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Abstract Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output power of solar panels. Traditional solutions like perturb and observe (P&O) and Incremental Conductance (IC) are commonly utilized to follow the MPP under various environmental circumstances. However, these algorithms suffer from slow tracking speed and low dynamics under fast-changing environment conditions. To cope with these demerits, a data-driven artificial neural network (ANN) algorithm for MPPT is proposed in this paper. By leveraging the learning capabilities of the ANN, the PV operating point can be adapted to dynamic changes in solar irradiation and temperature. Consequently, it offers promising solutions for MPPT in fast-changing environments as well as overcoming the limitations of traditional MPPT techniques. In this paper, simulations verification and experimental validation of a proposed data-driven ANN-MPPT technique are presented. Additionally, the proposed technique is analyzed and compared to traditional MPPT methods. The numerical and experimental findings indicate that, of the examined MPPT methods, the proposed ANN-MPPT approach achieves the highest MPPT efficiency at 98.16% and the shortest tracking time of 1.3 s.

Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output power of solar panels. Traditional solutions like perturb and observe (P&O) and Incremental Conductance (IC) are commonly utilized to follow the MPP under various environmental circumstances. However, these algorithms suffer from slow tracking speed and low dynamics under fast-changing environment conditions. To cope with these demerits, a data-driven artificial neural network (ANN) algorithm for MPPT is proposed in this paper. By leveraging the learning capabilities of the ANN, the PV operating point can be adapted to dynamic changes in solar irradiation and temperature. Consequently, it offers promising solutions for MPPT in fast-changing environments as well as overcoming the limitations of traditional MPPT techniques. In this paper, simulations verification and experimental validation of a proposed data-driven ANN-MPPT technique are presented. Additionally, the proposed technique is analyzed and compared to traditional MPPT methods. The numerical and experimental findings indicate that, of the examined MPPT methods, the proposed ANN-MPPT approach achieves the highest MPPT efficiency at 98.16% and the shortest tracking time of 1.3 s. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are growing in popularity so, power electronic converter research and development is necessary to provide high power, low cost, as well as efficient methods for EV battery charging. Two kinds of EV chargers exist i.e. on-board and off-board. However, off-board chargers have to be utilized for DC rapid and ultra-quick charging to reduce EV volume and weight significantly. This paper reviews the state-of-the-art literature on power electronics converter systems for both AC-DC and DC-DC power stages for off-board chargers, which interface with the utility grid, PV systems, and EVs. In addition to the novel topologies of AC-DC rectifiers and DC-DC converters, the control schemes of AC-DC rectifiers and DC-DC converters are also explained in this manuscript. Comparisons are made regarding their topologies, isolation, power & voltage ranges, efficiency, bi-directional power capabilities, control variables, advantages, and disadvantages.

The impact of air pollution on the world has been highlighted by the World Health Organization (WHO), which has contributed to the growth of emission-free and ecologically friendly transportation options such as electric vehicles (EVs). Between 2019 and 2020, EV sales climbed by 43%, and by 2025, 14 million EVs are expected to be sold worldwide. On the other hand, EVs have drawbacks such as expensive batteries, short lifespans, lack of EV dependability, limited driving range, and long charging periods. Integrating renewable energy sources (RESs) such as biomass, solar, and wind power into EV charging infrastructures is gaining popularity. PV solar-powered EV charging has benefits like cheaper fuel costs, easier installation, less demand on the grid for power, and cost savings. Hybrid and on-board charging systems offer benefits such as reduced weight, faster charging, and improved communication protocols. On-board chargers have higher energy transfer but are more expensive and difficult to integrate with charging stations. Off-board charging systems include public, rapid, induction, and home charging stations. Advanced charging management systems can optimize charging to preserve battery life. It is recommended that future research concentrate on the development of sophisticated control algorithms, integration of wireless charging, extension of fast-charging infrastructure, innovation in materials, compatibility with many vehicles, and optimization of grid interface to enhance efficiency and user experience. The charging of electric vehicles in standalone and grid-connected photovoltaic systems is covered in this paper, along with an explanation of the various modes of operation for these systems. There is also a quick explanation of the various kinds of EV chargers, their power capacities, and their specifications. The research and comparisons in this paper are expected to be useful resources for scholars who are interested in learning more about EV charging facilities.

