Abstract: Anomaly recognition of safety monitoring data of hydropower station is a prerequisite for scientific evaluation of dam safety. Traditional 3σ criterion is prone to cause miss judgment when applied to the online anomaly identification of “step type” and “oscillating type” monitoring data series. In view of this, we established an improved criterion by replacing the general position parameter and general scale parameter in the 3σ criteria with Andrews M-estimator and standard quartile range. Engineering practice and sensitivity analysis prove that the method could effectively eliminate the adverse effects of anomalies on the recognition results. The proportion of anti-anomaly amounts 25%, and the accuracy and reliability of anomaly recognition are improved obviously.

Key words: anomaly identification, 3σ criterion, M-estimator, standard quartile range, dam safety condition