Fault Diagnosis of HVDC Systems Using Machine Learning Based Methods

With the development of high-power electronic technology, HVDC system is applied in the power system because of advantages in large-capacity and long-distance transmission, stability, and flexibility. Therefore, as the guarantee of reliable operating of HVDC system, fault diagnosis of the HVDC system is of great significance. In the current variety methods used in fault diagnosis, Machine Learning based methods have become a hotspot. To this end, the performance of several commonly used machine learning classifiers is compared in HVDC system. First of all, nine faults both in AC systems and DC systems of the HVDC system are set in the HVDC model in Simulink. Therefore, 10 operating states corresponding to the faults and normal operating are considered as the output classes of classifier. Seven parameters, such as DC voltage and DC current, are selected as fault feature parameters of each sample. By simulating the HVDC system in 10 operating states (including normal operating state) correspondingly, 20000 samples, each containing seven parameters, be obtained during the fault period. Then, the training sample set and the test sample set are established by 80% and 20% of the whole sample set. Subsequently, Decision Trees, the Support Vector Machine (SVM), K-Nearest Neighborhood Classifier (KNN), Ensemble classifiers, Discriminant Analysis, Backward Propagation Neural Network (BP-NN), long Short-Term Memory Neural Network (LSTM-NN), Extreme Learning Machine (ELM) was trained and tested. The accuracy of testing is used as the performance index of the model. In particular, for BP-NN, the impact of different transfer functions and learning rules combinations on the accuracy of the model was tested. For ELM, the impact of different activation functions on accuracy is tested. The results have shown that ELM and Bagged Trees have the best performance in HVDC fault diagnosis. The accuracy of these two methods are 92.23% and 96.5% respectively. However, in order to achieve better accuracy in ELM model, a large number of hidden layer nodes are set so that training time increases sharply

Fault Diagnosis of HVDC Systems Using Machine Learning Based Methods

With the development of high-power electronic technology, HVDC system is applied in the power system because of advantages in large-capacity and long-distance transmission, stability, and flexibility. Therefore, as the guarantee of reliable operating of HVDC system, fault diagnosis of the HVDC system is of great significance. In the current variety methods used in fault diagnosis, Machine Learning based methods have become a hotspot. To this end, the performance of several commonly used machine learning classifiers is compared in HVDC system. First of all, nine faults both in AC systems and DC systems of the HVDC system are set in the HVDC model in Simulink. Therefore, 10 operating states corresponding to the faults and normal operating are considered as the output classes of classifier. Seven parameters, such as DC voltage and DC current, are selected as fault feature parameters of each sample. By simulating the HVDC system in 10 operating states (including normal operating state) correspondingly, 20000 samples, each containing seven parameters, be obtained during the fault period. Then, the training sample set and the test sample set are established by 80% and 20% of the whole sample set. Subsequently, Decision Trees, the Support Vector Machine (SVM), K-Nearest Neighborhood Classifier (KNN), Ensemble classifiers, Discriminant Analysis, Backward Propagation Neural Network (BP-NN), long Short-Term Memory Neural Network (LSTM-NN), Extreme Learning Machine (ELM) was trained and tested. The accuracy of testing is used as the performance index of the model. In particular, for BP-NN, the impact of different transfer functions and learning rules combinations on the accuracy of the model was tested. For ELM, the impact of different activation functions on accuracy is tested. The results have shown that ELM and Bagged Trees have the best performance in HVDC fault diagnosis. The accuracy of these two methods are 92.23% and 96.5% respectively. However, in order to achieve better accuracy in ELM model, a large number of hidden layer nodes are set so that training time increases sharply

Fault management in networks incorporating Superconducting Cables (SCs) using Artificial Intelligence (AI) techniques.

