Photovoltaic Inverter Research Articles

Junction Temperature Prediction and Lifetime Assessment in a PV Inverter Using a 10-year Mission profile

The expansion of great-scale photovoltaic (PV) power plants indicates the need for an accurate lifetime assessment of inverters to maintain energy supply availability. In this context, the study contributes in two ways. First, we use machine-learning (ML) models for junction temperature prediction. Second, we perform reliability assessments using a 10-year mission profile in three Brazilian cities. The thermal loadings are obtained through a look-up table approach. Although the ML models exhibit different performances in regression, other factors must be considered, such as easy-to-apply, interpretability, and generalization capability. The reliability assessment is typically based on an annual mission profile, assuming damage repeats until failure. However, only the historical series can confirm whether this choice was acceptable, pessimistic, or optimistic. For instance, in Campos do Jordão-SP, if the chosen mission profile is 2014, the expected failure of 10\% of inverter samples occurs three years earlier than suggested by the historical series. Regardless of the methodology used to estimate thermal loading or accumulated damage, the mission profile significantly influences photovoltaic inverter reliability, indicating that if more data is available, the chosen mission profile should align with the historical series.

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.

Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study.

Simplified Carrier-based PWM for Three-Level Transformer-Less Grid-connected Photovoltaic Inverters

Simplified Carrier-based PWM for Three-Level Transformer-Less Grid-connected Photovoltaic Inverters

Solar Power Plant Optimization using Automatic Transfer Switch (ATS) and Low Voltage Disconnect (LVD)

The automatic system model uses a Solar Charge Controller (SCC) to turn off the solar panel system at the minimum battery point so that the battery is safe and durable and an Automatic Transfer Switch (ATS) to automatically transfer the electricity network from State Electricity Company or Grid or solar panels. In this study, Solar Panel Priority Grid electricity is used if the solar panel is insufficient and hybrid system with solar panel priority with 2 cut out battery usage. The results of this study are 450WP Solar Panel, 100A SCC MPPT, 12V/100Ah Battery, and 3000 Watt Modified Sine Wave Inverter, Miniature Circuit Breaker, Contactor, Relay Switch, Time Delay Relay and Indicators. The results of the switching process test between the Solar Power Plant source and grid with ATS control can run automatically in Grid Priority Mode, meaning Solar Power Plant as a backup, or Solar Power Plant Priority Mode where the grid source is used as a power backup system. In the Battery Capacity Optimization System, Low Voltage Disconnect (LVD) Protection can work well, namely it can cut off the voltage from the inverter if the battery is in a low voltage state at a rating below 10.8 Volts, Auto Cut Charging Protection testing can charge the battery up to 13.8 Volts and Cycle Use, namely the process of this system can work to store energy while releasing energy to run the load. The results of this study can be used as a reference for the best choice of the most efficient Solar Power Plant system.

Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants.

Optimization of a Smart Greenhouse with a Solar Energy System for Floating Raft Hydroponic Cultivation: Implementation and Performance Evaluation

Abstract The rapid growth of the global population has increased food demand, posing challenges for traditional agriculture due to limited natural resources. Hydroponic systems offer a sustainable solution by optimizing the growing environment. This study evaluates the performance of a floating-raft hydroponic smart greenhouse powered by solar energy. The system integrates advanced technologies and renewable energy sources, including three 120 Wp and 130 Wp solar panels arranged in a series-parallel configuration, a solar charge controller, two parallel 12V 65Ah and 12V 200Ah batteries, a microcontroller, DHT22 sensors, and actuators such as LED lights and a mist-type DC pump sprayer. These components effectively regulate humidity, temperature, lighting, and spraying, with data transmission to the Antares platform via a monitoring system utilizing NodeMCU ESP8266. The greenhouse maintained optimal environmental conditions, reducing the temperature from 38°C to 32°C within 30 minutes. The system’s daily electrical load was 1006.36 Wh, leaving an energy reserve of 68.35%. The plants cultivated in the greenhouse responded favorably to the system. This study highlights the potential of combining renewable energy with smart agricultural technologies to address the limitations of traditional farming practices and enhance sustainability.

Analysing the Performance of Large-Scale Rooftop Solar PV System Using Helioscope, PVsyst and PV*SOL Photovoltaic Simulation Software: As a Renewable Energy Strategy

