Open-circuit fault diagnosis and maintenance in multi-pulse parallel and series TRU topologies

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Transformer Rectifier Units (TRUs) are a reliable way for DC generation in several electric applications. These units are formed by multiple three-phase uncontrolled bridge rectifiers connected according to two main topologies (parallel and series), and fed by a phase-shifting transformer, which can have different configurations. Fault diagnosis of the uncontrolled bridge rectifier diodes is one of the most important concerns on the electronic devices, nonetheless, rectifier units are inherently not protected in front of Open-Circuit (O/C) faults, which cause malfunction and performance deterioration. In order to solve this drawback, the proposed fault diagnosis method is based on the O/C fault signature observed in the DC-link output voltage of TRUs rectifier. It allows detecting the O/C diodes of parallel and series TRUs with different phase-shifting transformer configurations and for the most usual fault scenarios. Moreover, it also helps the prediction of diodes that could be exposed to failure after the fault, which provides corrective maintenance for the TRU development. The proposed method is illustrated from MATLABTM numerical simulations of a 12-pulse TRU, and is validated with experimental tests.This work supported in part by the Research Project Estabilidad de Redes MVdc Integrando Tecnologias de Energias Renovables, Almacenamiento de Energia y Convertidores de Fuente de Impedancia, RTI2018-095720-B-C33, in part by the Ministerio de Ciencia, Innovación y Universidades, and in part by the European Union.Peer ReviewedPostprint (author's final draft

Converter fault diagnosis and post-fault operation of a doubly-fed induction generator for a wind turbine

Wind energy has become one of the most important alternative energy resources because of the global warming crisis. Wind turbines are often erected off-shore because of favourable wind conditions, requiring lower towers than on-shore. The doubly-fed induction generator is one of the most widely used generators with wind turbines. In such a wind turbine the power converters are less robust than the generator and other mechanical parts. If any switch failure occurs in the converters, the wind turbine may be seriously damaged and have to stop. Therefore, converter health monitoring and fault diagnosis are important to improve system reliability. Moreover, to avoid shutting down the wind turbine, converter fault diagnosis may permit a change in control strategy and/or reconfigure the power converters to permit post-fault operation. This research focuses on switch fault diagnosis and post-fault operation for the converters of the doubly-fed induction generator. The effects of an open-switch fault and a short-circuit switch fault are analysed. Several existing open-switch fault diagnosis methods are examined but are found to be unsuitable for the doubly-fed induction generator. The causes of false alarms with these methods are investigated. A proposed diagnosis method, with false alarm suppression, has the fault detection capability equivalent to the best of the existing methods, but improves system reliability. After any open-switch fault is detected, reconfiguration to a four-switch topology is activated to avoid shutting down the system. Short-circuit switch faults are also investigated. Possible methods to deal with this fault are discussed and demonstrated in simulation. Operating the doubly-fed induction generator as a squirrel cage generator with aerodynamic power control of turbine blades is suggested if this fault occurs in the machine-side converter, while constant dc voltage control is suitable for a short-circuit switch fault in the grid-side converter.Wind energy has become one of the most important alternative energy resources because of the global warming crisis. Wind turbines are often erected off-shore because of favourable wind conditions, requiring lower towers than on-shore. The doubly-fed induction generator is one of the most widely used generators with wind turbines. In such a wind turbine the power converters are less robust than the generator and other mechanical parts. If any switch failure occurs in the converters, the wind turbine may be seriously damaged and have to stop. Therefore, converter health monitoring and fault diagnosis are important to improve system reliability. Moreover, to avoid shutting down the wind turbine, converter fault diagnosis may permit a change in control strategy and/or reconfigure the power converters to permit post-fault operation. This research focuses on switch fault diagnosis and post-fault operation for the converters of the doubly-fed induction generator. The effects of an open-switch fault and a short-circuit switch fault are analysed. Several existing open-switch fault diagnosis methods are examined but are found to be unsuitable for the doubly-fed induction generator. The causes of false alarms with these methods are investigated. A proposed diagnosis method, with false alarm suppression, has the fault detection capability equivalent to the best of the existing methods, but improves system reliability. After any open-switch fault is detected, reconfiguration to a four-switch topology is activated to avoid shutting down the system. Short-circuit switch faults are also investigated. Possible methods to deal with this fault are discussed and demonstrated in simulation. Operating the doubly-fed induction generator as a squirrel cage generator with aerodynamic power control of turbine blades is suggested if this fault occurs in the machine-side converter, while constant dc voltage control is suitable for a short-circuit switch fault in the grid-side converter

