Department of Electrical Engineering

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Author: search.filters.author.Dr. Mujtaba Hussain Jaffery, Assistant Profesor [Supervisor]

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    Mitigation Of Harmonics Using Soft Computing
    (COMSATS University Islambad Lahore Campus, 2020) Muhammad Waleed Rafique,; SP18-REE-008; Dr. Mujtaba Hussain Jaffery, Assistant Profesor [Supervisor]; LHR TP 7467
    Excessive use of non-linear devices in industries results in current harmonics that degrade the power quality and has an unfavorable impact on the entire performance of power system. In this research, a Hybrid Shunt Active Power Filter (HSAPF) is implemented for compensation of reactive power and harmonic current component for balanced load by improving the power factor, total harmonic distortion (THD), and performance of the system. For extracting three-phase reference current for HSAPF, a novel control technique for harmonics mitigation based on Particle Swarm Optimization (PSO) and Fuzzy logic Controller (FLC) is proposed in this research. SIMULINK is used to implement Pq0 and Id-Iq control approaches for harmonics mitigation, with a PI controller for voltage regulation and a hysteresis controller for the reference current generation. Following a comparative analysis of both techniques i-e Pq0 and Id-Iq control techniques, implement the Soft computing technique (PSO) on PI controller technique on any of these techniques that perform better for harmonics mitigation. As the performance of the PI controller is dependent on its gains, the best gains produce better results and improve system response. The given results show that PSO technique is an efficient technique that helped to give better performance as compared to the conventional PI controller by improving steady-state response by giving the best gains to PI controller. Because of the system's robustness and non linearity, a single PI controller does not provide better performance So, by using gains obtained from PSO adaptively tuning of PI controller has been carried out by using FLC that can help to improve the dynamic performance of HSAPF. Therefore, from the comparative analysis, it can be inferred that PSO based Adaptive Fuzzy PI system has more efficient results with a minimum THD, improved stability time, and a power factor nearer to unity as compared to other techniques.
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    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.