In Malaysia, government initiatives and policymakers play significant role in creating effective strategies aiming at enhancing the usage of residential, industrial and commercial solar PV systems. However, the most significant barrier to the widespread adoption of photovoltaic (PV) systems is their high installation costs; as a result, numerous studies on PV system modification are now being conducted. Several simulation tools are available for the efficient and extensive integration of solar energy. To predict energy output using climate data and assess performance ration in accordance, such software can be optimized in terms of parameters like PV module number, inverter, tilt angle, and module design structure. The accuracy of these figures fluctuates, though, because climate factors affect how productive photovoltaic systems are. In this current paper, the simulation and design of a 6.9 MW solar power plant in University Tun Hussein Onn Malaysia, have been investigated using three popular software PVsyst v5.06, Helioscope and PV*SOL. This study's main objective is to assess the potential of solar renewable energy sources to produce electrical energy under the Supply Agreement of Renewable Energy (SARE) program using flexible solar system design modelling tools, PVsyst, Helioscope, and PV *SOL. The comparison results showed that Helioscope and PVsyst are considered to be the most dependable software. PVsyst appraised the definite energy production cost with lowermost relative error of 0.12 % and Helioscope estimated performance ratio with lowest relative error of 4.74%. In terms of energy losses, environmental element contributing to the most energy losses where PVsyst recorded 11.1% photovoltaic (PV) loss caused by irradiation and temperature changes. Whereby Helioscope clearly shows that environmental power loss of 8.4% represents the largest portion caused by irradiation and temperature changes. PV*SOL recorded an energy loss of 7.3% due to shading, temperature and reflection on module interface.

A Noninvasive Sliding Mode Observer Based Approach for Detecting Open-Circuit Faults in HERIC Inverters

This article presents a new technique for detecting and localizing the open-circuit (OC) faults in the semiconductor switches of the Highly Efficient and Reliable Inverter Concept (HERIC) converter. It is a transformerless Photovoltaic (PV) inverter based on the full-bridge topology, with two extra switches on the AC side called ac bypass switches. The proposed method uses a sliding mode observer (SMO) to detect faults in the AC bypass switch and main switches of the full bridge inverter. To diagnose OC faults in the AC bypass leg, two parallel SMOs are created based on the converter’s state space model. Similarly, two SMOs are designed for the main switches. A residual is generated by combining the observed and measured grid currents to accurately locate the OC fault within the inverter system. To localize the faulty main switch, once the fault is detected the inverter is reconfigured and based on the grid current the faulty switch is identified. A major benefit of this method is that it does not rely on additional sensors for fault diagnosis, making the system more robust. The effectiveness and reliability of this approach are demonstrated through simulation studies. In addition, a laboratory prototype is developed to validate the practical applicability of the method.

Common Mode and Terminal Voltages With Effect of Device Junction Capacitance in Five-Level Transformerless PV Inverter

Common Mode and Terminal Voltages With Effect of Device Junction Capacitance in Five-Level Transformerless PV Inverter

Analysis and Decoupling of Multisource EMI in High-Power PV Inverter

Analysis and Decoupling of Multisource EMI in High-Power PV Inverter

계통연계형 PV(Photovoltaic) 설계 및 과도상태 특성 연구

This study investigated the design of a grid-connected photovoltaic (PV) system based on solar power generation and its characteristics during ground-fault incidents. Various approaches to enhance the stability and efficiency of the PV system were explored. Experimental results confirmed that the integration of solar arrays, DC/DC converters, and inverters optimized the output characteristics of the system. In particular, the analysis focused on current changes and control mechanisms during ground fault incidents and compared the effectiveness of DC circuit breakers and superconducting fault current limiters. Simulations were performed to propose solutions to address the problem of sudden increases in fault current. The results demonstrated the effectiveness of DC circuit breakers in limiting fault currents. Additionally, the application of fault current limiters improved both fault current limitation and response times for disconnection. The results of this study enhance the safety and economic operation of solar power systems and facilitate the adoption of eco-friendly energy. The results can be used as a reference for the design and operation of solar power systems in the future and can support the implementation of renewable energy policies.

Real-Time Power Regulation of Flexible User-Side Resources in Distribution Networks via Dual Ascent Method

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method.

Anti-islanding image detection and optimization of distributed power supply using neural network architecture search

Anti-islanding detection (AILD) for distributed power sources plays an important role on the stable operation of power grid systems and the safety of electrical systems. In order to improve the detection accuracy, we propose neural network architecture search (NAS) based approach for anti-islanding image detection and optimization of distributed power sources. It combines the fast region-based convolutional neural network (Faster-R-CNN) with the differentiable architecture search (Darts), utilizing electrical signal image data acquired from cameras or photovoltaic (PV) inverters, thereby enhancing the accuracy and efficiency of detection. We conduct a comparative analysis of two methods for obtaining distributed power supply (DPS) anti-islanding image data: camera-based acquisition and PV inverter-based acquisition. Our observation reveals that the image data acquired through cameras is more conducive for the learning process of DCNN. The proposed algorithm was compared with other convolutional neural network (CNN) models, validating its performance superiority. This provides a novel perspective and methodology for the advancement of AILD for distributed power sources, and enhances the security and stability of DPS.

Design Of PV Plant to Improve a Voltage Profile Of 11 KV Grid in The South West of Libya

The use of photovoltaic (PV) system for electricity generation is growing up in the last decades. Thesouthern part of Libya suffers from voltage fluctuation and intermittence of electricity during thesummer time. The aim of this paper is firstly to study and analysis a distribution grid of small villagelocated in the southern west region of Libya. We analyzed the existed 11KV distribution overheadline supplied electricity of the village called Takarkibah using Power-World Simulator in order tointegrate it with PV plant. The results obtained show that injection of PV power about 70 % of thegrid power capacity gives good grid performance. After that, we use SAM simulation program todesign PV plant to improve the voltage profile in the distribution grid. Six designs (scenarios) aresimulated using different solar modules and inverters. The simulation results obtained from SAMillustrated the power capacity, energy yield, and losses of the designed PV plant.

A Novel Overall Efficiency Index for a Single Phase Standalone Solar PV Hybrid Inverters

A Novel Overall Efficiency Index for a Single Phase Standalone Solar PV Hybrid Inverters

Supporting strategy for investment evaluation of photovoltaic power generation engineering projects using multi-criteria decision analysis methods

This scientific study examines the evaluation of photovoltaic power generation projects through the application of multi-criteria decision analysis methods. Two groups of large-scale grid-connected PV power generation system projects with a nominal power of 50 MW and 500 MW respectively were analyzed and evaluated. These systems were designed to be installed in the same wider geographical area in northern Greece, but they were differentiated in the part of the PV circuit. Twelve systems were analyzed in which either monocrystalline silicon panels or poly-crystalline silicon panels or bifacial photovoltaic panels were to be installed. In these systems either central photovoltaic inverters or photovoltaic string inverters were considered for installation. The following criteria were used to evaluate the investment in these projects. These criteria were related to the profitability, the financial cost, the technical level, and the electrical energy production of the systems and these were the initial investment cost, the operation and maintenance cost, the levelized cost of electricity, the net present value, the internal rate of return, the capital recovery or payback period, the technical level of the photovoltaic circuit, the technical maturity of the photovoltaic circuit, and the annual electricity production. The evaluation of these criteria was initially conducted with fixed weighting coefficients followed by a sensitivity analysis of these weighting coefficients. The results of the evaluation using the PROMETHEE, AHP and TOPSIS multi-criteria decision analysis methods showed that the PV power generation systems which should be preferred are those with increased nominal power, where monocrystalline silicon technology panels are employed following the central inverter topology.

Revolutionizing PV grid integration: Metaheuristic optimization of fractional PI controllers in T-type neutral point piloted inverters for enhanced performance

This study investigates the efficacy of FOPI regulators as a substitute for conventional proportional integral (PI) controllers in grid-connected PV inverters. The research's objective is to improve the dynamic efficiency of these systems by integrating intelligent optimization techniques that utilize both PI and FOPI controllers and employing different metaheuristic optimization procedures. The study introduces four new optimization algorithms: the Tyrannosaurus Optimization Algorithm (TROA), the Nutcracker Optimization Algorithm (NOA), the Golden Eagle Optimizer (GEO), and the Jellyfish Search Optimizer (JSO). These are made to meet the needs of multi-objective optimization. The proposal suggests using two PI/FOPI regulators to regulate both voltage and amperage provided by the multilayer inverter. The proposal employs a T-type three-level inverter, known for its superior conversion efficiency over conventional inverters. Matlab-Simulink simulations demonstrate that the Nutcracker optimization algorithm (NOA) outperforms other metaheuristic techniques for optimizing dynamic behavior, such as overshoot, settling time, execution, and rising time. Comparisons with existing methods, such as the Manta Rays Foraging Optimization (MRFO) and Grey Wolf Optimizer (GWO), show that all four new algorithms consistently outperform these existing algorithms. The data suggest that using NOA improves stability, efficiency, and power factor while reducing the inverter's THD.

A solar-powered multi-functional portable charging device (SPMFPCD) with internet-of-things (IoT)-based real-time monitoring—An innovative scheme towards energy access and management

In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet- of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution.

Real-time solar PV generation in a building using LSTM-based time series forecasting

The increasing demand for renewable energy has forced the researchers to lay importance on studying the behavior of these sources by monitoring the operational data and performing data analysis. The application of data analytics to renewable energy is one of the most powerful methods to maintain the system's reliability and stability. As the power system becomes more complex the necessity to perform data analytics also increases. When data analytic techniques are applied to solar energy generations through Photovoltaic (PV) dataset, the possible behavior of PV generation performance which is affected by changes in environmental conditions can be predicted and further analytical approaches allow us to detect possible PV panel and inverter failures. This paper is an attempt towards applying the intelligent data analytics approaches to solar PV generation of a real-time photovoltaic plant. The main purpose of the data analytics platform is to analyze the energy yield, assess the PV system performance, and forecast solar PV generation. In this article, Long Short-Term Memory (LSTM) machine learning model is developed to assess and interpret the available information from the gathered data of the PV plant. The proposed model is a sequence-to-sequence regression-based forecasting model which performs time series forecasting in a real-time 100 kW PV plant in the University building of SRM Institute of Science and Technology, Kattankulathur, Tamilnadu.