Journal ArticleDOI
Supervisory Protection and Automated Event Diagnosis Using PMU Data
TLDR
In this article, a new framework for supervisory protection and situational awareness to enhance grid operations and protection using modern wide-area monitoring systems is presented, which analyzes only the PMU data with the strongest or the most prominent disturbance signature.Abstract:
This paper presents a new framework for supervisory protection and situational awareness to enhance grid operations and protection using modern wide-area monitoring systems. In contrast to earlier approaches dealing with the combined processing of data from multiple phasor measurement units (PMUs), the proposed approach analyzes only the PMU data with the strongest or the most prominent disturbance signature. The specific contributions of this paper are: a) new criteria for identification of PMU with the strongest signature, b) simplified approach for quick detection of faults, c) early classification of eight other disturbances suitable for near real-time response, d) time-frequency transform-based feature extraction techniques for speedy and reliable classifiers, and e) a promising approach to locate disturbances within narrow geographical constraints. The contributions are verified with exhaustive simulation data from the Western Electricity Coordination Council system model and limited real PMU data.read more
Citations
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Journal ArticleDOI
Real-Time Identification of Dynamic Events in Power Systems Using PMU Data, and Potential Applications—Models, Promises, and Challenges
Sukumar Brahma,Rajesh Kavasseri,Huiping Cao,Nilanjan Ray Chaudhuri,Theodoros Alexopoulos,Yinan Cui +5 more
TL;DR: Two underlying models for the task of real-time identification of dynamic events leading to a layer of situational awareness that can become a reality due to increased penetration of phasor measurement units in transmission systems are explored.
Journal ArticleDOI
Anomaly Detection Using Optimally Placed $\mu \text{PMU}$ Sensors in Distribution Grids
Mahdi Jamei,Anna Scaglione,Ciaran Roberts,Emma Stewart,Sean Peisert,Chuck McParland,Alex McEachern +6 more
TL;DR: A hierarchical architecture for monitoring the grid is proposed and a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter are established.
Journal ArticleDOI
Real-Time Multiple Event Detection and Classification in Power System Using Signal Energy Transformations
TL;DR: A method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data is proposed and a time series classification based method using energy similarity measure (ESM) is proposed.
Journal ArticleDOI
Applications of synchrophasor technologies in power systems
TL;DR: This paper presents a comprehensive summary of synchrophasor technology, its architecture, optimal placement techniques and its applications in electric power transmission and distribution systems.
Journal ArticleDOI
A Novel Event Detection Method Using PMU Data With High Precision
TL;DR: Numerical simulations on the real-time and synthetic PMU data show that the DPSDT method can accurately detect the start-time of an event and the event placement with relatively high precision.
References
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Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Journal ArticleDOI
Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis
Le Xie,Yang Chen,P. R. Kumar +2 more
TL;DR: An early event detection algorithm based on the change of core subspaces of the PMU data at the occurrence of an event is proposed and theoretical justification for the algorithm is provided using linear dynamical system theory.
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Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I)
TL;DR: A comprehensive feasibility analysis of ELM is conducted and it is revealed that there also exists some activation functions, which makes the corresponding ELM degrade the generalization capability.
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Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier
TL;DR: New FDST algorithms for fast and accurate time-frequency representation and an efficient classification algorithm for identifying PQ disturbances are proposed and compared with techniques proposed earlier.
Posted Content
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)
TL;DR: It is proved that the generalization capability of ELM with Gaussian kernel is essentially worse than that of FNN withGaussian kernel, and it is found that the well-developed coefficient regularization technique can essentially improve thegeneralization capability.
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