Security assessment for a cumulative sum-based fault detector in transmission lines

Noori, M. R.; Jamali, S.; Shahrtash, S. Mohammad

doi:10.1109/eeeic.2011.5874792

Cited by 14 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cumulative Sum (CuSum) is a process of cumulative summation of the difference between the current cycle and the reference cycle in order to locate any abrupt jumping in amplitude of the derivative signal. This reduces the computational burden on relay operator 5,17‐19 . CuSum can be executed by sample by sample process or window approach.…”

Section: Fault Detection Techniquementioning

confidence: 99%

“…In reference 7 a sparse auto‐encoder (SAE) technique has been presented to discriminate both AC and DC faults but it results in high computational overhead. Therefore this paper introduces a combination of two simple methods namely the Cumulative Sum (CuSum) average 17,18 and Teager Energy Operator (TEO) to detect the faulty signals. CuSum detects the abrupt changes in the disturbed signals and offers better performance in terms of speed and accuracy and it is immune towards noise, frequency drift in the signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault analysis in photovoltaic generation based DC microgrid using multifractal detrended fluctuation analysis

Prasad

Dash

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Section: Fault Detection Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault analysis in photovoltaic generation based DC microgrid using multifractal detrended fluctuation analysis

Prasad

Dash

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

“…CUSUM has been used for detecting changes in different applications, such as surveillance, security, quality control, and power system applications [21]. Furthermore, two-sided CUSUM has been effectively applied to fault detection in power system applications.…”

Section: A Cumulative Sum (Cusum)mentioning

confidence: 99%

“…Furthermore, two-sided CUSUM has been effectively applied to fault detection in power system applications. However, the CUSUM has a drift parameter δ that is a prior constant variable which is initialized once, and the algorithm continues to use its value all the time [21]. Such a global variable and a single calculation might limit the applicability of CUSUM for monitoring streaming sensory data.…”

Section: A Cumulative Sum (Cusum)mentioning

confidence: 99%

An Online Adaptive Algorithm for Change Detection in Streaming Sensory Data

Fathy

Barnaghi

Tafazolli

2019

IEEE Systems Journal

View full text Add to dashboard Cite

There has been a keen interest in detecting abrupt sequential changes in streaming data obtained from sensors in wireless sensor networks for Internet of Things applications, such as fire/fault detection, activity recognition, and environmental monitoring. Such applications require (near) online detection of instantaneous changes. This paper proposes an online, adaptive filteringbased change detection (OFCD) algorithm. Our method is based on a convex combination of two decoupled least mean square windowed filters with differing sizes. Both filters are applied independently on data streams obtained from sensor nodes such that their convex combination parameter is employed as an indicator of abrupt changes in mean values. An extension of our method (OFCD) based on a cooperative scheme between multiple sensors (COFCD) is also presented. It provides an enhancement of both convergence and steady-state accuracy of the convex weight parameter. Our conducted experiments show that our approach can be applied in distributed networks in an online fashion. It also provides better performance and less complexity compared with the state-of-the-art on both of single and multiple sensors. Index Terms-Cooperative (diffusion-based) strategy, mean change detection, multi-sensory data, streaming data. I. INTRODUCTION R ECENT advances in sensing and actuator technologies in wireless sensor network (WSN) and the further evolution of the Internet of Things (IoT) paradigm enable each sensor in a network to collect large quantities of measurement and observation data streams. This empowers monitoring and detecting a wide range of real-world phenomena in areas, such as environmental monitoring, segmentation, quality control, healthcare, and smart city [1]-[3].

show abstract

“…The CUSUM has been widely used today, across industries, to monitor deviations of a process with respect to a target value and also to find evidence of change in the mean of a process. It has been successfully employed in power system fault detection in particular [14,15]. The CUSUM method is easy to handle and useful for detecting the locations of change points.…”

Section: Detection Parameter Algorithm Based On Cumulative Sum Theorymentioning

confidence: 99%

Novel Segmentation Technique for Measured Three-Phase Voltage Dips

et al. 2015

View full text Add to dashboard Cite

This paper focuses on issues arising from the need to automatically analyze disturbances in the future (smart) grid. Accurate time allocation of events and the sequences of events is an important part of such an analysis. The performance of a joint causal and anti-causal (CaC) segmentation method has been analyzed with a set of real measurement signals, using an alternative detection technique based on a cumulative sum (CUSUM) algorithm. The results show that the location in time of underlying transitions in the power system can be more accurately estimated by combining CaC segmentation methods.

show abstract

Security assessment for a cumulative sum-based fault detector in transmission lines

Cited by 14 publications

References 8 publications

Fault analysis in photovoltaic generation based DC microgrid using multifractal detrended fluctuation analysis

Fault analysis in photovoltaic generation based DC microgrid using multifractal detrended fluctuation analysis

An Online Adaptive Algorithm for Change Detection in Streaming Sensory Data

Novel Segmentation Technique for Measured Three-Phase Voltage Dips

Contact Info

Product

Resources

About