Research Progress Presentation_Julai2022.pptx

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INTEGRATED COMPUTATIONAL INTELLIGENCE BASED TECHNIQUES FOR SOLVING ECONOMIC POWER DISPATCH PROBLEM IN POWER SYSTEM PLANNING, OPERATION AND SECURITY
Student Name: NOR LAILI ISMAIL
Student ID: 2019868418
Programme Code: EE950
Supervisors: PROF. IR. DR. ISMAIL BIN MUSIRIN
	                	  PM. DR. NOFRI YENITA BINTI DAHLAN
Part:  6

Introduction 
*  Economic dispatch (ED) is a process to determine the optimum output of power generated to meet the demand while fulfilling the equality and inequality constraints and producing the lowest possible cost.
*  This study aims to formulate a new optimization technique termed as Hybrid Evolutionary-Barnacles Mating Optimizer (HEBMO). It will be utilized to solve the economic load dispatch problems.
*   The depth is explored further by developing a new multi-objective optimization technique termed as Multi-Objective Hybrid Evolutionary-Barnacles Mating Optimizer (MOHEBMO) for solving the minimization of total fuel cost and emission of
 harmful gasses concurrently in a common fitness function.
*  Integration between the proposed HEBMO with artificial neural network (ANN) is expected to give benefit in the scheduling of input features of the generator and other parameters for the purpose of predicting total fuel cost and loss in power system operation. 
*  All the three proposed algorithms will be validated on 2 reliability test systems (RTS) to represent a small and medium power transmission model namely the IEEE 30-Bus RTS and IEEE 57-Bus RTS. 
*  Comparative studies will be conducted with respect to the traditional Evolutionary Programming (EP) and Barnacles Mating Optimizer (BMO) for the purpose of performance evaluation for HEBMO and MOHEBMO. 
*  
Objectives
1. To formulate a new hybrid technique termed Hybrid Evolutionary-Barnacles Mating Optimization (HEBMO)  for solving economic load dispatch.
2. To develop a new Multi-Objective Hybrid Evolutionary-Barnacles Mating Optimization (MOHEBMO) technique for minimizing coal consumption and pollution emission in the hybrid renewable energy of thermal-wind system.
3. To integrate the proposed HEBMO with ANN for scheduling of input features of the generator and other parameters for the purpose of predicting total fuel cost and loss in power system operation. This model will be termed as Integrated HEBMO -ANN.
5. 
30%
Obj 1=100%
Obj 2=100%
Obj 1=100%
Obj 2=90%
Progress Status
#1
Problem Statement
Objectives
Scope of Study
2
1
3
LR
Research Methodolgy
Data Collection and Analysis
Objective 2: Multi-objective Optimization-current work
w1 and w2= weight coefficient
Two conflict objectives optimized simultaneously by using weighted sum or scalarization method

The effectiveness of proposed technique, Multi-Objective Hybrid Evolutionary-Barnacles Mating Optimization (MOHEBMO) was compared with Multi-Objective Barnacles Mating Optimization (MOBMO) and Multi-Objective Evolutionary Programming (MOEP).

Ftotal=w1Fcost+w2Femission
Flowchart of MOHEBMO for cost and emission minimizing by applying weighted sum method
*  Flowchart of MOBMO for cost and emission minimizing by applying weighted sum method
*  Flowchart of MOEP for cost and emission minimizing by applying weighted sum method
*  
Related results for Obj 2
Gantt Chart
List of Publication
1. Computational Intelligence Based Technique for Fuel Cost   Minimization  in Small and Bulk Power- IJATCSE, Scopus indexed journal, April 2020.
2. Hybrid Optimization Based Technique for Ramp Rate Limit in Economic Dispatch Problem- Mathematical Statistician and Engineering Applications (MSEA). Scopus indexed journal.
Status of Thesis Writing
*   -Chapter 1 Introduction - 80%
*  - Chapter 2 Literature Review- 30%
*  Chapter 3 Formulations Of Hybrid Evolutionary-barnacles Mating Optimization   (HEBMO) For Economic Power Dispatch In Power System- 100%
*  - Chapter 4 Multi-objective Hybrid Evolutionary Programming -Barnacles Mating Optimization (MOHEBMO) Technique For Minimizing Generation Cost And Pollution Emission In Power System- 60% 
*  
*  
What’s Next? 
*  Integration of HEBMO and ANN for total fuel cost and loss prediction.
Thank you

Research Progress Presentation_Julai2022.pptx

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