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Title
Abstract
Tests and Results
Material and Methods
Statistical Parameters and Software Use
Results and Discussion
Architecture Overview
Heterogeneous Data Set Integration (Hdsi)
Related Works
Experiments and Results
Experimental Results and Validation
Conclusion
Database Studies
Statistical Distribution Analysis
Risk Analysis
Conclusions
Introduction
References
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Systems Engineering

Application of expert systems

Profile image of Ian CarterIan Carter

1989, Artificial Intelligence in Engineering

https://doi.org/10.1016/0954-1810(89)90025-3
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Abstract

What are expert systems? What are their purposes? What are the impacts resulting from their implementations? This book aims to answer these questions and more. Written by experts in the field, chapters cover various concepts related to expert systems, including computational intelligence, signal processing, real time systems, systems optimization, electric power systems, support vector machines, fault diagnosis, asset management, and smart cities. Chapters systematically discuss the thematic concepts of expert systems and present a broad range of technical and theoretical information. As such, in addition to being of interest to a professional audience, the book is useful as a text for undergraduate and graduate courses. The prerequisites for understanding this book's content are basic, requiring only elementary knowledge of computation. The first part of this book (Chaps. 1 -5) examines the various uses and applications of expert systems. These include support decision systems, alert diagnosis systems, rule-based expert systems applied on heterogeneous data sources, detection of insects in coffee agriculture, influence of linear/cyclic polyethylenes on the electric percolation of microemulsions, and proposition of multi-agent for modeling smart parking. The second part of this book (Chaps. 5 -8) presents solutions that comprise technologies of expert systems in electric power systems. It describes topics related to efficient asset management practices for power systems using expert procedures, intelligent systems for estimation of gases dissolved in insulating mineral oil from physicochemical tests, and systems based on computational intelligence to estimate failure rates in power transformers.

Related papers

Expert Systems in Engineering Applications
spyros G tzafestas

Springer, Berlin, 1993

This book provides a set of important contributions presenting a number of expert systems that deal with modern engineering applications. The book is divided in five parts. Part 1--General issues. Part 2--Expert systems in engineering domains. Pat 3--Expert systems in fault diagnosis. Part 4--Expert systems in robotics and manufacturing. Part 5--Expert systems catalogs ( AI and expert systems tools).

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USING EXPERT SYSTEMS FOR FAULT MANAGEMENT IN ELECTRICAL POWER SYSTEMS
GJESR Journal

Expert Systems are extensively being used in power system fault managements and it has become an area of strong research interest in the past few years. This paper is primarily concerned with applications that relate to the overall power system monitoring, control, operation, protection and planning with emphasis on protective relaying, substation control and related monitoring functions. It gives both a survey of the present research efforts and a discussion of future possibilities and trends in this area.

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Expert systems: An overview
Stephen Kaisler

IEEE Journal of Oceanic Engineering, 1986

The purpose of this article is to introduce readers to the basic principles of rule-based expert systems. Four topics are discussed in subsequent sections: (1) Definition; (2) Structure of an expert system; (3) State of the art and (4) Impact and future research.

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Object oriented expert systems for real-time power system alarm processing Part I. Selection of a toolkit
Kamrul Hasan

Electric Power Systems Research, 1994

Alarm processing is a traditional feature of energy management systems and has not changed significantly over several generations of SCADA design. This paper describes the first part of a project on the use of an artificial intelligence system for alarm processing and fault diagnosis. The paper presents an evaluative study of three real-time artificial intelligence toolkits: Muse, Kappa and Nexpert Object. The evaluation is based on the knowledge representation and reasoning paradigm, the user's interface, integration with external programs and real-time databases, portability, price and execution speed, and the capabilities of such toolkits to recognize the power system conditions responsible for causing alarms, under conditions of uncertainty and missing alarm information. A specific application of the Kappa toolkit to an alarm processing system is described in Part II (Electr. Power Syst. Res., 30 (1994) 77–82).

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An object-oriented expert system for power system alarm processing and fault identification
Kamrul Hasan

1994

Alarm processing is a traditional feature of energy management systems (EMS) and has not changed significantly over several generations of SCADA design. However recent applications of artificial intelligence have dramatically altered the methods of handling this information. This paper describes two parts of a project carried out at the University of Dundee for Scottish Hydro-Electric plc (HE) on the use of an artificial intelligence system for alarm processing and fault diagnosis. The first part of the project was an overview and comparison study of three real-time object-oriented toolkits: Muse, Kappa and Nexpert Object. The study is based on the capabilities of such toolkits to handle the power system alarm processing integration with external programs and real-time databases, portability, price and execution speed. Some advantages and drawbacks of each toolkit are also pointed out. The second part of the project was the implementation of an object-oriented expert system using the KappaPC toolkit operating on a 486 IBM compatible PC under Microsoft Windows 3.1. The structure of the object-oriented expert system captures the heuristic knowledge used for power system operation. The knowledge-base is automatically updated by the existing SCADA system as the power system status changes. The paper also describes the features of the real-time object-oriented expert system which includes the need for fast deep-level reasoning, easy maintainability of the object-oriented programming and the end user's interface

