TEACHING LEARNING BASED OPTIMIZATION ALGORITHM(a solution to find global optimization)

Guided by: Prof. L.N. PathyBiswaranjan (1321209035)Jayaprakash(1201209161)Prajna (120120181)Rajnikanta(1201209207)Sherin(1201209158)Swostik(1201209191)

Contents

• Motivation• Economic load dispatch• Generators used in power plant• Methods for solving economic load dispatch• Introduction to TLBO• Teacher phase, learner phase, self earning phase• Advantages and disadvantages• Progress• Future work

MOTIVATION

This algorithm is purely based to our day-to-day life , how a student behaves inside a class,What he learns from the teacher & from his friends & viceversa overall how it affects him to optimize his performance (positively).So we choose to do our project on this concept.

ECONOMIC LOAD DISPATCH

Economic load dispatch is a process of scheduling the required load demand among available generation units so that the overall cost of generation is minimized.

TYPES OF GENERATORS USED IN POWER PLANT

1.Hydro power plant – Zero operating cost.So it is not included in ELD but can be used for

hydro thermal scheduling.2.Nuclear power plant- Operates at constant load So it not included in ELD.3.Thermal power plant

So it come under Economic Load Dispatch Cost of generation of thermal power plant:

Fi(Pgi)=ai*Pgi2+bi*Pgi+ci

where Pgi=output of ith unitai,bi,ci=constant coefficients for ith unit.

Problem formulationOBJECTIVE FUNCTION :- Min F(Pg)=total cost =subjected to: 1. Equality constraint

Pd = 2.Inequality constraint

Pgi(min) ≤ Pgi ≤ Pgi(max)

Where =cost of generation of ith unit Ng=number of generators Pd=total load or demand Pgi(min)= minimum output of ith unit Pgi(max)=maximum output of ith unit

TYPES OF METHODS TO SOLVE ECONOMIC LOAD DISPATCH PROBLEM

-- CONVENTIONAL METHOD:-- Lagrangian multiplier method.- Non-linear based algorithm.

- Integer Programming problem- Hessian Matrix

- SOFT-COMPUTING METHODS:-- particle swarm optimization .- TLBO(Teacher learning based optimization ).- Genetic algorithm etc.

PROBLEM FOR ECONOMIC LOAD DISPATCH

• PROBLEM:-The fuel cost functions for three thermal plants in rupees/h are given byC1 = 500 + 5.3 P1 + 0.004 P1^2 ; P1 in MWC2 = 400 + 5.5 P2 + 0.006 P2^2 ; P2 in MWC3 = 200 + 5.8 P3 + 0.009 P3^2 ; P3 in MWThe total load , Pd is 800MW.Generation limits:200 =< P1 =< 450 MW150 =< P2 =< 350 MW

100 =< P3 =< 225 MW

TEACHING LEARNING BASED OPTIMIZATION

• Every individual learns from other individuals to improve themselves.

• Inspired from class room teaching process• This algorithm simulates three fundamental

modes of learning1. Through the teacher (Teacher phase)2. Interacting with other learners (Learner phase)3. Through self learning (self learning phase)

• TLBO A Population Based Algorithm

• Group of students Population(any feasible solution)

• Different subjects Different design variable

• Result scores Fitness value of problem• Teacher Best solution

INITIAL POPULATION CREATION Pgi=(Pgi)min+ Rand (Pgimax-Pgimin)

for i= 1,2,………,(Ng-1)(Pgi)Ng=Pd -

We have taken 20 students in our program but here we have shown the initial population creation of 3 students.

P1 P2 P3 Cost

Student1 300 300 200 6760Student2 325 335 140 6749.25 TeacherStudent3 250 350 200 6855

Teacher phase• During this phase teacher gives knowledge to

student .• Students modify themselves.

Xi,new=Xi,old+r1 (Xteacher- TFXmean)

• Xmean =mean result of the class .

• XTeacher =best learner

• TF = teaching factor=round[1+rand(0,1){2,-1}]• r1 is the random number

• Xmean=[275 325 200]

• Xteacher= [325 335 140]

• X3,new=X3,old+r1 (Xteacher- TFXmean) =[250 350 200]

+1*([325 335 140]-1*[275 325 200])=[250 350 200]+[50 10 -60]=[300 360 140]Here r1=1 and TF=1(the value is improved)

Simple Calculation

LEARNER PHASE OF TLBO ALGORITHM

• Learners learn from other learners.• They are chosen randomly or from the

neighbourhood positions.• Learning from neighbours is easy and

compatible .• While learning from non-neighbour learners

though difficult improve the search ability thereby improving the global performance.

• The learners are arranged in a M*N vector• This vector is called position matrix• Our assumption is position=the number i.e position

of each learner is fixed (for ex. Exam hall sitting arrangement)

POSITION MATRIX

1 2 3 4

5 6 7 8

9 10 11 12

13 14 15 16

17 18 19 20

• Every learner is coded with an integer.

• Thus every learner modified its position by looking best nearby position as follows

if (Xj > Xk) Xj,new=Xj,old +rj (Xj-Xk)

else Xj,new=Xj,old +rj (Xk-Xj)

LEARNER NO. NEAR BY POSITION

BEST NEAR BY

1 2 , 5 5

2 1, 3, 6 3

3 2, 4, 7 4

SELF LEARNING PHASE

• Not every learner includes in this process• Searching is ambiguous as it is a self motivated

process.• The equation is Xi,new(K)=Xi,old(K)+r4(Xi,old(K)-Xi,old(K-1))• K=iteration number• r4=random number[0,1]

ALGORITHM

Advantage of TLBO in comparison toother conventional methods

More accurate

Does not require any derivative.

Follows the entire path to find its solution.

Disadvantages of TLBO

• It consumes lot of memory space.

• It involves lot of iterations so is a time consuming method.

PROGRESS

• Programming Completed 1. population generation 2. Teacher phase3. Learner phase

FUTURE WORKS AND APPLICATION OF TLBO

• To apply the TLBO in different power system problems

REFERENCE

[1] M.Dorigo .v. Maniezzo, A.colorni,Ant system:optimization by a colony of cooperating agents, IEEE Trans.Syst., Man, Cybern. Part B:Cybern. 26(1) (1996)29-41[2] C.S.Suresh, N.Anima, Data clustering Based on Teaching-Learning-Based optimization SEMCCO 2011 part II, LNCS 7077, 2011, pp. 148-156.[3] T.Vedat, Design of planer steel frames using teaching-learning based optimization. Eng. Struct. 34(2012) 225-232.[4] R.Venkata Rao, V.D. Kalyankar, Parameter optimization of mordern Machining processes using teaching-learning-based optimization algorithm.

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