Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

Alamaniotis, Miltiadis; Agarwal, Vivek

doi:10.4018/ijmstr.2014040102

Title: Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

Journal Article · Tue Apr 01 00:00:00 EDT 2014 · International Journal of Monitoring and Surveillance Technologies Research

DOI:https://doi.org/10.4018/ijmstr.2014040102· OSTI ID:1367854

Alamaniotis, Miltiadis ^[1]; Agarwal, Vivek ^[2]

Purdue Univ., West Lafayette, IN (United States). School of Nuclear Engineering
Idaho National Lab. (INL), Idaho Falls, ID (United States). Dept. of Human Factors, Controls and Statistics

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, artificially intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. Our proposed methodology implements an anticipatory system aiming at controlling energy systems in a robust way. Initially a set of support vector regressors is adopted for making predictions over critical system parameters. Furthermore, the predicted values are fed into a two stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions into a single one at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1367854

Report Number(s):: INL/JOU-14-33370

Journal Information:: International Journal of Monitoring and Surveillance Technologies Research, Vol. 2, Issue 2; ISSN 2166-7241

Publisher:: IGI PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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