A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems.

Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller's performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm's performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis.

Accurate photovoltaic (PV) diagnosis is of paramount importance for reducing investment risk and increasing the bankability of the PV technology. The application of fault diagnostic solutions and troubleshooting on operating PV power plants is vital for ensuring optimal energy harvesting, increased power generation production and optimised field operation and maintenance (O&M) activities. This study aims to give an overview of the existing approaches for PV plant diagnosis, focusing on unmanned aerial vehicle (UAV)-based approaches, that can support PV plant diagnostics using imaging techniques and data-driven analytics. This review paper initially outlines the different degradation mechanisms, failure modes and patterns that PV systems are subjected and then reports the main diagnostic techniques. Furthermore, the essential equipment and sensor's requirements for diagnosing failures in monitored PV systems using UAV-based approaches are provided. Moreover, the study summarizes the operating conditions and the various failure types that can be detected by such diagnostic approaches. Finally, it provides recommendations and insights on how to develop a fully functional UAV-based diagnostic tool, capable of detecting and classifying accurately failure modes in PV systems, while also locating the exact position of faulty modules.

Adaptive terminal synergetic-backstepping technique based machine learning regression algorithm for MPPT control of PV systems under real climatic conditions.

This paper deals with a comparative evaluation of nonlinear controllers based on the linear regression technique, which is a machine learning algorithm for maximum power point tracking. In the past decade, most photovoltaic systems have been equipped with classical algorithms such as perturb and observe, hill climbing, and incremental conductance. The simplicity of these techniques and their ease of implementation were seen as the main reasons for their utilization in photovoltaic systems. However, researchers' attention has recently been attracted by artificial intelligence-based techniques such as linear regression, which offer better performance within the bounds of the nonlinearity of photovoltaic system characteristics. An adaptive terminal synergetic backstepping controller is developed in this paper for a single-ended primary inductance converter. This control scheme is based on the combination of a non-singular terminal synergetic technique with an integral backstepping technique and equally a neural network for the approximation of unmeasured or inaccessible variables that guarantees the finite-time convergence. The proposed controller was further verified under virtual and real environmental conditions, and the numerical results obtained from Matlab/Simulink software under various test conditions, including load variations, show that the adaptive terminal synergetic backstepping controller gives satisfactory performance compared to the adaptive integral backstepping controller used in the same climatic conditions.

Recognizing the preventive quality in the adoption of innovations: The case of third-party ownership photovoltaic systems in Finland.

This study explores the role of the preventive quality of innovations on their adoption intention. The preventive quality of innovations is a distinctive feature of innovations that is directed towards avoiding a future, possibly harmful event. Empirically grounded in third-party ownership of photovoltaic systems in Finland, this study examines data collected from an online survey measuring respondent intention to adopt. A series of hypotheses theoretically grounded in the Diffusion of Innovations theory and the preventive quality of innovations were tested through Partial Least Squares Structural Equation Modeling with SmartPLS4. Findings for the overall sample reveal that the preventive quality and the relative advantage of innovations influenced adoption intention positively. Findings highlight the preventive quality of innovations as a construct that acts as the greatest contributor to the dependent variable.

Energy consumption and carbon emission assessment of battery swapping systems for electric motorcycle.

This work investigated different conceptual models for electric motorcycles, which are electric motorcycles with a home charging system, electric motorcycles using a battery swapping system, and electric motorcycles with battery swapping and photovoltaic systems, in four Southeast Asia countries. The current research focused on analyzing the impact of factors such as the number of battery packs in a swapping station, variation in battery swapping demand, the season, and photovoltaic panel size on energy utilization and carbon emissions associated with the entire energy supply chain. The objective of the current study was to evaluate and compare the well-to-wheel emissions using these different conceptual models, considering the varying energy mixes in four countries with a significant prevalence of motorcycle ownership, Thailand, Vietnam, Malaysia, and Indonesia. The results revealed that by using a 3 kW photovoltaic system, the dependency on grid energy can be significantly reduced and thus provide the highest benefits in terms of reduction of fossil fuel use and CO2 emissions. Although switching from internal combustion engine motorcycles to electric motorcycles could substantially reduce carbon emissions, it is only feasible when the primary resources used for generating electricity are sufficiently clean or battery swapping stations are equipped with a 3 kW photovoltaic system. In these four countries, electric motorcycles with battery swapping systems could accelerate the transition to a net-zero carbon emission society by reducing CO2 emissions by around 2.6-3.0 Mt-CO2 per year in the right environment. Prioritizing the decarbonization of power generation should be the primary focus, considering its critical role as a bottleneck within the system. The findings of this research hold significant value for decision-makers and investors who are actively pursuing smart city development and aiming to harness the potential of renewable energy sources.

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters.

