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Intelligent Fault Diagnosis of Power System

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 27053

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Prof. Dr. Andrzej Bień

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Department of Power Electronics and Automation of Energy Processing Systems, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: measurements of electricity parameters; digital processing of electrical signals

Special Issue Information

The energy system, in particular, electricity, is an important element of the functioning of modern civilization. Electricity supplies important and big as well as small consumers, both of whom are susceptible to varying degrees of shortages in the supply of electricity and disorders energy associated with the generation or reception.

The diagnostics of power system operation is important for its reliable power supply and for maintaining the quality of electricity. Equally important is the analysis of event-related disorders arising already in the system, i.e., monitoring and post-processing. Today, the basic tool in such activities is widely understood to be information technology (IT), starting from analog–digital processing, through to ongoing processing, sending and collecting data, and data analysis of often large data sets, i.e., big data. Synchrophasor technology is a very good example of such activities. Used and developed in this area are: construction of equipment operating in a difficult electromagnetic compatibility (EMC) environment, construction of data transmission systems (with their synchronization), analysis of collected data with the help of digital signal processing (DSP) and artificial intelligence (AI), and more.

Prof. Dr. Andrzej Bień
Guest Editor

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Keywords

measuring systems in the power industry, filtering, and EMC immunity
distributed measuring systems in the power industry
synchronization measurement data
voltage and current analysis in electricity
system stability
digital signal analysis

Published Papers (11 papers)

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Research

21 pages, 714 KiB

Open AccessArticle

WAMs Based Eigenvalue Space Model for High Impedance Fault Detection

by Gian Paramo and Arturo S. Bretas

Appl. Sci. 2021, 11(24), 12148; https://0-doi-org.brum.beds.ac.uk/10.3390/app112412148 - 20 Dec 2021

Cited by 7 | Viewed by 1850

Abstract

High impedance faults present unique challenges for power system protection engineers. The first challenge is the detection of the fault, given the low current magnitudes. The second challenge is to locate the fault to allow corrective measures to be taken. Corrective actions are essential as they mitigate safety hazards and equipment damage. The problem of high impedance fault detection and location is not a new one, and despite the safety and reliability implications, relatively few efforts have been made to find a general solution. This work presents a hybrid data driven and analytical-based model for high impedance fault detection in distribution systems. The first step is to estimate a state space model of the power line being monitored. From the state space model, eigenvalues are calculated, and their dynamic behavior is used to develop zones of protection. These zones of protection are generated analytically using machine learning tools. High impedance faults are detected as they drive the eigenvalues outside of their zones. A metric called eigenvalue drift coefficient was formulated in this work to facilitate the generalization of this solution. The performance of this technique is evaluated through case studies based on the IEEE 5-Bus system modeled in Matlab. Test results are encouraging indicating potential for real-life applications. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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20 pages, 5331 KiB

Open AccessArticle

Application of Probabilistic Neural Networks Using High-Frequency Components’ Differential Current for Transformer Protection Schemes to Discriminate between External Faults and Internal Winding Faults in Power Transformers

by Pathomthat Chiradeja, Chaichan Pothisarn, Nattanon Phannil, Santipont Ananwattananporn, Monthon Leelajindakrairerk, Atthapol Ngaopitakkul, Surakit Thongsuk, Vinai Pornpojratanakul, Sulee Bunjongjit and Suntiti Yoomak

Appl. Sci. 2021, 11(22), 10619; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210619 - 11 Nov 2021

Cited by 6 | Viewed by 1479

Abstract

Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a ¼ cycle DWT as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the ¼ cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand’s electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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15 pages, 2428 KiB

Open AccessArticle

A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

by Tierui Zou, Nader Aljohani, Keerthiraj Nagaraj, Sheng Zou, Cody Ruben, Arturo Bretas, Alina Zare and Janise McNair

Appl. Sci. 2021, 11(17), 8074; https://0-doi-org.brum.beds.ac.uk/10.3390/app11178074 - 31 Aug 2021

Cited by 5 | Viewed by 1530

Abstract

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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15 pages, 919 KiB

Open AccessArticle

A Bi-Level Model for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

by Nader Aljohani and Arturo Bretas

Appl. Sci. 2021, 11(14), 6540; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146540 - 16 Jul 2021

Cited by 9 | Viewed by 1853

Abstract

Power system state estimation is an important component of the status and healthiness of the underlying electric power grid real-time monitoring. However, such a component is prone to cyber-physical attacks. The majority of research in cyber-physical power systems security focuses on detecting measurements False-Data Injection attacks. While this is important, measurement model parameters are also a most important part of the state estimation process. Measurement model parameters though, also known as static-data, are not monitored in real-life applications. Measurement model solutions ultimately provide estimated states. A state-of-the-art model presents a two-step process towards simultaneous false-data injection security: detection and correction. Detection steps are

χ^{2}

statistical hypothesis test based, while correction steps consider the augmented state vector approach. In addition, the correction step uses an iterative solution of a relaxed non-linear model with no guarantee of optimal solution. This paper presents a linear programming method to detect and correct cyber-attacks in the measurement model parameters. The presented bi-level model integrates the detection and correction steps. Temporal and spatio characteristics of the power grid are used to provide an online detection and correction tool for attacks pertaining the parameters of the measurement model. The presented model is implemented on the

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118 bus system. Comparative test results with the state-of-the-art model highlight improved accuracy. An easy-to-implement model, built on the classical weighted least squares solution, without hard-to-derive parameters, highlights potential aspects towards real-life applications. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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13 pages, 1932 KiB

Open AccessCommunication

PV Fault Detection Using Positive Unlabeled Learning

by Kristen Jaskie, Joshua Martin and Andreas Spanias

Appl. Sci. 2021, 11(12), 5599; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125599 - 17 Jun 2021

Cited by 11 | Viewed by 1777

Abstract

Solar array management and photovoltaic (PV) fault detection is critical for optimal and robust performance of solar plants. PV faults cause substantial power reduction along with health and fire hazards. Traditional machine learning solutions require large, labeled datasets which are often expensive and/or difficult to obtain. This data can be location and sensor specific, noisy, and resource intensive. In this paper, we develop and demonstrate new semi supervised solutions for PV fault detection. More specifically, we demonstrate that a little-known area of semi-supervised machine learning called positive unlabeled learning can effectively learn solar fault detection models using only a fraction of the labeled data required by traditional techniques. We further introduce a new feedback enhanced positive unlabeled learning algorithm that can increase model accuracy and performance in situations such as solar fault detection when few sensor features are available. Using these algorithms, we create a positive unlabeled solar fault detection model that can match and even exceed the performance of a fully supervised fault classifier using only 5% of the total labeled data. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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14 pages, 3259 KiB

Open AccessArticle

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

by Hongjiang Cui, Ying Guan, Huayue Chen and Wu Deng

Appl. Sci. 2021, 11(12), 5385; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125385 - 10 Jun 2021

Cited by 99 | Viewed by 3122

Abstract

In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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21 pages, 637 KiB

Open AccessFeature PaperArticle

Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis

by Saverio Farsoni, Silvio Simani and Paolo Castaldi

Appl. Sci. 2021, 11(11), 5035; https://0-doi-org.brum.beds.ac.uk/10.3390/app11115035 - 29 May 2021

Cited by 12 | Viewed by 2074

Abstract

The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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15 pages, 880 KiB

Open AccessFeature PaperArticle

Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers

by Nader Aljohani, Tierui Zou, Arturo S. Bretas and Newton G. Bretas

Appl. Sci. 2021, 11(10), 4419; https://0-doi-org.brum.beds.ac.uk/10.3390/app11104419 - 13 May 2021

Cited by 2 | Viewed by 1463

Abstract

In the modern power system networks, grid observability has greatly increased due to the deployment of various metering technologies. Such technologies enhanced the real-time monitoring of the grid. The collection of observations are processed by the state estimator in which many applications have relied on. Traditionally, state estimation on power grids has been done considering a centralized architecture. With grid deregulation, and awareness of information privacy and security, much attention has been given to multi-area state estimation. Considering such, state-of-the-art solutions consider a weighted norm of residual measurement model, which might hinder masked gross errors contained in the null-space of the Jacobian matrix. Towards the solution of this, a distributed innovation-based model is presented. Measurement innovation is used towards error composition. The measurement error is an independent random variable, where the residual is not. Thus, the masked component is recovered through measurement innovation. Model solution is obtained through an Alternating Direction Method of Multipliers (ADMM), which requires minimal information communication. The presented framework is validated using the

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14 and

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118 bus systems. Easy-to-implement model, build-on the classical weighted norm of the residual solution, and without hard-to-design parameters highlight potential aspects towards real-life implementation. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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14 pages, 731 KiB

Open AccessFeature PaperArticle

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

by Francesco Castellani, Davide Astolfi and Francesco Natili

Appl. Sci. 2021, 11(8), 3307; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083307 - 07 Apr 2021

Cited by 25 | Viewed by 3050

Abstract

The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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21 pages, 6527 KiB

Open AccessArticle

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

by Rafia Nishat Toma and Jong-Myon Kim

Appl. Sci. 2020, 10(15), 5251; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155251 - 30 Jul 2020

Cited by 77 | Viewed by 5046

Abstract

Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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12 pages, 2055 KiB

Open AccessArticle

Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

by Jittiphong Klomjit and Atthapol Ngaopitakkul

Appl. Sci. 2020, 10(11), 3967; https://0-doi-org.brum.beds.ac.uk/10.3390/app10113967 - 07 Jun 2020

Cited by 7 | Viewed by 2315

Abstract

This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems. Full article

(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)

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