In the modern induction motor (IM) drive system, the fault-tolerant control (FTC) solution is becoming more and more popular. This approach significantly increases the security of the system. To choose the best control strategy, fault detection (FD) and fault classification (FC) methods are required. Current sensors (CS) are one of the measuring devices that can be damaged, which in the case of the drive system with IM precludes the correct operation of vector control structures. Due to the need to ensure current feedback and the operation of flux estimators, it is necessary to immediately compensate for the detected damage and classify its type. In the case of the IM drives, there are individual suggestions regarding methods of classifying the type of CS damage during drive operation. This article proposes the use of the classical multilayer perceptron (MLP) neural network to implement the CS neural fault classifier. The online work of this classifier was coordinated with the active FTC structure, which contained an algorithm for the detection and compensation of failure of one of the two CSs used in the rotor field-oriented control (DRFOC) structure. This article describes this structure and the method of designing the neural fault classifier (NN-FC). The operation of the NN-FC was verified by simulation tests of the drive system with an integrated FTC strategy. These tests showed the high efficiency of the developed fault classifier operating in the post-fault mode after compensating the previously detected CS fault and ensuring uninterrupted operation of the drive system.
In modern areas of knowledge related to electric drive automation, there is often a need to predict the state variables of the drive system state variables, such as phase current and voltage, electromagnetic torque, stator and rotor flux, and others. This need arises mainly from the use of predictive control algorithms but also from the need to monitor the state of the drive to diagnose possible faults that have not yet occurred but may occur in the future. This paper presents a method for predicting stator phase current signals using a network composed of long-short-term memory units, allowing the simultaneous prediction of two signals. The developed network was trained on a set of current signals generated by software. Its operation was verified by simulation tests in a direct rotor flux-oriented control (DRFOC) structure for an induction motor drive in the Matlab/Simulink environment. An important property of this method is the possibility of obtaining a filtering action on the output of the network, whose intensity can be controlled by varying the sampling frequency of the training signals.
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