A Framework for Expert Systems Development Integrated to a SCADA/EMS Environment

Silva, V.N.A.L. da; Linden, Ricardo; Ribeiro, Guilherme Fernando

doi:10.1109/isap.2007.4441684

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Using the fuzzy discriminant method, the problem of discriminating malfunction is solved. Silva et al [10] introduced a new expert system framework which includes two applications to meet the daily needs of control centers: An alarm processing system and a power system recovery support system. This framework can improve the rules of expert systems.…”

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]. However, such alarm methods with a classification function are all filtered according to established rules in expert systems.…”

Section: Introductionmentioning

confidence: 99%