Department of Electrical Engineering

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Has files: YesSubject: search.filters.subject.Dr. Muhammad Yaqoob Javed

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    Implementation of Machine Learning Models for Predicting Hydrogen Production from Renewable Energy
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Adnan Ayub; CIIT/FA23-REE-002/LHR; Dr. Muhammad Yaqoob Javed; LHR TP 10044
    The world continues to be largely reliant on fossil fuels, such as coal, oil and natural gas, which are major contributors of greenhouse gases, air pollution and climate change caused by the release of gases like CO2, NO o and SO2. Even though renewable energy sources such as solar and wind provide a cleaner substitute, their nature as intermittent and weather-dependent sources become a big problem in terms of large scale and long-term energy storage making traditional battery systems economically impractical. Consequently, the utilization of the surplus renewable energy through the process of water electrolysis to create green hydrogen has become a viable and alternative way of storing energy in the long term. This work suggests a two-stage machine learning-based predictive model of hydrogen production when using renewable energy, based on real-world working data of a 40.5 MW grid-connected photovoltaic (PV) power facility. The initial step involves predicting photovoltaic power output from meteorological variables using multiple regression and deep learning models, such as Support Vector Regression (SVR), Random Forest, Decision Tree, and Gated Recurrent Units (GRU). The second stage is to incorporate the predicted solar energy into a hybrid electrochemical model of hydrogen production, and the same machine learning models serve as data-driven correction models to more effectively predict hydrogen yield. It is a two-stage method that integrates both physical modelling and machine learning to model nonlinear system behaviour and real-world losses of operation. The findings show that SVR was always better in both phases than the other models with an R 2 value of 0.965 in photovoltaic power prediction and 0.968 in hydrogen production prediction. The given framework minimized the mistake in the production of hydrogen annually to about 3 percent, which is much better than theoretical models and deep learning alternatives. Moreover, a Sobol based global sensitivity analysis revealed that Global Horizontal Irradiance (GHI) made the greatest contribution to the uncertainty in the hydrogen production process, then AC power output and temperature at the module. The outcomes of these studies support the premise that the two stages proposed framework is a viable, precise, and large-scale solution to real-time forecasting, optimization of the system, and successful grid integration of green hydrogen system.
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    An Efficient Fault Detection Method for Grid Connected Solar Photovoltaic System
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Zain Rasool; FA21-REE-001; Dr. Muhammad Yaqoob Javed; LHR TP 9369
    Photovoltaic (PV) module faults have harmful effects on both the efficiency of power generation and overall safety. Among these faults, current mismatch is the most common type, leading to a decrease in output current and causing distinct steps in the (current and voltage) I-V representative curves as well as multiple spikes in the P-V curves. Consequently, the power output of PV units is significantly impacted. This research delves into the scrutiny of faulty PV units in real-world PV power sites, specifically focusing on current inequality faults resulting from partial shadowing, hot spots, and cracks. There are various techniques used to detect the current faults. These techniques are ground fault detection and interruption (GFDI), over current protection (OCP), Insulation monitoring devices (IMD), and Arc fault current interruption (AFCI). Other than these devices various classification algorithms have also been developed which can be employed to classify the detected PV faults while the system is running. In this research the dataset from previous research is used to train regression tree, SVM, and logistic regression classifiers. Amongst these classifiers, regression tree classifier has presented an accuracy of up-to 99%, while the previous research presented an accuracy of 98%. This research distinguishes between different fault features within the I-V curve steps and proposes computational analytics and statistical techniques for diagnosing PV unit mismatch faults. The inclusion of PV system reduces the carbon footprint paving ways to green energy, in addition to saving fuel and generation costs on a yearly basis. As such this research aligns itself with the Sustainable Development Goals (SDGs) set by the United Nations.