The growing interest in deploying photovoltaic systems and achieving their benefits as sustainable energy supplier raises the need to seek reliable medium-term and long-term operations with optimal performance and efficient use of economic resources. Cleaning scheduling is one of the activities that can positively impact performance. This work proposes a methodological framework to define the optimal scheduling of the cleaning activities of photovoltaic systems. The framework integrates a forecast model of the performance ratio, including the environmental variables' effect. In addition, an economic analysis involving the economic losses and maintenance costs of cleaning is used. This framework is applied to a case study of a photovoltaic system located in Yumbo, Colombia. Based on the historical data on irradiance, active energy, temperature, rainfall, and wind speed, the obtained forecast model of the photovoltaic system's performance ratio in a 60-day horizon has a mean absolute percentage error lesser of than 11%. The next cleaning date is forecasted to be beyond the horizon in a 19-day range, which will decrease as time goes by. This framework was applied to historical data and compared to actual cleaning dates performed by the utility company. The results show a loss of USD 33.616 due to unnecessary, early, or late cleaning activities. [ABSTRACT FROM AUTHOR]

The growing interest in deploying photovoltaic systems and achieving their benefits as sustainable energy supplier raises the need to seek reliable medium-term and long-term operations with optimal performance and efficient use of economic resources. Cleaning scheduling is one of the activities that can positively impact performance. This work proposes a methodological framework to define the optimal scheduling of the cleaning activities of photovoltaic systems. The framework integrates a forecast model of the performance ratio, including the environmental variables’ effect. In addition, an economic analysis involving the economic losses and maintenance costs of cleaning is used. This framework is applied to a case study of a photovoltaic system located in Yumbo, Colombia. Based on the historical data on irradiance, active energy, temperature, rainfall, and wind speed, the obtained forecast model of the photovoltaic system’s performance ratio in a 60-day horizon has a mean absolute percentage error lesser of than 11%. The next cleaning date is forecasted to be beyond the horizon in a 19-day range, which will decrease as time goes by. This framework was applied to historical data and compared to actual cleaning dates performed by the utility company. The results show a loss of USD 33.616 due to unnecessary, early, or late cleaning activities.

Photovoltaic (PV) plants can be built rapidly when compared with other conventional electrical plants; hence, they are a competent candidate for supplying the electricity grid. The output power of the PV modules can be used in plug-in electric vehicles (PEVs) DC charging stations to reduce the burden on the electricity grid, particularly during peak load hours. To integrate PV modules and electric vehicles (EVs) with the electricity grid, the modular multilevel converters (MMCs) topologies producing staircase voltage waveforms are preferred as they are able to deliver less total harmonic distortion (THD) and higher efficiency in addition to lower voltage stress on semiconductor switches. In conventional centralized MMC topologies, a direct connection to a high-DC-link input voltage is required which is not appropriate for PV plants. A new MMC topology for PV/EV/grid integration is proposed in this paper, where the individual PV arrays are directly connected to each phase of the AC grid to harvest the maximum available power point. A current-source converter (CSC) based on a single-stage isolated SEPIC converter is adopted as the submodule (SM) for the proposed MMC topology given its outstanding features, such as low input ripple current, high efficiency, high power factor, and flexible output voltage higher or lower than the input voltage. The single-stage SMs can operate in both DC/DC and DC/AC operating modes. Proper controllers for each mode of operation are designed and applied to supply constant current from either the PV modules or the battery cells by eliminating the second-order harmonic component. The performance of the proposed converter is verified by simulations and a downscaled prototype controlled by TMSF28335 DSP. [ABSTRACT FROM AUTHOR]

