Department of Electrical Engineering
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Item Modeling of High-Efficiency Hybrid PV-TEG System with Intelligent Control to Harvest Maximum Energy under Various Non-Static Operating Conditions(COMSATS University Islamabad Lahore Campus, 2022) By: Ali Nasir; , FA20-REE-009; Dr. Muhammad Yaqoob Javed Contributor(s): , Assistant Profesor [Supervisor]; LHR TP 8077As the world is moving towards cleaner energy to cater the effects of global warming, the existing renewable energy resources needs to be hybridized with other resources for better output using the same input as before. Photovoltaics-Thermoelectric Generator (PV-TEG) energy system is one such example. The energy from the sun primarily contains two components which carry energy i.e., visible and thermal spectrum, in normal operation of PV energy system, only the visible spectrum is utilized and a considerable amount of energy from the sun is wasted from the thermal spectrum in form of heat. High cell temperature and dynamic temperature spread (DTS) causes current mismatching problem and causes hot spots, resulting in either reduction of efficiency or permanent structural damage due to thermal stresses. The heat which is concentrated at the back of PV panels can be converted into useful energy using series parallel connection of TEG modules resulting in PV panels cooling as well as added energy at the output. For the PV-TEG energy system the controllability aspect is crucial as the main problem lies in the optimization and harvesting of energy from these two sources, the non-linear energy generation nature of the PV and TEG energy systems due to changing conditions i.e., partial shading (PS) and dynamic temperature spread (DTS), makes it hard to attain the full potential of PV and TEG systems using classical/analogue techniques of maximum power point tracking (MPPT). This research work employed the novel implementation Flying Squirrel Search Optimization (FSSO) to find the maximum power point (MPP) for PV-TEG energy system. Compared to already implemented algorithms, FSSO owns the distinctive superiority of simple implementation structure. Meanwhile, its own random and adaptive parameters selection principle greatly boosts the convergence performance which is lacking in all the existing algorithms. To validate the superiority of the results from FSSO, multiple case studies are made to compare the results with the existing promising algorithms such as Particle swarm optimization (PSO), Fruit-Fly optimization (FFO), Perturb and observe (P&O) and Incremental conductance (INC). The simulations confirmed the robustness of FSSO, as it achieves more power and improved tracking time compared to otherItem Performance Improvement of Smart Grid by Implementing Hybrid Load Forecasting Method Using Neural Networks(COMSATS University Islamabad Lahore Campus, 2024) Muhammad Asad,; SP21-REE-004; Dr. Muhammad Farooq-i-Azam Assistant Professor Assistant Profesor [Supervisor]; LHR TP 8065The increasing use of renewable energy resources in power systems worldwide has led to the development of smart grids, which allow for bidirectional communication between supply and demand. Accurate load forecasting is crucial for maximizing the efficiency of these smart grids, as it allows for the switching between renewable and other energy resources, reducing overall costs and transmission losses. Neural network-based load forecasting models are performing better than traditional statistical models. The hybridization of neural network-based models with optimized learning algorithms is the focus of our research. In this work, we propose a novel approach for load forecasting in smart grids. The approach includes four key components: refactoring the smart grid dataset for load forecasting, gathering a highly relevant set of parameters affecting domestic load, constructing four hybrid models with appropriate tuning parameters, and comparative analysis of these models. We devise proper format and structure the data used for load forecasting, ensuring that it is in the best possible form for accurate predictions. We carefully select the most important parameters for medium and short-term load forecasting, ensuring that the model is able to take into account the most relevant factors. We use an empirical approach to determine the best set of parameters for our models, ensuring that they are optimized for accurate predictions.We have modelled three machine learning hybrid models with ANN, hybridized with genetic algorithm, particle swarm optimization, and Levenberg Marquardt, along with one deep learning model RNN-LSTM. Our comparative results show that ANN-GA and ANN-PSO performed better in medium-term forecast with accuracy rates of 93.7 and 98.26 percent, respectively. While ANN-LM and RNN LSTM performed better in short-term forecast achieving accuracy rates of 97 and 99 percent, respectively. Our results demonstrate that our proposed approach is effective in achieving highly accurate load forecasts, making it a valuable tool for optimizing the performance of smart grids. iItem 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 Javedhe 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.Item Fuzzy Model Predictive Controller for Attitude Control of Satellite(COMSATS University Islamabad Lahore Campus, 2023) Rabiya Noor,; SP20-REE-008; ,Dr. Mujtaba Hussain Jaffery Assistant Profesor; LHR TP 7894The technological advancement in satellite has tremendously improved in recent years. Generally, satellite applications have been classified into four categories i.e., navigation, communication, weather and earth observation missions. The successful execution of these applications heavily relies on the performance of attitude determination and control subsystem of