Integrated Dwt-Differentiation Algorithm for Fault Detection and Relay Coordination in Micro Grid

The Bidirectional flow of current makes it difficult to detect fault in the microgrid. The level of fault current changes continuously with change in load, it leads to selectivity and sensitivity issue of relay. In this paper integrated DWT-differentiation algorithm is proposed for fault detection and relay coordination, the input waveform of fault current is proceed with discrete wavelet transform. Time scale function of DWT used to extract exact feature from signal which helps in further effective analysis. The Optimization function of relay is mainly depends on PSM (plug setting multiplier) and TDS (Time dial span). The Fault current used to calculate this parameter are already analyzed from DWT. Standard 9 bus IEEE system is used as reference. Fault is detected at 21 different locations; initially primary protection is activated and secondary protection operates only if first selected pair of relay fails to operate .The differential algorithm select best pair of backup relay and relay coordination is carried out resulting in reduction of operating Time

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Development of a machine learning algorithm for fault detection in a cantilever beam

Noise & Vibration Worldwide ◽

10.1177/09574565211000450 ◽

2021 ◽

pp. 095745652110004

Author(s):

Amit Kumar Gorai ◽

Tarapada Roy ◽

Sumeet Mishra

Keyword(s):

Mechanical Properties ◽

Fault Detection ◽

Cantilever Beam ◽

Learning Algorithm ◽

Model Development ◽

Vibration Signal ◽

Beam Specimen ◽

Discrete Wavelet ◽

Feed Forward Network ◽

Model Training

The mechanical properties of a component change with any type of damage such as crack development, generation of holes, bend, excessive wear, and tear. The change in mechanical properties causes the material to behave differently in terms of noise and vibration under different loading conditions. Thus, the present study aims to develop an artificial neural network model using vibration signal data for early fault detection in a cantilever beam. The discrete wavelet transform coefficients of de-noised vibration signals were used for model development. The vibration signal was recorded using the OROS OR35 module for different fault conditions (no fault, notch fault, and hole fault) of a cantilever beam. A feed-forward network was trained using backpropagation to map the input features to output. A total of 603 training datasets (201 datasets for three types of cantilever beam—no fault, notch fault, and hole fault) were used for training, and 201 datasets were used for testing of the model. The testing dataset was recorded for a hole fault cantilever beam specimen. The results indicated that the proposed model predicted the test samples with 78.6% accuracy. To increase the accuracy of prediction, more data need to be used in the model training.

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Sympathetic Trippings Blocking in Over Current Relay Coordination Algorithm during Steady State & Transient Network Topologies

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2016-0018 ◽

2016 ◽

Vol 17 (3) ◽

pp. 311-326 ◽

Cited By ~ 1

Author(s):

Manohar Singh ◽

B.K. Panigrahi ◽

T. Vishnuvardhan

Keyword(s):

Distribution System ◽

Search Algorithm ◽

Operating Time ◽

Network Configuration ◽

Coordination Problem ◽

Relay Coordination ◽

Network Topologies ◽

Time Gap ◽

Transient Network ◽

Nuisance Tripping

Abstract In this paper an improved over current relay coordination protection technique is proposed. The proposed technique eliminates the sympathetic/nuisance tripping in an interconnected distribution system. Nuisance trippings are eliminated by incorporating the additional selectivity constraints in conventional over current relay coordination problem. Whenever the fault is cleared only from one end of the line in bidirectional fault feed lines, transient network configuration comes into existence. Transient network configuration causes the redistribution of fault in the distribution system and leads to further nuisance tripping of relays. This problem in this paper is solved by minimising the operating time gap between primary relays located at near end and far end of a faulty line to the best minimum possible value. The differential search algorithm is applied for optimization of highly non-linear over current relay coordination problem in this paper. The result presented in this paper shows that the proposed over current relay coordination technique is immune against the sympathetic/nuisance tripping and operating time difference between primary relays at near end and far end is also minimised within acceptable time margin successfully.

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Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid

Applied Sciences ◽

10.3390/app10144965 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4965

Author(s):

Yordanos Dametw Mamuya ◽

Yih-Der Lee ◽

Jing-Wen Shen ◽

Md Shafiullah ◽

Cheng-Chien Kuo

Keyword(s):

Machine Learning ◽

Fault Detection ◽

Radial Distribution ◽

Fault Classification ◽

Discrete Wavelet ◽

Efficiency Coefficient ◽

Distribution Grid ◽

Phase Current ◽

Three Phase ◽

Types Of Faults

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.

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Analysis of Local Measurement-Based Algorithms for Fault Detection in a Multi-Terminal HVDC Grid

Energies ◽

10.3390/en12244808 ◽

2019 ◽

Vol 12 (24) ◽

pp. 4808 ◽

Cited By ~ 1

Author(s):

María José Pérez Molina ◽

Dunixe Marene Larruskain ◽

Pablo Eguía López ◽

Agurtzane Etxegarai

Keyword(s):

Fault Detection ◽

Fault Location ◽

Operating Time ◽

Rate Of Change ◽

Local Measurement ◽

Backup Protection ◽

High Voltage Direct Current ◽

Fault Resistance ◽

Operation Speed ◽

Protection Systems

One of the most important challenges of developing multi-terminal (MT) high voltage direct current (HVDC) grids is the system performance under fault conditions. It must be highlighted that the operating time of the protection system needs to be shorter than a few milliseconds. Due to this restrictive requirement of speed, local measurement based algorithms are mostly used as primary protection since they present an appropriate operation speed. This paper focuses on the analysis of local measurement based algorithms, specifically overcurrent, undervoltage, rate-of-change-of-current, and rate-of-change-of-voltage algorithms. A review of these fault detection algorithms is presented. Furthermore, these algorithms are applied to a multi-terminal grid, where the influence of fault location and fault resistance is assessed. Then, their performances are compared in terms of detection speed and maximum current interrupted by the HVDC circuit breakers. This analysis aims to enhance the protection systems by facilitating the selection of the most suitable algorithm for primary or backup protection systems. In addition, two new fault type identification algorithms based on the rate-of-change-of-voltage and rate-of-change-of-current are proposed and analyzed. The paper finally includes a comparison between the previously reviewed local measurement based algorithms found in the literature and the simulation results of the present work.

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