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AAHES: A hybrid expert system realization of adaptive autonomy for Smart Grid

A. Fereidunian ; M.A. Zamani ; F. Boroomand ; H.R. Jamalabadi ; H. Lesani ; C. Lucas ; Shahab Shariat Torbaghan (Institutionen för energi och miljö, Elteknik) ; M. Meydani
IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2010; Gothenburg; Sweden; 11 October 2010 through 13 October 2010 p. Art. no. 5638929. (2010)
[Konferensbidrag, refereegranskat]

Smart grid expectations objectify the need for optimizing power distribution systems greater than ever. Distribution Automation (DA) is an integral part of the SG solution; however, disregarding human factors in the DA systems can make it more problematic than beneficial. As a consequence, Human-Automation Interaction (HAI) theories can be employed to optimize the DA systems in a human-centered manner. Earlier we introduced a novel framework for the realization of Adaptive Autonomy (AA) concept in the power distribution network using expert systems. This research presents a hybrid expert system for the realization of AA, using both Artificial Neural Networks (ANN) and Logistic Regression (LR) models, referred to as AAHES, respectively. AAHES uses neural networks and logistic regression as an expert system inference engine. This system fuses LR and ANN models' outputs which will results in a progress, comparing to both individual models. The practical list of environmental conditions and superior experts' judgments are used as the expert systems database. Since training samples will affect the expert systems performance, the AAHES is implemented using six different training sets. Finally, the results are interpreted in order to find the best training set. As revealed by the results, the presented AAHES can effectively determine the proper level of automation for changing the performance shaping factors of the HAI systems in the smart grid environment.

Nyckelord: Adaptive autonomy , Expert system , Human-Automation Interaction (HAI) , Level of Automation (LOA) , Logistic regression , Neural network , Power distribution automation , Smart grid

Denna post skapades 2015-05-04.
CPL Pubid: 216330


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Institutioner (Chalmers)

Institutionen för energi och miljö, Elteknik



Chalmers infrastruktur