Robust support vector machine‐based zero‐crossing detector for different power system applications

Ghosh, None Minati; Koley, Chiranjib; Roy, Nirmal Kumar

doi:10.1049/iet-smt.2018.5025

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…For given motor parameters, torque and speed area of BLDC motor are identified. Robust ZCP detection mechanism is developed in [23] using support vector machine. In this study, authors considered noise level up to 20% and THD level 50%.…”

Section: Introductionmentioning

confidence: 99%

Non-Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Logistic Regression Model

Veeramsetty¹,

Srinivasula²,

Salkuti

2022

Computers

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Non-Zero crossing point detection in a sinusoidal signal is essential in case of various power system and power electronics applications like power system protection and power converters controller design. In this paper 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, logistic regression model is used to predict the non-zero crossing point in a distorted signal based on input features like slope, intercept, correlation and RMSE. Logistic regression model is trained and tested in Google Colab environment. As per simulation results, it is observed that logistic regression model is able to predict all non-zero-crossing point in a distorted signal.

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Section: Introductionmentioning

confidence: 99%

Non-Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Logistic Regression Model

Veeramsetty¹,

Srinivasula²,

Salkuti

2022

Computers

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“…This tool performance is examined up to 7.27% total harmonic distortion (THD) only. Support vector machine based ZCP detection tools are developed in [10] by considering the 100 microseconds sample-distorted sinusoidal signal.…”

Section: Introductionmentioning

confidence: 99%

Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks

Veeramsetty

Edudodla²,

Salkuti

2021

Algorithms

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Zero-crossing point detection is necessary to establish a consistent performance in various power system applications, such as grid synchronization, power conversion and switch-gear protection. In this paper, zero-crossing points of a sinusoidal signal are detected using deep neural networks. In order to train and evaluate the deep neural network model, new datasets for sinusoidal signals having noise levels from 5% to 50% and harmonic distortion from 10% to 50% are developed. This complete study is implemented in Google Colab using deep learning framework Keras. Results shows that the proposed deep learning model is able to detect zero-crossing points in a distorted sinusoidal signal with good accuracy.

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Detection of zero crossing points for a sinusoidal signal under the presence of harmonics and noise using decision trees

Edudodla,

Veldandi,

Veeramsetty

2022

AIP Conference Proceedings

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Robust support vector machine‐based zero‐crossing detector for different power system applications

Cited by 3 publications

References 26 publications

Non-Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Logistic Regression Model

Non-Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Logistic Regression Model

Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks

Detection of zero crossing points for a sinusoidal signal under the presence of harmonics and noise using decision trees

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