Internal Fault Diagnosis of MMC-HVDC Based on Classification Algorithms in Machine Learning

With the development of the HVDC system, MMC-HVDC is now the most advanced technology that has been put into use. In power systems, faults happen during the operation due to natural reasons or devices physical issues, which would cause serious economic losses and other implications. Thus, fault detection and analysis are extremely important, especially in the HVDC system. Existing works in literature mainly focus on the faults detection and analysis on the system side such as short circuit of the AC side, and open circuit of the DC side. However, little attention has been paid to the fault detection and analysis inside the converters. With the technology development of converter devices, replacing the whole converter becomes more expensive. Thus, my research mainly focuses on the detection and classification of the faults within the internal of the MMC module. In this research, an SPS model of MMC-HVDC is built as the example. Faults including short circuit and open circuit located inside the MMC module are simulated. Machine learning algorithms are chosen as the tool to achieve the goal of detecting faults and locating the fault position inside the MMC module precisely. After comparing the basic characteristics and properly application situations of various methods of machine learning, Coarse KNN, Complex Tree and Bagged Tree (Random Forest) are deployed to solve the problem. The performance of the methods are analyzed and compared, to get the most proper method in solving the problem

Internal Fault Diagnosis of MMC-HVDC Based on Classification Algorithms in Machine Learning

With the development of the HVDC system, MMC-HVDC is now the most advanced technology that has been put into use. In power systems, faults happen during the operation due to natural reasons or devices physical issues, which would cause serious economic losses and other implications. Thus, fault detection and analysis are extremely important, especially in the HVDC system. Existing works in literature mainly focus on the faults detection and analysis on the system side such as short circuit of the AC side, and open circuit of the DC side. However, little attention has been paid to the fault detection and analysis inside the converters. With the technology development of converter devices, replacing the whole converter becomes more expensive. Thus, my research mainly focuses on the detection and classification of the faults within the internal of the MMC module. In this research, an SPS model of MMC-HVDC is built as the example. Faults including short circuit and open circuit located inside the MMC module are simulated. Machine learning algorithms are chosen as the tool to achieve the goal of detecting faults and locating the fault position inside the MMC module precisely. After comparing the basic characteristics and properly application situations of various methods of machine learning, Coarse KNN, Complex Tree and Bagged Tree (Random Forest) are deployed to solve the problem. The performance of the methods are analyzed and compared, to get the most proper method in solving the problem

Average value of the DC-link output voltage in multi-phase uncontrolled bridge rectifiers under supply voltage balance and unbalance conditions

Average value of the DC-link output voltage is a variable of interest in multi-phase uncontrolled bridge rectifiers. The aim of this paper is to present a new, effort-saving procedure capable of providing an accurate value of this variable, a value which can be later corrected considering the usually omitted voltage drops. The proposed method, based on the Cauchy’s formula (1841), allows the limitations of the existing methods to be overcome and can be used under supply voltage balance and unbalance conditions. Time-domain simulations and experimental tests were conducted to show the usefulness of the method and validate its accuracy. Under supply voltage balance conditions, the new method allows results as accurate as those provided by analytical expressions available in the literature or time-domain simulations performed by any software to be obtained. Moreover, under supply voltage unbalance conditions, this method outperforms analytical expressions available in the literature and at least equals time-domain simulations performed by any software in terms of accuracy of the obtained results. Therefore, under supply voltage balance and unbalance conditions, the proposed method makes the mathematical effort required to elaborate analytical expressions or the computational effort required to perform time-domain simulations unnecessary. In addition, the new method provides suitable estimates of values experimentally determined.This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades under Grant RTI2018-095720-B-C33.Peer ReviewedPostprint (author's final draft

