M.Phil / MS

Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/42

This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.

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A 3D Accelerometer Based Human Activity Classification
(COMSATS University Islamabad Lahore Campus, 2018) Aftab Paul,; FA16-REE-009; Contributor(s): Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]; LHR TP 7475
Recently, Automated Human Activity Recognition has been extensively used for long term health monitoring of healthy individuals and to provide assisted living for the elderly. Long-term health monitoring systems have been successfully implemented for the prevention of chronic diseases like heart disease, obesity, workers syndrome, and other diseases related to sedentary lifestyle. Activities of Daily Living such as sitting, standing, walking, working in office, jogging, and running etc. can be efficiently classified using sensors such as 3D accelerometers, gyroscopes, and magnetometers. Such systems have demonstrated very high classification accuracies for activities performed for longer durations of time. However, these systems are unable to detect and classify transitory activities where the subject switches from one basic activity to another. For example, if a subject stands up from the chair to walk out of the room and then goes downstairs to reach ground floor of the office building has transitions from stationary sitting to standing and then walking, walking to going downstairs and then walking again and these transitions may not be classified correctly by existing automated human activity recognition systems since the models are trained using nonrealtime segmented data for each individual activity class. This research aims to develop a system for the detection of transitory activities. A Mobile phone-based accelerometer is used to record these activities from the chest of subject through MyNeuroHealth application. Data is collected, pre-processed, and classified into different activity classes. This data is used to train Artificial Neural Network to classify transitory activities. The proposed system achieved an accuracy of more than 50% with real-time data. Furthermore, it is also observed that using two accelerometers for collecting the movement data can enhance classification accuracy to 65%. Given that little or no work has been done in this dimension of HAR, this research may be extended to improve the accuracy of HAR for real-time automated long term health monitoring systems. X
A Bi-Level Fuzzy Control Algorithm For Load Manage
(COMSATS University Islamabad Lahore Campus, 2020) Hafiz Umar Khan,; SP19-REE-015; Dr. Muhammad Naeem Shahzad, Assistant Profesor [Supervisor]; LHR TP 7604
The aim of this thesis is to deal with power dynamics of production and consumption by using the Bi-Level Fuzzy Control Algorithm for Load Management of Islanded Multi-Microgrids in the form of a new coordination control reconfiguration scheme has been discussed, in this system many grids in a cluster join and their SOC’s are charged by photovoltaic power. Generation is utilized for charging purpose and to feed the load surge. At time when imbalance of power generation of multiple system or discharging occurs, the fuzzy control algorithm efficiently anticipates in the system and fulfil the need of any other load or soc by any best suitable option in another microgrid system. The findings offered the good capability for these systems in the form of efficient production, less loss of power, more reliability and effectively power the load and charging using central controller. Primary and secondary controllers are being communicate by designed algorithms. These central controllers based on algorithms used to charge and share the power of multigrid in real time to balance the load of system. These systems are very useful in contemporary world. More upgradation is still in the future work of many researchers to comprehend the unending power consumption and production rise.
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.
A Hybrid Framework For Attitude Estimation Of
(Publisher COMSATS University Islambad Lahore Campus, 2020) Talal Ali Khan; SP18-REE-005; Dr. Khuram ALi, Assistant Profesor [Supervisor]; LHR TP 7471
Inertial Measurement Unit (IMU) sensors are being actively used for motion tracking or attitude estimation due to their low weighted structure and cheap cost. IMU is a fusion of gyroscope, accelerometer, and magnetometer. There are many applications of IMU sensors. Some of which are gesture recognition, estimation of vehicular motion and orientation of objects, etc. Mostly gyroscope and accelerometer are used to estimate the attitude of any object where magnetometer provides the heading and course information. The gyroscope provides short-term stability because of inherited bias. Due to this bias an error starts to occur as the difference between actual measurement and expected measurement of the attitude tends to increase with time. This error can be large as 70 degrees/hour. This problem can be solved by using accelerometer in fusion with gyroscope. Accelerometer that measures accelerations acting on a body due to gravitation and other exterior forces. It is relatively easy to estimate attitude when there are no outliers but in presence of external interference its measurement is contaminated which creates jitters and lag in measured values. As the external forces are always present that’s why it never accurate. This is why we use these sensors in conjunction so; they can complement each other to give accurate results. To solve this problem, we have introduced an effective technique in which complementary filter is used to give accurate attitude estimate of the body in the presence of prolonged dynamic conditions and gyro bias. Two filters are used in cascade form where one filter deals with precise attitude estimation of the motion of a rigid body and external acceleration through synchronicity of accelerometer and gyroscope and the other filter deals with gyro bias estimation, this enables the proposed architecture to be used in a different application with minimum calibration. A novel threshold-based method is used for external acceleration detection so that external accelerations can be dealt efficiently. This will help in changing the confidence level on the measurements of accelerometer in case of prolonged accelerations and also compliments attitude estimation. Second filter is designed in a way that it estimates bias in the by using attitude estimation and accelerations profile and then the bias is removed from the system. The architecture is also compared with different estimators existing in the literature under different dynamical conditions on different datasets.
