Segmentation of Fault Networks Determined from Spatial Clustering of Earthquakes

We present a new method of data clustering applied to earthquake catalogs, with the goal of reconstructing the seismically active part of fault networks. We first use an original method to separate clustered events from uncorrelated seismicity using the distribution of volumes of tetrahedra defined by closest neighbor events in the original and randomized seismic catalogs. The spatial disorder of the complex geometry of fault networks is then taken into account by defining faults as probabilistic anisotropic kernels, whose structures are motivated by properties of discontinuous tectonic deformation and previous empirical observations of the geometry of faults and of earthquake clusters at many spatial and temporal scales. Combining this a priori knowledge with information theoretical arguments, we propose the Gaussian mixture approach implemented in an Expectation-Maximization (EM) procedure. A cross-validation scheme is then used and allows the determination of the number of kernels that should be used to provide an optimal data clustering of the catalog. This three-steps approach is applied to a high quality relocated catalog of the seismicity following the 1986 Mount Lewis ($M_l=5.7$) event in California and reveals that events cluster along planar patches of about 2 km$^2$, i.e. comparable to the size of the main event. The finite thickness of those clusters (about 290 m) suggests that events do not occur on well-defined euclidean fault core surfaces, but rather that the damage zone surrounding faults may be seismically active at depth. Finally, we propose a connection between our methodology and multi-scale spatial analysis, based on the derivation of spatial fractal dimension of about 1.8 for the set of hypocenters in the Mnt Lewis area, consistent with recent observations on relocated catalogs

Power System Stability Analysis Using Wide Area Measurement System

Advances in wide area measurement systems have transformed power system operation from simple visualization, state estimation, and post-mortem analysis tools to real-time protection and control at the systems level. Transient disturbances (such as lightning strikes) exist only for a fraction of a second but create transient stability issues and often trigger cascading type failures. The most common practice to prevent instabilities is with local generator out-of-step protection. Unfortunately, out-of-step protection operation of generators may not be fast enough, and an instability may take down nearby generators and the rest of the system by the time the local generator relay operates. Hence, it is important to assess power system stability over transmission lines as soon as the transient instability is detected instead of relying on purely localized out-of-step protection in generators. This thesis proposes a synchrophasor-based out-of-step prediction methodology at the transmission line level using wide area measurements from optimal phasor measurement unit (PMU) locations in the interconnected system. Voltage and current measurements from wide area measurement systems (WAMS) are utilized to find the swing angles. The proposed scheme was used to predict the first swing out-of-step condition in a Western Systems Coordinating Council (WSCC) 9 bus power system. A coherency analysis was first performed in this multi-machine system to determine the two coherent groups of generators. The coherent generator groups were then represented with a two-machine equivalent system, and the synchrophasor-based out-of-step prediction algorithm then applied to the reduced equivalent system. The coherency among the group of generators was determined within 100 ms for the contingency scenarios tested. The proposed technique is able to predict the instability 141.66 to 408.33 ms before the system actually reaches out-of-step conditions. The power swing trajectory is either a steady-state trajectory, monotonically increasing type (when the system becomes unstable), or oscillatory type (under stable conditions). Un- der large disturbance conditions, the swing could also become non-stationary. The mean and variance of the signal is not constant when it is monotonically increasing or non-stationary. An autoregressive integrated (ARI) approach was developed in this thesis, with differentiation of two successive samples done to make the mean and variance constant and facilitate time series prediction of the swing curve. Electromagnetic transient simulations with a real-time digital simulator (RTDS) were used to test the accuracy of the proposed algorithm with respect to predicting transient in- stability conditions. The studies show that the proposed method is computationally efficient and accurate for larger power systems. The proposed technique was also compared with a conventional two blinder technique and swing center voltage method. The proposed method was also implemented with actual PMU measurements from a relay (General Electric (GE) N60 relay). The testing was carried out with an interface between the N60 relay and the RTDS. The WSCC 9 bus system was modeled in the simulator and the analog time signals from the optimal location in the network communicated to the N60 relay. The synchrophasor data from the PMUs in the N60 were used to back-calculate the rotor angles of the generators in the system. Once the coherency was established, the swing curves for the coherent group of generators were found from time series prediction (ARI model). The test results with the actual PMUs match quite well with the results obtained from virtual PMU-based testing in the RTDS. The calculation times for the time series prediction are also very small. This thesis also discusses a novel out-of-step detection technique that was investigated in the course of this work for an IEEE Power Systems Relaying Committee J-5 Working Group document using real-time measurements of generator accelerating power. Using the derivative or second derivative of a measurement variable significantly amplifies the noise term and has limited the actual application of some methods in the literature, such as local measurements of voltage or voltage deviations at generator terminals. Another problem with the voltage based methods is taking an average over a period; the intermediate values cancel out and, as a result, just the first and last sample values are used to find the speed. This effectively means that the sample values in between are not used. The first solution proposed to overcome this is a polynomial fitting of the points of the calculated derivative points (to calculate speed). The second solution is the integral of the accelerating power method (this eliminates taking a derivative altogether). This technique shows the direct relationship of electrical power deviation to rotor acceleration and the integral of accelerating power to generator speed deviation. The accelerating power changes are straightforward to measure and the values obtained are more stable during transient conditions. A single machine infinite bus (SMIB) system was used for the purpose of verifying the proposed local measurement based method