HVDC Systems Fault Analysis Using Various Signal Processing Techniques

The detection and fast clearance of faults are important for the safe and optimal operation of HVDC systems. In HVDC systems, various types of AC faults (rectifier & inverter side) and DC faults can occur. It is therefore necessary to detect the faults and classify them for better protection and diagnostics purposes. Various techniques for fault detection and classification in HVDC systems using signal processing techniques are presented and investigated in this research work. In this research work, it is shown that the wavelet transformation can effectively detect abrupt changes in system signals which are indicative of a fault. This research has focused on DC faults at various distances along the lines and AC faults on the converter side. The DC line current is chosen as the input to the wavelet transform. The 5th level coefficients have been used to identify the various faults in the LCC-HVDC system. Moreover, the value of these coefficients has been used for the classification of the different faults. For more accurate classification of faults, the wavelet entropy principle is proposed. In LCC-HVDC systems, a different approach for fault identification and classification is proposed. In this investigation an algorithm is developed that provides the trade-off between large input data size and minimal number of neurons in the hidden layer, without compromising the accuracy. The claim is confirmed by the results provided from the investigation for various fault conditions and its corresponding ANN output which confirms the specific fault detection and its classification. A fault identification and classification strategy based on fuzzy logic for VSC–HVDC systems is proposed. Initially, the developed Fuzzy Inference Engine (FIE) detects AC faults occurring in the rectifier side and DC faults on the cable successfully. However, it could not identify the line on which the fault has occurred. Hence, to classify the faults occurring in either AC section or DC section of the HVDC system, the FIE has to be restructured with appropriate data input. Therefore, a FIE which identifies different types of fault and the corresponding line where the fault occurs anywhere in the HVDC system was developed. Initially the developed FIE with three input and seven output parameters results in an accuracy level of 99.47% being achieved. After a modified FIE was developed with five inputs and seven output parameters, 21 types of faults in the VSC HVDC system were successfully classified with 100% accuracy. The FIE was further developed to successfully classify with 100% accuracy faults in Multi-Terminal HVDC systems

Fault Detection of Inter-Turn Short-Circuited Stator Windings in Permanent Magnet Synchronous Machines

Vannkraftverk leverer grønn og pålitelig energi til befolkningen i Norge, og bidrar med rundt 88 % av landets årlige strømbehov. Uventede avbrudd og stans for kraftverkene vil resultere i store økonomiske tap, samt at kraftverkene ikke får levert nødvendig kraft til nettet. Med fremveksten av Industri 4.0 benytter industriene nyskapende teknologier som skytjenester, Kunstig Intelligens (KI) og tingenes internett for å forbedre de ulike operasjonene i selskapet. Innen vannkraft-industrien vil KI-baserte systemer bli brukt som grunnlag for prediktive vedlikehold. I dag utføres det meste av vedlikeholdsarbeid i henhold til en planlagt tidsplan, og industrien ser derfor på bruk av maskinlærings-metoder for tidlig feilgjenkjenning i vannkraftverkene. Denne masteroppgaven ser på anvendelsen av maskinlærings-algoritmer for å tidlig forutsi kortslutninger i aramturviklingene i en Permanent Magnet Synkronmaskin (PMSM), ved bruk av trefaset strøm-data. Data A ble samlet inn i et internt laboratorium med en Permanent Magnet Synkrongenerator (PMSG) som hadde en implementert 4.8 % kortslutning i aramturviklingen. Dataen bestod av sunne og defekte datasett med RMSverdier for den trefasede strømmen. Data B ble hentet fra et tidligere arbeid av den samme typen PMSM med en 6.0 % kortslutning i aramturviklingen. Data B bestod av signal-verdier for den trefasede strømmen. Ved bruk av Python ble de to datasettene visuelt inspisert og forbehandlet ved hjelp av ‘Z-score’-metoden for å fjerne avvikende verdier. Denne prosessen hadde imidlertid ingen merkbar effekt på nøyaktigheten til maskinlærings-modellene. Enkel signalbehandling i tidsplanet ble anvendt på strømdataene, men klarte ikke å oppdage kortslutningsfeilen implementert på den andre faseviklingen. Statistiske parameter som gjennomsnitt, standard avvik, skjevhet, kurtose, toppverdifaktor, peak-to-peak, RMS, klaringsfaktor, formfaktor og impulsfaktor ble beregnet for alle tre fasene. En Principal Component Analysis (PCA)- algoritme ble anvendt på datasettene med de statistiske parameterne og reduserte Data A fra 18 parameter til tre Principal Components. Data B ble redusert fra 33 parametere til fire Principal Components. Før dataen kjøres i maskinlørings-modellene, ble feilindikatorer som flagger verdier utenfor den 95. persentilen av gjennomsnittsverdiene til parameterne lagt til i datasettet . Fire overvåkede maskinlærings-modeller – ‘Random Forest’, ‘Decision Trees’, ‘k-NN’ og ‘Naive Bayes’ – ble kjørt for datasettene. Random Forest- og Decision Tree-modellene hadde en tendens til å overtilpasse maskinlærings-prediksjonene på datasettene som inneholdt de statistisk parameterne. Datasettet med PCA-komponentene reduserte overtilpasningen av disse modellene og forbedret nøyaktigheten til Naive Bayes-modellen. Ettersom Naive Bayes-modellen ga varierende resultater og ble ansett som inkonsekvent, samt overtilpasnings-tendensene til Random Forest og Decision Tree, ble k-NN-modellen vurdert som den mest pålitelige av maskinlærings-modellene. De beste feilindikatorene for Data A var kurtose- og skjevhet-indikatorene, mens klaringsfaktor og formfaktor ga best nøyaktighet for Data B. Videre arbeid bør unngå bruk av data som inneholder RMS-verdier, og fokusere på bruk av signalbaserte verdier slik som i Data B. Dataprosessering og feilmerking bør også utføres i frekvensplanet, ettersom en stor svakhet ved avhandlingen er at metodikken kun ble anvendt i tidplanet. Andre ytelsesindikatorer som robusthet bør også brukes for å vurdere ytelsen til maskinlærings-modellene

