Phasor measurement units (PMU) are essential equipment for real-time operation of the electrical system. However, due to complex factors, these data can easily be compromised by interference, lack of synchronization or lack of equipment. This will raise several levels of data quality problems, being able to directly affect applications that are based on data quality. This work aims to detect these anomalies based only on the data of the PMU, without the need for knowledge of electrical parameters of the network. For this purpose, an online algorithm based on the eccentricity and typicality of the sample in relation to its set of previous samples, or TEDA, was proposed, where its main objective was to define the most sensitive method as to the variations in media and variation over time, maximizing eccentricity and favoring the detection of more difficult outliers. To evaluate two methods, phasor frequency measurement data from a single PMU are used.
