12,404 research outputs found
On the Exploitation of Admittance Measurements for Wired Network Topology Derivation
The knowledge of the topology of a wired network is often of fundamental
importance. For instance, in the context of Power Line Communications (PLC)
networks it is helpful to implement data routing strategies, while in power
distribution networks and Smart Micro Grids (SMG) it is required for grid
monitoring and for power flow management. In this paper, we use the
transmission line theory to shed new light and to show how the topological
properties of a wired network can be found exploiting admittance measurements
at the nodes. An analytic proof is reported to show that the derivation of the
topology can be done in complex networks under certain assumptions. We also
analyze the effect of the network background noise on admittance measurements.
In this respect, we propose a topology derivation algorithm that works in the
presence of noise. We finally analyze the performance of the algorithm using
values that are typical of power line distribution networks.Comment: A version of this manuscript has been submitted to the IEEE
Transactions on Instrumentation and Measurement for possible publication. The
paper consists of 8 pages, 11 figures, 1 tabl
Inferring Power Grid Information with Power Line Communications: Review and Insights
High-frequency signals were widely studied in the last decade to identify
grid and channel conditions in PLNs. PLMs operating on the grid's physical
layer are capable of transmitting such signals to infer information about the
grid. Hence, PLC is a suitable communication technology for SG applications,
especially suited for grid monitoring and surveillance. In this paper, we
provide several contributions: 1) a classification of PLC-based applications;
2) a taxonomy of the related methodologies; 3) a review of the literature in
the area of PLC Grid Information Inference (GII); and, insights that can be
leveraged to further advance the field. We found research contributions
addressing PLMs for three main PLC-GII applications: topology inference,
anomaly detection, and physical layer key generation. In addition, various
PLC-GII measurement, processing, and analysis approaches were found to provide
distinctive features in measurement resolution, computation complexity, and
analysis accuracy. We utilize the outcome of our review to shed light on the
current limitations of the research contributions and suggest future research
directions in this field.Comment: IEEE Communication Surveys and Tutorials Journa
Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles
The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has
received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking
received support from the European Union’s Horizon 2020 research and innovation programme and Germany,
Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy,
Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL
Joint Undertaking under grant agreement No. 692455-2
Learning Graphs from Linear Measurements: Fundamental Trade-offs and Applications
We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain high-degree nodes), based on which we study fundamental trade-offs between the number of measurements, the complexity of the graph class, and the probability of error. We first derive a necessary condition on the number of measurements. Then, by considering a three-stage recovery scheme, we give a sufficient condition for recovery. Furthermore, assuming the measurements are Gaussian IID, we prove upper and lower bounds on the (worst-case) sample complexity for both noisy and noiseless recovery. In the special cases of the uniform distribution on trees with n nodes and the Erdős-Rényi (n,p) class, the fundamental trade-offs are tight up to multiplicative factors with noiseless measurements. In addition, for practical applications, we design and implement a polynomial-time (in n ) algorithm based on the three-stage recovery scheme. Experiments show that the heuristic algorithm outperforms basis pursuit on star graphs. We apply the heuristic algorithm to learn admittance matrices in electric grids. Simulations for several canonical graph classes and IEEE power system test cases demonstrate the effectiveness and robustness of the proposed algorithm for parameter reconstruction
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