ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.  In recent years, Neural networks have been very victorious in several signal processing and control applications.  Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods.

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems

Conventional automatic reclosures blindly operate for permanent, semi-permanent or transient faults on an overhead line without any discrimination after allowing some estimated time delay. Reclosing onto a line with uncleared fault often results in, not only loss of stability and synchronism but also damage to system equipments, as a consequence. The thesis focuses on methods to discriminate a temporary fault from a permanent one, and accurately determine fault extinctiontime in an extra high voltage (EHV) transmission line in a bid to develop a self-adaptive automatic reclosing scheme. The fault identification prior to reclosing is based on optimized artificial neural network associated with three training algorithms, namely, Standard Error Back-Propagation, Levenberg Marquardt and Resilient Back-Propagation algorithms. In addition, Taguchi's methodology is employed in optimizing the parameters of each algorithm used for training, and in deciding the number of hidden neurons of the neural network. To get data for training the neural networks, a range of faults are simulated on two case studies -single machine -infinite bus model (connected via EHVtransmission line) and a benchmark IEEE 9-bus electric system. The spectra of the fault voltage data are analyzed using Fast Fourier Transform, and it has been found out that the DC, the fundamental and the first four harmonic components can sufficiently and uniquely represent the condition of each fault. In each case study, the neural network is fed with the normalized energies of the DC, the fundamental and the first four harmonics of the faulted voltages, effectively trained with a set of training data, and verified with a dedicated testing data obtained from fault voltage signals generated on IEEE 14-bus electric system model. The results show the efficacy of the developed adaptive automatic reclosing scheme. This effectively means it is possible to avoid reclosing before any fault on a transmission line (be it temporary or permanent) is totally cleared

System configuration, fault detection, location, isolation and restoration: a review on LVDC Microgrid protections

Low voltage direct current (LVDC) distribution has gained the significant interest of research due to the advancements in power conversion technologies. However, the use of converters has given rise to several technical issues regarding their protections and controls of such devices under faulty conditions. Post-fault behaviour of converter-fed LVDC system involves both active converter control and passive circuit transient of similar time scale, which makes the protection for LVDC distribution significantly different and more challenging than low voltage AC. These protection and operational issues have handicapped the practical applications of DC distribution. This paper presents state-of-the-art protection schemes developed for DC Microgrids. With a close look at practical limitations such as the dependency on modelling accuracy, requirement on communications and so forth, a comprehensive evaluation is carried out on those system approaches in terms of system configurations， fault detection, location, isolation and restoration

The great societal transformations: epigenetic explorations: a transdisciplinary perspective on the evolution of modern knowledge societies ; part I, The Epigenetic Research Program (EPR) - basic building blocks ; part II, 'Great transformations' within modern societies - epigenetic transfer modules (TM)

'Die zwei Artikel in diesem Heft geben eine konzise Übersicht zu einer neuen, transdisziplinären Perspektive für die Analyse 'wissensbasierter Prozesse' innerhalb der unterschiedlichsten Bereiche in den Natur- und Sozialwissenschaften. Dieser neue Approach, der unter dem Namen 'epigenetisches Forschungsprogramm' (ERP) läuft, ermöglicht es, so die zentrale Botschaft dieses Reihenheftes, evolutionäre Entwicklungsmuster der 'sozialen Welt' - in ihren sozio-ökonomischen oder sozio-kulturellen Seiten - einzufangen und zu identifizieren. Dieses neuartige Leistungspotential erschließt sich durch den Aufbau eines konzeptionellen, theoretischen wie modellmäßigen Apparats von transdisziplinärem Geltungsbereich und durch die Differenzierung in zwei Ebenen, nämlich in einen theoretischen, modellbezogenen wie einen generellen anwendungsorientierten transdisziplinären Bereich und in Transfermodule sowie Datenfelder, welche einzelnen Disziplinfeldern zugeordnet werden können. Und hinsichtlich der Aufteilung der zwei Artikel offeriert der erste Teil eine Übersicht zu den einzelnen ERP-Bausteinen und der zweite Teil ein Set an 'Transfermodulen' speziell für die Analyse der Entwicklung moderner Gesellschaften und ihrer so vielfältig gewordenen und weit verteilten Wissensbasen.' (Autorenreferat)'The subsequent two parts give a precise overview of a new and transdisciplinary perspective for the study of 'knowledge based processes' in a wide variety of domains, including natural science fields and social science areas. The new approach which runs under the label of an 'epigenetic research program' (ERP) is able, so the core message, to capture the evolutionary development patterns of the socio-economic and the socio-cultural world. This achievement is brought about through the construction of a conceptual, theoretical and modeling apparatus of sufficient transdisciplinary generality and through the separation of two different levels, namely of ERP-meta-levels on the one hand and ERP-levels of application on the other hand. Meta-level elements are characterized, above all, by their transdisciplinary status, being not linked to any particular type of application domain, whereas building Blocks at application levels are clearly connected with special features in natural science or social science domains. With respect to the set of two consecutive articles, part I presents a general summary of the ERP-perspective and part II is devoted to a set of ERP 'transfer modules' mainly for the evolution of modern societies and their knowledge bases.' (author's abstract)

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

A review of networked microgrid protection: Architectures, challenges, solutions, and future trends

The design and selection of advanced protection schemes have become essential for the reliable and secure operation of networked microgrids. Various protection schemes that allow the correct operation of microgrids have been proposed for individual systems in different topologies and connections. Nevertheless, the protection schemes for networked microgrids are still in development, and further research is required to design and operate advanced protection in interconnected systems. The interconnection of these microgrids in different nodes with various interconnection technologies increases the fault occurrence and complicates the protection operation. This paper aims to point out the challenges in developing protection for networked microgrids, potential solutions, and research areas that need to be addressed for their development. First, this article presents a systematic analysis of the different microgrid clusters proposed since 2016, including several architectures of networked microgrids, operation modes, components, and utilization of renewable sources, which have not been widely explored in previous review papers. Second, the paper presents a discussion on the protection systems currently available for microgrid clusters, current challenges, and solutions that have been proposed for these systems. Finally, it discusses the trend of protection schemes in networked microgrids and presents some conclusions related to implementation