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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE) International Journal of Robotics and Control Systems
Afshin Balal
Texas Tech University
Control & Systems Engineering Electrical & Electronics Engineering

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Optimized generated power of a solar PV system using an intelligent tracking technique Afshin Balal; Mostafa Abedi; Farzad Shahabi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i4.pp2580-2592

Abstract

Partial shading condition (PSC) is common and complicated in all types of PV power plant. Therefore, the power production of solar system would be affected by the mismatch phenomena produced by PSC. Furthermore, when the array is partially shaded, the P–V characteristics become more complex which causes multiple peaks of the P-V curve. So, the simple maximum power point tracking (MPPT) methods such as perturb and observe (P&O) will fail. To address the above issue, this paper proposes a combination of two different approaches, implementing distributed MPPT (DMPPT) and optimized fuzzy/bee algorithm (OFBA). DMPPT can be utilized to maximize solar energy by allowing each module, or group of modules, be managed independently. Also, due to the output power oscillations around the operating point in the P&O method, an OFBA is employed which allowing for the decrease of output power oscillations without the usage of temperature and light sensors. The result shows that utilizing of DMPPT control approach in conjunction with the OFBA boosts the output generated power.
Using PV Fuzzy Tracking Algorithm to Charge Electric Vehicles Yao Lung Chuang; Miguel Herrera; Afshin Balal
International Journal of Robotics and Control Systems Vol 2, No 2 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i2.636

Abstract

Due to the possible shortage of oil and gas, increasing the number of cars, global warming, air pollution, and outages, there is a special need for renewable energy sources and electric vehicles (EVs). The new battery-electric vehicles BEVs can be charged by the power grid. However, the existing fossil fuel power plant cannot provide enough power for this purpose, and the only choice is renewable energy sources (RECs). Comparing RECs, solar energy is abundant and accessible in any part of the world. Needless to state that a maximum power point tracking (MPPT) system is required in order to extract maximum power from solar modules. In this paper, a charging strategy is proposed via using a solar system, a boost converter, and a fuzzy tracking algorithm. The main research contribution of the presented paper is to charge an EV without putting stress on the power grid. The effectiveness of this approach is demonstrated by the MATLAB Simulink and LTSPICE results.
Using machine learning prediction to design an optimized renewable energy system for a remote area in Italy Ali Rezaei; Afshin Balal; Yaser Pakzad Jafarabadi
International Journal of Applied Power Engineering (IJAPE) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v12.i3.pp331-340

Abstract

Due to the lack of fossil fuels, there is a significant demand to employ renewable energy systems (RES) worldwide. This paper proposes designing an optimized RES for a remote microgrid that relies solely on solar and wind sources. The proposed RES aims to provide reliable and efficient energy to the microgrid by using machine learning algorithms to forecast the power output of the solar and wind sources. This forecasting will help the system to anticipate and adjust to changes in the weather patterns that may affect the availability of solar and wind. In addition, the system advisor model (SAM) software is used to design the hybrid solar/wind system, considering factors such as the size of the microgrid and the available resources. The system comprises a 60-kW wind system of ten turbines and a 100-kW PV system spread out over the houses. The results show that random forest regression (RFR) models achieved a high level of accuracy in predicting solar power generation, as evidenced by their low mean squared error (MSE) and high R² values. Additionally, a proposed hybrid system can generate enough energy to meet the area's needs.
Co-Authors Ali Rezaei Farzad Shahabi Miguel Herrera Mostafa Abedi Yao Lung Chuang Yaser Pakzad Jafarabadi