Performance and Optimization of Commercial Solar PV and PTC Plants

Baseer, Mohammad Abdul; P, Praveen R.; Zubair, Muhammad; Khalil, A. Galal Abo; Saduni, Ibrahim Al

doi:10.35940/ijrte.e6247.018520

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Kumar et al (2010) [4] found that sustainable sources of energy are inexpensive, reliable, and environmentally friendly. Baseer et al (2020) [5] also discussed the limitations of non-renewable additives like solar panels and windmills in strengthening sustainable energy sources. To improve efficiency, Baseer et al (2022) [6] al.…”

Section: Introductionmentioning

confidence: 99%

Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques

Abdul Baseer,

Almunif,

Alsaduni

et al. 2023

Energies

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Renewable energy (RE) sources, such as wind, geothermal, bioenergy, and solar, have gained interest in developed regions. The rapid expansion of the economies in the Middle East requires massive increases in electricity production capacity, and currently fossil fuel reserves meet most of the power station demand. There is a considerable measure of unpredictability surrounding the locations of the concerned regions where RE can be used to generate electricity. This makes forecasting difficult for the investor to estimate future electricity production that could be generated in each area over the course of a specific period. Energy production forecasting with complex time-series data is a challenge. However, artificial neural networks (ANNs) are well suited for handling nonlinearity effectively. This research aims to investigate the various ANN models capable of providing reliable predictions for sustainable sources of power such as wind and solar. In addition to the ANN models, a state-of-the-art ensemble learning approach is used to improve the accuracy of predictions further. The proposed strategies can forecast RE generation accurately over short and long time frames, relying on historical data for precise predictions. This work proposes a new hybrid ensemble framework that strategically combines multiple complementary machine learning (ML) models to improve RE forecasting accuracy. The ensemble learning (EL) methodology outperforms long short-term memory (LSTM), light gradient boosting machine (LightGBM), and sequenced-GRU in predicting wind power (MAE: 0.782, MAPE: 0.702, RMSE: 0.833) and solar power (MAE: 1.082, MAPE: 0.921, RMSE: 1.055). It achieved an impressive R2 value of 0.9821, indicating its superior accuracy.

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Section: Introductionmentioning

confidence: 99%

Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques

Abdul Baseer,

Almunif,

Alsaduni

et al. 2023

Energies

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“…Blok šema solarne elektrane III MODEL SOLARNE ELEKTRANE U PROGRAMSKOM PAKETU SAMDanas su u upotrebi brojni softveri za projektovanje solarnih fotonaponskih sistema. Sistem Advisor Model (SAM) je efikasan besplatni inženjerski alat koji služi za projektovanje i analizu fotonaposnkih sistema, od malih stambenih solarnih elektrana do velikih industrijskih sistema[8]. Softver sadrži bazu podataka sa podacima o fotonaponskim modulima, invertorima, vremenskim parametrima, nivoima zračenja i ostalim podacima koji su neophodni za projektovanje sistema.…”

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Analiza rada solarne elektrane u NTP Čačak – System Advisor Model (SAM) modelovanje i poređenje sa stvarnim rezultatima

Dragićević¹,

Vujičić²,

Marjanović³

2022

EEE

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In the 21st century, the use of renewable resources has been propagated by the specter of climate change and its associated impact. Renewable energy technologies have been deployed at a high rate and show a significant potential to become a major player in the electricity supply in next decade. PV systems have experienced a significant growth in their rate of deployment as an alternative to conventional power sources. This study presents the modeling and simulation of a 25 kWp solar power plant in Čačak, Serbia. The power plant is situated on the roof of the Science and Technology Park Čačak. The model was developed using System Advisor Model (SAM) software. SAM was used to analyze the technical performances of based on local weather conditions and all the system components of installed photovoltaic system. The model was validated by comparing its predictions with the actual plant data. The proposed predictive model can be used to characterize power plant performance as well as analyze and evaluate the system's efficiency.

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Novel Hybrid Optimization Maximum Power Point Tracking and Normalized Intelligent Control Techniques for Smart Grid Linked Solar Photovoltaic System

2021

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Introduction of photovoltaic (PV) systems in grid causes some complex power quality problems such as harmonics, voltage fluctuation, and load imbalance that can affect the grid coordinated PV system performance. Therefore, herein, a novel normalized reasoning‐based fuzzy neural adaptive (NRFNA) control technique is proposed for the double‐stage grid coupled solar PV system. In addition, the utmost power in a double‐stage PV system is tracked at dissimilar environmental states by the proposed novel hybrid krill herd spider monkey (HKHSM)‐based maximum power point tracking (MPPT) algorithm. The current computation control method preserves the grid current in unity power factor as sinusoidal for the system at point common coupling (PCC). The simulation of this proposed work is done on MATLAB/Simulink. Subsequently, the performance of the developed replica results is validated under dissimilar conditions of power quality. Furthermore, the proposed HKHSM‐based MPPT along with a normalized reasoning herd spider monkey (NRHSM) control technique in a grid coordinated solar PV system has achieved 1.70% load current harmonics and 3.7% grid current harmonics. Consequently, the simulation outcomes are compared with the various conventional methods for proving the significance of the proposed system.

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Performance and Optimization of Commercial Solar PV and PTC Plants

Cited by 6 publications

References 15 publications

Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques

Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques

Analiza rada solarne elektrane u NTP Čačak – System Advisor Model (SAM) modelovanje i poređenje sa stvarnim rezultatima

Novel Hybrid Optimization Maximum Power Point Tracking and Normalized Intelligent Control Techniques for Smart Grid Linked Solar Photovoltaic System

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