Integrated Use of Artificial Neural Networks and Genetic Algorithms for Problems of Alarm Processing and Fault Diagnosis in Power Systems

Fritzen, Paulo Cícero; Cardoso, Ghendy; Zauk, João Montagner; Morais, Adriano Peres de; Bezerra, Ubiratan Holanda; Beck, Joaquim A. P. M.

doi:10.1007/978-3-642-12145-6_38

Cited by 2 publications

(2 citation statements)

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“…The rule-based system can selectively process alarm messages, and the artificial neural network can further classify the selected alarm messages. Fritzen et al [13] used an artificial neural network (ANN) and a genetic algorithm (GA) for alarm processing and fault diagnosis, which improves the diagnosis speed and generalization ability of the alarm system. Although such methods can improve the fault tolerance of alarm systems, they have problems, such as poor interpretation ability and difficulty in sample acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory

et al. 2019

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Alarm messages for grid monitoring are an important way to supervise the operation of power grids. Since the use of alarm messages is increasing exponentially due to the continuous expansion of the scale of power grids, a processing method for alarm messages based on statistics is proposed in this study. Entropy theory in information theory is introduced into the calculation of information value in power-grid alarming. By means of multiple entropy definitions, an evaluation index system for information value is constructed. Based on the analytic hierarchy process (AHP), various alarm-message entropies are used as indices to comprehensively assess the information value and level of each alarm message. Finally, an example is given to illustrate the effectiveness and practicality of the proposed method. This study provides a new idea for the intelligent classification of alarm messages.

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Section: Introductionmentioning

confidence: 99%

“…The abovementioned alarm methods can realize intelligent fault diagnosis in [9][10][11][12][13][14][15][16]. Alarm classification functions have also been used, for example in [10][11][12].…”

Section: Introductionmentioning

confidence: 99%