Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

Liu, Shengyuan; Zhao, Yuxuan; Li, Fangxing; Ding, Yi; Yang, Li; Yi, Shimin

doi:10.1109/tsg.2019.2941565

Cited by 90 publications

(41 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In ref. [47] illustration of the use of reduced synchrophasor data mechanism to detect HILF events is proposed. For this purpose, an algorithm consisting of SS and local outlier factor (LOF) concept is applied.…”

Section: Methods Used In Sbr Evaluationmentioning

confidence: 99%

Novel trends in resilience assessment of a distribution system using synchrophasor application: A literature review

Sonal

Ghosh

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary The gradual sprawl of power system toward smart grid and enhanced integration of distributed energy resources at the distribution end is transforming the conventional distribution system and is eventually making it self‐sustained. The distribution system is thus continuously evolving to prepare itself for any unforeseen natural, man‐made, and complex events. These events may impact the distribution system as high‐impact low‐frequency (HILF) scenarios. The high intensity and widespread damage of the power system due to HILF scenarios necessitate resilience. Resilience analysis aims to identify these region‐specific vulnerabilities of the electricity network and implement appropriate methods to quickly restore the system to its predisturbance state. Further to enhance the network visibility and situational awareness, integration of synchrophasor to resilience is significant. Therefore, synchrophasor technology integrated with resilience provides online wide area visibility of the distribution system. Using synchrophasor for resilience assessment propels an agile network for coordinating accurate and timely analysis of the system parameters. Therefore, understanding the concept of resilience, its evolution, detailed overview, synchrophasor‐based resilience (SBR) methods as well as its applications are necessary. This article outlines the key points of the synchrophasor technology based resilience technique, its significance, and lays a foundation for continuing research in this area. It offers detailed insight into the comprehensive review of this evolving concept. The article majorly focuses on all the aspects of SBR, its necessity in performance evaluation, synchrophasor measurement based resilience enhancement methods, and application of different SBR techniques in the distribution system. A detailed comparative analysis of SBR features, sample efficiency, result accuracy, merits, and demerits based on available literature is provided. Finally, future perspectives are discussed for implementing resilience assessment using synchrophasor technology in the distribution system.

show abstract

Section: Methods Used In Sbr Evaluationmentioning

confidence: 99%

Novel trends in resilience assessment of a distribution system using synchrophasor application: A literature review

Sonal

Ghosh

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Transient issues can be handled using offline simulation tools (power flow, electromagnetic transients) as well as decision‐making tools. Decision‐making based on situation awareness from supervisory control and data acquisition systems (SCADA) and WAMS 115 will be realized soon with the effective application of fast‐developing artificial intelligence technologies.…”

Section: Practical Challenges Issues and Industry Practicesmentioning

confidence: 99%

Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience

Aziz

Liu

Waseem

et al. 2020

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Background When a power system blackout occurs, it affects the economy of the country and every aspect of human life. Cascading failures can easily occur and cause a major blackout in the power grid due to the breakdown or failure of important nodes or links. Recently, transmission network reconfiguration (TNR) becomes a hot topic and has made many concerns after major blackouts of power systems. Aims TNR is the second‐stage action plan to restore power systems and plays a major role in the process of power system restoration. On the other hand, grid resilience involves a quick dynamic reconfiguration of power systems to minimize the propagation of attack influences on the grid. The motivations to include the works in this survey are based on the quality of the research performed in the transmission network reconfiguration problem for grid resilience. In this article, the state‐of‐the‐art review of recent progress in the network reconfiguration problem of the transmission system for grid resilience is discussed with practical challenges, technical issues, and power industry practices. Materials & Methods In this paper, complex network theory‐based indices with advantages, disadvantages, and their applications have been discussed to assess the important nodes and lines for network reconfiguration problem during sudden disturbances in power systems. Furthermore, optimization models have been presented with objective functions as well as their constraints. Taken together, optimization methodologies have been discussed to solve network reconfiguration problem with merits and demerits. Results This survey paper presents current trends in research and future research directions concerning transmission network reconfiguration for academic researchers and practicing engineers. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations. Discussion Based on a thorough comparison of literature some future perspectives are also discussed for transmission network reconfiguration problem for grid resilience. Conclusion This review paper provides a comprehensive review of current practices applied to transmission network reconfiguration. The core focus of this paper will remain on complex network theory‐based indices, optimization models, optimization methodologies, challenges, and technical issues, and discusses future direction for transmission network reconfiguration problem for grid resilience. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations.

show abstract

“…In recent year, researches about synchorphasor data have been studied worldwide in the literature as a very hot topic. Liu et al [17] proposed a novel datadriven algorithm based on local outlier factor (LOF) to detect and locate events in power systems using reduced PMU data. The unequal-interval reduction method was presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS.…”

Section: Synchrophasor Data Description a Related Work Of Synchmentioning

confidence: 99%

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

Deep learning techniques have been widely used for power system operations as a very hot topic in recent years. This paper proposed a spatiotemporal deep learning-based low frequency oscillation mode estimation method using synchrophasor data. The proposed deep learning method consists of the graph convolutional network (GCN) and the long short-term memory (LSTM). A graph network is used to model the power network in which the edges represent the connection relationships between system buses. Specifically, the GCN method is used to capture the topological structure of the power networks to obtain the spatial dependence. The LSTM model is used to capture the dynamic change of electrical variables that can be monitored by PMU devices through synchrophasor data to obtain the temporal dependence. Eventually, the proposed GCN-LSTM model is used to capture the spatiotemporal patterns and features of the system-wide synchrophasor data. To validate the effectiveness of the proposed method, we evaluate it on two classic IEEE simulation platform, i.e., IEEE 39-bus system and IEEE 118-bus system, by comparing with the Nonlinear Autoregressive Neural Network with External Input (NARX), the Gated Recurrent Unit (GRU) network, and single LSTM methods. It is demonstrated that the proposed GCN-LSTM method can achieve the best estimation results for different simulation platforms.

show abstract

Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

Cited by 90 publications

References 34 publications

Novel trends in resilience assessment of a distribution system using synchrophasor application: A literature review

Novel trends in resilience assessment of a distribution system using synchrophasor application: A literature review

Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Contact Info

Product

Resources

About