The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L-3PVSI and 3L-3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m2 to 800 W/m2, is kept at 800 W/m2 for 0.2 s, is then gradually decreased from 800 W/m2 to 200 W/m2 in 0.2 s, is then kept at 200 W/m2 for 0.2 s, and is then finally increased to 1000 W/m2 for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set-Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L-3PVSI inverter is above the threshold at an irradiance of 200 W/m2. The 3L-3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.

Life cycle assessment of photovoltaic electricity production by mono-crystalline solar systems: a case study in Canada.

Photovoltaic (PV) system is widely recognized as one of the cleanest technologies for electricity production, which transforms solar energy into electrical energy. However, there are considerable amounts of emissions during its life cycle. In this study, life cycle assessment (LCA) was used to evaluate the environmental and human health impacts of PV electricity production in Canada. The PV potential varies considerably among the provinces, with higher values in Manitoba (MB), Saskatchewan (SK), Alberta (AB), and southern Ontario (ON). A grid-connected slanted-roof mono-crystalline silicon (mono-Si) PV system with a capacity of 3 kWp (the peak power of the system in kilowatts) in Toronto, Ontario, was considered as the case study system. Ten impact categories were considered including (1) acidification, (2) carcinogenic, (3) ecotoxicity, (4) eutrophication, (5) fossil fuel depletion, (6) global warming, (7) non-carcinogenic, (8) ozone depletion, (9) respiratory effects, and (10) smog. Among the four components of the PV system, i.e., mono-Si panel, mounting system, inverter, and electric installation, the mono-Si panel production was the highest contributor in seven out of ten impact categories, including acidification (68%), eutrophication (60%), fossil fuel depletion (81%), global warming (77%), ozone depletion (88%), respiratory effects (74%), and smog (70%). For the other three processes, the electric installation contributed most to ecotoxicity at 58%, followed by the mounting system in the carcinogenic category (29%), and the inverter in the non-carcinogenic category (31%). By normalizing the impacts based on the reference scores in Canada, it was found that the ecotoxicity and carcinogenic categories had dominant contributions to the overall impact by 53% and 42%, respectively. The global warming potential impact was estimated as 79 gr CO2 eq /kWh, which is close to the mean value of 79.5 gr CO2 eq /kWh, reported in the literature. The sensitivity analysis indicated that a 10% increase in the panel and mounting system area will increase the ozone depletion and carcinogenic categories by 8.1% and 2.8%, respectively.

Framework for the analysis of renewable energy grid policies in the context of COVID-19.

COVID-19 is a severe global pandemic that has caught the whole world unprepared. In the absence of a clear timeline for this pandemic to end, it is need of the hour to investigate the effect of this pandemic on both previous and anticipated investments. Global economic unrest has hindered the ramping deployment of Renewable energy projects. The most quick actions that may be taken to mitigate the effects and to up-rise the investment portfolio policies are a very critical tool in hands of government for a very immediate effect have also been made without keeping the context of COVID-19 into account. New variants of diff rent nature are being discovered and every now and then new lock downs are happening. In this context different policies have to be evaluated under the pandemic scenario. A case study of a large scale renewable energy project for a higher education institute in Pakistan is being used to measure the difference during COVID and pre COVID times. This paper provides a framework to investigate the impact of COVID on renewable energy system projects under current net-metering, net-billing and self-consumption policies. A recent investment in a photovoltaic system is assessed based on previously projected financial benefits versus the pandemic effected ones. This research concludes that investing in photovoltaic systems are still a viable option even in an extreme pandemic situation with less than 0.5 years increase in payback period, and the government can still provide a stimulus for investing in green energy by implementing net-metering policies on a larger scale.

A low-cost system for real-time measuring of the sunlight incident angle using IoT.

The incidence angle of solar irradiance is an important parameter for sizing and locate photovoltaic systems, which affects the installation design and has a high influence in the power production of photovoltaic panels. This angle is traditionally estimated considering the geographical position, however, this approach ignores the existence of local elements that affect the generation, such as weather conditions, topography, constructions with high reflection, among others. Therefore, this work presents the design and construction of a measurement device with nine irradiance sensors, which are located at different angles on two orthogonal axes within a semisphere. Since the angles of the sensors are known, a model to determine the direction of the maximum incidence irradiance, at each instant of time, can be calculated from the on-site measurements. In this way, it is also possible to calculate the panel inclination and orientation producing the maximum power for a particular location. The device acquires the irradiance magnitude in the nine sensors in real time, and it is transmitted using the Internet to simplify data recollection. Finally, the device uses a low-cost platform, which makes possible the adoption of this solution in a wide range of applications, e.g. design, diagnostic or reconfiguration of PV arrays.

Low-cost system for sunlight incidence angle measurement using optical fiber.