A HYBRID MAC LAYER PROTOCOL FOR HETEROGENEOUS WIRELESS BODY AREA NETWORKS
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Hina Bushra; CIIT/FA14-REE-007/LHR; Dr. Ali Nawaz Khan; LHR TP 5627
The rapid advancements in wireless communication technologies and micro-electronics systems have fostered the development of small and intelligent micro-components that incorporate sensing devices and wireless communications into a single miniature circuit which is wearable or implantable inside the human body for medical and healthcare applications. These components when deployed over or across the body of subject communicate wirelessly to constitute Wireless Body Area Networks (WBANs). Since several WBAN nodes are sharing the wireless channel to report their collected data back to a base-station or sink node, the medium access mechanism must be robust, scalable, and energy efficient. Furthermore, it may also adapt to different data-rate requirements for different sensors in a heterogeneous WBAN. In this research, Adaptive Sleep and Dynamic GTS allocation algorithms in WBAN have been implemented and compared with the proposed protocol entitled Adaptive Dynamic MAC that takes into consideration number and type of sensors, variable data-rates and energy requirements through managing sleep/wakeup intervals for WBAN nodes and implementing adaptive schedules for communication within the network. Network performance is compared on the basis of network lifetime, throughput and channel utilization through analytical model that is also verified in a customized simulator. Simulation results are gathered by observing network performance under variable network energy conditions, change in the position of gateway node, and effect of heterogeneous sensor nodes on mentioned performance parameters. It has been shown that Adaptive Dynamic MAC performs better than Dynamic GTS Allocation framework in all mentioned parameters. Furthermore, proposed algorithm has better network lifetime and throughput characteristics in comparison to baseline Adaptive Sleep algorithm.
A Novel Renewable Powered Stand Alone Electric
(COMSATS University Islambad Lahore Campus, 2021) Hira Asghar,; SP19-REE-002; Dr. Muhammad Jawad, Assistant Profesor [Supervisor]; LHR TP 7476
The depletion of fossil fuels, gradually increasing environmental pollution and global warming lead to the electrification of the transport sector because the transport sector is one of the main reasons for the rising environmental concerns. However, the increasing number of electric vehicles (EVs) entering the power grid upsurges the electricity demand from the grid as it requires a massive amount of electricity to fulfill its charging needs. The existing electric grid and EV infrastructure is not capable enough to support increasing penetration of EVs, therefore the anticipated increase in the EV count brings new challenges regarding the EV charging needs. Most of the existing works have shared a common drawback of charging EVs by electricity provided from the electric grid. Thus, the primary purpose of utilizing EVs as a solution to decrease pollution is emitted due to the shifting of carbon emission from transportation to the electricity generation sector. This necessitates the usage of renewable energy resources, for instance, solar and wind energy to charge the electric vehicles to attain the environmental and economic benefits of EVs. Although renewable energy seems to be a promising solution due to its instability it may lead to an insufficient energy supply and cause an incomplete or interrupted charging of EVs.
