Long transmission lines have to be compensated to enhance the transport of active power. But a wrong design of the compensation may lead to subsynchronous resonances (SSR). For studies often park equivalent circuits are used. The parameters of the models are often determined analytically or by a three-phase short-circuit test. Models with this parameters give good results for frequencies of 50 Hz and 100 Hz resp. 60 Hz and 120 Hz. But SSR occurs at lower frequencies what arises the question of the reliability of the used models. Therefore in this publication a novel method for the determination of Park equivalent circuit parameters is presented. Herein the parameters are determined form time functions of the currents and the electromagnetic moment of the machine calculated by transient finite-element simulations. This parameters are used for network simulations and compared with the finite-element calculations. Compared to the parameters derived by a three-phase short-circuit a significant better accuracy of simulation results can be achieved by the presented method.
This paper deals with the early detection of fault conditions in induction motors using a combined model- and machine-learning-based approach with flexible adaptation to individual motors. The method is based on analytical modeling in the form of a multiple coupled circuit model and a feedforward neural network. In addition, the differential evolution algorithm independently identifies the parameters of the motor for the multiple coupled circuit model based on easily obtained measurement data from a healthy state. With the identified parameters, the multiple coupled circuit model is used to perform dynamic simulations of the various fault cases of the specific induction motor. The simulation data set of the stator currents is used to train the neural network for classification of different stator, rotor, mechanical, and voltage supply faults. Finally, the combined method is successfully validated with measured data of faults in an induction motor, proving the transferability of the simulation-trained neural network to a real environment. Neglecting bearing faults, the fault cases from the validation data are classified with an accuracy of 94.81%.
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