Adaptive control with an expert system based supervisory level

Adaptive control is presently one of the methods available which may be used to control plants with poorly modelled dynamics or time varying dynamics. Although many variations of adaptive controllers exist, a common characteristic of all adaptive control schemes, is that input/output measurements from the plant are used to adjust a control law in an on-line fashion. Ideally the adjustment mechanism of the adaptive controller is able to learn enough about the dynamics of the plant from input/output measurements to effectively control the plant. In practice, problems such as measurement noise, controller saturation, and incorrect model order, to name a few, may prevent proper adjustment of the controller and poor performance or instability result. In this work we set out to avoid the inadequacies of procedurally implemented safety nets, by introducing a two level control scheme in which an expert system based 'supervisor' at the upper level provides all the safety net functions for an adaptive controller at the lower level. The expert system is based on a shell called IPEX, (Interactive Process EXpert), that we developed specifically for the diagnosis and treatment of dynamic systems. Some of the more important functions that the IPEX system provides are: (1) temporal reasoning; (2) planning of diagnostic activities; and (3) interactive diagnosis. Also, because knowledge and control logic are separate, the incorporation of new diagnostic and treatment knowledge is relatively simple. We note that the flexibility available in the system to express diagnostic and treatment knowledge, allows much greater functionality than could ever be reasonably expected from procedural implementations of safety nets. The remainder of this chapter is divided into three sections. In section 1.1 we give a detailed review of the literature in the area of supervisory systems for adaptive controllers. In particular, we describe the evolution of safety nets from simple ad hoc techniques, up to the use of expert systems for more advanced supervision capabilities

Providing decision support for the condition-based maintenance of circuit breakers through data mining of trip coil current signatures

The focus of this paper centers on the condition assessment of 11kV-33kV distribution circuit breakers from the analysis of their trip coil current signatures captured using an innovative condition monitoring technology developed by others. Using available expert knowledge in conjunction with a structured process of data mining, thresholds associated with features representing each stage of a circuit breaker's operation may be defined and used to characterize varying states of circuit breaker condition. Knowledge and understanding of satisfactory and unsatisfactory breaker condition can be gained and made explicit from the analysis of captured trip signature data and subsequently used to form the basis of condition assessment and diagnostic rules implemented in a decision support system, used to inform condition-based decisions affecting circuit breaker maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of SP Power System's in-service circuit breakers. This knowledge then forms the basis of a decision support system for the condition assessment of these circuit breakers during routine trip testing

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad

TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system

Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes

Automated post-fault diagnosis of power system disturbances

In order to automate the analysis of SCADA and digital fault recorder (DFR) data for a transmission network operator in the UK, the authors have developed an industrial strength multi-agent system entitled protection engineering diagnostic agents (PEDA). The PEDA system integrates a number of legacy intelligent systems for analyzing power system data as autonomous intelligent agents. The integration achieved through multi-agent systems technology enhances the diagnostic support offered to engineers by focusing the analysis on the most pertinent DFR data based on the results of the analysis of SCADA. Since November 2004 the PEDA system has been operating online at a UK utility. In this paper the authors focus on the underlying intelligent system techniques, i.e. rule-based expert systems, model-based reasoning and state-of-the-art multi-agent system technology, that PEDA employs and the lessons learnt through its deployment and online use

On-line transformer condition monitoring through diagnostics and anomaly detection

This paper describes the end-to-end components of an on-line system for diagnostics and anomaly detection. The system provides condition monitoring capabilities for two in- service transmission transformers in the UK. These transformers are nearing the end of their design life, and it is hoped that intensive monitoring will enable them to stay in service for longer. The paper discusses the requirements on a system for interpreting data from the sensors installed on site, as well as describing the operation of specific diagnostic and anomaly detection techniques employed. The system is deployed on a substation computer, collecting and interpreting site data on-line

Issues in integrating existing multi-agent systems for power engineering applications

Multi-agent systems (MAS) have proven to be an effective platform for diagnostic and condition monitoring applications in the power industry. For example, a multi-agent system architecture, entitled condition monitoring multi-agent system (COMMAS) (McArthur et al., 2004), has been applied to the ultra high frequency (UHF) monitoring of partial discharge activity inside transformers. Additionally, a multi-agent system, entitled protection engineering diagnostic agents (PEDA) (Hossack et al., 2003), has demonstrated the use of MAS technology for automated and enhanced post-fault analysis of power systems disturbances based on SCADA and digital fault recorder (DFR) data. In this paper, the authors propose the integration of COMMAS and PEDA as a means of offering enhanced decision support to engineers tasked with managing transformer assets. By providing automatically interpreted data related to condition monitoring and power system disturbances, the proposed integrated system offer engineers a more comprehensive picture of the health of a given transformer. Defects and deterioration in performance can be correlated with the operating conditions it experiences. The integration of COMMAS and PEDA has highlighted the issues inherent to the inter-operation of existing multi-agent systems and, in particular, the issues surrounding the use of differing ontologies. The authors believe that these issues need to be addressed if there is to be widespread deployment of MAS technology within the power industry. This paper presents research undertaken to integrate the two MAS and to deal with ontology issues

Investigation of gas circulator response to load transients in nuclear power plant operation

Gas circulator units are a critical component of the Advanced Gas-cooled Reactor (AGR), one of the nuclear power plant (NPP) designs in current use within the UK. The condition monitoring of these assets is central to the safe and economic operation of the AGRs and is achieved through analysis of vibration data. Due to the dynamic nature of reactor operation, each plant item is subject to a variety of system transients of which engineers are required to identify and reason about with regards to asset health. The AGR design enables low power refueling (LPR) which results in a change in operational state for the gas circulators, with the vibration profile of each unit reacting accordingly. The changing conditions subject to these items during LPR and other such events may impact on the assets. From these assumptions, it is proposed that useful information on gas circulator condition can be determined from the analysis of vibration response to the LPR event. This paper presents an investigation into asset vibration during an LPR. A machine learning classification approach is used in order to define each transient instance and its behavioral features statistically. Classification and reasoning about the regular transients such as the LPR represents the primary stage in modeling higher complexity events for advanced event driven diagnostics, which may provide an enhancement to the current methodology, which uses alarm boundary limits