A Research On Clock-Synchronous Sleep And Wake
(Publisher COMSATS University Islambad Lahore Campus, 2016) Usama Masood; FA12-MSEE-018; Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]; LHR TP 6980
A Research on Clock-Synchronous Sleep and Wake Scheduling Scheme in Wireless Sensor Networks Network Wireless Sensor Network (WSN) consist of a unique set of resources like on-board battery and wireless communication devices with limited bandwidth. WSN offers a wide range of applications for monitoring space or targets. WSN is capable of performing simple processing tasks like tracking, detection of an event, or classification and may consist of multiple nodes that can process the information and communicate with nearby nodes in real-time for environmental monitoring, event detection, surveillance, object tracking, battlefield situation monitoring, and data collection etc. However, there are certain limitations in deploying WSN efficiently such as in terms of limited power resource for a single node in WSN, limited processing capability and varying network life time. It had been shown that wireless communication to and from sensor nodes consumes significantly more battery power in comparison to power expended in sensing, computation and memory access procedures. One of the possible solutions to this problem is to communicate as sparingly as possible through efficient sleep/wake scheduling for WSN nodes to extend node and network lifetime. A major research issue in WSN is to develop an energy efficient MAC protocol that not only provides increase in network lifetime but also addresses latency. In this research, a new MAC protocol is designed using sleep/wake scheduling for WSN. Though energy consumption in WSN is unavoidable due to communication necessity and for different stages like idle listening, retransmission, channel sensing and overhearing; this proposed protocol will help in decreasing this energy consumption. Energy efficiency and latency of the proposed sleep/wake scheduling scheme is evaluated and compared with the state of the art research. An AEL (Accounting for Energy and Latency) factor is introduced which is the deciding element for active and sleep cycles of the node. This said AEL factor defines the minimum duty cycle among the network nodes and is specified prior to nodes deployment depending upon application requirements. In the proposed protocol, the nodes adjust their duty cycles according to this AEL factor depending upon traffic load, their position and their connectivity in the network. The effect of this AEL factor on energy efficiency and delay is evaluated for different network densities in this research thesis. While incorporating sleep/wake scheduling for energy efficiency, delays are added in the network to route packet from node towards sink. Therefore, it is necessary to address latency for MAC protocol especially for delay constrained applications. This research thesis focuses on sleep/wake scheduling scheme ensuring energy efficiency, decreased latency and increased network lifetime by selecting an appropriate AEL value. The research includes the comparison of the proposed protocol with state of the art research and has shown significant percentage improvements in energy efficiency and delay from S-MAC and Anycast protocol
A Research On Feasibility Of Intra Vehicle
(Publisher COMSATS University Islambad Lahore Campus, 2018) Naeem Mirza,; FA13-MSEE-016; Dr. Ali Nawaz Khan, Assistant Profesor [Supervisor]
In Intra Vehicular Wireless Sensor Network (IVWSN), Engine Control Units (ECUs) gathers information about the vehicle from the sensors over wireless channels. This information is then transferred to On-Board Unit (OBU) through Controller Area Network (CAN) in order to monitor and maintain vehicular operations. CAN bus is widely used wired media for communication between ECU and sensors by several car manufacturers but it requires very careful sensor deployment and wiring. In this research thesis, we have achieved low cost and energy efficient communication between (1) sensor nodes and ECU using CAN bus and (2) OBU and ECU within Vehicular Ad Hoc Network (VANET) using Bluetooth low energy (BLE) CC2540/CC2541 modules that can be readily employed in densely urban environment for wireless inter vehicular (up to 50m range) as well as wireless intra vehicular (up to 10m range) communication. The BLE system was invented for the purpose of transmitting small packets of data at once, while consuming less power than Basic Rate & Enhanced Data Rate (BR & EDR) and IEEE 802.15.4 standard based ZigBee devices. A typical communication scenario involves either a Peripheral/ Central or Broadcaster/ Observer device pair. For our tests, we have chosen the Peripheral/ Central modes as they provide greater flexibility to configure application parameters and allow bidirectional communication. We have included comparison between BLE and CAN bus for communication between (1) ECUs and sensors and (2) ECU and OBU, in terms of energy efficiency, throughput, latency and coverage area. Furthermore, wireless communication between ECUs and sensors had been a
Active Power Management Of Standalone DC Micro And
(Publisher COMSATS University Islambad Lahore Campus, 2020) Tahir Riaz Sindhu; , FA16-REE-004; Dr. Muhammad Yaqoob Javed, Assistant Profesor; LHR TP 5629
Standalone DC microgrids are emerging as an efficient solution for integrating renewable energy sources in remote and off-grid areas. Effective active power management is essential to ensure system stability, reliability, and optimal utilization of available resources. This paper presents a comprehensive approach to active power management in a standalone DC microgrid incorporating renewable energy sources such as solar photovoltaic systems, energy storage units, and varying load demands. The proposed control strategy dynamically balances power generation, storage, and consumption by prioritizing critical loads and maintaining DC bus voltage within acceptable limits. Advanced control techniques, including droop control and energy management algorithms, are implemented to enhance system performance under fluctuating conditions. Simulation results demonstrate improved power sharing, reduced energy losses, and enhanced system stability. The study highlights the importance of intelligent power management strategies in achieving sustainable and reliable standalone DC microgrid operation.