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

A New Relaying Method for Third Zone Distance Relay Blocking During Power Swings

Due to the increasing complexity of modern bulk power systems, the power swing identification, blocking, and protection have become more challenging than they used to be. Among various transmission line protection methods, distance relays are the most commonly used type. One of the advantages of using distance relays is the zoned protection which provides redundancy. However, the additional redundancy comes with a problem that it increases the probability of incorrect operation. For example, the undesired operation of the third zone distance protection during power swing scenarios has been attributed as one of the major causes for creating large-scale blackouts. Some research works in the literature investigate proper identification of stable and unstable power swing conditions. Most research works dwell on identification of power swing conditions but do not address how the scheme could be used for blocking the third zone of distance relays during stable power swings. Also, the current power swing detection schemes are often very complex to implement for a relaying engineer or are not fast enough for blocking the third zone distance element. This research proposes a reliable and fast methodology for the third zone blocking (TZB) during power swings. The new mathematical formulations and derivations are based on sound time tested power system theory and are simpler to understand for a relaying engineer to implement this technique. The algorithm proposed in the research can prevent unnecessary tripping of distance relays during power swings. The algorithm also overcomes the shortcomings of the conventional power swing identification methods when applied for the third zone blocking. A first zero-crossing (FZC) concept is introduced as the criteria for identifying stable power swing or out-of-step phenomena. The analysis is based on system stability point of view and utilizes power-angle equations. The proposed algorithm could be applied at every discrete time interval or time step of a distance relay to detect power swing points. It could also be applied to any transmission line in the power system by finding an equivalent single machine infinite bus (SMIB) configuration individually for each line on a real-time basis, which is one of the primary advantages of the proposed method. In the thesis work, the proposed technique is first demonstrated using a simple single machine infinite bus system. The TZB algorithm is then tested using a modified Western Electricity Coordinating Council (WSCC) power system configuration using Power System Analysis Toolbox (PSAT) simulations. The code is written in MATLAB. The TZB method is then further analyzed using electromagnetic simulations with Real-Time Digital Simulator (RTDS) on WSCC system. The proposed method uses small time step simulations (50 μs) to take various aspects of power system complexity into consideration, such as different harmonics presents in the system, synchronous machine operation at different speeds, travelling wave representation of transmission lines instead of purely lumped parameter representation, etc. The investigations as mentioned above and the results show that the proposed TZB scheme is a straightforward and reliable technique, involving only a few calculation steps, and could be applied to any power system configuration. The main novelty of this technique is that it does not require a priori stability study to find the relay settings unlike conventional power swing identification or distance relay blocking techniques. The inputs to the relay are basic electrical quantities which could be easily measured locally on any transmission line. The local measurements would make the implementation of the proposed TZB simpler for relaying applications compared to Wide Area Measurement System (WAMS) based techniques. In a WAMS based relaying technique - the cost associated with the communication network, reliability of the communication network, impact of communication delay on relay, etc all become factors for actual industry use

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered