Fault detection and classification in smart grids using augmented K-NN algorithm

Hosseinzadeh, Javad; Masoodzadeh, Farokh; Roshandel, Emad

doi:10.1007/s42452-019-1672-0

Cited by 24 publications

(6 citation statements)

References 30 publications

(29 reference statements)

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“…There is a big gap between the intelligent construction of classroom teaching. Some areas are in a leading position in the research on the intelligent construction of mathematics classroom teaching [ 4 ]. Selvi and Muthulakshmi found that there are many problems in the intelligent classroom design of mathematics teaching, such as large redundancy and low teaching efficiency, and put forward an intelligent teaching method for teenagers [ 5 ].…”

Section: Related Workmentioning

confidence: 99%

Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

Zhang

2021

Computational Intelligence and Neuroscience

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With the increasing importance of mathematics in basic education, how to evaluate and analyze the intelligent effect of mathematics teaching classroom through scientific methods has become one of the indicators to evaluate the intelligent classroom. This paper studies the design and application of mathematics teaching intelligent classroom based on the PCA-NN (principal component analysis-neural network) algorithm. Firstly, this paper briefly describes the current research status of mathematics teaching intelligent classroom design and PCA-NN algorithm. Secondly, combined with the key factors of mathematics teaching, it formulates specific standards and puts forward an adaptive strategy of intelligent and personalized intelligent mathematics teaching classroom. Finally, the algorithm is verified by experiments. The results show that, for students with different mathematics basic levels, the mathematics teaching intelligent classroom based on the PCA-NN algorithm can effectively improve the quality of mathematics classroom teaching. Through the research on the factors such as teaching quality, effect, and interaction mode involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting teaching quality are determined. This paper analyzes and studies the system from different angles. The research results provide some help for the current quality evaluation of classroom teaching and use the PCA-NN algorithm to make quantitative analysis and multivariate verification of mathematics classroom teaching effect.

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Section: Related Workmentioning

confidence: 99%

Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

Zhang

2021

Computational Intelligence and Neuroscience

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“…The principal component analysis (PCA) approach tends to reduce the dataset size in the suggested strategy, while linear discriminant analysis (LDA) gives online classification before implementing the K-NN. High impedance faults (HIFs) can arise when transmission lines are linked or grounded to each other using a high impedance connection method, making this a complicated detection procedure (Hosseinzadeh et al, 2019) [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Shanmugapriya¹,

Baskaran²

2023

Intelligent Automation &Amp; Soft Computing

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Smart Grids (SG) is a power system development concept that has received significant attention nationally. SG signifies real-time data for specific communication requirements. The best capabilities for monitoring and controlling the grid are essential to system stability. One of the most critical needs for smartgrid execution is fast, precise, and economically synchronized measurements, which are made feasible by Phasor Measurement Units (PMU). PMUs can provide synchronized measurements and measure voltages as well as current phasors dynamically. PMUs utilize GPS time-stamping at Coordinated Universal Time (UTC) to capture electric phasors with great accuracy and precision. This research tends to Deep Learning (DL) advances to design a Residual Network (ResNet) model that can accurately identify and classify defects in grid-connected systems. As part of fault detection and probe, the proposed strategy uses a ResNet-50 technique to evaluate real-time measurement data from geographically scattered PMUs. As a result of its excellent signal classification efficiency and ability to extract high-quality signal features, its fault diagnosis performance is excellent. Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time. The proposed approaches classify the fault type with a precision of 98.5% and an accuracy of 99.1%. The long-short-term memory (LSTM), Convolutional Neural Network (CNN), and CNN-LSTM algorithms are applied to compare the networks. Real-world data tends to evaluate these networks.

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“…Principal Component Analysis is based on an eigendecomposition and allows easy execution. The approach is widely used in monitoring applications, e.g., aircraft engine condition monitoring [14,37], the monitoring of smart grids [38], and flight path monitoring [26,32].…”

Section: Feature Extractionmentioning

confidence: 99%

Steady-State Fault Detection with Full-Flight Data

et al. 2022

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Aircraft engine condition monitoring is a key technology for increasing safety and reducing maintenance expenses. Current engine condition monitoring approaches use a minimum of one steady-state snapshot per flight. Whilst being appropriate for trending gradual engine deterioration, snapshots result in a detrimental latency in fault detection. The increased availability of non-mandatory data acquisition hardware in modern airplanes provides so-called full-flight data sampled continuously during flight. These datasets enable the detection of engine faults within one flight by deriving a statistically relevant set of steady-state data points, thus, allowing the application of machine-learning approaches. It is shown that low-pass filtering before steady-state detection significantly increases the success rate in detecting steady-state data points. The application of Principal Component Analysis halves the number of relevant dimensions and provides a coordinate system of principal components retaining most of the variance. Consequently, clusters of data points with and without engine fault can be separated visually and numerically using a One-Class Support Vector Machine. High detection rates are demonstrated for various component faults and even for a minimum instrumentation suite using synthesized datasets derived from full-flight data of commercially operated flights. In addition to the tests conducted with synthesized data, the algorithm is verified based on operational in-flight measurements providing a proof-of-concept. Consequently, the availability of continuously sampled in-flight measurements combined with machine-learning methods allows fault detection within a single flight.

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Fault detection and classification in smart grids using augmented K-NN algorithm

Cited by 24 publications

References 30 publications

Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Steady-State Fault Detection with Full-Flight Data

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