27 research outputs found
Multi-agent systems for power engineering applications - part 2 : Technologies, standards and tools for building multi-agent systems
This is the second part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examined the potential value of MAS technology to the power industry, described fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications, and presented a comprehensive review of the power engineering applications for which MAS are being investigated. It also defined the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented. Given the significant and growing interest in this field, it is imperative that the power engineering community considers the standards, tools, supporting technologies and design methodologies available to those wishing to implement a MAS solution for a power engineering problem. The paper describes the various options available and makes recommendations on best practice. It also describes the problem of interoperability between different multi-agent systems and proposes how this may be tackled
Multi-agent systems for power engineering applications - part 1 : Concepts, approaches and technical challenges
This is the first part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented
An Integrated Research Infrastructure for Validating Cyber-Physical Energy Systems
Renewables are key enablers in the plight to reduce greenhouse gas emissions
and cope with anthropogenic global warming. The intermittent nature and limited
storage capabilities of renewables culminate in new challenges that power
system operators have to deal with in order to regulate power quality and
ensure security of supply. At the same time, the increased availability of
advanced automation and communication technologies provides new opportunities
for the derivation of intelligent solutions to tackle the challenges. Previous
work has shown various new methods of operating highly interconnected power
grids, and their corresponding components, in a more effective way. As a
consequence of these developments, the traditional power system is being
transformed into a cyber-physical energy system, a smart grid. Previous and
ongoing research have tended to mainly focus on how specific aspects of smart
grids can be validated, but until there exists no integrated approach for the
analysis and evaluation of complex cyber-physical systems configurations. This
paper introduces integrated research infrastructure that provides methods and
tools for validating smart grid systems in a holistic, cyber-physical manner.
The corresponding concepts are currently being developed further in the
European project ERIGrid.Comment: 8th International Conference on Industrial Applications of Holonic
and Multi-Agent Systems (HoloMAS 2017
Prediction Of Iron Losses Of Wound Core Distribution Transformers Based On Artificial Neural Networks
This paper presents an artificial neural network (ANN) approach to predicting and classifying distribution transformer specific iron losses, i.e., losses per weight unit. The ANN is trained to learn the relationship of several parameters affecting iron losses. For this reason, the ANN learning and testing sets are formed using actual industrial measurements, obtained from previous completed transformer constructions. Data comprise grain oriented steel electrical characteristics, cores constructional parameters, quality control measurements of cores production line and transformers assembly line measurements. It is shown that an average absolute error of 2.32% has been achieved in the prediction of individual core specific iron losses and an error of 2.2% in case of transformer specific losses. This is compared with average errors of 5.7% and 4.0% in prediction of specific iron losses of individual core and transformer, respectively, obtained by the current practice applying the typical loss curve to the same data