| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
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| Initial Amendment Date: | July 31, 2015 |
| Latest Amendment Date: | July 31, 2015 |
| Award Number: | 1508875 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Radhakisan Baheti
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | August 1, 2015 |
| End Date: | July 31, 2018 (Estimated) |
| Total Intended Award Amount: | $399,999.00 |
| Total Awarded Amount to Date: | $399,999.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
110 8TH ST TROY NY US 12180-3590 (518)276-6000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NY US 12180-3522 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | EPCN-Energy-Power-Ctrl-Netwrks |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Phasor measurements units (PMUs) in North America provide terabytes of synchronized phasor measurements of key operational parameters of power systems on a daily basis. In current practice of power system operation, the large amounts of PMU measurements are stored for past event analysis, and the in-situ data processing for real-time decision is beyond the capability of current technologies used in power systems. This proposal will develop a framework of collective processing of measurements in multiple PMUs across multiple time instants for various tasks in power system monitoring. The research goal is to develop efficient data management and information extraction methods that are suitable for real-time processing of large volumes of PMU data. The outcome of this project will positively impact the reliable operation of future power systems. The generic techniques for high-dimensional data analysis developed in this project can potentially find applications in other areas beyond power systems, e.g., Internet monitoring, social network analysis, image and video processing, etc. This proposal also contains an integrated educational agenda for K-12 students, undergraduates and graduate students.
This proposal for the first time bridges the areas of power system monitoring and high-dimensional analysis based on low-dimensional models. By developing new generic tools that are motivated by tasks in power system monitoring, this proposal will contribute to the development of the field of PMU-based power system monitoring and the field of high-dimensional data analysis. Focusing on improving data integrity and data accuracy of PMU measurements, this proposal will address the following challenges and open questions by:
1. Developing new computationally efficient missing data recovery methods to fill in the measurements that are lost during communication.
2. Developing new methods to detect events in power systems by collectively processing PMU measurements in multiple channels.
3. Developing new convex-optimization-based methods to detect cyber data attacks to PMU measurements.
4. Analyzing the likelihood and frequency of cyber data attacks to power systems.
All the developed methods will be numerically evaluated on actual PMU data in Central New York Power System.
This proposal will establish a framework of data-challenged power system monitoring by exploiting low-dimensional structures. It will extend the current understanding of low rank methods to the field of PMU data analysis. It will connect high-dimensional data analysis with graph theory through the research on the detection of cyber data attacks to power systems. This project will provide new insights to optimization-based high-dimensional data analysis by investigating the theoretical limits of proposed methods.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Data scarcity has been a major issue for power system monitoring until recent years. With the Smart Grid initiative, a large number of modern measuring devices, such as Phasor measurement units (PMUs) have been installed in power systems and constantly provide large volumes of data about system operating conditions and energy demand. Such data abundance is unprecedented in power grids and revolutionizing the practice of power system monitoring and control. The strong efforts in sensing and data acquisition, however, would be in vain if not equipped with efficient signal processing and information extraction methods. Currently, the real-time processing of the large amounts of data is still beyond the technical capability of the power industry.
One serious impediment to incorporating PMU into routine practices in a control center is the unreliable quality of PMU data, resulting from missing, bad data, or even potential cyber data attacks from malicious intruders. Our research goal in this project is to develop computationally efficient methods that can recover the missing data points and correct the bad measurements in PMU data in real time for the subsequent applications built on PMU data. The central idea of our approach is to exploit the spatial and temporal correlations in the data to develop model-free methods for data quality improvement. One distinctive advantage of our proposed approaches in this project is that our data recovery and error correction methods are accompanied by provable analytical guarantees. Since the reliability is critically important for power systems, approaches with theoretical guarantees may be more favorable for practical implementation.
The other issue studied by this proposal is the cyber data attacks, where an intruder can alter some measurements so as to mislead the operator about the state of the power system. Then the operator may take incorrect control actions, resulting in catastrophic outcomes. We develop methods that can detect and identify the cyber data attacks. The idea is to exploit the abnormal patterns in the temporal correlations in the measurements. We also analyze the likelihood and frequency of cyber data attacks by researching the optimal attack strategy of an intruder. This study quantifies the vulnerability of different parts of the power system. It helps the operator to take precautions to prevent the cyber data attacks.
Last Modified: 10/22/2018
Modified by: Meng Wang
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