Photovoltaic Inverter Research Articles

Analisis dan Desain Floating PV System di Beberapa Waduk Batam untuk Optimalisasi Bauran Energi Terbarukan

The climate crisis caused by the use of fossil fuels is prompting countries around the world to shift to renewable energy. Indonesia has substantial solar energy potential, but this must be balanced with land conservation efforts. PT PLN Batam, a subsidiary of PT PLN (Persero), shares the responsibility of meeting the national renewable energy mix target of 23% by 2025. Adding new power plants requires a large area, which is a significant challenge in the development of both fossil and renewable energy plants in Batam. The floating PV system is an alternative solution to this problem. This study aims to design a floating solar power generation system at Tembesi and Mukakuning Reservoirs, focusing on solar energy potential, energy production, and system design. The results indicate that solar irradiation at both locations reaches 1,650 kWh/m² with an average horizontal PSH of 4.52 hours/day and net PSH Modules of 4.32 hours/day. Energy production in Year-1 is 55.83 GWh with a performance ratio of 81.65%, resulting in annual energy production of 1,345 kWh/kWp. The floating PV system design at Tembesi and Mukakuning Reservoirs consists of 10 islands, each with 19 array boxes and 20 strings per array box, comprising 28 PV modules per string. A total of 106,400 PV modules with a capacity of 390 Wp each is utilized. The system employs 3,125 kVA PV inverters, 7,500 kVA MV transformers, and 35 MVA HV transformers to ensure high efficiency and reliability. The system design also considers environmental durability, including resistance to corrosion and water level fluctuations. Recommendations include optimizing the design based on seasonal variations and implementing effective monitoring and maintenance systems.

Power Factor Analysis of Grid-Connected Solar Inverter under Different Irradiance Levels throughout the Day

The power factor (PF) plays a crucial role in determining the quality of energy produced by grid-connected photovoltaic (PV) systems. When irradiation levels are high, typically during peak sunlight hours, the PV panels generate more electricity. In this scenario, the PF tends to be higher because the real power output closely matches the apparent power drawn from the grid. Whereas, when irradiation levels are low, such as during cloudy weather or nighttime, the PV panels produce less electricity. In these conditions, the power factor may decrease because the real power output diminishes compared to the apparent power drawn from the grid. This could be due to reduced efficiency or increased reactive power flow. PF decreases linearly at solar irradiance values lower than 220 (W/m2). At the same time, it approaches unity at higher solar irradiance values than 220 (W/m2). In this study, the variation of the power coefficient of the grid-connected PV solar system depending on solar irradiation was modeled and analyzed using MATLAB/Simulink 41016490. The analytical expression of the power factor depending on the change in solar irradiation was found using the curve fitting method.

Leakage current suppression methods for single-phase photovoltaic inverters

Given the swift expansion of the worlds population and economy, the decline in environmental quality caused by global warming, frequent climate disasters, ecosystem damage, sea level rise and other problems makes the world energy structure is changing, which means many countries start to develop their new energy industry and technology in order to meet their growing demand for energy. Therefore, promoting the energy transformation and innovation has become a new focus, and photovoltaic (PV) power generation as a green energy source is the focus of attention. PV inverters are essential components of photovoltaic array systems since they are the principal equipment capable of converting the fluctuating DC voltage produced by solar panels into mains frequency alternating current. However, the leakage current problem appeared during its operation has become one of the most important focuses of electrical engineers in recent years. This paper takes three aspects which is topology, filter and modulation mode to discuss how to suppress common mode leakage current in inverters. This paper reviews the existing research results and looks forward to their future development trends, which provides a reference for the following research.

Novel five-level inverter based on DC power-capacitor series charge-discharge switching strategy

Photovoltaic power generation systems generally include four modules: solar cells, batteries, inverters and controllers. Among them, the inverter converts the direct current generated by the photovoltaic array into a power conversion device that meets the grid-connected requirements for industrial frequency alternating current, which is the key equipment for grid-connected photovoltaic power generation system. Most multilevel inverters in the market today are based on the Neutral-Point-Clamped (NPC) structure to generate voltages at different levels, and then control the magnitude and direction of the voltages through H-bridge circuits. This topology has been maturely used in three-level inverters, but if we want to build five-level, seven-level and more level numbers of inverters based on NPC, the number of capacitors used is increasing, and the circuit becomes more and more complex. Therefore, this paper proposes a five-level inverter topology based on single power supply-single capacitor series charging and discharging. (Power-capacitor series charge-discharge switching, PCSCDS). This inverter has a simple structure, comprehensible control strategy and requires only one DC power supply and one large capacitor to generate a five-level voltage. The topology is subsequently simulated and analyzed by MATLAB/Simulink simulation, which proves the effectiveness of the novel inverter.

