Artificial immune systems applied to voltage disturbance diagnosis in distribution electrical systems

Chavarette, Fábio Roberto; Lotufo, Anna Diva P.; Minussi, Carlos R.

doi:10.1109/ptc.2013.6652127

Cited by 18 publications

(33 citation statements)

References 24 publications

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“…The censor phase of the NSA consists primarily of generating a detector set from the data that were randomly chosen and verifying which data can recognize a non-proper pattern. The detectors are similar to the mature T cells that can recognise pathogenic agents ( Lima et al, 2013 ).…”

Section: Negative Selection Algorithmmentioning

confidence: 99%

“…The Ag is the signal to be analyzed, unknown by the NSA and represented by expression ( 1 ). The detectors, that is, the lymphocytes ( Ab ), are expressed according to expression ( 2 ) ( Lima et al, 2013 ), ( de Castro & Timmis, 2002 ):…”

Section: Negative Selection Algorithmmentioning

confidence: 99%

“…A deviation, i.e., a tolerance rate, is proposed where the combination with the patterns is acceptable and can improve the diagnosis ( Lima et al, 2013 ):…”

Section: Matching Criterion and Affinity Ratementioning

confidence: 99%

“…To evaluate and validate the efficiency of this new approach, the results were compared with a conventional diagnosis system based only in the NSA, proposed and presented in Lima, Lotufo, and Minussi (2013 ). This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems

Chavarette

Lopes

Lotufo

et al. 2016

Expert Systems with Applications

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a b s t r a c tThis paper presents a new artificial immune algorithm with continuous-learning, which is inspired by the biological immune system, to realize the voltage diagnosis in electrical distribution systems. This conception allows one to compose a diagnosis system that can continuously learn without reinitialization when new disturbances occur due to the evolution of the electrical system. Two artificial immune algorithms, which are the negative selection algorithm and the clonal selection algorithm, are used for the pattern recognition process and the learning process, respectively. The principal application of this new method aids the operation during failures, supervises the protection system, and can evolve with the power systems to continuously acquire new knowledge. This new methodology has a direct impact in the area of diagnosis in electrical systems, as well as, in the pattern recognition problem, because the main contribution and novelty of this method is the continuous learning capability, which enables the system to learn unknown patterns without having to restart the knowledge. This is the major advantage of this methodology. To evaluate the efficiency and performance of this new method, failure simulations were performed in a real distribution system with 134 buses using the EMTP software. The results show robustness and efficiency.

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Section: Negative Selection Algorithmmentioning

confidence: 99%

Section: Negative Selection Algorithmmentioning

confidence: 99%

“…A deviation, i.e., a tolerance rate, is proposed where the combination with the patterns is acceptable and can improve the diagnosis ( Lima et al, 2013 ):…”

Section: Matching Criterion and Affinity Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems

Chavarette

Lopes

Lotufo

et al. 2016

Expert Systems with Applications

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“…Type of Disturbance Total Match (%) [9] Voltage 96.21 [13] Voltage 99.11 [10] Voltage 99.31 [11] Voltage 99.66 [6] High-impedance fault 97.85 [12] High The proposed method presents a matching index and a precision that is superior to other similar works that are available in the literature. It should be emphasized that the MNSA proposed in this work, is an efficient technic, because the modification implemented provides a better learning in the censor phase, improving the accuracy in diagnosis.…”

Section: Referencementioning

confidence: 99%

A modified negative selection algorithm applied in the diagnosis of voltage disturbances in distribution electrical systems

Chavarette

Minussi

Bessa

et al. 2015

2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)

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This paper presents a modified negative selection algorithm for the diagnosis of disturbance in distribution electrical systems. This study analyzes voltage disturbances and high-impedance faults, based on three phase current and voltage electric measures, which are obtained at the substations. The principal application is to support operation decision aid during faults, as well as to supervise the protection system. To evaluate the performance of the proposed method, simulations were executed using the EMTP software for a distribution test system containing 134 bus. The results obtained were compared with the specialized literature.

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Analysis of the Energy Quality of the Electronic Laboratory of the Federal University of Tocantins Using Artificial Immunological Systems

Silva

Moreira

Freitas

2019

Proceedings of the 4th Brazilian Technology Symposium (BTSym'18)

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Artificial immune systems applied to voltage disturbance diagnosis in distribution electrical systems

Cited by 18 publications

References 24 publications

An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems

An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems

A modified negative selection algorithm applied in the diagnosis of voltage disturbances in distribution electrical systems

Analysis of the Energy Quality of the Electronic Laboratory of the Federal University of Tocantins Using Artificial Immunological Systems

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