Optimized generated power of a solar PV system using an intelligent tracking technique

Balal, Afshin; Abedi, Mostafa; Shahabi, Farzad

doi:10.11591/ijpeds.v12.i4.pp2580-2592

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…Ambient temperature and soiling effect decrease the PR by 0.7% and 0.03%, respectively, with an increase of 1 °C temperature [51]. The maximum power point of a solar cell varies during the day due to variations in sun energy and temperature [52]. The cold climate region has higher PR values due to lower temperatures than the hot climate region [42], [53].…”

Section: Continuous Checking Of Current and Voltage Generationmentioning

confidence: 99%

Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review

Verma,

Yadav

2024

IJAPE

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The world is moving forward to a transition in the form of increasing the contribution of renewable energy sources in the energy sector, and among these, solar photovoltaic-based power generation is catching pace. Several factors are responsible for the lowering of outputs due to different degradation causes such as hotspots, corrosion, humidity, ultraviolet (UV) irradiation, temperature effects, dust, aging, weathering, yellowing, snail trails, discoloration, junction box failure, delamination, cracks, and faults from the solar photovoltaic (PV) plants. This paper presents a comprehensive review of the various form of degradation and their implications on solar PV power plant performance. The review has been carried out considering the different degradation causes and their identification methods in solar PV plant. Further, the analysis has been done on the basis of the earlier studies to understand the rates of degradation for various solar PV power plants in various climatic conditions. The PV technologies used in solar power plants are also responsible for the change in the performance of power plants over time; therefore, degradation based on different solar PV cell technologies is also analyzed. The visual inspection tools like thermal imaging with IR cameras help identify areas with abnormal heat patterns, indicating potential issues like cell or interconnect failures, loose electrical connections, or bypass diode malfunctions while EL cameras are used to identify low-level electrical excitation and defects such as cracks, hotspots, and cell-level degradation.

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Section: Continuous Checking Of Current and Voltage Generationmentioning

confidence: 99%

Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review

Verma,

Yadav

2024

IJAPE

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“…In order to extract solar energy, there is a need for a grid-tied inverter, DC-DC converter, maximum power point (MPPT) tracking system, batteries, and solar panels [25][26][27]. There are quite a few MPPT tracking techniques, including basic methods and optimized methods [28][29][30]. Fig.…”

Section: Pv Systemmentioning

confidence: 99%

Techno-Economic Analysis of a 12-kW Photovoltaic System Using an Efficient Multiple Linear Regression Model Prediction

Pourmaleki

Agutu²,

Rezaei

et al. 2022

IJRCS

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Renewable energy sources are expected to replace traditional energy sources such as oil and gas in the future. It goes without saying that solar energy has been demonstrated to be a key source of green energy. Solar energy is used because it is abundant, pollution-free, and easily available. However, the power utility market requires highly exact solar energy forecasts. These challenges need the creation of a device that can precisely predict solar energy output via processing the location's weather data, which is accomplished through the use of machine learning and multiple linear regression (MLR). Some elements, such as the number of cloudy days, humidity, temperature, wind condition, and precipitation, should be addressed while simulating solar power output. In this paper, a 12-kW photovoltaic (PV) system on the rooftop of a house in Isfahan was studied using the System Advisor Model (SAM). The most significant research contribution of the proposed paper is to predict the output power of a solar system with the lowest possible error. According to the simulation results, by using the MLR model, the predicted power has an error of 6 % with the actual power, which is a very good estimation. In addition, this system meets each household's energy needs plus an additional 8430 kWh per year, resulting in being paid by utility companies, a fewer number of outages, and lower air pollution levels.

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“…There are ecologic energies as the sun, biomass, and others form of energies. In this paper we are interested in the system of photovoltaic which produces electrical energy from the sun [1]- [8]. The production of electrical energy from solar energy depends on multiple factors such as climatic conditions, electrical configurations and the various faults that could take place, and other noncritical factors [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of PV module based on neural network using performance indices

Mekhaznia,

Khenfer

2023

IJPEDS

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Solar energy is an inexhaustible and clean renewable energy. Its exploitation through photovoltaic panels is clearly increasing in the world as ecologic energy. But like the conventional power grids, this new green energy could be affected by several defects that could reduce its performance, cause negative economic and safety impacts. These faults are multiple and of different natures. their consequences are based on their dangerousness. They can cause malfunction, power reduction and even total shutdown of the PV. The goal of this contribution is to implement an artificial method based on the ANN to diagnose the faults of the solar modules and to study the interest of the performance indices for the interpretation of the results of the diagnosis of the PV module. The results obtained are widely commented by different performance indices of confusion matrix and ROC curves. The neural network-based diagnostic system has a high accuracy and training was efficient because the curves of the cases are in the vicinity of unity i.e., a perfect classification.

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Optimized generated power of a solar PV system using an intelligent tracking technique

Cited by 10 publications

References 34 publications

Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review

Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review

Techno-Economic Analysis of a 12-kW Photovoltaic System Using an Efficient Multiple Linear Regression Model Prediction

Diagnosis of PV module based on neural network using performance indices

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