Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

Niazazari, Iman; Livani, Hanif; Ghasemkhani, Amir; Liu, Yunchuan; Yang, Lei

doi:10.1109/naps50074.2021.9449678

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Zhang et al [24], [25] consider features of a smart grid and different types of wavelets, and employ a transfer learning method for HIF detection. Niazazari et al [26] use a CNN and DWT to detect HIF. Fan and Yin [27] present an analysis of CNN's differentials, using transfer learning to solve the problem of missing data; in which different scenarios of HIF variation and other system operations, such as switching loads and capacitors bank are used, the performance of CNN is compared to a MLP and the cross-entropy loss calculation for performance analysis is done.…”

Section: Related Workmentioning

confidence: 99%

“…In total, 100 images (i.e., samples) are obtained and classified as: (i) 0, for normal system operation condition, including capacitors bank switching; and (ii) 1, for HIF condition (i.e., capturing only the beginning, middle or end of a fault, or a complete HIF). Thus, a total of 50 samples are obtained in each category, 80% are used in training and 20% for testing/validation [24], [26], [27], [41].…”

Section: ) Convolutional Neural Networkmentioning

confidence: 99%

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Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Diefenthäler,

Sausen,

De Campos

et al. 2023

IEEE Access

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High impedance faults constitute one of the biggest challenges in electrical power systems. In overhead distribution systems, faults are caused by tree branches that touch the electrical grid or by the rupture of energized conductors on low conductivity soils. They are also known as low-current faults, which are not detected by conventional protection systems, compromising the quality of the power supply and causing hazardous risks to the electrical system. This paper aims to address the problem of high impedance faults detection using Artificial Neural Networks: two Multi Layer Perceptron networks, being one Neural Pattern Recognition and another Neural Fitting, and a Convolutional Neural Network. The neural networks are trained and analyzed in scenarios based on a medium-voltage distribution grid model, located in the Basque Country, Spain. The network topologies are implemented, repeatedly trained considering multiple architectures, and validated in other scenarios with different location, time, and duration of the fault using the Matlab software. After, the criteria of accuracy, reliability, security, safety and sensitivity are evaluated. At last, a comparative analysis between them is carried out, and from the results obtained, a superior performance of the Convolutional Neural Network in compared to the Multi Layer Perceptron networks is observed.

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Section: Related Workmentioning

confidence: 99%

Section: ) Convolutional Neural Networkmentioning

confidence: 99%

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Diefenthäler,

Sausen,

De Campos

et al. 2023

IEEE Access

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“…While phasor measurement units (PMUs) are used to measure synchro-phasors [10], WMUs are used to measure synchrowaveforms [3], [11], [12]. The term WMU is gradually starting to appear in the academic literature, e.g., see [2], [7], and also in the industry reports, e.g., see [13], [14]. Given the much higher reporting rate of WMUs than PMUs, and also because WMUs have much less internal filtering than PMUs, WMUs are capable of capturing several details about the voltage waveforms and current waveforms that are inherently impossible for PMUs to capture.…”

Section: Introductionmentioning

confidence: 99%

A Synchronized Lissajous-Based Method to Detect and Classify Events in Synchro-Waveform Measurements in Power Distribution Networks

Izadi

Mohsenian‐Rad

2022

IEEE Trans. Smart Grid

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Waveform measurement units (WMUs) are a new class of smart grid sensors. They capture synchro-waveforms, i.e., time-synchronized high-resolution voltage waveform and current waveform measurements. In this paper, we propose new methods to detect and classify power quality events in power distribution systems by using synchro-waveform measurements. The methods are built upon a novel graphical concept, called synchronized Lissajous curve. The proposed event detection and event classification methods work by analyzing the shape of the synchronized Lissajous curves during disturbances and events. The impact of challenging factors, such as the angle, the location, and other parameters of the event are discussed. We show that these challenges can be addressed if we treat the synchronized Lissajous curves as images, instead of as time series as in the raw synchronized waveform measurements. Hence, we can take advantage of the recent advancements in the field of image processing so as to capture the overall characterizing patterns in the shapes of the synchronized Lissajous curves. We develop a Convolutional Neural Network (CNN) method to classify the events, where the input is the synchronized Lissajous images. The effectiveness of the proposed event detection and classification methods is demonstrated through computer simulations, including hardware-in-the-loop simulations, and real-world field data. Multiple case studies verify the performance of the proposed methods. The proposed event detection method can accurately detect events, and identify the start time and the end time of each event. The proposed event classification method can classify power quality events with high accuracy. The proposed detection and classification methods do not require any prior knowledge about the network. They use data from as few as only two WMUs.

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A Review on Incipient Fault Detection, Location and Classification in Underground Cable

Das,

Mishra,

Swain

et al. 2024

Lecture Notes in Electrical Engineering

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Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

Cited by 5 publications

References 17 publications

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

A Synchronized Lissajous-Based Method to Detect and Classify Events in Synchro-Waveform Measurements in Power Distribution Networks

A Review on Incipient Fault Detection, Location and Classification in Underground Cable

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