The development and optimization of renewable energy systems are some of the most necessary topics to advance towards secure and sustainable energy models. Photovoltaic energy is one of those sustainable options that could contribute to the reduction of greenhouse gas emissions. The optimal angle of solar incidence producing the highest absorption in a day is an important parameter to install photovoltaic systems. This value is often estimated using simulation models based on geographic location; however, those models ignore the influence of nearby obstruction objects, albedo, and local weather conditions. Such a problem is addressed in this work by designing a system to estimate the optimum angle of solar incidence for the photovoltaic panels. The system is based on an arrangement of 33 measurement points spaced in arcs every 45 degrees in azimuth and every 22.5 degrees in elevation, which provides a wide range for analysis. The light captured by each optical fiber is transmitted to a flat array where the power is measured using a single RGB camera.

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review.

The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

Environmental impacts of solar photovoltaic systems: A critical review of recent progress and future outlook.

Photovoltaic (PV) systems are regarded as clean and sustainable sources of energy. Although the operation of PV systems exhibits minimal pollution during their lifetime, the probable environmental impacts of such systems from manufacturing until disposal cannot be ignored. The production of hazardous contaminates, water resources pollution, and emissions of air pollutants during the manufacturing process as well as the impact of PV installations on land use are important environmental factors to consider. The present study aims at developing a comprehensive analysis of all possible environmental challenges as well as presenting novel design proposals to mitigate and solve the aforementioned environmental problems. The emissions of greenhouse gas (GHG) from various PV systems were also explored and compared with fossil fuel energy resources. The results revealed that the negative environmental impacts of PV systems could be substantially mitigated using optimized design, development of novel materials, minimize the use of hazardous materials, recycling whenever possible, and careful site selection. Such mitigation actions will reduce the emissions of GHG to the environment, decrease the accumulation of solid wastes, and preserve valuable water resources. The carbon footprint emission from PV systems was found to be in the range of 14-73 g CO2-eq/kWh, which is 10 to 53 orders of magnitude lower than emission reported from the burning of oil (742 g CO2-eq/kWh from oil). It was concluded that the carbon footprint of the PV system could be decreased further by one order of magnitude using novel manufacturing materials. Recycling solar cell materials can also contribute up to a 42% reduction in GHG emissions. The present study offers a valuable management strategy that can be used to improve the sustainability of PV manufacturing processes, improve its economic value, and mitigate its negative impacts on the environment.

Dependence of new environmental nano organic semiconductor nickel-(II)-tetraphenyl-21H,23H-porphyrin films on substrate type for energy storage applications.

Characterization of organic nickel-(II)-tetraphenyl-21H,23H-porphyrin films as a function of substrate type was performed for energy storage applications and consequently environmental enhancement. Nickel-(II)-tetraphenyl-21H,23H-porphyrin films show an amorphous phase. They have a crystallite size of 8-11 nm. Strain caused a shift of different humps' positions. The measured transmittance has high values within the range of 85-91%, and the absorption coefficient values were included within the high-absorption region. Both optical gap and fundamental gap, refractive index, carrier-concentration-to-effective-mass ratio and lattice dielectric constant were calculated, and they were found to be increased, except refractive index and lattice dielectric constant. The obtained data indicated that nickel-(II)-tetraphenyl-21H,23H-porphyrin films are a candidate for energy storage applications.

Analysis of PV Systems Installed in Paraná

HIGHLIGHTS Comparative study of the operation of eight real cases of systems installed in Paraná. There is a convergence between the values estimated by the Atlas and the ones calculated. It was possible to identify the cities that presented the greatest figures of merit.

Abstract With the development of renewable energies in the world, there is also an increase in solar photovoltaic systems globally. In Brazil, and in the state of Paraná, there is an exponential growth of this form of energy generation, which causes the necessity to study the performance of the installed systems. Therefore, this article analyzed eight photovoltaic systems installed in the state of Paraná, under the aspect of figures of merit parameters, through calculations of final yield, performance ratio and capacity factor. In addition, the calculated values were compared to the values estimated by the Solar Energy Atlas of the State of Paraná. As a result, the largest average differences in final yield, between the calculations and the Atlas, were found in the cities of Cascavel, while the smallest were observed in Goioerê.

Performance Estimation of Grid Connected Photovoltaic Systems in Different Regions in Paraná

Abstract The obtaining of electric energy from solar energy through photovoltaic systems shows great growth due to the solar potential present in the Brazilian territory. In the State of Paraná, studies are recurrent in public and private sectors on the development and performance of this technology. The installation and commissioning processes of photovoltaic plants are linked to the expectation of energy generation and the performance of the system through the figures of merit. The feasibility of the project can be confirmed when the results of these parameters are satisfactory and correspond to averages obtained from already consolidated surveys. The six photovoltaic systems implemented at the Federal Technological University of Paraná have expectations of generation and performance consistent with those of previous studies and will contribute to scientific advancement on the behavior of systems with different technologies located in different regions in the state.

