Department of Electrical Engineering

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Author: search.filters.author.Dr, Muhammad Yaqoob Javeed, Assistant Profesor [Supervisor]

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    Robust Kalman Filter For 2-D Nonlinear Systems
    (Publisher COMSATS University Islambad Lahore Campus, 2020) Talha Zeb,; SP18-REE-030; Dr, Muhammad Yaqoob Javeed, Assistant Profesor [Supervisor]; LHR TP 6449
    Kalman filter (KF), being an optimal estimator for linear systems and known statistics of Gaussian noise, is an optimal filter thus widely used method for state estimation. However, the central obstruction in its implementation is that the dynamics of the model must be exactly known. Many advances in robust control and estimation to reduce the effects of model uncertainties have attracted various studies on robust Kalman filtering. There are three renowned approaches for robust linear state estimation i.e. H∞ filtering, set valued filtering and guaranteed cost (GC) filtering; all of them are used with Kalman filtering framework due to its wider applicability. Since, the exact minimum of estimation error variance is not mathematically computed, the usual choice, therefore, is to enforce a constant upper bound on error variances for all acceptable uncertainties. This upper bound is then minimized instead of obtaining (global) minimum variance. Thus, the robust KFs (RKFs) are sub-optimal but adaptable to practical scenarios. Since, all of the above methods perform de-regularization, they either suffer from inherent conservativeness or existence conditions. Practically, these conditions cannot be ensured to be satisfied at all arbitrary iterations. If the conditions are not met at any iteration, the filter may perform poorly. However, a RKF based on least square (LS) approach employed unique Regularized Least Square (RLS) criterion for uncertain models. The distinctive features of this method are it performs regularization thus avoids existence conditions and vector optimization problem is reduced to scalar minimization problem. Hence, this approach, according to the best of our knowledge, is most suitable for online implementation.
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    Performance Enhancement Of Maximum Power …
    (Publisher COMSATS University Islambad Lahore Campus, 2023) Muhammad Saqib Ashraf,; SP17-REE-022; Dr, Muhammad Yaqoob Javeed, Assistant Profesor [Supervisor]; LHR TP 6442
    The demand for electricity has been increased tremendously in the last decade due to the rapid increase in population and exponential growth. The main source of electricity is the conventional energy sources i.e., coal, petroleum and natural gas, etc. However, these non renewable energy sources are getting depleted quickly and putting adverse effects on the environment by the emission of greenhouse gases. Moreover, the current conventional energy system is also facing difficulties in fulfilling the electricity demands, due to increasing power outages, coal prices, and the amount of electricity wastage during transmission through the grids. Therefore, renewable energy sources i.e., solar energy, wind power, and hydroelectricity have been getting immense popularity in the world and they have very little effect on the environment. However, solar energy has numerous advantages over others. Therefore, researchers have been investigating various techniques to convert solar energy to electricity with maximum efficiency. One of the critical problems is the complex partial shading, this occurs when the clouds or any other object stops the light from hitting the panels and this results in two or more peaks that globalize the MPP and it becomes a nonlinear problem. To solve this problem, in this research work a hybrid technique based on Perturb and Observe (P&O) and Dragonfly Algorithm (DA) has been implemented to track maximum power point under both partial shading and complex partial shading scenarios. The simulations are done on Simulink MATLAB and different techniques i.e., P&O, DA, Particle Swarm Optimization (PSO), and Cuckoo Search Algorithm (CSA) are compared with the purposed hybrid technique under four different irradiances i.e., uniform irradiance, partial shading 1, partial shading 2 and complex partial shading. In all cases, the results have been compared and it shows that the developed technique is superior to the compared techniques in terms of transient and power loss. Similarly, GMPP is tracked faster than any method compared with, because the P&O which works before DA reduces the search space and time to achieve GMPP as well.