Machine-learning based control of bi-modular multilevel PWM inverter for high power applications.

This paper presents the topology and machine learning-based intelligent control of high-power PV inverter for maximum power extraction and optimal energy utilization. Modular converters with reduced components economic and reliable for high power applications. The proposed integrated intelligent machine learning based control delivers power conversion control with maximum power extraction and supervisory control for optimal load demand control. The topology of the inverter, operating modes, power control and supervisory control aspects are presented. Simulation is carried out in MATLAB/SIMULINK to verify the feasibility of the proposed inverter and control algorithm. The experimental study is presented to validate the simulation results. The operational performance of the proposed topology is evaluated in terms of operational parameters such as regulation of output power, and load relay control and is compared to existing topologies. The economic performance is also evaluated in terms of power switch sizing and reliability in power delivery concerning switch or power sources failure.

Assessing power profile characteristics in solar PV-storage integrated electricity markets: A quantitative study

Integrating solar PV inverters and storage devices into the modern power grid generates multiple power profiles with varying magnitudes. The intermittent nature of PV necessitates installing storage devices to reduce unit commitment challenges and accommodate reserve power. This paper proposes various operational factors to determine the boundaries for individual and aggregated power profiles within a cluster of PV systems and storage units. The study utilizes dispatchability and operating reserve factors (primary and secondary) during storage discharge mode to extract dispatchable power and primary and secondary reserve power. Similarly, storage arbitrage and non-dispatchability factors extract arbitrage, non-dispatchable, and a portion of supplemental or tertiary reserve power during storage charging mode. An optimization model leveraging mixed-integer linear programming is formulated to minimize operational expenditures considering these hybrid PV-storage power profiles. Extrapolated electricity market rates for hybrid power profiles are projected using the prevailing prices of photovoltaic (PV) systems and storage technologies. This paper utilizes the proposed aggregated operational factors as constraints while extracting these power profiles for a cluster of PV-storages. The efficacy of the proposed optimization framework is tested on a modified IEEE 34-bus system by adding 10 PV-storage devices on 10 buses. The final quantitative values correspond to the percent penetration and arbitrage of these power profiles.

A Novel Chaos Control Strategy for a Single-Phase Photovoltaic Energy Storage Inverter

The single-phase photovoltaic energy storage inverter represents a pivotal component within photovoltaic energy storage systems. Its operational dynamics are often intricate due to its inherent characteristics and the prevalent usage of nonlinear switching elements, leading to nonlinear characteristic bifurcation such as bifurcation and chaos. In this paper, a deep investigation of a single-phase H-bridge photovoltaic energy storage inverter under proportional–integral (PI) control is made, and a sinusoidal delayed feedback control (SDFC) strategy to mitigate the nonlinear characteristics is proposed. A frequency domain mapping model of the system is established, then, by analyzing the Jacobian matrix and equilibrium points, the bifurcation diagram is formed, and finally, the stable operational domains are determined under double and triple bifurcation parameters. Through simulation experiments, the efficacy of this strategy is validated. The findings show that through the control strategy, the stable operational envelope of the inverter can be greatly expanded and the nonlinear dynamic phenomena can be notably suppressed.

Photovoltaic systems, costs, and electrical and electronic waste in the Legal Amazon: An evaluation of the Luz para Todos Program

Despite its significance in regulating the global climate and maintaining biodiversity, the vastness of the Amazon geography imposes challenges for its residents, including limited access to electricity. The Luz para Todos Program proposes to universalize access to the public electricity service in remote regions of the Legal Amazon with renewable energy sources. From this perspective, the work assessed the ability of sector agents to meet the demand for universalization of the Luz para Todos Program, the economic aspects involved in the universalization process, and the waste management and reverse logistics of these systems. The findings indicate a demand for up to 15 million photovoltaic modules, batteries, and inverters with a maximum cost exceeding BRL 38 billion (USD 7.4 billion). Over 33 years, between 58 and 234 thousand tons of electronic waste would be generated. The program should change the panorama of installed photovoltaic capacity in the northern region of the country, thus placing considerable demand upon the solar industry to supply equipment for the installation of thousands of standalone systems across a vast territory, generating waste in places with inadequate infrastructure to manage them in an environmentally sensitive manner. The diversification of renewable energy sources could alleviate the burden on the service chain, curtail the waste generated by photovoltaic equipment, and stimulate the development of a service chain in regions gaining access to electricity services.

