main project of m tech
DESCRIPTION
Presentation of M Tech ProjectTRANSCRIPT
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..