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Expert Systems with Applications
mahmoud Saafan

Expert Systems with Applications, 2005

Control structures and algorithms (conventional control, fuzzy control, data-based control, modelfree control, sliding mode control, neuro-fuzzy control), Theory and applications of soft computing, Systems modelling, identification and optimization (including nature-inspired algorithms), Computeraided design of control system, Applications to mechatronics systems (including automotive systems and mobile robots, embedded systems, control of power plants, servo systems, electrical driving systems)

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Expert Alarm System for Prediction of Chemistry Faults in a Power Station
Ashoka Komalla

International journal of artificial intelligence, 2017

The integration of rule based techniques in traditional real-time systems is a promising approach to cope with the growing complexity of real-world applications. Real-time expert systems are on-line knowledge-based systems that combine analytical process models with conventional process control to monitor complex industrial processes and to assist in problem identification. Intelligent system for alarm diagnosis and root cause analysis and operator guidance in process plants is an important area for which the real time expert system is being used. Fault monitoring and prediction is of prime importance in a Power Plant. This paper proposes an expert system to aid plant operation engineers in diagnosing the cause of abnormal water / steam parameters in a Fossil fuel Power station. The diagnostic decisions are written in the form of fault trees. Fault trees fare developed or all steam water cycle parameters. This system enables the operator to take preventive measures in time, avoiding...

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Applications of artificial intelligence in power systems
ali hasan

Electric Power Systems Research, 1997

Application of Artificial Intelligence technologies (mainly in the form of Expert Systems) to power systems has been an active area of research for about a decade and significant successes have been achieved. This paper lists the literature related to artificial intelligence applications to power systems and notes the artificial intelligence technologies that are becoming important in conjunction with expert systems.

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Vinod Chandra S S

International Journal of Electrical and Computer Engineering (IJECE), 2014

The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator's actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system.

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Object oriented expert systems for real-time power system alarm processing
Kamrul Hasan

Electric Power Systems Research, 1994

Alarm processing is a traditional feature of energy management systems and has not changed significantly over several generations of SCADA design. This paper describes the first part of a project on the use of an artificial intelligence system for alarm processing and fault diagnosis. The paper presents an evaluative study of three real-time artificial intelligence toolkits: Muse, Kappa and Nexpert Object. The evaluation is based on the knowledge representation and reasoning paradigm, the user's interface, integration with external programs and real-time databases, portability, price and execution speed, and the capabilities of such toolkits to recognize the power system conditions responsible for causing alarms, under conditions of uncertainty and missing alarm information. A specific application of the Kappa toolkit to an alarm processing system is described in Part II (Electr. Power Syst. Res., 30 (1994) [77][78][79][80][81][82].

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Expert Systems for Monitoring and Control
Daniel Dvorak

Many large-scale industrial processes and services are centrally monitored and controlled under the supervision of trained operators. Common examples are electrical power plants, chemical refineries, air-traffic control, and telephone networks --- all impressively complex systems that are challenging to understand and operate correctly. The task of the operator is one of continuous, real-time monitoring and control, with feedback. The job can be difficult when the physical system is complex (tight coupling and complex interactions). Also, there may be faults not only in the system but also in its sensors and controls. Deciding the correct control action during a crisis can be difficult; a bad decision can be disastrous. This paper surveys existing work in the field of knowledge-based systems that assist plant/process operators in the task of monitoring and control. The goal here is to better define the information processing problems and identify key requirements for an automated op...

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Object oriented expert systems for real-time power system alarm processing Part II. Application of a toolkit
Kamrul Hasan

Electric Power Systems Research, 1994

This paper presents the second part of a project on the use of an artificial intelligence system to process alarms and to perform fault diagnostics. The paper describes the implementation of an object oriented expert system using the KappaPC toolkit which was chosen in Part I (Electr. Power Syst. Res., 30 (1994) 69–75) for initial development of a prototype real-time alarm processing and fault diagnosis system. The structure of the object oriented expert system captures the heuristic knowledge used for power system operation. The knowledge base is automatically updated by the existing SCADA system as the power system status changes. The paper also describes the features of the real-time object oriented expert system which include the need for fast deep-level reasoning, easy maintainability of the object oriented programming and the end-user's interface.

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Artificial intelligence in power system operations
Bruce Wollenberg

Proceedings of the IEEE, 1987

Power system operators often reach a cognitive barrier when information arrives too fast during a power system emergency. At such times it becomes difficult to reach a correct diagnosis of the problem or to formulate the correct decision when actions must be taken. Artificial Intelligence gives designers of Energy Management Systems a way to solve many of the diagnosis and decision problems so as to make the EMS more useful. This paper explores reasons why AI techniques, such as knowledgebased expert systems, are being used in EMS designs and the differences between knowledgebased expert systems and traditional numeric a l p rithm development. The differences between expert systems and the numeric approach extend to the basic conception and design of the applications. This is illustrated using a relay fault diagnosis system, showing both the traditional and rapid prototyping approaches to its development. Finally, issues concerned with the implementation of AI in EMS computers are explored along with the authors' predictions of possible AI applications to power system operations.

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