Department of Electrical Engineering

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    Estimation of Wake Effect in Wind Farms using Machine Learning Algorithms
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Usama Riaz; SP22-REE-001; Dr. Aamer Bilal Asghar; LHR TP 9501
    The need for renewable energy is always growing, making wind farm efficiency opti mization a vital area of study. Wind turbines that are dispersed throughout the landscape on wind farms use the kinetic energy of the wind to generate electricity. The idea that a wind turbine running in the wake of another turbine lowers wind speed after the rotor and reduces power generation is introduced by these massive wind farms with several turbines. The flow field in the wake of first row turbines is characterized by strong wind velocity deficit and high turbulent intensity as well. For this reason, a further downstream turbine in a wind farm can capture lesser wind energy than the first row turbine. Most importantly wake effect modeling is done in this study since it is important in determining the real en ergy production of a wind farm enhanced by this feature. In this study, machine learning algorithms are used to determine the wake effect of a wind turbine in a wind farm. Although the wind turbines appear simple in their working principle, their control is actually complex and difficult to maintain operational and structural integrity and performance. Each turbine produces a ‘wake’ when it takes wind energy; this is a major factor in the overall dynamics of the turbines. These wakes, which are areas of turbulent flow and decreased wind speed behind the turbines, are a major barrier to efficient wind energy harvesting. Moreover, the extension of the mechanical life of the turbines in not only increase efficiency but also sus tain turbulence in the wake. The present research work, specifically focuses on estimating wake effects in wind farms for machine learning algorithms. The data used for this aim is archival data of wind farm recordings, but in this case, the data is computationally syn thesized. The goal is to improve accuracy and efficacy while reducing wake estimating loss compared to traditional methods. Utilizing machine learning models such as Artificial Neural Networks (ANN), Random Forest Regression, Decision Tree Regression, Support Vector Machines (SVM)andtheadaptive neuro-fuzzy inference system (ANFIS) which are used to capture complex architecture and forecast accuracy. The outcomes of this research have the potential to revolutionize wind farm optimization by providing a more adaptive and faster wake estimation process. Among the models tested, the Random Forest Model performed the best with an outstanding R2 score of 0.99, demonstrating its effectiveness in accurately estimating wake effects. In comparison, the Decision Tree Regression also showed strong performance with R2 score of 0.97, although it exhibited a slightly higher MSEandRMSE
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    Estimation of Syngas Production in Biomass Using Machine Learning Algorithms
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Nabeel Arshad; FA22-REE-006; Dr.Aamer Bilal Asghar; LHR TP 9499
    Renewable energy technologies can be beneficial for the economic development of any country on the planet. Also, with the present situation in the energy sector, the high cost of fuel, the use of renewable energy resources has seemed to have gained importance. The energy resources that pertain to solar, biomass, and wind energies are clean energy friendly to the environment. In the modern world, fossil fuels meet the requirements of the transportation sector in large volume and bring several negative impacts viz., air pollution, noise, and global warming. Besides, the fast decline of below-ground petroleum resources that arise with the rapid consumption of fossil fuels is accompanied by another grave problem for the transportation sector. Usually, the gasifier's temperature is greater than 1000°C. Before coal is put into the gasifier, it must go through a process called pyrolysis, a sequence of intricate physical and chemical reactions that occur without oxygen or air and gradually increase in temperature from 150 to 700 degrees Celsius. High molecular weight charcoal and volatile substances, such as CO, H2, H2O, CO2, and CH4, are the process byproducts. In this research work, machine learning algorithms such as Artificial Neural Network (ANN), Random Forest (RF), GBR, and XGB will be used to increase the efficiency of biomass gasification using AI-based machine learning algorithms. A dataset of biomass converting into syngas and other useful products is obtained to estimate the output of syngas, H2, CH4, and composition of different gases based on input parameters, namely carbon, temperature, sulfur, oxygen, nitrogen and ash, etc. The algorithms have an advantage in their capacity to train a model using a dataset consisting of numerical values and achieve an optimal fit point by incorporating efficiency parameters. Root Mean Square Error (RMSE) is a frequently used metric to estimate the precision of predictions. The RMSE values will be computed for each scenario. The approach will be experimented with under temperature variations, and other relevant constraints.