Multi-resolution Analysis Algorithm for Fast Fault Classification and Location in Distribution Systems

Jiménez-Aparicio, Miguel; Reno, Matthew J.; Barba, Pedro; Bidram, Ali

doi:10.1109/sege52446.2021.9535096

Cited by 16 publications

(5 citation statements)

References 16 publications

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“…In general, very high accuracies can be achieved for fault classification and location. The works in [49]- [51] report average location errors of as little as 13 meters for short distribution lines using Discrete Wavelet Transform (DWT), DMD, and MM for extracting features, and RF for fault location. The work in [52] explores the combination of time and frequency-domain features using both MM and Stationary WT (SWT) for signal decomposition.…”

Section: Introductionmentioning

confidence: 99%

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

Jimenez-Aparicio,

Patel,

Reno

et al. 2023

IEEE Access

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This work provides a detailed protection analysis of a fast, Traveling-Wave (TW), Machine-Learning (ML), local, non-directional, economic, and setting-less protection scheme. The goal is to provide an objective evaluation of how a data-driven TW protection scheme would perform when deployed on a power distribution system as a faster alternative to over-current (OC) protection. Fault simulations consider scenarios from none to high-penetration of renewable energy resources to show its suitability for future applications on Distributed Energy Resources (DER)-dominated distribution systems. A modified IEEE 34 node system with solar Photovoltaic (PV) in multiple locations is modeled in PSCAD/EMTDC. The TW, ML method's protection scheme is built upon an efficient signal-processing stage, using the Discrete Wavelet Transform, and scaled-down Random Forest models that classify the fault location into several protection zones. The analysis is focused on the quantification of the sensitivity and selectivity of the proposed protection scheme. Furthermore, estimations of the false trip probability and average unnecessary load loss are included, as these events may occur due to the misoperation and miscoordination of the proposed datadriven, signal-based relays. Results show high TW detection rates and fault location accuracies, which cause a small and bounded percentage of imprecise fault locations and unnecessary load loss. Similarly, the same IEEE 34 node system is modeled in OpenDSS and a custom OC protection scheme is designed. The comparison between both methods leads to the conclusion that TW, ML methods can be a significantly faster aid to traditional protection.

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Section: Introductionmentioning

confidence: 99%

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

Jimenez-Aparicio,

Patel,

Reno

et al. 2023

IEEE Access

Self Cite

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“…Different groups of techniques can be applied to obtain information of the first microseconds after a fault occurs [1]. Multiple algorithms have been designed to perform fault location and classification using extremely short portions of the data [2,3]. This paper does not aim to provide a fault location method, but to provide an in-depth explanation of the fault signatures in the dozens of microseconds that follow a fault detection.…”

Section: Introductionmentioning

confidence: 99%

“…A widespread option is to decompose the signals by frequency bands applying WTs and to study them independently. This method has been successfully used for event characterization, fault classification, and fault location [2,17,18,[20][21][22][23]. This method is especially suitable for studying TWs as the fault signatures, and its frequency components are heavily influenced by the fault location and the propagation path.…”

Section: Introductionmentioning

confidence: 99%

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Traveling Wave Energy Analysis of Faults on Power Distribution Systems

2022

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This paper explores the most important factors that define the Traveling Wave (TW) propagation on distribution systems. The factors considered in this work are: the distance to the fault location, the fault type, and the crossing of system elements (such as regulators, capacitor banks, laterals, and extra loads within the protection zones). This work uses a realistic, yet simplified, distribution system composed of two protection zones, in which, several combinations of the previously mentioned factors are considered. The simulated fault measurements undergo a signal processing stage in which, first, they are decomposed into independent modes using the Karrenbauer transform. Second, a time–frequency representation is obtained using the Stationary Wavelet Transform (SWT), dividing the signal into several frequency bands. Finally, the Parseval’s Energy (PE) theorem is applied to calculate the signal energy in each frequency band. A qualitative analysis is performed based on the previously calculated energies to outline which are the factors that most affect the TW energy during propagation. The results show that distance, the presence of regulators, either in the propagation path or upstream, and the type of fault are the main factors that affect TW propagation across the system, and therefore they should be considered for TW-based protection schemes for distribution systems.

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“…For this reason, many approaches use a time-frequency decomposition to analyze the TW frequency components. The works in [22,23] used the continuous wavelet transform (CWT) for this purpose, while [24,25] opted for discrete WTs. The reference [26] used the S-transform instead.…”

Section: Introductionmentioning

confidence: 99%

Embedded, Real-Time, and Distributed Traveling Wave Fault Location Method Using Graph Convolutional Neural Networks

et al. 2022

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This work proposes and develops an implementation of a fault location method to provide a fast and resilient protection scheme for power distribution systems. The method analyzes the transient dynamics of traveling waves (TWs) to generate features using the discrete wavelet transform (DWT), which are then used to train several graph convolutional network (GCN) models. Faults are simulated in the IEEE 34-node system, which is divided into three protection zones (PZs). The goal is to identify the PZ in which the fault occurs. The GCN models create a distributed protection scheme, as all nodes are able to retrieve a prediction. Given that message-passing between nodes occurs both during training and in the execution of the model, the resiliency of such schemes to communication losses was analyzed and demonstrated. One of the models, which only uses voltage measurements, was implemented on a Texas Instruments F28379D development board. The execution times were monitored to assess the speed of the protection scheme. It is shown that the proposed method can be executed in approximately a millisecond, which is comparable to existing TW protection in the transmission system. For experimental purposes, a DWT-based detection method is employed. A design of a setup to playback TWs using two development boards is also addressed.

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Multi-resolution Analysis Algorithm for Fast Fault Classification and Location in Distribution Systems

Cited by 16 publications

References 16 publications

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV

Traveling Wave Energy Analysis of Faults on Power Distribution Systems

Embedded, Real-Time, and Distributed Traveling Wave Fault Location Method Using Graph Convolutional Neural Networks

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