Abstract: In order to monitor the temperature of an electromechanical machine using electrical operating data of the machine, structural data concerning a geometry, a thermal conductivity and an electrical conductivity of elements of the machine is imported. Using the structural data and the electrical operating data, electrical energy losses in the machine are continuously simulated in a spatially resolved manner by means of an electrical simulation model of the machine. Furthermore, a temperature distribution in the machine is continuously simulated by means of a thermal simulation model of the machine using the structural data and the simulated electrical energy losses. In accordance with the simulated temperature distribution, a temperature value is then determined for a component of the machine and output in order to monitor its temperature.

Abstract: An apparatus, system and method for detecting anomalies in a grid are disclosed. The method includes transforming data acquired from the grid based on at least one of a Fast Fourier Transformation and a spectrogram of the data, wherein the data acquired includes data associated with at least one of grid voltage, grid current, grid frequency, phase; fitting the data using a fitting function initialized using at least the transformed data, wherein the fitting function includes at least one of a sinusoidal function; or generating a lower representation of at least one of the data acquired and the transformed data; and detecting the anomaly in the grid based on at least one outlier detected in the fitted data or the lower representation of data using at least one of a parameter deviation and the similarity index.

Abstract: A computer-implemented method of providing a machine learning model for condition monitoring of an electric power converter is provided. The method includes: obtaining a first batch of input data that includes a number of samples of one or more operating parameters of the converter during at least one operating state of the converter; reducing the number of samples of the first batch by clustering the samples of the first batch into a first set of clusters, (e.g., according to a first clustering algorithm, e.g., based on a clustering feature tree, such as BIRCH), and determining at least one representative sample for each cluster; providing the representative samples for training the machine learning model; and/or training the machine learning model based on the representative samples.

Abstract: A method of optimizing a control loop of a converter, such as a control system of the converter, includes acquiring actual values of a drive system powered by the converter, inferring, based on at least one machine learning model and the actual values, one or more adjustments of control parameters of the control loop for improving the control accuracy, and outputting the one or more adjustments for adapting control parameter values.