The transport sector generates a considerable amount of greenhouse gas (GHG) emissions worldwide, especially road transport, which accounts for 95% of the total GHGs. It is commonly known that Electric vehicles (EVs) can significantly reduce GHG emissions. However, with a fossil-fuel-based power generation system, EVs can produce more GHGs and therefore cannot be regarded as purely environmentally friendly. As a result, renewable energy sources (RES) such as photovoltaic (PV) can be integrated into the EV charging infrastructure to improve the sustainability of the transportation system. This paper reviews the state-of-the-art literature on power electronics converter systems, which interface with the utility grid, PV systems, and EVs. Comparisons are made in terms of their topologies, isolation, power and voltage ranges, efficiency, and bi-directional power capability for V2G operation. Specific attention is devoted to bidirectional isolated and non-isolated EV-interfaced converters in non-integrated architectures. A brief description of EV charger types, their power levels, and standards is provided. It is anticipated that the studies and comparisons in this paper would be advantageous as an all-in-one source of information for researchers seeking information related to EV charging infrastructures. [ABSTRACT FROM AUTHOR]

• A hosting capacity calculation method suitable for MV and LV distribution system planning. • Realistic assessment of the hosting capacity using time-series with real data. • Novel handling of uncertainties by using the probability of a limit violation as the performance index. • Showing the importance of correctly modelling the background voltage. • The MV network significantly impacts the hosting capacity for the LV network. This paper presents an approach to estimate the hosting capacity for distribution networks considering the impact of PV penetration at different voltage levels. The estimation and the method were selected such that the results were most suitable for distribution system planning. A time-series based method was used as it covers significant aspects needed for prioritising network reinforcement. The MV background voltage was modelled varying in time, assuming the same penetration level in the other LV networks supplied by the same MV system. The hosting capacity is defined as the maximum acceptable PV size per customer for a given PV penetration. Based on the different possible combinations of PV location, the probability of overvoltage and overloading is used as a performance index. The planning risk is used as a limit for the performance criterion. The method can be automated for a large number of networks due to using an IEC 61970-based input format. It also enables linking DSO network models to customer smart metre databases. The severity and risks of limit violations are analysed with different metrics from the time-series simulations. The change in background voltage with increasing penetration is shown to impact the results significantly. When considering it, the estimated hosting capacity was reduced by 32 %, on average. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) plants can be built rapidly when compared with other conventional electrical plants; hence, they are a competent candidate for supplying the electricity grid. The output power of the PV modules can be used in plug-in electric vehicles (PEVs) DC charging stations to reduce the burden on the electricity grid, particularly during peak load hours. To integrate PV modules and electric vehicles (EVs) with the electricity grid, the modular multilevel converters (MMCs) topologies producing staircase voltage waveforms are preferred as they are able to deliver less total harmonic distortion (THD) and higher efficiency in addition to lower voltage stress on semiconductor switches. In conventional centralized MMC topologies, a direct connection to a high-DC-link input voltage is required which is not appropriate for PV plants. A new MMC topology for PV/EV/grid integration is proposed in this paper, where the individual PV arrays are directly connected to each phase of the AC grid to harvest the maximum available power point. A current-source converter (CSC) based on a single-stage isolated SEPIC converter is adopted as the submodule (SM) for the proposed MMC topology given its outstanding features, such as low input ripple current, high efficiency, high power factor, and flexible output voltage higher or lower than the input voltage. The single-stage SMs can operate in both DC/DC and DC/AC operating modes. Proper controllers for each mode of operation are designed and applied to supply constant current from either the PV modules or the battery cells by eliminating the second-order harmonic component. The performance of the proposed converter is verified by simulations and a downscaled prototype controlled by TMSF28335 DSP.

