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

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

Miltiadis Alamaniotis (School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA) and Vivek Agarwal (Department of Human Factors, Controls and Statistics, Idaho National Laboratory, Idaho Falls, ID, USA)
DOI: 10.4018/ijmstr.2014040102
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Abstract

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, 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. The 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. The predicted values are used as input to a two-stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions 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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Introduction

Energy systems are comprised of several smaller and simpler components operating in a synergistic way for energy generation, delivery, and distribution. The plethora of components, together with their interactions, significantly increases the complexity of those systems making their control rather challenging task. In particular, any control decision is taken only after several factors are taken into consideration. However, not all factors or operational parameters are well known at each instance; hence, control decisions are made under some uncertainty.

Anticipatory decision systems are a class of systems that make decisions taking into account both the current and future states (Tsoukalas, 1998). While the current state contains the most recent measured values of the system under monitoring, anticipation of future states is performed by analytical prediction models (Agarwal et al., 2013). Analytical models are utilized to make predictions regarding the system’s own state variables and/or its environment. In other words, anticipatory systems mimic the way humans make their decisions, observe the current situation, and foresee possible future situations that may be attained by specific actions (Rosen, 2012).

Intelligent control focuses on realizing complex automatic control systems by utilizing functions, such as solution comparison, solution search using a heuristic criterion, prediction making, and generally by employing functions and tools from the artificial intelligence realm (Tsoukalas & Uhrig, 1996; Harris et al., 1993). One of the crucial factors in human thinking is the ability to learn how to make predictions over the output of actions. However, learning how to predict requires prior observation of the same or similar phenomenon. By observing, humans develop inherent ways of how to think and anticipate the possible output following specific actions. Thus, anticipation is a crucial factor for implementing intelligent control systems (Uhrig et al., 1994).

The majority of proposed anticipatory systems to implement intelligent control employ tools from artificial intelligence and statistics. Ikonomopoulos et al. (1993) proposed an anticipatory system based on the integration of neural networks and fuzzy logic for measuring operational parameters in complex systems, while Xinging et al. (1996) implemented a neurofuzzy system for complex system control. Uhrig and Tsoukalas (2003) applied anticipatory control for complex nuclear systems by using intelligent multi-agent systems. A hybrid expert system-neural network methodology was presented by Tsoukalas and Reyes-Jimenez (1990), and a possibilistic-probabilistic formalism by Tsoukalas (1989). Furthermore, the use of dynamic control equations together with data mining tools was presented for controlling complex power systems like wind turbines in (Kusiak et al. 2009). Further, fuzzy logic rules were used for anticipation in Chiu et al.’s article (1991), while a distributed intelligence approach for the anticipation of transportation infrastructures is proposed by Van Dam et al. (2004). The above examples strictly adopt a single model for making predictions; therefore, the examples exhibit significant limitations in prediction accuracy.

This paper presents a new methodology for implementing anticipatory control systems. The proposed methodology combines support vector regression, SVR, (Bishop, 2006) and fuzzy logic (Ross, 2013). In particular a set of SVR models is adopted for prediction making (Alamaniotis et al., 2012), while fuzzy logic is employed for fusing the individual SVR predictions and decision making. The proposed system is applied for making control decisions in complex energy systems (Alamaniotis et al., July 2014). In the current manuscript, the anticipatory control system is adopted for controlling the maintenance actions of a turbine, which is the basic component in all energy generation systems.

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