On the identifiability, parameter identification and fault diagnosis of induction machines

PhD ThesisDue to their reliability and low cost, induction machines have been widely utilized in a large variety of industrial applications. Although these machines are rugged and reliable, they are subjected to various stresses that might result in some unavoidable parameter changes and modes of failures. A common practice in induction machine parameter identification and fault diagnosis techniques is to employ a machine model and use the external measurements of voltage, current, speed, and/or torque in model solution. With this approach, it might be possible to get an infinite number of mathematical solutions representing the machine parameters, depending on the employed machine model. It is therefore crucial to investigate such possibility of obtaining incorrect parameter sets, i.e. to test the identifiability of the model before being used for parameter identification and fault diagnosis purposes. This project focuses on the identifiability of induction machine models and their use in parameter identification and fault diagnosis. Two commonly used steady-states induction machine models namely T-model and inverse Γ- model have been considered in this thesis. The classical transfer function and bond graph identifiability analysis approaches, which have been previously employed for the T-model, are applied in this thesis to investigate the identifiability of the inverse Γ-model. A novel algorithm, the Alternating Conditional Expectation, is employed here for the first time to study the identifiability of both the T- and inverse Γ-models of the induction machine. The results obtained from the proposed algorithm show that the parameters of the commonly utilised Tmodel are non-identifiable while those of the inverse Γ-model are uniquely identifiable when using external measurements. The identifiability analysis results are experimentally verified by the particle swarm optimization and Levenberg-Marquardt model-based parameter identification approaches developed in this thesis. To overcome the non-identifiability problem of the T-model, a new technique for induction machine parameter estimation from external measurements based on a combination of the induction machine’s T- and inverse Γ-models is proposed. Results for both supply-fed and inverter-fed operations show the success of the technique in identifying the parameters of the machine using only readily available measurements of steady-state machine current, voltage and speed, without the need for extra hardware. ii A diagnosis scheme to detect stator winding faults in induction machines is also proposed in this thesis. The scheme uses time domain features derived from 3-phase stator currents in conjunction with particle swarm optimization algorithm to check characteristic parameters of the machine and detect the fault accordingly. The validity and effectiveness of the proposed technique has been evaluated for different common faults including interturn short-circuit, stator winding asymmetry (increased resistance in one or more stator phases) and combined faults, i.e. a mixture of stator winding asymmetry and interturn short-circuit. Results show the accuracy of the proposed technique and it is ability to detect the presence of the fault and provide information about its type and location. Extensive simulations using Matlab/SIMULINK and experimental tests have been carried out to verify the identifiability analysis and show the effectiveness of the proposed parameter identification and fault diagnoses schemes. The constructed test rig includes a 1.1 kW threephase test induction machine coupled to a dynamometer loading unit and driven by a variable frequency inverter that allows operation at different speeds. All the experiment analyses provided in the thesis are based on terminal voltages, stator currents and rotor speed that are usually measured and used in machine control.Libya, through the Engineering Faculty of Misurata- Misurata Universit

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis