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College of Engineering and Architecture
Consideration of CommunicationTime Delays in
Wide-Area Control
Anjan BoseWashington State University
Pullman, WA, USA
IEEE Powertech 2019
Milan, Italy
June 26, 2019
College of Engineering and Architecture
New England Synchrophasor System
2
PhasorPoint
ROSE
ISO-NE
PDC
DQMS
External
Entity
ISO-NE
Network
• 44 stations
• 90 PMUs
• 429
Phasors
• NYISO
• PJM
• MISO
College of Engineering and Architecture
Challenges of the PMU Data Exchange Scheme
• Raw synchrophasor data exchange only
Large volumes of data• Bandwidth: cost, performance
High maintenance• Each entity has to model and maintain its own and other region’s PMU data
• There is no central PMU Registry
• No data quality information or outage status
Lacks coordination• Each entity processes and analyzes data separately (vendors and applications)
• Operators see different results and displays
• Interpretation discrepancy
• Bi-lateral data exchange structure
Duplicated outgoing data streams because each ISO/RTO has
multiple peers• Network cost, maintenance
3
College of Engineering and Architecture
Proof-of-Concept Cloud Based WAMS• Objective: demonstrate a cloud-hosted distributed
platform for real-time PMU data collection, storage, processing and dissemination to achieve wide-area monitoring Security Network latency Fault tolerance Data consistency Connectivity to powerful cloud-hosted data
analytics tools Potentially low cost: in cloud, we “rent as needed”
rather than “own”
• Project collaborations among ISO New England Inc. Cornell University Washington State University New York Power Authority (Phase II) 4
College of Engineering and Architecture
Proof-of-Concept Project on Cloud-Hosted Wide Area Monitoring
5
Benefits:
• Supplemental and backup to the traditional SE
• A new platform for collaborations between control areas (operators see the same wide view of the bulk system)
• Explore all benefits and concerns of the cloud computing and advance the technology in the power industry
• A new and efficient way for synchrophasor data exchange and repository, further advance the synchrophasor technology
College of Engineering and Architecture
Cloud WAMS Deployment: Live Data Streams
6
College of Engineering and Architecture
Cloud WAMS Deployment: Data Archive + Analytics
7
ISO-NE hosted distribution point
PMU1
PMU2
PMUm-1
PMUm
Re
-pla
ye
d C
37
Da
ta
TCP
Sender
31
PMU
Stream
s
Cornell hosted distribution point
PMU1
PMU2
PMUm-1
PMUm
Re
-pla
ye
d C
37
Da
ta
TCP
Sender
42
PMU
Stream
s
C37.118
Cloud hosted
Ingress point in
“data collector”
role.
Cloud hosted
Ingress point in
“data collector”
role.
Cloud hosted
Ingress point in
“data collector”
role.
Cloud hosted
Ingress point in
“data collector”
role.
Cloud hosted
Ingress point in
“data collector”
role.
Cloud hosted
Ingress point in
“data collector”
role.
Archived
data
Archived
data
Freeze-Frame File
System
Freeze-Frame File
System
College of Engineering and Architecture
Cyber Security
• Amazon Virtual Private Cloud (VPC) Logically isolated section of AWS under
users’ complete control
• SSH Tunnel for data stream
• ISO-NE data source Historical data playback w/ simulated real-
time timestamps Inside firewall
• Cloud Data Storage Encrypted using a key
• Generated by and stored in Amazon AWS• Managed by users
8
College of Engineering and Architecture
Cyber Security Performance Cost
• EC2 Latency (from data sources to LSE) Average = 245ms 1st Percentile = 211ms 99th Percentile = 255ms
• VPC Latency (from data sources to LSE) Average = 261ms 1st Percentile = 228ms 99th Percentile = 270ms
• Delta is approximately +15ms; the numbers do not include SE compute time (75ms-100ms)
• Adding SSH tunnels added less than 2ms
• AES 256 encryption has no impact on performance (noise level) 9
College of Engineering and Architecture
Latency
10
L1: One way from DataSource to CloudRelay to Application (SE)
L3se: Round trip from DataSource to SE to DataSource
L3raw: Round trip from DataSource to CloudRelay to DataSource
L2: Round trip from Ingress Relay to Egress relay
College of Engineering and Architecture
Histogram: L3 Raw Data Round Trip Latencies
11
Num
ber
of O
ccurr
ences
0
500
1000
1500
2000
2500
0 100 200 300 400 500 600
ISO-NE Data Source
Oregon
Viginia
College of Engineering and Architecture
Histogram: L3 Raw Data Round Trip Latencies
12
Num
ber
of O
ccurr
ences
0
500
1000
1500
2000
2500
0 100 200 300 400 500 600
Cornell Data Source
Oregon
Virginia
College of Engineering and Architecture
Histogram: SE Computation Time
13
• Total latency for SE is obtained by adding the raw latency and the SE
Computation time. Typical would be 75-150ms depending on data source and
cloud data center location
• SE Computation time here is from a later SE version with much-improved
computation time relative to that achieved in the original experiments
College of Engineering and Architecture
Histogram: L3 SE Results Round Trip Latencies
14
Num
ber
of O
ccurr
ences
0
500
1000
1500
2000
2500
0 100 200 300 400 500 600
Oregon
Virginia
College of Engineering and Architecture
Fault Tolerance and Data Consistency
15
• Fault Tolerance– Two parallel systems
– Independent• Manual redundancy
• Loss of one data center did
not impact results from
other data center
– Full back-up redundancy
was restored within 3
minutes after data center
shutdowns
• Consistency– No raw data loss
– Returned raw data and LSE
results from the two data
centers were identical
– Within ~100 ms
College of Engineering and Architecture
OpenPDC (Visualizer) Displaying SE Results
16
College of Engineering and Architecture
Freeze Frame File System Role
• A standard POSIX file system, but augmented to provide millisecond accuracy for time-based data access.
• Integrates with the popular Spark data analytics framework via Spark “resilient distributed data” (RDD) objects
These are small scripts written in Python, Java or Scala
Used to extract data, transform it. Automatically cached for speed. Flexible, but in our work, these RDDs usually extract tensors.
Spark has tens of millions of lines of powerful tools that can operate directly on data in this RDD form.
• Includes traditional computational frameworks, like Matlab
• Also more modern ones, like machine learning tools (neural network models, Bayesian learning models, etc)
17
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