With the increasing penetration of renewable energy sources, the immense growth in energy demand and the ageing of existing system infrastructure, future power systems have started to face reliability and resiliency challenges. To mitigate these issues, the need for bulk power corridors which enable the effective sharing of the available power capacity, between countries and from remote renewable energy sources, is rendered imperative. In this context, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes have been considered as a promising solution towards the modernisation of power systems. As opposed to conventional copper cables, SCs are characterised by a plethora of technically-attractive features such as compact structure, higher current-carrying capability, lower losses, higher power transfer at lower operating voltages and over longer distances, and reduced environmental impact. The performance of SCs is mainly determined by the structure of the cable and the electro-magneto-thermal properties of the HTS tapes, accounting for the critical current, critical temperature and critical magnetic field. Particularly, during steady state conditions, HTS tapes operate in superconducting mode, providing tangible benefits to power system operation such as a current-flowing path with approximately zero resistance. However, under certain transient conditions (e.g., electric faults), when the fault current flowing through HTS tapes reaches values higher than the critical current, HTS tapes start to quench. The quenching phenomenon is accompanied by a rapid increase in the equivalent resistance and temperature of SCs, the generation of Joule heating and the subsequent reduction in fault current magnitudes. Consequently, the transition of SCs from superconducting state to resistive state, during transient conditions, introduces many variables in the fault management of such cable technologies. Therefore, in order to exploit the technological advantages offered by SC applications, accommodate their wide-scale deployment within future energy grids, and accelerate their commercialisation, the detailed evaluation of their transient response and the consequent development of reliable fault management solutions are vital prerequisites. On that front, one of the main objectives of this thesis is to provide a detailed fault signature characterisation of AC and DC SCs and develop effective and practically feasible solutions for the fault management of AC and High Voltage Direct Current (HVDC) grids which incorporate SCs. As the fault management (i.e., fault detection, fault location, and protection) of SCs has proven to be a multi-variable problem, considering the complex structure, the unique features of SCs, and the quenching phenomenon, there is a need for advanced methods with immunity to these factors. In this context, the utilisation of Artificial Intelligence (AI) methods can be considered a very promising solution due to their capability to expose hidden patterns and acquire useful insights from the available data. Specifically, data-driven methods exhibit multifarious characteristics which allow them to provide innovative solutions for complex problems. Given their capacity for advanced learning and extensive data analysis, these methods merit thorough investigation for the fault management of SCs. Their inherent potential to adapt and uncover patterns in large datasets presents a compelling rationale for their exploration in enhancing the reliability and performance of superconducting cable systems. Therefore, this thesis proposes the development of novel, data-driven protection schemes which incorporate fault detection and classification elements for AC and multi-terminal HVDC systems with SCs, by exploiting the advantages of the latest trends in AI applications. In particular this thesis utilises cutting-edge developments and innovations in the field of AI, such as deep learning algorithms (i.e., CNN), and state-of-the-art techniques such as the XGBoost model which is a powerful ensemble learning algorithm. The developed schemes have been validated using simulation-based analysis. The obtained results confirm the enhanced sensitivity, speed, and discrimination capability of the developed schemes under various fault conditions and against other transient events, highlighting their superiority over other proposed methods or existing techniques. Furthermore, the generalisation capability of AI-assisted schemes has been verified against many adverse factors such as high values of fault resistance and noisy measurement. To further evaluate the practical feasibility and assess the time performance of the proposed schemes, real-time Software In the Loop (SIL) testing has been utilised. Another very important task for the effective fault management of AC and DC SCs is the estimation of the accurate fault location. Identifying the precise location of faults is crucial for SCs, given their complex structure and the challenging repair process. As such, this thesis proposes the design of a data-driven fault location scheme for AC systems with SCs. The developed scheme utilises pattern recognition techniques, such as image analysis, for feature extraction. It also incorporates AI algorithms in order to formulate the fault location problem as an AI regression problem. It is demonstrated that the scheme can accurately estimate the fault location along the SCs length and ensure increased reliability against a wide range of fault scenarios and noisy measurements. Further comparative analysis with other data-driven schemes validates the superiority of the proposed approach. In the final chapter the thesis summarises the key observations and outlines potential steps for further research in the field of fault management of superconducting-based systems.With