Download Intro - Clemson University

PANEL DISCUSSION P5
Impact of Distributed Generation on
Harmonics and Power Quality
Chair: Siri Varadan, Nexant, Inc.
Panelists:
Elham Makram, Clemson University
Thomas Baldwin, Florida State University
Mark McGranaghan, Electrotec Concepts
Presentation Topics and Order

Effect of Harmonics on Distributed Generation –
– Elham Makram

Harmonic and Distributed Generation Interaction Issues in
the U.S. Navy All-Electric Ship Program –
– Tom Baldwin

PQ Issues for DG Applications –
– Mark Mc Granahan

Software Aspects of PQ in a DG Context –
– Siri Varadan

General Discussion
DG Conference, Clemson, SC
March 13-15, 2002
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‘Independent’ Modules to Ensure
Vendor-Independence in Utilitywide PQ Monitoring Systems
by
Mehmet Kemal Celik
Overview of the Presentation
Introduction
 Modular System Design
 Benefits
 Functional Design
 A Customized Analysis Example
 Conclusions

DG Conference, Clemson, SC
March 13-15, 2002
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Power Quality Monitoring
Systems

Integrated systems of several PQ monitors are
being set up
– increasing number of monitors
– emerging computer technologies on hardware and
software area
– different architectures that serve all PQ monitors with a
single large database
» client-server
» Intranet/Internet
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Power Quality Monitoring
Systems

Such integrated designs have several advantages
–
–
–
–
–
–
–
efficient and fast analysis of large volumes of data
establish centralized PQ data
usage of standard PQ indices within the utility
standardization of customer complaint evaluation
modular, expendable and portable system design
reduction in system maintenance and expansion costs
standard data analysis tools on LAN/WAN, Intranet,
etc.
– centralized security system
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Main PQ Monitoring System
Components

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Communication System - physical media (fibre
optics, copper, wireless, etc), modems (DSL,
telephone, etc.), Ethernet network components
(switches, routers, etc), etc.
Information Technology (IT) System - computers
(data servers, polling stations, client stations, etc),
system software (operating system, etc), database
system, protocol converters, user applications
(GUI, analytical applications, alarm and
information dispersal software, web publishing,
etc.), etc.
Monitoring System
- instrumentation (PQ
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monitors, RTUs, sensors,
etc),
March 13-15,
2002 data retrieval and
PQ Monitoring System
Components
Monitoring system
Polling
server
IT system
Communication system
ODBC
database
Database/Application
server
WAN
End users
PQ Monitors
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Implementation Benefits


Removes vendor dependencies - The best
alternatives, specific to meet all of the utility’s
requirements, are used
Easy implementation of analysis applications
– Analytical applications can be written that use the
‘standardized databases
– Industry standard and vendor independent applications,
each best suited to meet utility’s requirements


Ease in implementation of web reporting
applications - Latest web hosting and interactive
site technologies can be used.
Economic benefits
With Clemson,
several
DG -Conference,
SC alternative
systems, price of theMarch
individual
modules is more
13-15, 2002
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Operational Benefits

Maintenance of a standard database
– Allows cheap and regular maintenance.
Facilitates easy database expansion. Single
database ensures that there is no duplication of
data from all current and potential future power
quality monitors.

Existing trained personnel can quickly come
up to speed with the operation &
maintenance of the system - No new
elaborate training is necessary
DG Conference, Clemson, SC
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Future Expansion Benefits
Future subsystem components can be
selected solely on their merit - ‘Vendorindependence’
 Modular design allows

– utilization of future advances in
instrumentation, communication, and software
technologies
– utilization of upgrading without a major
investment in a complete system overhaul
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Conceptual Design

Future expandability; plug-and-play concept
Instrumentation
Required software
- Communication
- Data logging
- Configuration
Meter server
Database
Database server
LAN
End user
Fiber Optics and
DSL modems
Current PQ meters
Leased Lines or other
media
Future PQ meters
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Functional Design
Applications Software
Polling & Downloading
Proprietary
Database
Data Managing, Analysis
& Reporting
WEB Reporting
ODBC
Database
PQ System
Characterization
Future
Applications
User Interface
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Software Modules
3F component
analysis
Data from
Communication server
Harmonic
analysis
‘Basic’ software
is installed on the
Communication server
Web reporting
ODBC
database
Event reporting
Application
server
PQ characterization
Independent software
modules, each of which
can be modified/upgraded
independently
Import/Export of data
to other formats
(PQDIF , COMTRADE , etc)
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Data Flow
Polling
Alarms
Proprietary
Database
Basic Software
3F Component Analysis
Harmonics Analysis
Data Managing &
Reporting
SQL
Standard
Interfaces
User
Interface
Data Analysis &
Reporting
ODBC
Database
WEB Reporting
PQ System
Characterization
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Data Analysis and Reporting
Data Managing
& Reporting
Data Analysis
& Reporting
ODBC compliant database
Oracle or SQL server
Disturbance reports
Event-based reports
Periodic reports
Trending reports
Rule-based
Expert
System
system
rules
ODBC data
Calculation
Modules
Display
Extract
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Data Analysis and Reporting

Calculation Modules
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RMS, Root Mean Square, (voltage or current)
SARFI
Fundamental voltages and currents of Fourier series
Total voltage harmonic RMS, etc.
Expert system rules constitute a simple if-and-then
logic for combining windowed time data with
relational and topological data
Extract module basically extracts the required
portion of the data from database tables
Display is in the form of graphs, tables and text
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Typical Customization – Disturbance
Aggregation
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Monitors track individual PQ disturbances
A deviation on a single phase at a single instant in
time will be recorded as a disturbance
An electrical system event may cause multiple
disturbances
For example, a single event: a tree branch blowing
against a 12kV line
– It can result in voltage sags on more than one phase
– Sags will be recorded at PQ monitors located at
different parts of feeder with small time lags
– Arcing may generate wave shape faults as well
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Conference,may
Clemson,
SC
– Furthermore, fault
result
in a series of
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March
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momentary outages, maybe followed by an interruption
Event Reporting (Disturbance
Aggregation)
Feeder 1
Feeder 2
M3
M4
M5
M2
M1
M6
Events
Time
(milliseconds)
Voltage sag
@t=1
Voltage sag
@t=2
Voltage sag
@t=2.5
Momentary
@t=3.5
Feeder 1 events
Voltage sag Voltage sag Waveshape
@t=3.6
@t=4
@t=4.5
Feeder 2 events
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Disturbance Aggregation

Two sets of disturbances, one on feeder 1, and
another on feeder 2
– voltage sag at t = 1 from PQ monitor 2 on feeder 1
– Voltage sag from PQ monitor 1 at t = 2 on feeder 1
– Voltage sag from PQ monitor 3 at t = 2.5 on feeder 1
– Momentary outage from PQ monitor 2 at t = 3.5 on feeder
1
– Voltage sag from PQ monitor 4 at t = 3.6 on feeder 2
– Voltage sag from PQ monitor 5 at t = 4 on feeder 2
– Waveform distortion from PQ monitor 6 at t = 4.5 on
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feeder 2
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Disturbance Aggregation

Sequence of disturbances
– all due to a tree branch blowing against a line on feeder
1
– Or, the disturbances on feeder 2 may be due to an
independent event on feeder 2, such as lightning
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Disturbance Aggregation
A properly sized moving time window that starts with the first
event of the sequence will capture most of the practical
situations, such as
event data that are from different phases on the same
monitor
that are far apart from each other only by infinitesimal
time intervals, etc.
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Disturbance Aggregation
However, fixed size moving time windowing may cause inaccurate
aggregation of events if it is not properly sized and used as the only
decision criteria
Window size of 3 milliseconds
Time
(milliseconds)
Voltage sag
@t=1
Voltage sag
@t=2
Voltage sag
@t=2.5
Momentary
@t=3.5
Feeder 1 events
Voltage sag Voltage sag Waveshape
@t=3.6
@t=4
@t=4.5
Feeder 2 events
DG Conference, Clemson, SC
March 13-15, 2002
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Disturbance Aggregation
If relational information is used with adaptive window sizing,
accuracy and robustness is enhanced
Same substation
Feeder 2
Feeder 1
M4
M3
M5
M2
M1
M6
Events
Window size increased due to spatial information
Time
(milliseconds)
Voltage sag
@t=1
Voltage sag
@t=2
Voltage sag
@t=2.5
Momentary
@t=3.5
Feeder 1 events
Voltage sag Voltage sag Waveshape
@t=3.6
@t=4
@t=4.5
Feeder 2 events
DG Conference, Clemson, SC
March 13-15, 2002
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Disturbance Aggregation
In actual implementation, many more variables are
considered, such as
severity of the events
sequence with respect to the location
relational information and connectivity
electrical and geographical distance
types of substation and feeder equipment used
protection schemes utilized, etc.
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Conclusions
Modular – system components can be
changed without major modifications
 Expandable – new monitoring systems can
be added without a complete system
overhaul
 Easier to maintain
 Standard unified set of tools across the users
 Customizable applications

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