A comprehensive fault diagnostic system using artificial intelligence for sub-transmission and urban distribution networks

Helicopter is a system on which operates a large number of specialities from electrical domain to mechanical domain. Today, diagnosis methods are segmented by field of expertise. Each expert treats the sub-system whose he is responsible for regardless of the results of other specialities.Therefore, there is no relation between specialities. Thus, diagnosis at system level is efficient but, due to the lack of correlation between subsystems, it is incomplete. Within the framework of a helicopter, the operator of maintenance uses all the data recorded during the flight, the results of expert treatments, but also his knowledge, his experience and his capacities of observation and analysis to provide an effective global diagnosis. In order to build relation between fields of expertise and so, to obtain a diagnosis at aircraft level which could be relevant, we try to set up a concept which gets closer, at most, to human judgment but which is not well adapted to industrial environment: the normality.During our study, we established that to reach our objective and to stick at best with the concept of normality, the most relevant solution consists in building a global normal signature. This signature could be illustrated as the image of the aircraft health, qualified as normal. This paper defines the normal signature and explains, in part, the process of its building.

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“…The use of a centralized diagnosis module could be interesting [4], but a number of skills are required.…”

Section: Eads Eurocoptermentioning

confidence: 99%

Normal signature characterization for system health assessment

Bect¹,

Simeu-Abazi

Maisonneuve³

et al. 2011

2011 Prognostics and System Health Managment Confernece

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“…The knowledge representation and inference procedures using the cause-effect network were presented for diagnosing multiple faults as well as single fault [6], [7]. Many other methods using artificial neural network (ANN) [8]- [11], artificial intelligence [12], and fuzzy relations [13] and [14] have been proposed in recent years. Reference [13] shows it is difficult to determine the parameter value, or how the parameter is decided for the different case.…”

Section: Introductionmentioning

confidence: 99%

Fault section diagnosis of power system using fuzzy logic

Chin

2003

IEEE Trans. Power Syst.

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This paper proposed a fast diagnosis system for estimating the fault section of power system by using hybrid cause-effect network and fuzzy logic. This proposed system builds the cause-effect networks, which represent the fuzzy relations of protective relays and circuit breakers (CBs) for selection of faulty sections in blackout areas. In order to deal with the uncertain information in the process of diagnosis, a fuzzy-logic method is derived. Since the cause-effect network is well suited for parallel processing and the fuzzy logic has fast solution advantage, the inferential speed can be improved significantly. An error detector also was developed in this system to handle malfunction of relays and circuit breakers. Many tests have been run to show its effectiveness. Results show that the proposed system is a very effective method.Index Terms-fault section diagnosis, cause-effect network, protective relays, circuit breakers, fuzzy logic.

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“…With particular reference to distribution systems, has found a great development the ESs technology (Huang et al , 1997; Hsu et al , 1991; Teo, 1997; Teo and Gooi, 1998; Montakhab and Adams, 1998). As for the GAs, a serious problem presented by the ESs is constituted by the high processing times for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A new methodology for distribution systems faults identification, location and characterization

Campoccia

Silvestre

Incontrera³

et al. 2005

COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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The electrical energy market liberalization has encouraged utilities to make new investments at distribution level so as to attain higher quality levels. Service continuity is one of the aspects of greater importance in the definition of the quality of the electrical energy, for this reason, the research in the field of faults diagnostic for distribution systems is spreading ever more. In this paper, a new methodology for diagnostic management of automated distribution systems is presented. The technique is based on the use of circuital models of the electrical system resulting from the composition of quadripoles. In this way, matrix relations and thus a non linear system can be deduced. The solution of the latter gives as a result the identification of the type of fault, of its characteristic parameters and location. The paper shows an application of the proposed methodology in which also its precision and robustness to parametric variations are tested

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A comprehensive fault diagnostic system using artificial intelligence for sub-transmission and urban distribution networks

Cited by 13 publications

References 10 publications

Normal signature characterization for system health assessment

Normal signature characterization for system health assessment

Fault section diagnosis of power system using fuzzy logic

A new methodology for distribution systems faults identification, location and characterization

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