Usman's Website - Projects

Battery Model Parameters Estimation

This work focuses on accurate estimation of Li-ion battery parameters. A two-stage strategy is proposed which combines a universal adaptive stabilizer based adaptive parameters estimation method with an optimization technique. The battery parameters obtained from the adaptive estimation method helps to design a precise search space interval of each battery parameter. The precise search space interval in the optimization stage, therefore, results in accurate estimates of Li-ion battery parameters and reduction of computational time. This work has been published in IEEE Access.

Indirect Field Oriented Controlled Electric Vehicle Drive System

We developed an indirect field oriented controlled electric vehicle drive setup powered by a 400 V, 6.6 Ah Li-ion battery bank. The performance of a Fractional-Order Proportional Integral (FO-PI) controller was compared with a PI controller as a speed regulator. The battery bank voltage and current results of FO-PI controller outperformed that of the PI controller. This work has been accepted in 14th IEEE Vehicle Power and Propulsion Conference (VPPC), 2018, Chicago, USA, and also published in IET Electrical Systems in Transportation Journal.

Implementation of a Three-Phase (Space-Vector PWM based) Direct Matrix Converter

In this project, the aim was to reduce the computational time of a space vector PWM technique for a three-phase direct matrix converter. The alternate of trigonometric functions, primary cause of intense computation of space vector PWM, was proposed. We developed the hardware of a three-phase direct matrix converter to verify our proposed method. This work has been accepted in the 4th International Conference on Electrical Engineering (ICEE), 2017, Pakistan.

DC Motor Parameters Estimation

In this work, a high gain universal adaptive stabilizer based adaptive parameters estimation technique is utilized for accurate estimation of a permanent magnet DC motor. Unlike recursive and optimization methods, our method estimates DC motor parameters within a single experimental run. This work has been published in Elsevier Control Engineering Practice Journal.

Dynamic Friction Characterization of a Linear Servo Motor

A robust state space sinusoidal reference tracking controller was employed to estimate the overall friction faced by the platform attached to a linear voice coil motor. The proposed strategy estimate not only overall friction encountered by the platform attached but also able to compensate for it. This work has been published in Journal of Robotics and Mechatronics.

DC-DC Buck Converter with MPPT for Battery Charging

This project aims to charge a 12 V Lead-Acid battery using a DC-DC buck converter from 150 W solar panel. In this project, the performance of overall system is assessed under high frequency and under the partial shading of PV panel.

Distribution Transformer kVA Load Estimation via Smart Meter Data

In this project, a hardware-free novel strategy for a distribution transformer kVA load estimation is developed that operates at the utility server level without requiring a fixed power factor assumption or reactive power load information across all residential customers. This strategy is a simple and effective fixed point iteration-based formulation for a balanced secondary distribution network, that is extended for an unbalanced three-phase underground secondary distribution network. This work also includes a detailed theoretical analysis on the convergence and stability of the algorithm. This work has been accepted for publication in Elsevier Electric Power System Research journal, and IEEE Electrical Power and Energy Conference (EPEC) 2021, Toronto, Canada.

Real-Time Behind-The-Meter Solar Generation Disaggregation

The accurate information of behind-the-meter (BTM) PV generation is substantial for modern power distribution system operations, such as load forecasting, demand response (DR), and time-of-use (TOU) pricing. This work develops a non-intrusive method to disaggregate BTM PV generation from net demand data. The proposed Universal-Adaptive Stabilization (UAS)-based framework is unsupervised, robust, and does not require detailed dynamics of the system. This work is under review in IEEE Transactions on Control System Technology, and IEEE Power and Energy Society General Meeting (PESGM), 2022, Denver, Colorado, USA.