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We present a novel numerical method for modelling of coupled magneto-mechanical behaviour and magnetic hysteresis losses in electrical machine cores under mechanical stress. An energy-based single-valued magneto-mechanical constitutive law is coupled to the Jiles-Atherton model of hysteresis. The material model is implemented in a 2-D finite element method, which solves for both the magnetic vector potential and mechanical displacement in the electrical machine core. The model is applied to analyse stator shrink fitting in a permanent-magnet synchronous machine and centrifugal stress in a high-speed solid-rotor induction machine. The losses in the machines are shown to increase due to the consideration of the mechanical stress.

Smart meters collect a lot of data on customer level electricity consumption and this, together with other data sources e.g. environmental information and public open data, provides an excellent basis for data mining. As a part of a recent smart grid project conducted in Finland, several different ways of mining smart meter data were studied. The project brought advances in customer classification and clustering, load profiling, spatial load analytics, behaviour change detection and load forecasting. acceptedVersion