The authors describe a learning classifier system (LCS) which employs genetic algorithms (GA) for adaptive online diagnosis of power transmission network faults. The system monitors switchgear indications produced by a transmission network, reporting fault diagnoses on any patterns indicative of faulted components. The system evaluates the accuracy of diagnoses via a fault simulator developed by National Grid Co. and adapts to reflect the current network topology by use of genetic algorithms.
{"title":"Adaptive alarm processor for fault diagnosis on power transmission networks","authors":"L. Kiernan, K. Warwick","doi":"10.1049/ISE.1993.0004","DOIUrl":"https://doi.org/10.1049/ISE.1993.0004","url":null,"abstract":"The authors describe a learning classifier system (LCS) which employs genetic algorithms (GA) for adaptive online diagnosis of power transmission network faults. The system monitors switchgear indications produced by a transmission network, reporting fault diagnoses on any patterns indicative of faulted components. The system evaluates the accuracy of diagnoses via a fault simulator developed by National Grid Co. and adapts to reflect the current network topology by use of genetic algorithms.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"6 1","pages":"25-37"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89480026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A conceptual and practical environment to design logic controllers is presented, which is based on a special version of Petri nets tailored to this aim. One of the purposes is to not only provide control engineers with powerful graphic tools to manipulate designs quickly and simulation tools to check system performance in particular operative conditions, but also with analytic tools to formally verify controller correctness. Most of the theoretical analysis links to algebraic theory of Petri nets with the fundamental concepts of net invariants and dead-locks. An example from an electrical power plant field is thoroughly examined through all design stages.
{"title":"Conceptual framework for the design of logic control","authors":"L. Ferrarini, C. Maffezzoni","doi":"10.1049/ISE.1993.0021","DOIUrl":"https://doi.org/10.1049/ISE.1993.0021","url":null,"abstract":"A conceptual and practical environment to design logic controllers is presented, which is based on a special version of Petri nets tailored to this aim. One of the purposes is to not only provide control engineers with powerful graphic tools to manipulate designs quickly and simulation tools to check system performance in particular operative conditions, but also with analytic tools to formally verify controller correctness. Most of the theoretical analysis links to algebraic theory of Petri nets with the fundamental concepts of net invariants and dead-locks. An example from an electrical power plant field is thoroughly examined through all design stages.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"1 1","pages":"246-256"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85580371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Power transmission network design is a promising application area for artificial intelligence techniques. Recently, several attempts have been made to integrate conventional numerical algorithms with more ambitious decision support systems. The authors present SELF, a knowledge-based system supporting a specific task in power transmission network design known as 'load-flow analysis'. SELF is the result of long-term cooperation in this field between CISE and the Electric Research Centre of ENEL (the Italian Electricity Board). It integrates a heuristic search approach to the design task with conventional numerical algorithms within the framework of a 'propose-critique-modify' design methodology. The aim of the system is to help the designer interpret numerical results and take design decisions. SELF has been tested on networks of significant size (up to 420 nodes and 600 connections) and is currently used by ENEL researchers.
{"title":"Applying KBS technology to power transmission network design","authors":"R. Cicoria, M. Gallanti, P. Pogliano","doi":"10.1049/ISE.1993.0005","DOIUrl":"https://doi.org/10.1049/ISE.1993.0005","url":null,"abstract":"Power transmission network design is a promising application area for artificial intelligence techniques. Recently, several attempts have been made to integrate conventional numerical algorithms with more ambitious decision support systems. The authors present SELF, a knowledge-based system supporting a specific task in power transmission network design known as 'load-flow analysis'. SELF is the result of long-term cooperation in this field between CISE and the Electric Research Centre of ENEL (the Italian Electricity Board). It integrates a heuristic search approach to the design task with conventional numerical algorithms within the framework of a 'propose-critique-modify' design methodology. The aim of the system is to help the designer interpret numerical results and take design decisions. SELF has been tested on networks of significant size (up to 420 nodes and 600 connections) and is currently used by ENEL researchers.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"9 1","pages":"38-51"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91338912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A good model is essential in simulating and analysing a system satisfactorily. This paper proposes a new computer-aided modelling methodology to construct parsimonious models for dynamic physical systems. A set of domain-independent modelling principles and domain-dependent modelling constraints has been formulated to guide parsimonious model construction. Models and the knowledge base for the enhancement of models have been described by bond graphs. After a parsimonious model is generated, two types of qualitative analysis are conducted; one is to derive unknown states from known states and the other is to predict the effects of parameter changes on the whole system. This methodology is only applicable when the structure of a dynamic system is known beforehand. A detailed case study is presented to demonstrate this approach.