The transport sector generates a considerable amount of greenhouse gas (GHG) emissions worldwide, especially road transport, which accounts for 95% of the total GHGs. It is commonly known that Electric vehicles (EVs) can significantly reduce GHG emissions. However, with a fossil-fuel-based power generation system, EVs can produce more GHGs and therefore cannot be regarded as purely environmentally friendly. As a result, renewable energy sources (RES) such as photovoltaic (PV) can be integrated into the EV charging infrastructure to improve the sustainability of the transportation system. This paper reviews the state-of-the-art literature on power electronics converter systems, which interface with the utility grid, PV systems, and EVs. Comparisons are made in terms of their topologies, isolation, power and voltage ranges, efficiency, and bi-directional power capability for V2G operation. Specific attention is devoted to bidirectional isolated and non-isolated EV-interfaced converters in non-integrated architectures. A brief description of EV charger types, their power levels, and standards is provided. It is anticipated that the studies and comparisons in this paper would be advantageous as an all-in-one source of information for researchers seeking information related to EV charging infrastructures.

Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image.

Charging electric vehicles (EVs) from photovoltaic panels (PV) provides a sustainable future for transportation. This paper presents the development of a 10 kW EV charger that can be powered from both a PV array and the three-phase ac grid. The goal is to realize a high power density and high-efficiency three-port power converter that integrates the EV, PV, and grid and meets the Chademo and combined charging standard/Combo EV charging standards. The EV port is designed to be isolated and bidirectional, so that both charging and vehicle-to-grid can be implemented. As PV and EV are both dc by nature, the converter uses a central dc link to exchange power between the EV and PV, thereby increasing efficiency. The use of silicon carbide devices and powdered alloy core inductors enables high switching frequency and power density. The closed-loop control allows four different power flows: ${\text{PV}\rightarrow \text{EV}, \text{EV}\rightarrow }$ grid, grid ${\rightarrow \text{EV}}$ , and ${\text{PV}\rightarrow }$ grid. Hence, the converter operates as a PV inverter, a bidirectional EV charger, and a combination of both. A 10 kW prototype has been successfully tested, and its experimental waveforms and measured efficiency are presented. It has three times the power density and higher partial and peak load efficiency when compared to existing solutions. [ABSTRACT FROM AUTHOR]

Integration of renewable energy including solar energy is growing faster than ever before. Solar energy supplies more than 1.3% of global power, and it is predicted to become the largest electricity source by 2050 with about 11% of global power consumption. However, the improper installation, non-frequently scheduled maintenance, and aging effect can accelerate the deterioration of PV system components, which directly increase the possibility of arc fault occurrence. The undetected arc faults pose a severe fire hazard to residential, commercial, and utility-scaled PV systems. To deliver electricity in a safe and reliable manner, such a dangerous event must be detected at early stage. This paper presents a comprehensive review of the-state-of-art techniques for DC arc faults detection in photovoltaic systems (PV). Different methods and the features used for detection are discussed and compared in detail. This paper also emphasizes the importance of DC arc fault simulation for characteristics study and fault diagnosis purpose. Several DC arc fault models have been reviewed and compared. [ABSTRACT FROM AUTHOR]

In this paper, a new investigation on islanding detection method is presented for PV-based VSC system. The proposed PV-VSC is represented as one of the distributed energy resource (DER) for a microgrid environment. A new multivariable dynamic model is presented in terms of two stage conversion with a dc–dc converter for the proposed PV-VSC system. The proposed VSC system is controlled primarily by a positive sequence P–Q loop, at the PCC. A new backstepping fast sliding mode control (BFSMC) is proposed for primary PWM control of VSC system. During sudden grid disconnection an islanding protection loop based on rate of change in voltage harmonic profile injection is proposed. A binary tree (BT) threshold filter is designed for the proposed hybrid islanding protection loop, to avoid false detection during other grid operational issues. UL 1741 (section 46) standard is followed for the PV-VSC system to consider the worst-case scenario. Performance of the proposed hybrid islanding protection scheme is investigated for IEEE 9 bus system with multiple DERs, implemented in MATLAB/Simulink environment. The effectiveness of the proposed scheme is determined by result analysis, where no significant effect on power quality is recorded during nominal grid operation. [ABSTRACT FROM PUBLISHER]

Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image. [ABSTRACT FROM AUTHOR]