Adaptive Nonlinear Gain Approximation of a Robust Nonlinear Controller to Handle Uncertain Disturbances for UAV Systems Using Neural Networks
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Waqas Ahmad; CIIT/FA23-REE-003/LHR; Dr. Mirza Tariq Hamayun; LHR TP 10045
The quadrotor unmanned aerial vehicle (UAV) has been a part of scientific studies owing to its applications in military, agriculture, infrastructure and construction, and environmental monitoring and conservation. However, its control design is challenging due to its nonlinear underactuated dynamics, external disturbances, parametric uncertainties, and the aerodynamic variations encountered during flight operations. Sliding mode control (SMC) is the most widely used control technique for handling nonlinear dynamics in the presence of external disturbances and modeling uncertainties. However, conventional SMC requires a priori knowledge of the upper bound on the overall disturbance at all times. Usually, these upper bounds on disturbances are derived from past data, environmental conditions, and empirical modeling; therefore, a conservative bound is used in SMC design, leading to high control effort and draining more energy from the system. To address this issue, adaptive sliding mode control (ASMC) strategies based on a neural network (NN) are presented in this dissertation. Neural networks are recognized as the leading approach to intelligent computation and are highly effective in handling nonlinearity, fault tolerance, adaptation, and continuous online learning. In this research, NN-based ASMC strategies are presented for a quadrotor UAV to improve flight performance by online estimation of adaptive modulation gain in response to variations in disturbance magnitude. Different neural network schemes are employed, including feed-forward neural networks (FNNs) using backpropagation (BP) and Levenberg-Marquardt (LM) algorithms for optimization, and a radial basis function neural network (RBFNN) with a Gaussian activation function, to adaptively estimate the modulation gains. Furthermore, the stability analysis of the proposed controllers is proven using Lyapunov theory.
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.
Analysis and modeling of lithium iron phosphate (LiFePO4) batteries for state of health estimation
(COMSATS University Islamabad Lahore Campus, 2022) Hamza Bashir,; FA19-REE-016; , Assistant Profesor [Supervisor]
Li-ion batteries have been chosen as the best candidate for portable, mobile and high power applications due to their high energy densities, long lifetimes, and high efficiencies compared to other battery types, like lead-acid and nickel-based batteries recently. On the other hand, these batteries are prone to failure due to charge imbalance in batteries linked in series or parallel, which may be catastrophic. Therefore, they must be carefully monitored in real-time. So, the Li-ion cells chemistry batteries have exceptional performance compared to other chemistries, but only if treated well (cell balancing, protection from overcharge, over-discharge, and short circuit conditions). Despite the advantages of lithium-ion batteries, these batteries still have a short life due to particular aging processes inside the batteries and improper cell balancing, which become evident under defined conditions. Reliable battery calendar ageing prediction, on the other hand, remains a crucial yet complex issue for improving the performance of related storage devices. So, manufacturers and researchers need to investigate the cell balancing process and aging processes in lithium-ion batteries to determine the aging effects that occur and the factors that cause a rapid decrease in their lifetimes. Nowadays, Lithium iron phosphate (LiFePO4) based chemistry batteries are considered to be one of the most valuable lithium-ion batteries in the market because of their high energy density, lack of memory effect, lower self-discharge, long lifetime, large cycle life number, inherently safe cathode structure under critical conditions, and non polluting characteristics. In this thesis, 3S-1P battery pack cell balancing system is implemented by using passive cell balancing technique including the thermal effects and an aging prediction model has been developed for lithium iron phosphate (LiFePO4) batteries using MATLAB/Simulink©, which has been able to predict the aged effect in terms of capacity fade and internal resistance increase. Thevenin equivalent circuit model is utilized and SOH is estimated. Parameter estimation is done using soft computing technique. Overall, different models of SOH estimations are studied and reviewed. The results are developed in MATLAB Simscape and further extended in MATLAB scripts. Possible effects of cell balancing and internal resistance has been discussed in the conclusion.
