Department of Electrical Engineering

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Author: search.filters.author.Dr. Aamer Bilal AsgharSubject: search.filters.subject.Dr. Aamer Bilal Asghar

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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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    A Data-Driven Approach for Design and Development of Batch Process Automation System of a Plant
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muhammad Subiyyal Fayyaz; SP20-REE-001; Dr. Aamer Bilal Asghar; LHR TP 8070
    Industrial automation processes make use of control systems to operate and observe machines as well as processes. Automation in industrial workplace gives advantages of enhancing quality and productivity whereas minimizing errors, improving safety, and adding flexibility to manufacturing process. The aim of this work is to implement the simulation-based data driven control automation in which I have to design, develop and automate the complete plant through Programmable Logic controllers (PLC). Keeping this in view, I have designed the redundant controller architecture by using Device Level Ring (DLR) topology; a cost-effective control solution in which the single point of failure on networking and SCADA/HMI layer will be minimized. V-Cycle process model will be followed during detail design and development phase of the project till complete simulation. Control Logix soft computing technique used for logic development. I have developed complete simulated data driven control project in which I have utilized different soft computing techniques and software’s for industrial automation including RS-Logix5000/Studio 5000 for logic development, RSLinx for communication between PLC controllers and SCADA/HMI layer are used. Factory talk View Studio will be used for graphics development. Also, I have used the soft computing techniques for reporting purpose. SQL Database management console utility have utilized for reporting DB. For simulation activity, I have used RS-Simulator. Simulated data driven automated SCADA/HMI system are ensure to meet the performance parameters i.e. increase productivity, increase efficiency, reduce variability and ensure safety. Major parameters are showed in form of Bar-Trends. Health Statuses of control hardware and communication diagnostics are very helpful for system health checks and monitoring in diagnostic HMI.