Strategies for Power Line Communications Smart Metering Network Deployment

Smart Grids are becoming a reality all over the world. Nowadays, the research efforts for the introduction and deployment of these grids are mainly focused on the development of the field of Smart Metering. This emerging application requires the use of technologies to access the significant number of points of supply (PoS) existing in the grid, covering the Low Voltage (LV) segment with the lowest possible costs. Power Line Communications (PLC) have been extensively used in electricity grids for a variety of purposes and, of late, have been the focus of renewed interest. PLC are really well suited for quick and inexpensive pervasive deployments. However, no LV grid is the same in any electricity company (utility), and the particularities of each grid evolution, architecture, circumstances and materials, makes it a challenge to deploy Smart Metering networks with PLC technologies, with the Smart Grid as an ultimate goal. This paper covers the evolution of Smart Metering networks, together with the evolution of PLC technologies until both worlds have converged to project PLC-enabled Smart Metering networks towards Smart Grid. This paper develops guidelines over a set of strategic aspects of PLC Smart Metering network deployment based on the knowledge gathered on real field; and introduces the future challenges of these networks in their evolution towards the Smart Grid.

Enhanced Operation of Electricity Distribution Grids Through Smart Metering PLC Network Monitoring, Analysis and Grid Conditioning

Low Voltage (LV) electricity distribution grid operations can be improved through a combination of new smart metering systems' capabilities based on real time Power Line Communications (PLC) and LV grid topology mapping. This paper presents two novel contributions. The first one is a new methodology developed for smart metering PLC network monitoring and analysis. It can be used to obtain relevant information from the grid, thus adding value to existing smart metering deployments and facilitating utility operational activities. A second contribution describes grid conditioning used to obtain LV feeder and phase identification of all connected smart electric meters. Real time availability of such information may help utilities with grid planning, fault location and a more accurate point of supply management.

System for ampacity monitoring and low sag overhead conductor evaluation

MELECON 2012 - 2012 16th IEEE Mediterranean Electrotechnical Conference, 25 Mar - 28 Mar 2012, Túnez

Model driven product line engineering : core asset and process implications

Reuse is at the heart of major improvements in productivity and quality in Software Engineering. Both Model Driven Engineering (MDE) and Software Product Line Engineering (SPLE) are software development paradigms that promote reuse. Specifically, they promote systematic reuse and a departure from craftsmanship towards an industrialization of the software development process. MDE and SPLE have established their benefits separately. Their combination, here called Model Driven Product Line Engineering (MDPLE), gathers together the advantages of both. Nevertheless, this blending requires MDE to be recasted in SPLE terms. This has implications on both the core assets and the software development process. The challenges are twofold: (i) models become central core assets from which products are obtained and (ii) the software development process needs to cater for the changes that SPLE and MDE introduce. This dissertation proposes a solution to the first challenge following a feature oriented approach, with an emphasis on reuse and early detection of inconsistencies. The second part is dedicated to assembly processes, a clear example of the complexity MDPLE introduces in software development processes. This work advocates for a new discipline inside the general software development process, i.e., the Assembly Plan Management, which raises the abstraction level and increases reuse in such processes. Different case studies illustrate the presented ideas.

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

Intrusion Detection Systems in SDN-based Self-Healing PMU Networks

Nowadays, Power grids are critical infrastructures on which everything else relies, and their correct behavior is of the highest priority. New smart devices are being deployed to be able to manage and control power grids more efficiently and avoid instability. However, the deployment of such smart devices like Phasor Measurement Units (PMU) and Phasor Data Concentrators (PDC), open new opportunities for cyber attackers to exploit network vulnerabilities. If a PDC is compromised, all data coming from PMUs to that PDC is lost, reducing network observability. Our approach to solve this problem is to develop an Intrusion detection System (IDS) in a Software-defined network (SDN). allowing the IDS system to detect compromised devices and use that information as an input for a self-healing SDN controller, which redirects the data of the PMUs to a new, uncompromised PDC, maintaining the maximum possible network observability at every moment. During this research, we have successfully implemented Self-healing in an example network with an SDN controller based on Ryu controller. We have also assessed intrinsic vulnerabilities of Wide Area Management Systems (WAMS) and SCADA networks, and developed some rules for the Intrusion Detection system which specifically protect vulnerabilities of these networks. The integration of the IDS and the SDN controller was also successful. \\To achieve this goal, the first steps will be to implement an existing Self-healing SDN controller and assess intrinsic vulnerabilities of Wide Area Measurement Systems (WAMS) and SCADA networks. After that, we will integrate the Ryu controller with Snort, and create the Snort rules that are specific for SCADA or WAMS systems and protocols.