Real time event detection and dynamic model identification

© Copyr i gh t 2014 O SIs o f t , LLC .

Presented by

Real time event

detection and dynamic

model identification

using PMU data

Raymond A. de Callafon, UCSD

Charles H. Wells, OSIsoft LLC


R.A. de Callafon

• Prof. in Mechanical and Aerospace Engineering

at the University of California, San Diego

• Research/background in dynamic systems & control

• Expertise in signal processing and parameter estimation

• Some applications:

– motion and adaptive control for servo systems

– state and parameter estimation in mechanical/electrical systems

– dynamic modeling of mechanical/electrical systems

2


C. Wells

• Resident visiting scholar at UCSD since 2012 as an

employee of OSIsoft, LLC

• Installed PMUs at OSIsoft headquarters in 2001 and

directed the development of the IEEE 1344 interface

• Design of the IEEE C37.118 software interface and Fast

Fourier Transform interface that performs moving window

FFTs on phasor data

3


Outline

Using PI-SDK program to detect events and quantify the

dynamics of an electricity grid (in real-time)…

• UCSD Microgrid and PMUs

• Our contributions to microgrid analysis

• Illustration of event detection and dynamic analysis

• Summary

4


The UCSD microgrid

• Daily population of 45000

• 2 times energy density of commercial

• 12 million sq. ft. of buildings, $200M/yr building growth

• Self generate 92% of annual demand

– 30 MW natural gas Cogen plant

– 2.8 MW of Fuel Cells installed

– 3 MW of Solar PV installed

5


Keeping track of the UCSD microgrid

• Data from Phasor

Measurement

Units (PMUs)

• 60Hz sampling

• Data stored in

OSIsoft PI

server(s)

• Data available

for UCSD

research

6


PMU data is growing…

– Measurements reported at standardized rates (typically 60 Hz), minimum of 14 signals per PMU.

– 1000*14*60=840K/s

– Time synchronizationis essential.

7


Why (micro)grid monitoring & analysis?

Improved Reliability

• Self-sustaining islanding to reduce cascading system failure

• Overall system less vulnerable to massive (natural) events

• Resolve variability of renewable energy on a local level

Improved Efficiency and Reduced Carbon Footprint

• Implementation of CHP with renewables on a localized level

• Reduce carbon footprint by maximizing efficiency of energy production and consumption on a local level

• Encourages third party investment in the local grid

8


Our contributions to microgrid analysis

Answers to the questions:

• How do we detect

individual events?

• How can we quantify

these events dynamically?

• What do these events tell

us about our the dynamics

of our (micro)grid?

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Real-time event detection

• Detection of Events via Filtered Rate of Change

• Approach:– Auto Regressive Moving

Average (ARMA) filter

– Definition of FRoC signal for Event Detection

• Detection and classificationof 14 events over 9 hours

• Direct link to event frames

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• Detection of Events via Filtered Rate of Change

• Approach:– Auto Regressive Moving

Average (ARMA) filter

– Definition of FRoC signal for Event Detection

• Detection and classificationof 14 events over 9 hours

• Direct link to event frames

11



• It works much better than

ROCOF signal defined

in the IEEE standard.

• Less false alarms for

event detection.

• Smaller threshold values

12


Classification of events

13

)()(

)()()1(

tCxtF

tBdtAxtx

detect beginning of event

ring down model



• Assume observed event in frequency F(t) is due to a deterministic system

where (unknown) input d(t) can be `impulse’ or `step’ or `known shape’

• Store a finite number of data points of F(t) in a special data matrix H

• Inspect rank of (null projection on) H: determines # modes

• Compute matrices A, B and C via Realization Algorithm.

• Extension of Ho-Kalman, Kung algorithm. Miller, de Callafon (2010)

• Applicable to multiple time-synchronized measurements! (multiple PMUs)

14

)()(

)()()1(

kCxkF

kBdkAxkx



• PI server receiving

multiple time-synchronized

PMU data

• Classification of one

MO Ring Down model

capturing grid dynamics

Clear advantage of centralized

data storage/processing

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Real-time detection and classification of events

Main Features:

• Automatic detection of disturbance/transient event

• Automatic estimation of Frequency, Damping and Dynamic Model.

Challenges:

• Distributed computation for centralized dynamics and control of grid dynamics.

• Data management and visualization of results to end-user.

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PI System tools used in this research

• PI Server 2012 (PMU data at 30 and 60 Hz)

• ProcessBook (ad hoc queries of the data)

• DataLink (extensive use for extracting data and

importing to MatLab)

• PI-AF (ease of finding data of interest)

– Coresight for viewing AF objects

• Interfaces:

– C37.118, OPC, Bacnet, Modbus

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• Automatic event detection

based on real-time data

streams

• Classification with models

• Models can be used to

simulate and/or control

Solution Results and Benefits

Summary

Business Challenge

• Wealth of real-time PMU

data at high sampling rates

• Automatic analysis of

multiple time-synchronized

data streams

• Get early warnings of events

• Real-time Filtered Rate of

Change (FRoC) signal

• Software for automatic

event classification in a

dynamic model

PMU data provides a wealth of information on the dynamics

of a (micro)grid…

Using a PI datalink server to collect PMU data

allows detection of events and quantify the

dynamics of an electricity grid in real-time…

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© Copyr i gh t 2014 O SIs o f t , LLC . 19

Raymond de Callafon

[email protected]

Professor in MAE, UCSD

Charles Wells

[email protected]

Visiting scholar at UCSD

mailto:[email protected]

mailto:[email protected]


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Real time event detection and dynamic model identification