Analysis of the islanded microgrid resilience under normal and extreme events using soft computing techniques
(COMSATS University Islamabad Lahore Campus, 2020) M. Shebaz Asiam,; FA18-REE-014; Prof. Dr. Sobia Baig Assistant Profesor [Supervisor]; LHR TP 7885
Analysis of the Islanded Microgrid Resilience under Normal and Extreme Events using Soft Computing Techniques Smart operational strategies are considered cost-effective through the implementation of microgrids (MGs). Their implementation can provide a continuous power supply for critical loads during these extreme choice events. It is reliable for increasing distributed system resilience against extreme disruption events because of its operational flexibility, islanding capability, and consumer engagement when a system malfunction is detected. The electrical power distribution system resilience must be enhanced to ensure continuous power during natural disasters. Smart operational strategies and system hardening may be applied to achieve this. When the power transmission and distribution network is subjected to extreme disruptions, power system hardening measures are widely used to secure the network. However, hardening the entire network will increase the overall cost. Smart operational strategies are considered cost-effective through the implementation of microgrids. The maximum capacity load considered for normal load mode is 1000 kW rating, the medium-capacity load for energy conservation is 500 Kw, whereas the critical load for an emergency is 250 kW. Their implementation can provide a continuous power supply for critical loads during these extreme events. It is a reliable choice for increasing distributed system resilience against extreme disruption events because of its operational flexibility, islanding capability, and consumer engagement when a system malfunction is detected. In this study, a bidirectional “Fuzzy Logic Controller (FLC)” has been realized to deal with system resilience during islanded grid mode/operation in the event of a major disaster as well as in normal conditions. The system involves the utilization of solar, wind, battery, and load. We propose to develop a control algorithm for the system's current state. This algorithm allows effectively boosts the resilience and makes the optimal selection between the emergency, energy conservation, and normal modes. The designed fuzzy rule-based system takes appropriate action depending on the current state of solar power, wind power, battery state of charge (SOC), and wind-solar forecast. This research work has produced simulation results in MATLAB/Simulink to demonstrate the system’s resilience in normal and abnormal conditions. Moreover, a performance comparison between the presented strategy and “Proportional Integral Derivative (PID)” controllers has been carried out. The main aim is to compare the performance of a MG’s load modes for the normal situation as well as for disrupted events. For comparison, the developed system is tested for various scenarios. From the simulation results, it is evident that the proposed FLC effectively boosts
Anomaly Detection And Short Term Forecasting
(Publisher COMSATS University Islambad Lahore Campus, 2021) Taimoor Muzaffar Gondal,; FA19-REE-012; Dr. Ikramullah Khosa, Assistant Profesor [Supervisor]; LHR TP 7473
Forecasting of Electric load always had an empirical impact on power operations. It is the most challenging and critical attribute of energy management system planning and operation. With the emergence of smart grid (SG) technology, the conventional grid has been promoted to a modern digital grid which ensures robustness, reliability, safety, and economic viability to the existing power systems. In contrast, it has introduced complex information and greater threats for electric load forecasting. As a result, cybersecurity has become a major challenge for the existing grid. Cyber attacks have numerous types but the most important one is the data integrity attack which results to reflect wrong projections. The data integrity attacks are termed as anomalies in time series data. Such anomalies can malignantly reshape a specific segment of input load data, which results in a remarkable deviation from original values. In this thesis, an anomaly detection model and a short-term load forecasting based model have been developed. Initially, anomaly detection has been implemented on electrical load for the data of duration three years. The LSTM model is trained and used to predict the electrical load and the mean square error is computed on test data. Based on maximum observed error, a threshold is selected to detect anomalies in the total data. After anomaly detection, the short-term load forecasting is performed using LSTM and Bi-LSTM methods and the results are compared on the basis of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Additionally, the computational complexity analysis for both algorithms is carried out. For short-term forecasting, hourly data of electrical load is considered. The Bi-LSTM model produced the MAPE of 8.79% while simple LSTM model showed 14.85%. The Bi-LSTM outperformed the conventional LSTM model with a margin of 6% MAPE overall. However, the computational time of LSTM model was short, almost 60% of the time taken by the Bi-LSTM model.