A CC/VC‐based power tracking method for photovoltaic inverter operated in voltage control mode

A CC/VC‐based power tracking method for photovoltaic inverter operated in voltage control mode

Optimal allocation of solar photovoltaic distributed generation for performance enhancement of electrical distribution networks considering optimal volt‐var regulation under uncertainty and high load variation

AbstractThis article proposes an optimal placement and sizing of photovoltaic (PV) power systems based distributed generation (DG) in radial electrical distribution networks considering the capability of PV inverters to regulate the voltage by optimal injecting and absorbing reactive power at the point of common coupling (PCC) using honey badger algorithm (HBA), as a recent and efficient optimization algorithm to solve the complicated optimal allocation. Several objective functions are achieved for distribution system performance enhancement: minimizing the power loss and the voltage deviation index (VDI) and maximizing the voltage stability index (VSI). Based on historical data and probabilistic models, seasonal hourly solar irradiance, ambient temperature, and load variation curves have been modeled, which simultaneously consider the light, normal, and heavy load demand. The essential components of distribution power systems have been characterized. To investigate the validity of the proposed approach, IEEE 33 and IEEE 69 BUS radial distribution test systems have been considered for power flow (PF) analyses, where Newton's Raphson method has been applied to solve the PF issue. The simulation results of different numerical scenarios have shown the effectiveness and validity of the newly proposed method to solve the optimal allocation problem considering optimal volt‐var regulation control of PV inverters compared to several valid and robust optimization algorithms.

Fault Condition Testing of Photovoltaic Inverter Controller Based on Active Support Technology using Hardware-In-the-Loop in RTDS

Abstract Photovoltaic power generation is an important component of sustainable power generation and has been widely utilized. Grid connected inverters are key equipment in photovoltaic integrated systems, which directly affect the stability of the system. In this paper, the transient model of photovoltaic inverter controller based on active support technology is derived. The transient mathematical model and corresponding self-closing loop control strategy are analyzed. Based on the RTDS, a hardware controller semi-physical simulation platform is established to simulate and test photovoltaic inverters under different fault and control methods. Finally, the simulation data obtained from the self-closing loop control method and the hardware control method are compared to verify the stability and reliability of the photovoltaic inverter hardware controller.

Three-Phase Transformerless PV Inverter With Reconfigurable LCL Filter and Reactive Power Capability

Three-Phase Transformerless PV Inverter With Reconfigurable <i>LCL</i> Filter and Reactive Power Capability

Innovative approaches for minimizing leakage current in three-phase transformerless PV inverters

Innovative approaches for minimizing leakage current in three-phase transformerless PV inverters

Optimal sizing of a fixed-tilt ground-mounted grid-connected photovoltaic system with bifacial modules using Harris Hawks Optimization

This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm’s stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system’s Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.

The Performance Comparison of Battery Charging Using MPPT and PWM Controllers on Amorphous Solar Panel-Based E-Scooters

Solar Panel-Based Electric Scooter (E-Scooter) is an electric vehicle in the form of a scooter that uses solar panels as a source of electrical energy for charging batteries and a BLDC motor as a wheel drive. With the development of the E-Scooter, it is necessary to know how to charge the battery. The purpose of this research is to determine the type of Solar Charge Controller (SCC) that can maximize battery charging on the E-Scooter. The research method is to compare SCC MPPT and SCC PWM. The research was conducted by monitoring the output current and voltage of the solar charger controller generated by the solar panels every 10 minutes. After that the recorded data will be stored in the monitoring device's storage memory for further processing. The results of the data that have been obtained on the e-scooter using the SCC MPPT type are capable of producing a power of 9.05 W with a current value of 0.33 A and a voltage of 27.47 V. Meanwhile, the PWM type SCC is capable of producing 8.22 W of power with a current value of 0.30 A and a voltage of 27.42 V. So that the MPPT type SCC is more economical than PWM for charging when the e-scooter is not moving. For the running conditions of the MPPT type SCC e-scooter is capable of producing 7.36 W of power with a current value of 0.27 A and a voltage of 27.26 V. Meanwhile, the PWM type SCC is capable of producing a current of 6.81 W with a value of 0.25 A and a voltage 27.24 V. So that the MPPT type SCC is more efficient than PWM for charging when the e-scooter is running.