{"title":"Automatic modelling and analysis of dynamic physical systems using qualitative reasoning and bond graphs","authors":"S. Xia, D. Linkens, S. Bennett","doi":"10.1049/ISE.1993.0017","DOIUrl":"https://doi.org/10.1049/ISE.1993.0017","url":null,"abstract":"A good model is essential in simulating and analysing a system satisfactorily. This paper proposes a new computer-aided modelling methodology to construct parsimonious models for dynamic physical systems. A set of domain-independent modelling principles and domain-dependent modelling constraints has been formulated to guide parsimonious model construction. Models and the knowledge base for the enhancement of models have been described by bond graphs. After a parsimonious model is generated, two types of qualitative analysis are conducted; one is to derive unknown states from known states and the other is to predict the effects of parameter changes on the whole system. This methodology is only applicable when the structure of a dynamic system is known beforehand. A detailed case study is presented to demonstrate this approach.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"9 1","pages":"201-212"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74742861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High flexibility and a quick response of the flexible manufacturing system make it extremely complicated to properly control the production activities. In this paper, the complicated FMS control problem is studied. The coloured Petri net is chosen to model the flexible manufacturing systems, presenting a generic functional view. Based on this model, an expert system is developed to improve the decision-making ability of the FMS control system. The expert system developed is flexible enough to be adapted to a variety of real-life applications. A simulation is performed on a practical flexible manufacturing system.
{"title":"An expert system for FMS control","authors":"Wang Chengen, Zhu Jianying, Wei Zhongxin","doi":"10.1049/ISE.1993.0019","DOIUrl":"https://doi.org/10.1049/ISE.1993.0019","url":null,"abstract":"High flexibility and a quick response of the flexible manufacturing system make it extremely complicated to properly control the production activities. In this paper, the complicated FMS control problem is studied. The coloured Petri net is chosen to model the flexible manufacturing systems, presenting a generic functional view. Based on this model, an expert system is developed to improve the decision-making ability of the FMS control system. The expert system developed is flexible enough to be adapted to a variety of real-life applications. A simulation is performed on a practical flexible manufacturing system.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"16 1","pages":"223-230"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89017851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The authors show that adaptive inverse control is a member of the class of control design techniques with an internal model control structure. By implication, therefore, adaptive inverse control is supported by the firm analytical foundation on which internal model control is now based. They present artificial neural network architectures for the implementation of nonlinear internal model control. This approach can be viewed as a nonlinear analogue of adaptive inverse control; the network models used are nothing more than nonlinear adaptive filters. The authors use two separate networks in the implementation of nonlinear IMC; one network models the plant, and the second network models the plant inverse. They conclude with a simulation example demonstrating nonlinear IMC using neural networks. >
{"title":"Adaptive filtering and neural networks for realisation of internal model control","authors":"K. Hunt, D. Sbarbaro","doi":"10.1049/ISE.1993.0008","DOIUrl":"https://doi.org/10.1049/ISE.1993.0008","url":null,"abstract":"The authors show that adaptive inverse control is a member of the class of control design techniques with an internal model control structure. By implication, therefore, adaptive inverse control is supported by the firm analytical foundation on which internal model control is now based. They present artificial neural network architectures for the implementation of nonlinear internal model control. This approach can be viewed as a nonlinear analogue of adaptive inverse control; the network models used are nothing more than nonlinear adaptive filters. The authors use two separate networks in the implementation of nonlinear IMC; one network models the plant, and the second network models the plant inverse. They conclude with a simulation example demonstrating nonlinear IMC using neural networks. >","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"9 1","pages":"67-76"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85099342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The modelling and identification of non-linear dynamical systems are considered in this paper. The emulation of an existing controller, a skilled human for example, is a special case of this general treatment. A technique is sought, capable of developing general black-box non-linear models with both numerical and symbolic data. The models themselves are expressed in a high-level human-understandable format and are induced from examples of past behaviour. In the case of human controllers, this approach removes reliance on the articulation of skilled behaviour. The studied approach is based on the automatic induction of decision trees and production rules from examples; these are particular cases of classifiers. The algorithms used are a product of the machine learning sub-field of artifical intelligence research. A formalism is developed whereby the modelling and control of general dynamical systems are transformed to classification problems, and therefore become amenable to processing by the induction algorithms mentioned above. Experimental results are presented describing the induction of executable models, both of skilled human control behaviour and of an exising automatic controller. Experiments were performed in simulations and on physical laboratory apparatus.
