Development and implementation of a power system fault diagnosis expert system

Minakawa, T.; Ichikawa, Yoshihiro; Kunugi, M.; Shimada, K.; Wada, Norio; Utsunomiya, Masaharu

doi:10.1109/59.387936

Cited by 69 publications

(18 citation statements)

References 8 publications

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“…Different techniques have been applied to analyze the generated signal-residuals, for example, in [14] a statistic method is presented, in [15] an expert system is proposed and neural networks are used in [16] [17].…”

Section: Fault Detection and Isolation Problemmentioning

confidence: 99%

Fault detection and isolation problem: Sliding mode fuzzy observers and neural networks

Anzurez-Marin

Espinosa-Juárez

Castillo–Toledo

2014

2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

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In this paper results of the application of a hybrid Fault Detection and Isolation scheme are presented. A TakagiSugeno fuzzy model is used to describe the system and a type of sliding mode observers are designed to estimate the system state vector; from this, the diagnostic signal-residual is generated by the comparison of measured and estimated output. Neural Networks are proposed in order to solve the fault isolation problem based on signal-residual. The faulted component is identified from the active signal-residuals by means of the application of the presented technique based on neural networks. This paper shows an application of the fault diagnosis technique, which was satisfactorily tested in a two-tank hydraulic system.

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Section: Fault Detection and Isolation Problemmentioning

confidence: 99%

Fault detection and isolation problem: Sliding mode fuzzy observers and neural networks

Anzurez-Marin

Espinosa-Juárez

Castillo–Toledo

2014

2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

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“…This explanation mechanism is intended for use in the real-time expert system with minimum of disturbances [11]. A fault diagnosis expert system was described by Minakawa et al [12]. This system uses the data from fault detection systems that locate fault points within electric stations.…”

Section: Introductionmentioning

confidence: 99%

A time-driven and event-driven approach for substation feeder incident analysis

Chen

Chang

Lee

2016

International Journal of Electrical Power & Energy Systems

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“…Under such circumstances, the system operator has to rely on the knowledge of the network, logical thinking and judgment to diagnose the fault and restore supply. To assist the system operator for fault diagnosis, various diagnostic knowledge base systems (DKES) using rule base, model-based reasoning and algorithmic techniques are reported [l, 31. Other approaches focus on the substation level using the sequence of event (SOE) recording [4], time-tagged data from circuit breakers and relays [5] and alarm processing in substation [6].…”

Section: Introductionmentioning

confidence: 99%

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

Teo¹

1997

IEEE Trans. Power Syst.

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This paper describes an intelligent diagnostic system for an interconnected distribution network developed to assist the system operator with fault identification during restoration. The intelligent process utilizes only those data available in a standard SCADA system such as the post fault network status, the list of the tripped breakers, main protection alarm, and the conventional event log. The fault diagnostic system is implemented by three independent mechanisms, namely the generic core rule, the generic relay setting inference and the specific postfault network matching and leaming. The generic core rule generates various possible fault locations and the generic relay inference examines whether each possible fault location is logical and valid. The specific network matching compares whether the post fault network and the related tripped breakers are identical to a previous fault event. Test results obtained from two distribution networks confirm that the developed system is practical, reliable and accurate.

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Development and implementation of a power system fault diagnosis expert system

Cited by 69 publications

References 8 publications

Fault detection and isolation problem: Sliding mode fuzzy observers and neural networks

Fault detection and isolation problem: Sliding mode fuzzy observers and neural networks

A time-driven and event-driven approach for substation feeder incident analysis

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

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