Browsing by Author "Dr. Muhammad Yaqoob Javed"

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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.
Comparative Analysis on Building Integrated PV System in Pakistan By:
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muhammad Zohaib Waqar; , SP20-REE-011; Dr. Muhammad Yaqoob Javed; LHR TP 8074
During the recent past years, the energy consumption throughout the whole country increases and unrenewable energy resources are exhausted. There is a need to introduce some other means of energy which will long lasting and can produce more energy. Renewable energy resources that are used to produce enormous amount of electricity are solar, hydral, wind and biomass energy. By 2020, the total installed capacity of energy in Pakistan is 34282 MW. Almost 2% of this energy generation is through PV and Wind. But due to current account deficit the import issue related to fuel is increasing day by day. There is a need to develop a cheap and self-sustained model related to energy generation in Pakistan. The population of Pakistan is approximately 230 million and now the culture of residential building is increasing in Pakistan. Due to which the energy demand is increasing rapidly as well. To fulfill those demands, the conventional source of energy is falling short day by day. So, the world is shifting towards the renewable source of energy and building integrated PV system is used for the purpose. Apparently in Pakistan, residential buildings are developing because of the rapid increase in population. So, to accommodate the people residential buildings are the most suitable. But it has the huge energy requirement that needs to fulfilled despite of the energy crisis in Pakistan. Therefore, Building Integrated PV system is used to install PV systems in these buildings to use renewable energy as a source to fulfill their energy requirements. In this research, PV integrated building of 133 KW Power will be analyzed and observed in Lahore. PV modules of 484 units, two invertors of almost 120 KW AC & power ratio of 1.109, batteries of 12 units having voltages of 1234 v and 3853 Ah will be used. In this grid connected building system plane is fixed and tilted at 90/6º, shading is linear and fixed load of 358 KW is observed. The total estimated production we need is to be 3132 MWh /Year. By analyzing the system, it is concluded that the total Produced Energy by the system is 118 MWh/ Year, total used energy is 3131.7 MWh/year. It is also concluded that Perf. Ratio PR is 76.74% and Solar Fraction SF is 3.83 %. Finally, the system is observed for both centralized systems and distributed systems to determine which system gives the most suitable and optimum outputs in different environmental conditions. Different environmental conditions like partial shading or uniform irradiance can be observed and the system can be designed accordingly. With the help of specific algorithms like DFO, P&O, INC and CS the outputs of the system can be deeply observed and the most suitable system can be designed according to the given condition to obtain the optimum outpu
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.
Module level power electronics in distrubted power system for solar PV application
(COMSATS University Islamabad Lahore Campus, 0022) Muhammad Talha Naveed; , SP20-REE-015; Dr. Muhammad Yaqoob Javed; LHR TP 7895
hotovoltaic (PV) solar energy is as promising as other renewable energies. Different researchers and engineers are attempting to increase the efficiency of solar PV system. As a result, for PV modules, this enhancement may be accomplished at almost the same level. As is well known, solar PV systems are less efficiency as a result of changing climatic conditions. Module-level power electronics (MLPE) do this by providing the performance improvements of a distributed transmission system in both partial and full shading conditions. As a result, MLPE successfully harvests the distributed maximum power point (DMPP) from solar to accept DC from PV or the grid. Each PV module is connected to the power system via a separate dc/dc converter with Maximum Power Point Tracking (MPPT) capabilities in the DMPP scheme. Each PV panel has a built in power optimization or micro-inverter that helps it work better in partial shade. In order to design the MLPE the efficiency of contemporary string inverters are comparing. The efficiency may be measured in a variety of situations, including uniform irradiation and partial shade. As a result, a DC-DC converter that is attached to each PV module is required to offset shading losses. The optimizer identifies the Local peak using a DC-DC converter from the unit, shuts down the modules during fire situations, troubleshoots, and monitoring a module in a highly efficient manner in this work. On the Matlab software tool, the effectiveness of the proposed power optimizer is displayed. To calculate efficiency, several firms such as SMA, Solar Edge, Huawei, Tigo, and Enphase can use helioscope to compare production and loses data. The results suggest that MLPE produces superior outcomes.
Wind Turbine output power estimation using soft computing techniques
(COMSATS University Islamabad Lahore Campus, 2020) Waleed Iqbal,; FA17-REE-021; LHR TP 7884; Dr. Muhammad Yaqoob Javed
he exponential increase in world population has increased the energy demand. This has resulted in the accelerated use of more conventional energy resources like fossil fuels which has caused the exhaustion of these resources. This has also triggered an increase in pollution thus harming the environment, leading to global warming. So there is an urgent need of finding alternate energy resources that are more environment friendly and are to meet out increasing energy demands. Accordingly, renewable energy is the best option for this purpose. Unambiguously, wind energy is the most obvious option due to its abundance everywhere and all the time. The only drawback of using wind as reliable energy resource is its dependence on natural factors, especially wind speed which depends on climatic conditions and varies from place to place. The wind turbines harness mechanical energy from the kinetic energy of wind and convert it into electrical energy. Fortunately, the accurate estimation of wind speed is possible. The stochastic nature of wind speed presents a challenging situation in estimation of wind power output. In this research, the mechanical power of wind turbine (WT) has been estimated using nonlinear input variables like wind speed (v), angular speed of WT blades (ωr), pitch of blades (β) and power coefficient (CP). The estimation performed using feed-forward back propagation neural network (FFBPNN) , recurrent neural network (RNN) and (ANFIS MODAL). Results are then compared with all networks. Five cases are considered for neural network which are designed based on number of hidden layers, different learning rates and activation functions, Both networks are implemented under similar conditions. The networks are trained using scaled conjugate gradient (SCG) algorithm. The primary factor used for the performance evaluation of networks is root mean square error (RMSE) while training time is considered as secondary factor. While in case of ANFIS cases design on the basis of input ,output membership function type ,number of input membership function for each input variable ,in this case primary factor regarding performance evaluation become (RMSE), The best performance is achieved within NN from FFBPNN using two hidden layers containing 100 tan-sigmoid (tansig) and 50 log-sigmoid (logsig) nodes respectively with the RMSE value of 0.49% while as compared with ANFIS modal best performance achieved using Gaussian input membership function 0.00175429 , 0.17 % using three inputs membership function while linear output membership function.