{"title":"Classification by induction: application to modelling and control of non-linear dynamical systems","authors":"K. Hunt","doi":"10.1049/ISE.1993.0020","DOIUrl":"https://doi.org/10.1049/ISE.1993.0020","url":null,"abstract":"The modelling and identification of non-linear dynamical systems are considered in this paper. The emulation of an existing controller, a skilled human for example, is a special case of this general treatment. A technique is sought, capable of developing general black-box non-linear models with both numerical and symbolic data. The models themselves are expressed in a high-level human-understandable format and are induced from examples of past behaviour. In the case of human controllers, this approach removes reliance on the articulation of skilled behaviour. The studied approach is based on the automatic induction of decision trees and production rules from examples; these are particular cases of classifiers. The algorithms used are a product of the machine learning sub-field of artifical intelligence research. A formalism is developed whereby the modelling and control of general dynamical systems are transformed to classification problems, and therefore become amenable to processing by the induction algorithms mentioned above. Experimental results are presented describing the induction of executable models, both of skilled human control behaviour and of an exising automatic controller. Experiments were performed in simulations and on physical laboratory apparatus.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"11 1","pages":"231-245"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76820644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anton Beschta, O. Dressler, Hartmut Freitag, Michael Montag, Peter Stmi
The basic idea of model-based diagnosis is to exploit knowledge about the structure and behaviour of the physical system to be diagnosed in order to identify diagnoses from discrepancies between observed and predicted behaviour. In this paper, we describe the application of model-based diagnosis to the problem of fault localisation in power transmission networks. We also show that the resulting system, DPNet, has an extended competence w.r.t. rule-based approaches (treatment of unknown and multiple faults) while requiring less development effort because of the use of component libraries in the model-based approach.
{"title":"A model-based approach to fault localisation in power transmission networks","authors":"Anton Beschta, O. Dressler, Hartmut Freitag, Michael Montag, Peter Stmi","doi":"10.1049/ISE.1993.0002","DOIUrl":"https://doi.org/10.1049/ISE.1993.0002","url":null,"abstract":"The basic idea of model-based diagnosis is to exploit knowledge about the structure and behaviour of the physical system to be diagnosed in order to identify diagnoses from discrepancies between observed and predicted behaviour. In this paper, we describe the application of model-based diagnosis to the problem of fault localisation in power transmission networks. We also show that the resulting system, DPNet, has an extended competence w.r.t. rule-based approaches (treatment of unknown and multiple faults) while requiring less development effort because of the use of component libraries in the model-based approach.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"102 1","pages":"3-14"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78215131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Brunner, W. Nejdl, H. Schwarzjirg, Monika Sturm
The authors have implemented a real-time expert system as part of a supervisory and data acquisition system for the Public Utilities Board of Singapore, controlling its 22 kV distribution network. It works as an operator support tool by diagnosing network disturbances and device malfunctions, and by presenting a switching sequence which can be executed immediately to restore supply. The authors describe how the expert system is integrated into a large supervisory control and data acquisition system for power distribution networks. The necessary techniques to cover online processing of real-time data, intelligent alarm processing and network reconfiguration/restoration are discussed. The expert system is based on a hierarchic multilevel problem-solving architecture, integrating model-based, heuristic and algorithmic techniques acting on an object-oriented data structure. Techniques such as the automatic creation and update of the knowledge base, event filtering, online event processing, and structural and temporal focusing have been implemented to enable the system to perform its task online and in real time.
{"title":"On-line expert system for power system diagnosis and restoration","authors":"Thomas Brunner, W. Nejdl, H. Schwarzjirg, Monika Sturm","doi":"10.1049/ISE.1993.0003","DOIUrl":"https://doi.org/10.1049/ISE.1993.0003","url":null,"abstract":"The authors have implemented a real-time expert system as part of a supervisory and data acquisition system for the Public Utilities Board of Singapore, controlling its 22 kV distribution network. It works as an operator support tool by diagnosing network disturbances and device malfunctions, and by presenting a switching sequence which can be executed immediately to restore supply. The authors describe how the expert system is integrated into a large supervisory control and data acquisition system for power distribution networks. The necessary techniques to cover online processing of real-time data, intelligent alarm processing and network reconfiguration/restoration are discussed. The expert system is based on a hierarchic multilevel problem-solving architecture, integrating model-based, heuristic and algorithmic techniques acting on an object-oriented data structure. Techniques such as the automatic creation and update of the knowledge base, event filtering, online event processing, and structural and temporal focusing have been implemented to enable the system to perform its task online and in real time.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"1 1","pages":"15-24"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72800648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper discusses methods for applying restrictions to qualitative models of physical systems in order to ensure more specific behaviour. Restriction is performed by adding constraints; directly on exogenous parameters, and indirectly on non-exogenous or dependent parameters. The former are called primary restrictions, and the latter are called derived restrictions. We show the connection between primary and derived restrictions, and how to determine the consistency of multiple restrictions. Examples are given of how a specific model is augmented using restrictions.
{"title":"Restriction of qualitative models to ensure more specific behaviour","authors":"D. Hibler, G. Biswas","doi":"10.1049/ISE.1993.0013","DOIUrl":"https://doi.org/10.1049/ISE.1993.0013","url":null,"abstract":"This paper discusses methods for applying restrictions to qualitative models of physical systems in order to ensure more specific behaviour. Restriction is performed by adding constraints; directly on exogenous parameters, and indirectly on non-exogenous or dependent parameters. The former are called primary restrictions, and the latter are called derived restrictions. We show the connection between primary and derived restrictions, and how to determine the consistency of multiple restrictions. Examples are given of how a specific model is augmented using restrictions.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"7 1","pages":"133-144"},"PeriodicalIF":0.0,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82036485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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