Projects

If something is important enough, even if the odds are against you, you should still do it.
-Elon Musk



I am currently working on harmonic filters and

compensation of transients in microgrids.


Completed Projects:

  • Design XY Table Point to Point Motion Control, Using Integral Reinforcement Learning method:
    Modeling and Simulation in MATLAB for XY table have been conducted in this project. The design of the system space model and the system dynamics form the initial part of the project. The second part of the project focuses on output feedback design, integral reinforcement learning and also the implementation of the auto-tuning PID controllers.

  • Design Continuous Time Integral Reinforcement Learning for solving a power system’s optimal load-frequency control.
    The case study was adopted from Vrabie, D. et al. (2009). Adaptive optimal control for continuous-time linear systems based on policy iteration. Automatica, 45 (2): 477–484

  • Tighter Piecewise Linear Approximation of Generators Cost Function, MILP and Goal Programming Approach:
    This project presents a novel method for a tighter piecewise linear approximation of generating units’ cost functions. A mixed-integer linear programing (MILP) is devised to linearize the cost functions. The proposed formulation is based on the concept of goal programming in which the goal is to precisely determine an approximation of the original cost function. The solution provides the optimal segmentations in terms of break points and the associated slopes that minimize the overall deviation of the approximated curve from the original cost function. The proposed method is not limited to quadratic curves and is capable of approximation of various types of curves. The convexity of cost functions is not required, and thus the proposed method is applicable to convex and nonconvex class of functions.

  • Tighter Piecewise Linear Approximation Using Trapezoid Edges Relaxation:
    To Be Updated…

Unit 1

Arch-Chord Equal
Segmentation
Our
Method

Abs.
Error (L1 norm)

8222.4

4500

1885.1822

Max. Abs.
Deviation (L inf. norm)
87.51 22.5

16.34

Picture1
[1] http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5773468


  • A High Efficiency DC/DC Boost Converter for Photovoltaic Applications:
    In this paper, a non isolated interleaved, dc/dc boost converter with a high efficiency is proposed for using in photovoltaic system applications. For realizing zero voltage soft switching (ZVS), two active clamp circuits are used for each phases of the boost converter. By utilizing a voltage doubler configuration at the converter’s output terminal and connecting the secondary side of coupled inductors in series, high conversion ration can be achieved. The capacitor is also connected in series with output capacitors to transfer leakage energy to the output. Interleaved structure is used in input side to minimize current ripple and reduce magnetic component. So, the converter not only operates with a higher voltage gain, but also is able to operate more efficiently and can be used in photovoltaic (PV) applications.
    A High Efficiency DC/DC Boost Converter for Photovoltaic Applications. Available from:
    Link.

  • Multi-Agent Collaborative Model in Electrical Energy Market: A Distributed-Based Approach:
    This paper proposes a novel multi-agent collaborative algorithm to unveil economic potentials of local collaborations between generation companies’ (GenCos). We investigate the potential of local collaborations to further increase GenCos’ profit after market clearing by the system operator. A multi-agent based analytical target cascading (M-ATC), which is a distributed optimization algorithm, is developed to accommodate the need to model GenCos as self-interested agents. The main novelty of M-ATC is twofold. It enables the agents to simultaneously determine the amount of local energy exchange as well as the optimal cost for energy transactions. In addition, a novel dynamic cutting plane procedure is presented to convert the distributed model to an agent-based model. The proposed algorithm guarantees economic feasibility and information privacy of the self-interested agents in a complete regulation-free environment. The practicality and efficiency of the proposed model is tested on a unit commitment problem.
    Available from:
    Link .

  • Cyber/Physical Attack Mitigation by Electric Vehicles Charging Coordination:
    In this project the potential of plug-in electric vehicles (PEVs) as a new defending asset is analyzed for power grid resiliency. TBU
    Keywords—Cyber/Physical Systems, Electric Vehicles, Nash Equilibrium.

  • A Consensus-Based Decentralized Framework for Energy- Water Coordination in multi-area power systems:
    In this paper, a consensus-based decentralized optimization framework for multi-criteria objectives is presented to address the energy-water interdependency in a multi-area power system. TBU
    Keywords: Game theory, multi-criteriea optimization, decentralized/distributed optimization, multi-agent Systems, Consensus protocols.

  • A Consensus-Based Decentralized Lexicographic Optimization Approach for Multi-carrier Electricity-Water Coordination:
    This paper presents a consensus-based decentralized lexicographic optimization method for coordination of multi-carrier multi-area energy systems consisting of electricity and water. Interdependencies of the electricity and water systems are mathematically modeled, and a multi-objective optimization problem is formulated for electricity-water scheduling in each area. TBU
    Keywords: Multi-carrier energy systems, electricity and water, lexicographic optimization, decentralized optimization.