Where is My Next Hop ? The Case of Indian Ocean Islands

Internet has become a foundation of our modern society. However, all regions or countries do not have the same Internet access regarding quality especially in the Indian Ocean Area (IOA). To improve this quality it is important to have a deep knowledge of the Internet physical and logical topology and associated performance. However, these knowledges are not shared by Internet service providers. In this paper, we describe a large scale measurement study in which we deploy probes in different IOA countries, we generate network traces, develop a tool to extract useful information and analyze these information. We show that most of the IOA traffic exits through one point even if there exists multiple exit points

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

Water-Tree Modelling and Detection for Underground Cables

In recent years, aging infrastructure has become a major concern for the power industry. Since its inception in early 20th century, the electrical system has been the cornerstone of an industrial society. Stable and uninterrupted delivery of electrical power is now a base necessity for the modern world. As the times march-on, however, the electrical infrastructure ages and there is the inevitable need to renew and replace the existing system. Unfortunately, due to time and financial constraints, many electrical systems today are forced to operate beyond their original design and power utilities must find ways to prolong the lifespan of older equipment. Thus, the concept of preventative maintenance arises. Preventative maintenance allows old equipment to operate longer and at better efficiency, but in order to implement preventative maintenance, the operators must know minute details of the electrical system, especially some of the harder to assess issues such water-tree. Water-tree induced insulation degradation is a problem typically associated with older cable systems. It is a very high impedance phenomenon and it is difficult to detect using traditional methods such as Tan-Delta or Partial Discharge. The proposed dissertation studies water-tree development in underground cables, potential methods to detect water-tree location and water-tree severity estimation. The dissertation begins by developing mathematical models of water-tree using finite element analysis. The method focuses on surface-originated vented tree, the most prominent type of water-tree fault in the field. Using the standard operation parameters of North American electrical systems, the water-tree boundary conditions are defined. By applying finite element analysis technique, the complex water-tree structure is broken down to homogeneous components. The result is a generalized representation of water-tree capacitance at different stages of development. The result from the finite element analysis is used to model water-tree in large system. Both empirical measurements and the mathematical model show that the impedance of early-stage water-tree is extremely large. As the result, traditional detection methods such Tan-Delta or Partial Discharge are not effective due to the excessively high accuracy requirement. A high-frequency pulse detection method is developed instead. The water-tree impedance is capacitive in nature and it can be reduced to manageable level by high-frequency inputs. The method is able to determine the location of early-stage water-tree in long-distance cables using economically feasible equipment. A pattern recognition method is developed to estimate the severity of water-tree using its pulse response from the high-frequency test method. The early-warning system for water-tree appearance is a tool developed to assist the practical implementation of the high-frequency pulse detection method. Although the equipment used by the detection method is economically feasible, it is still a specialized test and not designed for constant monitoring of the system. The test also place heavy stress on the cable and it is most effective when the cable is taken offline. As the result, utilities need a method to estimate the likelihood of water-tree presence before subjecting the cable to the specialized test. The early-warning system takes advantage of naturally occurring high-frequency events in the system and uses a deviation-comparison method to estimate the probability of water-tree presence on the cable. If the likelihood is high, then the utility can use the high-frequency pulse detection method to obtain accurate results. Specific pulse response patterns can be used to calculate the capacitance of water-tree. The calculated result, however, is subjected to margins of error due to limitations from the real system. There are both long-term and short-term methods to improve the accuracy. Computation algorithm improvement allows immediate improvement on accuracy of the capacitance estimation. The probability distribution of the calculation solution showed that improvements in waveform time-step measurement allow fundamental improves to the overall result