Enhancing the AC network stability with a grid-forming control for single-stage PV inverter

This article proposes a PLL-less grid-forming (GFor) control for a PV central power plant topology designed to maintain the DC voltage and improve the AC voltage stability. The PV single-stage or central topology stands as the most widely adopted power plant structure. However, no GFor control capable of regulating the DC bus has yet been proposed for this layout. This work presents a PLL-less GFor control that can effectively control the DC voltage for a single-stage PV inverter, whether it operates at the Maximum Power Point (MPP) or with reserve. Moreover, this control can incorporate Maximum Power Point Tracking (MPPT) controls typically employed in grid-following structures. The primary advantage of this GFor control is the voltage support and stability it provides and the capability of working with a low Short Circuit Ratio (SCR). The performance and viability of the proposed control was analysed with several simulations of various faults (symmetrical and asymmetrical), frequency (large phase jump and frequency excursion with high ROCOF) and resource events (irradiation changes and DC voltage reference tracking analysis). The results demonstrate that the proposed GFor control presented not only meets the grid-code requirements but also exhibits the behaviour expected during the different analysed events.

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in photovoltaic (PV) solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. In addition, an error compensation mechanism that uses an error node to capture the temporal fluctuation caused by system degradation or failures is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The results show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92 %–95 % from 1 h to one week ahead forecasting.

Voltage control strategy of a high-permeability photovoltaic distribution network based on cluster division

The use of distributed photovoltaics (PVs) on a large scale often causes voltage over-limit problems in distribution networks. This paper proposes a distributed photovoltaic cluster collaborative optimization voltage control strategy based on an improved community algorithm to address the issue of centralized control being unable to respond quickly to the randomness of distributed photovoltaics and the difficulty of achieving overall coordination with local control. First, by improving the community algorithm, the division of reactive and active clusters, considering the power balance and node coupling degree, is realized. Then, the cluster-coordinated voltage control strategy is proposed by making full use of the power control ability of a photovoltaic inverter. Finally, a voltage regulation ability evaluation index is proposed to assess the node regulation ability within the cluster and select key nodes. This effectively reduces the number of control nodes. The simulation analysis of the improved IEEE 69 distribution network shows that the proposed voltage control strategy can mitigate the issue of voltage over-limit in high-permeability distributed photovoltaic access distribution and enhance the photovoltaic consumption capacity.

Multi-Criteria Decision Support System for Automatically Selecting Photovoltaic Sets to Maximise Micro Solar Generation

Technological advancements have improved solar energy generation and reduced the cost of installing photovoltaic (PV) systems. However, challenges such as low energy-conversion efficiency and the unpredictability of electricity generation due to shading or climate conditions persist. Despite decreasing costs, access to solar energy generation technologies remains limited. This paper proposes a multi-criteria decision support system (MCDSS) for selecting the most suitable PV set (comprising PV modules, inverters, and batteries) for microgrid installations. The MCDSS employs two multi-criteria decision-making methods (MCDM) for analysis and decision-making: AHP and TOPSIS. The system was tested in two case studies: Barreiras, with a global efficiency of 14.4% and an internal rate of return (IRR) of 56.0%, and Curitiba, with a worldwide efficiency of 14.8% and an IRR of 52.0%. The research provided a framework for assessing and selecting PV sets based on efficiency, cost, and return on investment. Methodologically, it integrates multiple MCDM techniques, demonstrating their applicability in renewable energy. Managerially, it offers a practical tool for decision-makers in the energy sector to enhance the feasibility and attractiveness of microgeneration projects. This research highlights the potential of MCDSS to improve the efficiency and accessibility of solar energy generation.

Impact of temporal resolution on the design and reliability of residential energy systems

Future energy systems incorporating high shares of intermittent renewable energy sources are often designed using optimization-based, bottom-up energy system models. However, such models are generally limited to single years and hourly resolutions. This study quantifies the precision loss between hourly and sub-hourly-resolved data for the design and operation of a self-sufficient residential multi-energy system with respect to total costs, system design, and reliability using both averaging and sampling data methods. In this case study, the total annual cost is underestimated by 1.7% with the average hourly data relative to the fully-resolved minute resolution data, mainly due to the sizing of the photovoltaic inverter and battery. This is a result of the sub-hourly peaks in the supply and demand data that are evened out, significantly impacting the sub-electrical system. The results show up to 89 kWh of the annual lost load of the total electrical and thermal load, and a penalty cost of up to €894 (+24%) based on the value of the lost load. Another method, which employs regular sampling of the original time series, shows unpredictable behavior with respect to the tendency of either over- or underestimating system costs and components’ capacities depending on the selected samples. Both the sampling and averaging methods highlight that while hourly resolution may suffice for total system cost approximations, it falls short of sizing dynamically-operated components and meeting stringent reliability requirements. Future research may aim to enhance the temporal resolution of global intermittent renewable energy sources and reduce the computational expenses associated with minute-level resolutions.