Department of Electrical Engineering

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Author: search.filters.author.Dr. Mujtaba Jaffery, Assistant Profesor [Supervisor]Subject: search.filters.subject.FA17

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    Financial Risk Assessment Based On Disaster Induce
    (Publisher COMSATS University Islambad Lahore Campus, 2021) Rafal Ali Sheikh,; FA17-REE-002; Dr. Mujtaba Jaffery, Assistant Profesor [Supervisor]
    The security, authenticity and protection of electric power framework is a major dilemma now-a days because it is not a single system many other systems are dependent on it. Severe weather conditions are badly effecting our power infrastructures causing billions of dollar of financial losses. Apart from the economic losses, power outages disrupt the lives of millions of people including industrial and commercial customers. United States has a huge power infrastructure and face many problems due to these disasters. The aim of this thesis is to assess the financial risk associated with disaster-induced power outages. These disasters include mostly severe weather events including hurricanes, thunder storm, wildfire, snowfall, heavy wind and winter storms. It is necessary to take precautionary measures to avoid huge losses and also save the humanity from disasters. The prediction of financial losses and the factors being involved for power outage events is important. To accomplish this task a detailed exploratory and statistical analysis is required to see the correlation between different key parameters. For this purpose a machine learning algorithm Random Forest is used on publicly available data set of the United States from 2000 2016, containing data of about 49 States and comprising of 51 different variables. Exploratory analysis is carried out to form a base for this research work. Random Forest is a useful classifier used both for classification and regression. In this algorithm predicted results are compared with actual output in order to find the error and accuracy. In this research random forest is used for the purpose of predicting the financial losses against each disaster category for the top 8 financially affected states of US. It has one more advantage of predicting the importance of the features being involved for the losses. For the evaluation of results three types of error are calculated including MAE, MAPE and RMSE. It was found that outage duration and customers affected have the highest importance for each disaster in parameters ranking, and the major losses are due to Hurricanes, thunders storm and winter storms.
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    Performance Analysis Of Mars Powered Descent Based
    (Publisher COMSATS University Islambad Lahore Campus, 2021) Adnan Khalid,; FA17-REE-017; Dr. Mujtaba Jaffery, Assistant Profesor [Supervisor]; LHR TP 5638
    It is imperative to find new places other than Earth for the survival of human beings, which can be in our solar system or outside the solar system. Mars could be the alternative to Earth in future for us to live. In this context many missions have been performed by different space agencies to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing of unmanned and manned missions on Mars. Mars landing consist of different phases (Hypersonic Entry, Parachute Descent, Terminal Descent comprising of Gravity turn and Powered descent), however in this work the focus is the powered descent phase of landing. Firstly, the main objective of this work is to minimize the landing error during powered descend landing phase. The second objective involves the constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID, LQR have been developed for the stated problem, however predictive control algorithm with constraint handling ability hasn’t been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it came out to be very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of different variants of predictive algorithm. Along with MPC, Multiple MPC, Explicit MPC and Multiple Explicit MPC are the variants that have been explored and implemented for powered descent phase. The comparison is done among the variants of MPC in terms of feasibility, hard constraints and computational time. Moreover, other conventional control algorithms like PID and LQR are used as a comparative study with the proposed predictive algorithms. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB. It was concluded that MPC and its variant can handle input/output constraints without causing feasibility issue