application of ann controller in a dstatcom

APPLICATION OF ANN CONTROLLER IN A DSTATCOM

ANIL K.DAHIYA G.SIVA VIKRAM YADAV

Asst. Professor Roll No: 208249

Electrical engineering Dept MTECH.-PED

NIT Kurukshetra-136119 Electrical engineering Dept

Haryana-136119 NIT Kurukshetra

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CONTENTS1.power quality2. Facts & controllers3. D-STATCOM4. PI5. ANN6. Analysis of distribution system7. Modeling of distribution system8. Simulation results9. conclusion

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1.Power Quality

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POWER AND QUALITY

POWER QUALITY ISSUESPOWER ELECTRONIC LOADS NONLINEAR LOADS CRITICAL LOADS

HOW TO SOLVE THE PQ PROBLEM?

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3.FACTSWhat is FACTS?

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FACT Device evolution?

Different Fact Devices

Fact Controllers

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Different FACT devices and performance

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4.D-STATCOMRole of D-STATCOM?

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Working principles?

Controller Techniques

Operating modes?

SVpwm controlling technique. SRF controlling technique.

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CONTENTS

Fig4.1.Single line diagram of DSTATCOM

Fig4.2.Equivalent diagram of DSTATCOM

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XCV

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Pwm effect on VSC 3 phase Ac sinusoidal output output

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D-STATCOM (formulae)

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P=(2V2 /X) sin(δ/2)

Q=(2V2 /X) [1-cos(δ/2)]Vs

Vr

δ

IX

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PID Technology

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Invention of Artificial Neural Networks?

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What is a neuron, neural network?

Different ANN models?

Feed-Forward Neural NetworksFeed-back Neural NetworksRecurrent Neural Networks

ARTIFICIAL NEURAL NETWORKS

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ANN ARCHITECTURE

Dendrites

Soma (cell body)

Axon

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Architecture

synapses

axondendrites

Interconnection of 2 neurons (schematic diagram)

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z z

Linear activation

Threshold activation Hyperbolic tangent activation

Logistic activation

u

u

e

eutanhu

2

2

1

1

1

1 zz

e

1, 0,sign( )

1, 0.

if zz z

if z

z

z

z

z

1

-1

1

0

0

Σ

Σ

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LEARNING METHODS

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TYPE OF ARCHETECTURE

GRADIENT DESCENT HEBBIAN

SINGLE LAYERFEEDFORWARD

ADALINE, HOPFIELD HOPFIELD

MULTI LAYER FEEDFORWARD

CCN,MLFF,RBF

NEOCOGNITRON

RECURRENT NEURAL N/W

RNN BAM, BSB, HOPFIELD

Fig. Multilayer feed forward neural network

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GRADIENT DESCENT (training example) N+1 --------------- iteration t------------------target output value η-----------------learning rate intialize each weight unit to some random value(0.5 to -0.5) Untill termination condition is met,

1. For each (x,t) vector intraining samples.

2. Input the instance ( x) vector to s unit weight calculation and compute the output “o”.

3. For each linear unit weight Wi,

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➲ Squared Error Above it has mentioned, that the whole learning

process is in order to reduce the error, but how can man error describe? Generally the function squared error is used.

Notice: this function sums all the error over all of the networks output units after a whole set of training examples has been computed.

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then the value-vector can be updated by:

– where E(~w) is the gradient of E:∇

so for each value k can be updated by:

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But in practice, because the function sums all the error over a whole set of the training data, so need the algorithm with this function more time to compute, and can easily be effected by local minimum, so construct man a new function, named stochastic squared error:

As can be seen, the function computes error only about a example. The gradient of Ed(~w) is easily made out:

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How to improve the gain performance of controller?

+

+

+

-

-

-

x1

x2 x2

x1

+ -

- +

This...

PI Controller performance

input

output

input

output

PI

ANN

Improving performance of Controller

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Building a ANN controller using MATLAB Simulink

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Training a ANN controller using MATLAB Simulink, NNTool

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Extracting a ANN controller using MATLAB Simulink, NNTool

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7.Analysis of Distribution System

Essential parameters:

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Single line diagram of D-statcom

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Per Unit scaling

Claculated values:

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VSC CIRCUIT SPECIFICATIONS

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Modelling a Dstribution System

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CONTENTS

mH

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FIG2.ANN CONTROLLER CONGIGURATION

FIG1.PI CONTROLLER CONGIGURATIONS

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TABLE: PI CONTROLLER SPECIFICATIONS

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D-STATCOM DATA SPECIFICATIONS

700kw

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Simulation Results VBUS1 PERFOMANCE VBUS3 PERFORMANCE

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S

a

g

SW

E

L

L

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CONCLUSIONTYPE OF MODE %VOLTAGE REGULATION

Uncompensated 20%

Compensated using “PI controller” 10%

Compensated using “ANN controller” 5.8%

Fast acting, better voltage Regulation, Reactive power compensation performance by ANN based controller compared to PI Based controller

In a Dstatcom

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REFERENCES

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Thank you for your listening

ANY QUERIES PLEASE?

application of ann controller in a dstatcom

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