satellite. Therefore, researchers have been continuously working to improve the satellite attitude regulation under challenging space environment by applying various control strategies i.e., PD, PID, LQR, MPC. MPC is a famous control technique, as it can handle the operational constraints of the system effectively. In satellite, actuator torque has limited capability and it can result in the saturation problem. MPC can regulate the satellite attitude without violating the actuator constraints hence ensuring system stability. However, the mathematical model of satellite is highly nonlinear and for nadir pointing satellite, it can be treated as simple linear system for a constrained operating range. Although, it impacts the accuracy of the nonlinear model when a model-based controller is used. This results in a compromise between simplicity and accuracy that is faced by every control engineer. Therefore, in this work a systematic approach to improve the attitude accuracy has been explored while deriving linear subsystems of the nonlinear satellite model for the controller design. In this approach, the mathematical model of the plant is represented by various linear subsystems in terms of Takagi-Sugeno Fuzzy rules which are independent of each other and a control law is designed for each rule using MPC. The output of each MPC is accumulated based on Parallel Distribution technique hence providing command to the actuator. Eventually, the torque produced by the actuator is applied on nonlinear plant. Comparison of the response specifications (transient and steady state response) between PID, MPC and Fuzzy-MPC controllers under different cases such as external disturbance, actuator constraints, large reference angles and initial angles has been discussed. The simulation results indicated that proposed Fuzzy-MPC exhibits consistent and improved performance in all four cases in comparison to other controllers. The pointing accuracy is successfully achieved while catering to the challenge of actuator constraints under external disturbances of LEO.Item Utilization of modular DC distribution converters for residential buildings considering load variations: A through comparison with distribution system B(COMSATS University Islamabad Lahore Campus, 2022) Sidra Khan,; FA19-REE-014; Dr. Muhammad Jawad Associate Professor; LHR TP 7890The present era witnessed substantial research and development in the field of DC distribution because of swift escalation in the installation of solar panels on roof top and employment of loads (DC in nature) in residential sector. In this regard, the notion of DC microgrids has recently received a lot of study attention. Efficiency is the parameter that once wiped DC out of the power system because of the tussle of currents between AC and DC (the Tesla-Edison regime). Efficiency can therefore be regarded as the decision-making parameter for adoption of AC/DC at distribution scale. The main aim of this study is to compare the efficiency/energy savings of a AC power distribution network to a DC distribution power network in modern homes. There are various variables that determine when HVDC is superior than HVAC in power transmission systems, and the same is true in the case of distribution A number of significant variables have an impact on the effectiveness of distribution networks, particularly DC distribution network. The critical analysis indicated the loopholes in the DC distribution system efficiency or AC vs DC distribution system research studies that had previously been given.Moreover, the current research presents a modular approach on the distribution converters for efficiency enhancement of DC distribution systems.To achieve the aforementioned goals, the AC and DC distribution system models are implemented using real data traces of a modern home. The loads are classified based of their types, like AC category loads, DC category loads, and independent category loads. The independent loads can run on either an AC power supply or a DC power supply. The voltage levels are chosen considering the defined standards. The power electronic converters (PECs) are installed wherever required to drive a load in the premise e.g., AC loads in DC system are require an inverters (DC AC converter) for their operation.The load variation with respect to time and PECs variation with respect to loading are also considered in the analysis powered through utility grid as well as standalone solar system. Mathematical model is devised using bottom-up approach i.e., from load to the grid. The mathematical model is supported with Matlab simulations. Moreover, efficiency enhancement of distribution converters and in-turn efficiency enhancement of DC distribution system is also presented by employing modular architecture. A detailed comparison is established in the end among AC, DC and modular architecture-based distribution system. The analysis concludes with the DC system having an efficiency of 89.39% averaged over 24 hours of the day,Item Nowcasting of RSSL in wireless communication channel over the sea using machine learning algorithms(COMSATS University Islamabad Lahore Campus, 0023) Farwa Jafar,; FA20-REE-005; Dr. Khurram Zaidi; LHR TP 7896The presence of naturally occurring evaporation duct (ED) phenomenon is very high in the tropical/equatorial regions of the world. Although, refractivity estimation of EM and Radio waves in ED is well studied in the literature, still, the signal propagation through ED over-the-horizon needs to be thoroughly researched to help determine the received-signal-strength-level (RSSL) for a reliable wireless communication link. In order to accurately predict RSSL in ED, we have acquired RSSL (avg.) per-minute data for three months over-the-horizon distance of 50 km (Tx-Rx) from onshore-to-offshore Oil & Gas