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

Control and operation of wind power plants connected to DC grids

Remote offshore wind power plants (WPPs) are being linked through high-voltage de voltage-source converter (VSC-HVdc) transmission to the main grids. The current deployments of HVdc grid connections for offshore WPPs are point-to-point transmission systems. Moreover, WPPs connected to the offshore VSC-HVdc form an offshore ac grid which operates non­ synchronously to the main grids. lt is characterized by extensive submarine cabling and, in the case offull-scale power converter-based wind turbines, by being purely converter-based. This thesis goes into two main aspects regarding the operation of HVdc-connected WPPs: i) reactive power and voltage control and ii) fault ride through (FRT) in the ac offshore grids. Optimization-based reactive power control strategies are enhanced to the application of an ac grid consisting ofone grid-forming and several grid-connected converters. A reactive power and voltage control method is introduced which aims to increase the annual energy production from a single WPP. In the industrial application, several WPPs might be clustered which leads to multi-layered controllers and operation boundaries. Taking this into account, an operation strategy with reasonable communication requirements is suggested and evaluated against conventional methods . The work further propases a control framework for the grid-form ing offshore VSC-HVdc. Special emphasis is put on the FRT of unbalanced faults in the offshore grid and the provision of controlled currents for ease of fault detection. Furthermore, the internal variables of the offshore modular multi-level VSC-HVdc are analyzed. Moreover, tour FRT strategies for the grid­ connected converters are evaluated for unbalanced faults in the offshore grid. This consequently implies that control strategies in symmetrical components are considered. Furthermore, the reduction of over-modulation and over-voltages by the power converters in the offshore grid is dealt with.Los parques eólicos marinos suelen conectarse a redes eléctricas terrestres a través de corriente continua de alta tensión (siglas en inglés: HVdc) utilizando convertidores de fuente de tensión (siglas en inglés: VSC) cuando la corriente alterna de alta tensión (siglas en inglés: HVac) resulta tecnológicamente e económicamente desfavorable. Los parques eólicos conectados al convertidor HVdc marino crean redes eléctricos marinas de corriente alterna que operan asíncronamente a las redes terrestres. Dichas redes se caracterizan por tener cables submarinos, y, en el caso de aerogeneradores con convertidores de plena potencia, resultan en redes constituidas únicamente por convertidores de potencia. Esta tesis investiga dos de los aspectos principales de la operación de parques eólicos marinos conectados en corriente continua de alta tensión: i) la regulación de potencia reactiva y tensión y ii) la operación durante faltas eléctricas en las redes marinas. Se han propuesto estrategias de optimización del control de reactiva para su aplicación a una red ac con varios convertidores conectados. Se ha introducido un método de regulación de potencia reactiva y tensión cuyo objetivo es incrementar la generación eléctrica del parque eólico. En la implementación práctica, varios parques eólicos podrían pertenecer a la misma red lo cual conduce a reguladores multicapas y a la consideración las interfaces entre los operadores. Teniendo esto en cuenta, se propone una estrategia de regulación de potencia reactiva asumiendo unos tiempos de comunicación razonables, y se compara a conceptos convencionales. La segunda parte de la tesis sugiere un método de control para el convertidor marino en secuencia directa e inversa. Está diseñado para la operación normal y la operación durante faltas asimétricas y permite la inyección de corrientes reguladas para la detección de la falta. Además, se analizan las variables internas del convertidor modular multinivel (siglas en inglés: MMC) en estas situaciones. Asimismo, se han evaluado cuatro estrategias de respuesta a faltas asimétricas por parte de los convertidores de los aerogeneradores. Estas estrategias también incluyen el control en secuencia directa e inversa. Finalmente, se investiga la reducción de sobremodulación en los convertidores y sobretensiones en la red marina.Hochspannungs–Gleichstrom–Übertragung (HGÜ) stellt eine effiziente Lösung zur Netzanbindung weit entfernter Offshore–Windkraftanlagen dar. Die derzeit verwendeten Punkt–zu–Punkt–Anbindungen basieren dabei auf spannungsgeführten Umrichtertopologien. Das seeseitige Wechselstromnetz verbindet die Windkraftanlagen mit der netzbildenden HGÜ–Umrichterstation. Es charakterisiert sich im Vergleich zu gewöhnlichen Netzen durch das ausschließliche Verwenden von Seekabeln und, im Fall einer Verwendung von Windkraftanlagen mit Vollumrichtern, durch das Fehlen gewöhnlicher, direkt gekoppelter Synchrongeneratoren. Die vorliegende Dissertation behandelt zwei Kernaspekte bezüglich dem Betrieb HGÜ–angebundener Windparks: i) die kontinuierliche Regelung der Blindleistung und Spannung und ii) das Umrichterverhalten bei Spannungseinbrüchen aufgrund von Netzkurzschlüssen [engl. fault ride through (FRT)] im seeseitigen Wechselspannungsnetz. Hierfür werden Blindleistungsoptimierungsverfahren präsentiert, die für die Anwendung in Wechselstromnetzen mit einem netzbildenden Umrichter und weiteren netzsynchronen Umrichtern geeignet sind. Die vorgeschlagene Blindleistung– und Spannungsregelungsmethode verringert die Energieverluste im seeseitigen Netz und erhöht damit die Energieausbeute des Systems. Häufig werden verschiedene Windparks zu Clustern zusammengeschlossen, die mehrschichtige Regelungsansätze fordern. Hierfür wird ein weiteres Verfahren vorgeschlagen, das ähnliche Kommunikationsanforderungen wie herkömmliche Betriebsverfahren aufweist, jedoch geringere Verluste verursacht. Die Arbeit untersucht ferner ein dynamisches Regelungsverfahren für den seeseitigen HGÜ–Umrichter. Dabei wird speziell das Verhalten während unsymmetrischer Kurzschlüsse im seeseitigen Netz berücksichtigt. Darüber hinaus wird der Betrieb des modularen Mehrpunktumrichters (engl. MMC) für diese Anwendung analysiert. Bezüglich des Verhaltens netzsynchroner Umrichter während asymmetrischer Spannungseinbrüche im seeseitigen Netz werden weiterhin vier Verfahren untersucht. Diese zielen unter anderem auf die Verringerung von möglicher Übermodulation der Umrichter und Überspannungen im seeseitigen Netz ab

Performance measurements and analysis of the existing wireless communication technology in Iraq.

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityIraq may be considered as the largest wireless market in the Gulf region. A key driving factor in the market of wireless communication, it has seen enormous growth in the mobile phone market over the last five years leading to almost 24 million subscribers in 2011. Moreover, there are several technologies and services working in Iraq; three GSM Operators, three CDMA national operators and three CDMA provinces operators. The recent growth in the mobile phone market is based on the Global System for Mobile (GSM) communications and Code Division Multiple Access (CDMA) standards creating the next-generation wireless technologies in the Iraqi Wireless Communication market. One of the essential issues of this research is to investigate the performance of the decreased Quality Of Service (QoS) caused by interferences in the services on GSM/CDMA operators in Iraq. Many issues should be studied and taken into consideration, such as; does the Multi-Coalition Forces cause the interferences, jamming, higher rate of calls drop and false ringing; or are they caused by bad design and planning? Do we need to optimise our network due to the large number of users? All these factors are investigated and the measurements of most service providers and government agencies will be gathered. A detailed analysis was included from the providers with measurements of performance and the reasons for the deterioration of wireless services. The novel contributions of this thesis is the extensive radio measurement campaign over the three mobile an CDMA operator networks and the analysis and recommendations that were drawn to suggest the best approach to improve the QoS of Wireless communication technologies. Awareness of actual reasons behind the deterioration of services will be raised to the Iraqi Government, CMC and the wireless service providers