A modular neural network scheme applied to fault diagnosis in electric power systems

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer.The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.Flores, A.; Quiles Cucarella, E.; García Moreno, E.; Morant Anglada, FJ.; Correcher Salvador, A. (2014). A modular neural network scheme applied to fault diagnosis in electric power systems. Scientific World Journal. 2014:1-13. doi:10.1155/2014/176463S1132014Yongli, Z., Limin, H., & Jinling, L. (2006). Bayesian Networks-Based Approach for Power Systems Fault Diagnosis. IEEE Transactions on Power Delivery, 21(2), 634-639. doi:10.1109/tpwrd.2005.858774Aggarwal, R., & Song, Y. (1997). Artificial neural networks in power systems. Part 1: General introduction to neural computing. Power Engineering Journal, 11(3), 129-134. doi:10.1049/pe:19970306Faria, L., Silva, A., Vale, Z., & Marques, A. (2009). Training Control Centers’ Operators in Incident Diagnosis and Power Restoration Using Intelligent Tutoring Systems. IEEE Transactions on Learning Technologies, 2(2), 135-147. doi:10.1109/tlt.2009.16Rigatos, G., Piccolo, A., & Siano, P. (2009). Neural network-based approach for early detection of cascading events in electric power systems. IET Generation, Transmission & Distribution, 3(7), 650-665. doi:10.1049/iet-gtd.2008.0475Guo, W., Wen, F., Ledwich, G., Liao, Z., He, X., & Liang, J. (2010). An Analytic Model for Fault Diagnosis in Power Systems Considering Malfunctions of Protective Relays and Circuit Breakers. IEEE Transactions on Power Delivery, 25(3), 1393-1401. doi:10.1109/tpwrd.2010.2048344Ravikumar, B., Thukaram, D., & Khincha, H. P. (2008). Application of support vector machines for fault diagnosis in power transmission system. IET Generation, Transmission & Distribution, 2(1), 119. doi:10.1049/iet-gtd:20070071Aggarwal, R., & Yonghua Song. (1998). Artificial neural networks in power systems. Part 2: Types of artificial neural networks. Power Engineering Journal, 12(1), 41-47. doi:10.1049/pe:19980110Salim, R. H., de Oliveira, K., Filomena, A. D., Resener, M., & Bretas, A. S. (2008). Hybrid Fault Diagnosis Scheme Implementation for Power Distribution Systems Automation. IEEE Transactions on Power Delivery, 23(4), 1846-1856. doi:10.1109/tpwrd.2008.91791

Smart Grid Technologies in Europe: An Overview

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.