the increasing penetration of renewable energy sources, the immense growth in energy demand and the ageing of existing system infrastructure, future power systems have started to face reliability and resiliency challenges. To mitigate these issues, the need for bulk power corridors which enable the effective sharing of the available power capacity, between countries and from remote renewable energy sources, is rendered imperative. In this context, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes have been considered as a promising solution towards the modernisation of power systems. As opposed to conventional copper cables, SCs are characterised by a plethora of technically-attractive features such as compact structure, higher current-carrying capability, lower losses, higher power transfer at lower operating voltages and over longer distances, and reduced environmental impact. The performance of SCs is mainly determined by the structure of the cable and the electro-magneto-thermal properties of the HTS tapes, accounting for the critical current, critical temperature and critical magnetic field. Particularly, during steady state conditions, HTS tapes operate in superconducting mode, providing tangible benefits to power system operation such as a current-flowing path with approximately zero resistance. However, under certain transient conditions (e.g., electric faults), when the fault current flowing through HTS tapes reaches values higher than the critical current, HTS tapes start to quench. The quenching phenomenon is accompanied by a rapid increase in the equivalent resistance and temperature of SCs, the generation of Joule heating and the subsequent reduction in fault current magnitudes. Consequently, the transition of SCs from superconducting state to resistive state, during transient conditions, introduces many variables in the fault management of such cable technologies. Therefore, in order to exploit the technological advantages offered by SC applications, accommodate their wide-scale deployment within future energy grids, and accelerate their commercialisation, the detailed evaluation of their transient response and the consequent development of reliable fault management solutions are vital prerequisites. On that front, one of the main objectives of this thesis is to provide a detailed fault signature characterisation of AC and DC SCs and develop effective and practically feasible solutions for the fault management of AC and High Voltage Direct Current (HVDC) grids which incorporate SCs. As the fault management (i.e., fault detection, fault location, and protection) of SCs has proven to be a multi-variable problem, considering the complex structure, the unique features of SCs, and the quenching phenomenon, there is a need for advanced methods with immunity to these factors. In this context, the utilisation of Artificial Intelligence (AI) methods can be considered a very promising solution due to their capability to expose hidden patterns and acquire useful insights from the available data. Specifically, data-driven methods exhibit multifarious characteristics which allow them to provide innovative solutions for complex problems. Given their capacity for advanced learning and extensive data analysis, these methods merit thorough investigation for the fault management of SCs. Their inherent potential to adapt and uncover patterns in large datasets presents a compelling rationale for their exploration in enhancing the reliability and performance of superconducting cable systems. Therefore, this thesis proposes the development of novel, data-driven protection schemes which incorporate fault detection and classification elements for AC and multi-terminal HVDC systems with SCs, by exploiting the advantages of the latest trends in AI applications. In particular this thesis utilises cutting-edge developments and innovations in the field of AI, such as deep learning algorithms (i.e., CNN), and state-of-the-art techniques such as the XGBoost model which is a powerful ensemble learning algorithm. The developed schemes have been validated using simulation-based analysis. The obtained results confirm the enhanced sensitivity, speed, and discrimination capability of the developed schemes under various fault conditions and against other transient events, highlighting their superiority over other proposed methods or existing techniques. Furthermore, the generalisation capability of AI-assisted schemes has been verified against many adverse factors such as high values of fault resistance and noisy measurement. To further evaluate the practical feasibility and assess the time performance of the proposed schemes, real-time Software In the Loop (SIL) testing has been utilised. Another very important task for the effective fault management of AC and DC SCs is the estimation of the accurate fault location. Identifying the precise location of faults is crucial for SCs, given their complex structure and the challenging repair process. As such, this thesis proposes the design of a data-driven fault location scheme for AC systems with SCs. The developed scheme utilises pattern recognition techniques, such as image analysis, for feature extraction. It also incorporates AI algorithms in order to formulate the fault location problem as an AI regression problem. It is demonstrated that the scheme can accurately estimate the fault location along the SCs length and ensure increased reliability against a wide range of fault scenarios and noisy measurements. Further comparative analysis with other data-driven schemes validates the superiority of the proposed approach. In the final chapter the thesis summarises the key observations and outlines potential steps for further research in the field of fault management of superconducting-based systems

