varying characteristics, necessitating advanced control methods with superior fast-tracking capability. Therefore, in this paper, a decoupling duty cycle optimization model predictive control for DP-DDWEPGS is proposed. Based on the characteristics of switching multiplexing, NSC finite control set model predictive control (FCS-MPC) decouples the current prediction and the cost function, reduces the number of candidate voltage vectors in each operation, and shortens the operation time by 70%. To address the issues of high ripple value and increased error due to decoupling in FCS-MPC, duty cycle optimization control is added, greatly reducing the fluctuations in electromagnetic force and power of the permanent magnet linear generator (PMLG). Based on the established simulation model, the feasibility and superiority of the multiport converter and decoupling duty cycle optimization model predictive current control method are verified.