Platform. This data was collected using fixed antenna heights in ED. Applying deep learning algorithms on real-time RSSL data, we have nowcasted the future RSSL values for next 5 seconds timescale in this thesis. A thorough comparison is made between the CNN and LSTM deep learning methods for real-time series prediction analysis. These deep learning networks are linked with numerous convolution layers to grasp the nonlinear mapping between measured and future RSSL values. Coding and Simulation work is performed in Python 3.9 environment and results are generated in Kaggle Notebook. CNN and LSTM networks have never been used earlier for predicting “signal strength” over-the-horizon and over-the-sea under ED environment. The contribution of this research is to bridge this gap and examine the accuracy of LSTM and CNN for nowcasting RSSL data. According to what we've discovered, both of these neural network models are capable of achieving adequate to high prediction power given that the "datasets" are suitably big. Both methods, when taken as a whole, are reliable with regard to their hyperparameters. However, with increasing number of training courses, LSTM didn’t improve its performance, whereas CNNs performed correspondingly more accurate each time. For 3rd training, CNN has given the most optimal fitting of training data as compare to test data. The RMSE achieved for CNN after third training was 4.47 which is the least of all simulations. Hence, CNNs proved to be superior, since they operate one order of magnitude quicker than LSTM. We proposed that the early predictive capability, speed, and resilience of CNN open its door to nowcasting’s future.Item Propulsion control of aerial vehicle using online control allocation approach(COMSATS University Islamabad Lahore Campus, 2020) Javairia Mehar,; FA18-REE-004; Dr. Mirza Tariq Hamayun; LHR TP 7886Propulsion Control of Aerial Vehicle using On-Line Control Allocation Approach Control Allocation (CA) is one of the reconfiguration approaches to manage actuator redundancy in over actuated systems in an effective way, so that the fault tolerance can be achieved. There is a wide range of algorithms and approaches which include linear as well as quadratic programming-based CA schemes. Such schemes can deal with constraints on the control input, but these methods cannot be represented in explicit form.Reliability is an important property in Fault Tolerant Control domain and managing redundant actuators optimally plays a vital role to achieve this goal. In this thesis to handle the issue happening in actuators channels through Integral sliding mode FTC scheme by taking advantage of redundant actuators through online control allocation scheme designed for Propulsion Control application, (where only the engines thrust is available) for a safe landing. It is presumed that the health information of actuators is available for the proposed FTC scheme. Here, both lateral and longitudinal models of a large airplane are considered to design separate controller for each modes of operation. The viability level of the actuators is utilized by the control distribution conspire to reallocate the control signs to adjust engines push whenever actuators-failures happens without reconfiguring the controller. The main objective in this thesis is safe landing when system is totally dependent upon engines thrust. The effectiveness of the proposed propulsion control scheme against deficiencies is tried in simulations using MATLAB in view of an enormous vehicle airplane model. The basic purpose to design this scheme is emergency safe landing in case of total hydraulic failures. The proposed scheme using online control allocation is compared with direct control allocation to show its effectiveness.Item Design and Implementation of Industrial Internet of Things Smart Factory(COMSATS University Islamabad, Lahore Campus, 2020) Tahir Raza Jilani,; FA18-REE-023; LHR TP 7887; Dr. Abbas JavedWith the emanation of Industry 4.0 doors to new research fields are open and many industries are moving towards Industry 4.0. Smart manufacturing is a part of Industry 4.0 that connects the factory assets with the digital world and responds in real-time to meet the changes in the manufacturing process. This structure is adopted to improve efficiency and increase productivity and growth rate. This research has been divided into two phases. The first phase consists of the design and implementation of Industrial Internet of Things (IIoT), and the second phase is to develop the controller to maintain the pH quality of the manufacturing industry. In the first phase of this research, an IIoT-based smart factory is designed in which all sensors, actuators, and controllers are connected through communication and sensor layer. The idea behind a smart factory is to manufacture the products for the consumer and stakeholders at the right time. A user interface is also designed to connect the consumers and stakeholders with the industry. Asset and inventory is also monitored with the help of this interface. This smart industry is designed according to the Industry 4.0 standard called Reference Architecture Model of Industrie 4.0 (RAMI4.0). That is a fully integrated, collaborative system to meet the changing demand and customer needs. The second phase is to control and monitor the pH process to produce quality products. The control of pH is very important in the different manufacturing industries. The pH control is considered a benchmark problem due to its complexities, nonlinearities, and frequently changing dynamics. In the chemical industry, pH controlling is very important to produce quality products. The pH controlling process is based on the chemical reactions that occur in Continuous Stirred Tank Reactor (CSTR). In this research, a stable system with dead-time is considered for the pH controlling process. An intelligent rule-based FUZZY logic controller and, conventional PID controller is designed using the Ziegler Nichols tuning method. These controllers are designed to improve the quality of the product. Intelligent machine learning-based controllers are also developed to improve the quality. Performance analysis of tested models shows that material cost for FUZZY logic was 2.96% less than PID controller and 6.22% less than the time-based model. These models reduced the production time by 45 min. Elman, Layer Recurrent, and Feed-Forwardrward neural networks are designed to improve product quality. During the comparison of the trained models, it is found that the performance of Layer Recurrent NN is much better than other methods with a mean square error of 4.8401, and a standard deviation of 1.6049. The performance analysis of the trained model shows that the material cost of the FFNN model was 2.49%, with an error of 2.7%, while the material cost of the RNN model was 1.76% with an error of 2%. The computational time of the RNN model to predict the dosing quantity is 835.6 msec, while FFNN takes 709.5 msec for the prediction of dosing quantity.Item Impact Assessment of Electrical Vehicles on Main Grid and Optimal Charging Station Infrastructure Strategy(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Aizaz Mouhy-Ud-Din,; FA19-REE-011; Dr. Jehangir Arshad Assistant Professor; LHR TP 8068United nations have set sustainable cities and communities as a leading goal to protect the environment globally. The world’s transportation system has been shifting from conventional fuel vehicles to Electric Vehicles (EVs) to avoid greenhouse gas emissions. The Pakistan government has developed an EV policy that aims to shift maximum transport to EVs as the air quality index (AQI) of metropolitan cities has reached the highest AQI due to uncontrollable automotive gas emissions. The increase in EV sales and subsequent installation of EV charging stations may pose many problems to the existing power system. The purpose of this study is to evaluate the effects of electric vehicle charging on grid networks of Pakistan by creating charging profiles and propose an effective solar PV based hybrid generation charging station infrastructure with complete economic analysis using HOMER Grid software. Two charging profiles are developed, one using the SOC-based automated charging while the other profile is developed based on the desire of the EV owner using Python and HOMER Grid Software. The study concludes that EVs should be charged during the daytime with solar PV based Level 3 charging as this system has lowest Net Present cost i.e., USD 2.88 million, and has the lowest CO2 emissions. It is not feasible to install Level 3 charging station without supplement source such as solar distributed generation because charging station consumption reaches 1.58 GWh/yr. without using solar PV integration that will impact the grid.Item Handoff Management in Light Fidelity Networks(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Waseem Ahmad,; SP21-RCS-005; Dr. Muhammad Farooq-i-Azam; LHR TP 8057Handoff Management in Light Fidelity Networks Many technologies have been considered to handle the speedy developing range of cell phones and their extended requests for service providers. Among those technologies, Li-Fi is the one that can provide better communication and cover the spectrum shortage. However, Li-Fi has a short range and different service outages. Therefore, Li-Fi is integrated with Wi-Fi to provide better communication in the form of a hybrid Wi-Fi Li-Fi system. This thesis investigates handoff in hybrid light fidelity (Li-Fi) and wireless fidelity (Wi-Fi) networks, i.e., HLWNets. The hybrid network has more challenges to overcome the issue of the small spectrum regions of Li-Fi access points and the interference issue of two different networks. Li-Fi has a short coverage range, while Wi-Fi covers a great area. Due to this, to provide reliable coverage in HLWNets, we integrated Li-Fi networks with Wi-Fi networks. In HLWNets, we face the issue of frequent handoffs. Different methods have been introduced to resolve this issue. In this research, our proposed method is presented based on a signal-to-interference ratio (SINR) scheme instead of reference signal received power (RSRP) because it is a standard scheme but requires additional signals during transmission. The simulation results show that the proposed technique reduces frequent handoffs between Wi-Fi and Li-Fi. During the proposed method, we introduced the AWGN channel while transmitting data from the transmitter to provide reliable data on the receiver. This method also offers better throughput than a standalone Li-Fi system. We have developed a simulation network with handoff between fully working access points of hybrid Li-Fi and Wi-Fi. The simulation results work on the throughput and diminish the packet delay and drop ratio. The algorithm used for load balancing is based on evolutionary game theory for hybrid Li-Fi/Wi-Fi networks. The proposed greedy algorithm also overcomes the frequent handoffs in a hybrid Li-Fi/Wi-Fi network and provides rapid information transmission that improves the quality of service (QoS). We used vertical handoff and horizontal handoff based on the threshold. The greedy algorithm is used to decrease the data loss and improve throughput. The user is connected to a Li-Fi access point (AP) mostly. When light intensity decreases, we communicate to other AP based on the threshold value. If no high-intensity Li-Fi AP is available, the user is associated with Wi-Fi AP. The RSSI method moves the user from VIII Wi-Fi AP to Li-Fi AP. This process decreases the data loss rate and improves throughput and fast data transmission.