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission

Copyright: © 2022 by the authors. Open circuit failure mode in insulated‐gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions. In addition, very little attention has been paid to the importance of developing fast and accurate techniques for the real‐life application of open‐circuit failures of IGBT fault diagnosis. To achieve high classification accuracy and reduced computation time, a fault diagnosis framework with a combination of the AC‐side three‐phase current, and the upper and lower bridges’ currents of the MMCs to automatically classify health conditions of MMCs is proposed. In this framework, the principal component analysis (PCA) is used for feature extrac-tion. Then, two classification algorithms—multiclass support vector machine (SVM) based on error-correcting output codes (ECOC) and multinomial logistic regression (MLR)—are used for classifi-cation. The effectiveness of the proposed framework is validated by a two‐terminal simulation model of the MMC‐high‐voltage direct current (HVDC) transmission power system using PSCAD/EMTDC software. The simulation results demonstrate that the proposed framework is highly effective in diagnosing the health conditions of MMCs compared to recently published re-sults.National Natural Science Foundation of China, grant no. 51105291; by the Shaanxi Provincial Science and Technology Agency, nos. 2020GY124, 2019GY-125, and 2018JQ5127; Key Laboratory Project of the Department of Education of Shaanxi Province, nos. 19JS034 and 18JS045

A New MMC Topology Which Decreases the Sub Module Voltage Fluctuations at Lower Switching Frequencies and Improves Converter Efficiency

Modular Multi-level inverters (MMCs) are becoming more common because of their suitability for applications in smart grids and multi-terminal HVDC transmission networks. The comparative study between the two classic topologies of MMC (AC side cascaded and DC side cascaded topologies) indicates some disadvantages which can affect their performance. The sub module voltage ripple and switching losses are one of the main issues and the reason for the appearance of the circulating current is sub module capacitor voltage ripple. Hence, the sub module capacitor needs to be large enough to constrain the voltage ripple when operating at lower switching frequencies. However, this is prohibitively uneconomical for the high voltage applications. There is always a trade off in MMC design between the switching frequency and sub module voltage ripple

Signal Processing and Classification Tools for Intelligent Distributed Monitoring and Analysis of the Smart Grid

This paper proposes a novel framework for an intelligent monitoring system that supervises the performance of the future power system. The increased complexity of the power system could endanger the reliability, voltage quality, operational security or resilience of the power system. A distributed structure for such a monitoring system is described and some of the advanced signal processing techniques or tools that could be used in such a monitoring system are given. Several examples for seeking the spatial locations and finding the underlying causes of disturbances are included

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

Presently, the use of a credit card has become an integral part of contemporary banking and financial system. Predicting potential credit card defaulters or debtors is a crucial business opportunity for financial institutions. For now, some machine learning methods have been applied to achieve this task. However, with the dynamic and imbalanced nature of credit card default data, it is challenging for classical machine learning algorithms to proffer robust models with optimal performance. Research has shown that the performance of machine learning algorithms can be significantly improved when provided with optimal features. In this paper, we propose an unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE). The SSAE was optimized to achieve improved performance. The proposed SSAE learned excellent feature representations that were used to train the classifiers. The performance of the proposed approach is compared with an instance where the classifiers were trained using the raw data. Also, a comparison is made with previous scholarly works, and the proposed approach showed superior performance over other methods