Application Of Superconductors To Improve
(COMSATS University Islambad Lahore Campus, 2020) Amjad Ali,; SP18-REE-016; Dr. Muhammad Jawad, Assistant Profesor [Supervisor]; LHR TP 7465
With ever increasing demand of electricity and depleting conventional energy sources with their environmental concerns, renewable energy sources are an excellent alternative for producing clean energy. Among many renewable energy sources, wind energy has huge potential to contribute part of energy demand. However, as a result of unprecedented integration of wind energy in low voltage and high voltage grids, ensuring stability and power quality following grid codes is a challenge lately. Wind Energy Conversion System (WECS) suffers from two main problems, 1) fluctuations in output power due to unpredictable behavior of wind, and 2) retention of connection during grid faults. The optimal solution to increase Fault Ride Through (FRT) capability and smoothing of output power fluctuations in PMSG based WECS is still a point of research in literature. In this research Work, the application of superconductors to enhance the performance of Permanent Magnet Synchronous Generator (PMSG) based WECS is investigated. The presented work considers optimal integration of Superconducting Coil (SC) with PMSG based WECS. Fractional Order PI (FOPI) control is used for control of SC circuit. Harmony Search (HS) optimization technique is used for finding optimum parameter values of the SC and FOPI controllers. The proposed strategy is then compared with PI based control of SC for disturbance in the form of wind gust and grid fault.
Area Efficient Design of Memristor based SRAM Cell for Cache Memory
(COMSATS University Islamabad Lahore Campus, 2023) Hafiz Muhammad Abubaker Shakoor,; SP21-REE-009; Dr.Ejaz Ahmad Aansari; LHR TP 8066
Artificial Neural Network Modeling Approach For
(Publisher COMSATS University Islambad Lahore Campus, 2021) Syed Muhammad Saad Farooq; , SP19-REE-001; Dr. Mujtaba Hussain Jaffery, Assistant Profesor [Supervisor]; lhr tp 7464
circulating Fluidized Bed (CFB) gasifiers are used to convert solid fuel into liquid fuel. Artificial Neural Network (ANN) and Neuro-fuzzy controllers have immense potential to improve the efficiency of the gasifier because Circulating Fluidized Bed gasifiers exhibit complex computational behavior and nonlinear process, based on their thermodynamic and electrochemistry. The focus of this report is to discuss Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling approach to estimate solid circulation rate at high pressure in the Circulating Fluidized Bed gasifier. The data obtained on laboratory scale prototype in chemical engineering laboratory which is already published in the literature review to observe the flow rate of biomass solid fuel. Both, ANN and ANFIS model worked on 217 samples of experimental data, in which pressure (𝑏𝑎𝑟 − 𝑎𝑏𝑠), single mean diameter (SMD), total valve opening (𝑐𝑚/𝑠), mass flow rate (𝑔/𝑠) and riser dp (𝑚𝑚 − 𝐻20) have been included as the major focus of the study. Moreover, Neural Network toolbox and Neuro fuzzy toolbox are used in MATLAB 2019a. These two different architectures of neural network i.e. Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) use four input features and one output feature with multiple neurons in the hidden layers, to estimate the flow of solid particles in the riser. The output results are compared based on their Mean Square Error (MSE), Regression analysis(𝑅2), Mean Average Error (MAE) and Mean Absolute Percentage Error (MAPE). This report discusses in detail about the superiority of Neuro-Fuzzy controller over Artificial Neural Network. Each input is important variable for Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) model for the improvement of Circulating Fluidized Bed performance in terms of syngas and input feedstock to boiler.
Attitude Control Of Satellite Using Computationall
(Publisher COMSATS University Islambad Lahore Campus, 2020) Muhammad Shah Rukh Ahmad,; CIIT/FA12-MSEE-011/LHR; Dr. Mujtaba H. Jaffery, Assistant Profesor [Supervisor]
Attitude control System (ACS) of spacecraft performs the attitude control to counter the effects of disturbances present in the space environment. MPC (Model Predictive Control), controller is a control algorithm which uses the mathematical model of the plant and also considers input and output constraints for the calculation of optimal control law in real-time. The issues with the conventional MPC are its computational time and feasibility at each sampling instant. The larger computational time and infeasible optimization solution in real-time applications is a cause of concern. Previous work has only considered the implementation of MPC by considering the inputs constraints. The use of MPC to consider output constraints and effect of feasibility and computation time has not been explored. Therefore computationally efficient two variant algorithms of MPC i.e., Optimal MPC (OMPC) and Laguerre OMPC (LOMPC), have been selected as control algorithms to study feasibility and computational time by linking dual mode, closed-loop paradigm and Laguerre function techniques for linear and non-linear model. Linear model approximates non-linear model because of small angle approximations. Also changes in satellites attitude angles because of disturbances fall in the domain of small angles approximation regions. The effect of changing the position of Laguerre Poles on the closed loop performance and computation time was explored. The input constraints were considered on the control torque, whereas output constraints were considered for roll, pitch and yaw angles. The conclusion is that the in the case of linear approximated model, use of OMPC algorithms with Laguerre functions improves computational time. Both OMPC and LOMPC do not affect the closed loop performance even if infeasible solutions are produced. When OMPC and LOMPC algorithms were applied to the non-linear model, diverging non-zero steady-state errors were introduced and plant became unstable. It was evident from the results that OMPC and LOMPC were not able to control the non-linear model.
Automated Water Filtration for Modern Urban Infrastructure
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Soman Qaseem; CIIT/SP23-REE-001/LHR; Dr. Fawad Azeem; LHR TP 9996
To provide clean drinking water that is free of impurities, harmful bacteria, and chemical toxins, Water filtration plants are essential. Due to an increase in the urban population, the demand for higher-quality water has also increased. These filtration plants help by reducing the reliance on tap water, which in turn helps prevent waterborne diseases as they play a vital role in supporting industrial, agricultural, and municipal services through a pure and consistent water supply. Due to the demand for clean water in modern urban environments, it has become necessary to implement systems of advanced water filtration. Nowadays, water filtration systems often suffer from inefficiencies and are subject to human error, due to which manual monitoring and control of the water filtration process can be unproductive. Integrating an automation system with a water filtration plant provides an ingenious way to significantly improve efficiency by making real-time adjustments based on the demands and changes in water quality. This will also help minimize operating costs and further provide optimization of resources, which in turn can help the system to proactively identify failures and issues, such as filter clogging or chemical imbalances, allowing for a timely intervention. A water filtration automation system can be a complex network that manages the filtration process by constantly monitoring water quality parameters and system parameters, including flow rate, pressure, and water quality sensors, which provide real- time data for monitoring and decision making.
Blood glucose forecasting in type 1 diabetes mellitus patients using machine learning techniques
(comsats university Islamabad lahore campus, 2022) Hatim Butt,; FA19-REE-009; LHR TP 7889
Diabetes Mellitus is a metabolic disease that causes the body to lose control over blood glucose regulation. Patients with Type 1 diabetes completely rely on insulin therapy by themselves or using some automated insulin delivery systems. In both the cases, it is pertinent to have good estimate of future blood glucose levels. An efficient diabetes management demands accurate prediction of future blood glucose levels, failure of which results in short and long term health complications. With the modern exordium of quantified-self such as continuous glucose monitoring(CGM) systems, a patient can have access to their personalized glycemic profile which can be utilized for accurate prediction of future blood glucose levels. In recent years, machine learning methodologies have sparked a lot of interest in predicting glucose levels in diabetic patients, leading to the development of a variety of methods and techniques. However, the prediction accuracies of these methods are not good enough to be declared them as reliable predictors for evaluating glycemic conditions. In this research work we utilized multi-layered Long Short Term Memory(LSTM) network a famous deep learning technique based on recurrent neural network(RNN) for making prediction of blood glucose levels in patients with type 1 diabetes. The proposed framework predicts the future blood glucose level using Ohio T1DM dataset at prediction horizon(PH) of 30 and 60 minutes. Experimentation was also carried out on better feature representation to model in order to achieve higher prediction accuracy. The effect of different input feature sets, towards improvement of prediction accuracy was also been investigated. The results on Ohio T1DM Dataset (2018), that contain eight weeks’ worth of data shows that our method achieves the lowest RMSE score of 14.76mg/dL and 25.48mg/dL for prediction horizon of 30min and 60min respectively. The obtained results are the best known as per our knowledge using this dataset. The proposed methodology can be utilized in closed loop systems for precise insulin delivery to patient for their better glycemic control.