Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning

With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly. Whenever critical temperatures cannot be measured economically on a sensor base, a thermal model lends itself to estimate those unknown quantities. Thermal models for electric power systems are usually required to be both, real-time capable and of high estimation accuracy. Moreover, ease of implementation and time to production play an increasingly important role. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based lumped-parameter models, and data-driven nonlinear function approximation with supervised machine learning. A quasi-linear parameter-varying system is identified solely from empirical data, where relationships between scheduling variables and system matrices are inferred statistically and automatically. At the same time, a TNN has physically interpretable states through its state-space representation, is end-to-end trainable -- similar to deep learning models -- with automatic differentiation, and requires no material, geometry, nor expert knowledge for its design. Experiments on an electric motor data set show that a TNN achieves higher temperature estimation accuracies than previous white-/grey- or black-box models with a mean squared error of $3.18~\text{K}^2$ and a worst-case error of $5.84~\text{K}$ at 64 model parameters.Comment: Preprint; Fix typos, streamline math. notation; 10 page

Über den Wolken - über dem Gesetz? Das Arbeitsverhältnis des fliegenden Luftfahrtpersonals in der deutschen Zivilluftfahrt

Wallscheid O. Über den Wolken - über dem Gesetz? Das Arbeitsverhältnis des fliegenden Luftfahrtpersonals in der deutschen Zivilluftfahrt. Arbeit und sozialer Schutz. Vol 25. Berlin: Logos; 2013

A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors

Torque control of electric drives is a challenging task, as high dynamics need to be achieved despite different input and state constraints while also pursuing secondary objectives, e.g., maximizing power efficiency. Whereas most state-of-the-art methods generally necessitate thorough knowledge about the system model, a model-free deep reinforcement learning torque controller is proposed. In particular, the deep Q-learning algorithm is utilized which has been successfully used in different application scenarios with a finite action set in the recent past. This nicely fits the considered system, a permanent magnet synchronous motor supplied by a two-level voltage source inverter, since the latter is a power supply unit with a limited amount of distinct switching states. This contribution investigates the deep Q-learning finite control set framework and its design, including the conception of a reward function that incorporates the demands concerning torque tracking, efficiency maximization and compliance with operation limits. In addition, a comprehensive hyperparameter optimization is presented, which addresses the many degrees of freedom of the deep Q-learning algorithm striving for an optimal controller configuration. Advantages and remaining challenges of the proposed algorithm are disclosed through an extensive validation, which includes a direct comparison with a state-of-the-art model predictive direct torque controller