Over the last decade, data has become a highly valuable resource. Electrical power grids deal with large quantities of data, and continuously collect this for analytical purposes. Anomalies that occur within this data is important to identify since they could cause nonoptimal performance within the substations, or in worse cases damage to the substations themselves. However, large datasets in the order of millions are hard or even impossible to gain a reasonable overview of the data manually. When collecting data from electrical power grids, predefined triggering criteria are often used to indicate that an event has occurred within the specific system. This makes it difficult to search for events that are unknown to the operator of the deployed acquisition system. Clustering, an unsupervised machine learning method, can be utilised for fault prediction within systems generating large amounts of multivariate time-series data without labels and can group data more efficiently and without the bias of a human operator. A large number of clustering techniques exist, as well as methods for extracting information from the data itself, and identification of these was of utmost importance. This thesis work presents a study of the methods involved in the creation of such a clustering system which is suitable for the specific type of data. The objective of the study was to identify methods that enables finding the underlying structures of the data and cluster the data based on these. The signals were split into multiple frequency sub-bands and from these features could be extracted and evaluated. Using suitable combinations of features the data was clustered with two different clustering algorithms, CLARA and CLARANS, and evaluated with established quality analysis methods. The results indicate that CLARA performed overall best on all the tested feature sets. The formed clusters hold valuable information such as indications of unknown events within the system, and if similar events are clustered together this can assist a human operator further to investigate the importance of the clusters themselves. A further conclusion from the results is that research into the use of more optimised clustering algorithms is necessary so that expansion into larger datasets can be considered.