A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy.

The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance.

Dynamic life cycle economic and environmental assessment of residential solar photovoltaic systems.

With the increasing implementation of solar photovoltaic (PV) systems, comprehensive methods and tools are required to dynamically assess their economic and environmental costs and benefits under varied spatial and temporal contexts. This study integrated system dynamics modeling with life cycle assessment and life cycle cost assessment to evaluate the cumulative energy demand, carbon footprint, water footprint, and life cycle cost of residential grid-connected (GC) and standalone (SA) solar PV systems. The system dynamics model was specifically used for simulating the hourly solar energy generation, use, and storage during the use phase of the solar PVs. The modeling framework was then applied to a residential prototype house in Boston, MA to investigate various PV panel and battery sizing scenarios. When the SA design is under consideration, the maximum life cycle economic saving can be achieved with 20 panels with no battery in the prototype house, which increases the life cycle economic savings by 511.6% as compared to a baseline system sized based upon the engineering rule-of-thumb (40 panels and 40 batteries), yet decreases the demand met by 55.7%. However, the optimized environmental performance was achieved with significantly larger panel (up to 300 units) and battery (up to 320 units) sizes. These optimized configurations increase the life cycle environmental savings of the baseline system byup to 64.6%, but significantly decrease the life cycle economic saving by up to 6868.4%. There is a clear environmental and economic tradeoff when sizing the SA systems. When the GC system design is under consideration, both the economic and environmental benefits are the highest when no battery is installed, and the benefits increase with the increase of panel size. However, when policy constraints such as limitations/caps of grid sell are in place, tradeoffs would present as whether or not to install batteries for excess energy storage.

Fully decentralized robust backstepping voltage control of photovoltaic systems for DC islanded microgrids based on disturbance observer method.

In this paper, a new backstepping-based nonlinear technique for control of photovoltaic systems in DC islanded microgrids (MGs) is proposed. In contrast to most existing droop/non-droop control strategies that require an exact model of the system including line impedances, loads, other distributed generation units (DGUs) parameters, and even the MG configuration, the proposed method is taking dynamics and uncertainties into account using a designed disturbance observer. Moreover, the proposed method rapidly reaches the reference values and exhibits a more accurate robust performance using local quantities measurement, irrespective of parametric uncertainties, unmodeled dynamics, unknown loads, disturbances, and the number/structure of DGs within the MG. Finally, a low-voltage DC MG is built where the robust performance of the proposed method for different operating conditions including load variation, tracking capability, nonlinear loads, and plug-play of DGs is verified.

Knowledge-Based Sensors for Controlling A High-Concentration Photovoltaic Tracker.

To reduce the cost of generated electrical energy, high-concentration photovoltaic systems have been proposed to reduce the amount of semiconductor material needed by concentrating sunlight using lenses and mirrors. Due to the concentration of energy, the use of tracker or pointing systems is necessary in order to obtain the desired amount of electrical energy. However, a high degree of inaccuracy and imprecision is observed in the real installation of concentration photovoltaic systems. The main objective of this work is to design a knowledge-based controller for a high-concentration photovoltaic system (HCPV) tracker. The methodology proposed consists of using fuzzy rule-based systems (FRBS) and to implement the controller in a real system by means of Internet of Things (IoT) technologies. FRBS have demonstrated correct adaptation to problems having a high degree of inaccuracy and uncertainty, and IoT technology allows use of constrained resource devices, cloud computer architecture, and a platform to store and monitor the data obtained. As a result, two knowledge-based controllers are presented in this paper: the first based on a pointing device and the second based on the measure of the electrical current generated, which showed the best performance in the experiments carried out. New factors that increase imprecision and uncertainty in HCPV solar tracker installations are presented in the experiments carried out in the real installation.

A robust control scheme for grid-connected photovoltaic converters with low-voltage ride-through ability without phase-locked loop.

A robust control scheme with low-voltage ride-through ability is presented for grid-connected photovoltaic converters that operate under harsh conditions such as voltage sags and unknown disturbances and parameters. The proposed strategy allows for flexible active and reactive power injection into the grid during asymmetrical voltage sags without using a phase-locked loop or positive and negative sequence components. In addition, the same controllers are used under both normal operation and voltage sags, thus, lowering the control system's complexity. The scheme is conceived by combining uncertainty-and-disturbance-estimation compensation and repetitive control for the dc voltage and phase currents, respectively. Controller design is carried out systematically and closed-loop stability is proven through Lyapunov's analysis. Several simulation case studies are presented using the SimPowerSystemsTM toolbox of MATLAB/Simulink computing environment to demonstrate the performance of the proposed control system under voltage sags, unknown disturbances, abrupt changes in operating conditions, and parametric uncertainties.