main project of m tech

A NOVEL SHUNT COMPENSATOR BASED ON CONDUCTANCE ESTIMATION BY NEURAL NETWORKS

ByJ.Mahesh

13TR1D4909

ABSTRACT Main objectives of neural network application in DSTATCOM

(Distribution Static Compensator) are to enhance the efficiency, robustness, tracking capability according to requirements

A control algorithm based on load conductance estimation using the neural network is implemented proposed control algorithm is used for extraction of load fundamental conductance and susceptance components of distorted load currents.

The proposed control algorithm is used for extraction of load fundamental conductance and susceptance components of distorted load currents.

Power quality

The IEEE defines POWER QUALITY as the ability of a system or an equipment to function satisfactorily in its electromagnetic environment without introducing intolerable electromagnetic disturbances to anything in the environment.

Power Quality mainly deals with:Continuity of the supply,“Quality” of the voltage

POWER QUALITY PROBLEMS POWER QUALITY IMPROVEMENT

Power Factor Harmonic Distortion Voltage Transients Voltage Sags or Dips Voltage Swells

Power factor correction, Harmonic filtering, Special line notch filtering, Transient voltage surge

suppression, Proper earthing systems.

Power quality problems/improvement

LOAD COMPENSATION

• Load balancing• Power Factor Correction

Objectives of load

Compensation

NEURAL NETWORKS

What is Neural Networ

k?Historical

BackgroundWhy to

use Neural

Network?

LEARNING PROCESS

Types of Learning: Supervised & Un-Supervised

DATA FILTERS

Adaptive Filters

Zero-Phase Filter

Kalman Filter

Active Filters Passive Filters

Filters

DSTATCOM

Basic PrincipleThe operating principles of a DSTATCOM are based on the exact equivalence of the conventional rotating synchronous compensator.

SCHEMATIC DIAGRAM OF DSTATCOM

ESTIMATION OF REFERENCE SUPPLY CURRENTS USING NEURAL NETWORK BASED CONDUCTANCE BASED CONTROL ALGORITHM

Discrete,Ts = 5e-006 s.

powergui

v+-

Voltage Measurement

A B C

N

Transformer

A

B

C

a

b

c

A

B

C

a

b

c

A B Ca b c

A

B

C

a

b

c

A

B

C

a

b

c

Gate A B C

+ -

Subsystem4

Scope7

Scope

A

B

C

N

Loads ILn

Itn

Vdc

Gate

i+

-

Current Measurement1

i+ -

Current Measurement

Controller

Simulation model for the proposed circuit

PERFORMANCE OF DSTATCOM UNDER UNBALANCED LINEAR LOAD- SOURCE VOLTAGE (VSA), AND SOURCE CURRENTS(ISA, ISB, ISC)

Performance of DSTATCOM under unbalanced linear load- Dc-link voltage (Vdc), Source current (Isa), Controller current (Ica), and Load current (Ila)

CONCLUSION Test results have proved the effectiveness of proposed neural

network algorithm for reactive power compensation, harmonics elimination, load balancing, and neutral current compensation under linear/ nonlinear loads.

THANK YOU..