The applications for wide area monitoring, protection, and control systems (WAMPC) at the control center, help with providing resilient, efficient, and secure operation of the transmission system of the smart grid. The increased proliferation of phasor measurement units (PMUs) in this space has inspired many prudent applications to assist in the process of decision making in the control centers. Machine learning (ML) based decision support systems have become viable with the availability of abundant high-resolution wide area operational PMU data. We propose a deep neural network (DNN) based supervisory protection and event diagnosis system and demonstrate that it works with very high degree of confidence. The system introduces a supervisory layer that processes the data streams collected from PMUs and detects disturbances in the power systems that may have gone unnoticed by the local monitoring and protection system. Then, we investigate compromise of the insights of this ML based supervisory control by crafting adversaries that corrupt the PMU data via minimal coordinated manipulation and identification of the spatio-temporal regions in the multidimensional PMU data in a way that the DNN classifier makes wrong event predictions. This dataset contains images that represent PMU data described in the reference paper. Each image has a dimension of [300X20X3] comprising of 300 time points, 10 voltage and 10 frequency measurements, and 3 fundamental color intensities. Each of the image represents the instance of a disturbance. We consider a disturbance pattern length of 5s, with 0.5 s before the trigger and 4.5 s after the trigger. Voltage and frequency data streams from 10 PMUs at a sampling rate of 60 frames per second, were aggregated to form these pseudo color images. The data-set consisted of three sub-folders: 1. 344 instances of faults located in the sub-folder “DB_FLT” 2. 140 instances of loss of generation located in the sub-folder “DB_GNL” 3. 21 instances of synchronous motor switching events located in the sub-folder “DB_SMS”.
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Research Trends and Applications of PMUs
This work is a survey of current trends in applications of PMUs. PMUs have the potential to solve major problems in the areas of power system estimation, protection, and stability. A variety of methods are being used for these purposes, including statistical techniques, mathematical transformations, probability, and AI. The results produced by the techniques reviewed in this work are promising, but there is work to be performed in the context of implementation and standardization. As the smart grid initiative continues to advance, the number of intelligent devices monitoring the power grid continues to increase. PMUs are at the center of this initiative, and as a result, each year more PMUs are deployed across the grid. Since their introduction, myriad solutions based on PMU-technology have been suggested. The high sampling rates and synchronized measurements provided by PMUs are expected to drive significant advancements across multiple fields, such as the protection, estimation, and control of the power grid. This work offers a review of contemporary research trends and applications of PMU technology. Most solutions presented in this work were published in the last five years, and techniques showing potential for significant impact are highlighted in greater detail. Being a relatively new technology, there are several issues that must be addressed before PMU-based solutions can be successfully implemented. This survey found that key areas where improvements are needed include the establishment of PMU-observability, data processing algorithms, the handling of heterogeneous sampling rates, and the minimization of the investment in infrastructure for PMU communication. Solutions based on Bayesian estimation, as well as those having a distributed architectures, show great promise. The material presented in this document is tailored to both new researchers entering this field and experienced researchers wishing to become acquainted with emerging trends.
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- Award ID(s):
- 1809739
- NSF-PAR ID:
- 10343749
- Date Published:
- Journal Name:
- Energies
- Volume:
- 15
- Issue:
- 15
- ISSN:
- 1996-1073
- Page Range / eLocation ID:
- 5329
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/en15155329
