AI Summary & Key Takeaways
AI InsightThe proposed algorithm is implemented by using MatlabTM and C++ with a NI-DAQ data acquisition board.
📌 3 Key Takeaways
Abstract
Protection of an induction motor (IM) against possible faults, such as a stator winding fault, due to thermal deterioration, rotor bar and bearing failures, is very important in environments in which it is used intensively, as in industry as an actuator. In this work, a real time digital protection algorithm based on principal component analysis (PCA) and neural network method is presented for induction motors. The proposed protection algorithm covers internal winding faults (also known as stator faults), broken rotor bar faults, and bearing faults. Many laboratory experiments have been performed on a specially designed induction motor to evaluate the performance of the suggested protection algorithm. The hybrid protection algorithm described in this paper uses is based on a three phase rms supply. These currents are first preprocessed by PCA to extract distinctive features called residuals. The calculated residuals are applied to a feed-forward back-propagation neural network as input vectors for decision making. Outputs of the network are signals denoting winding fault, rotor bar fault, bearing fault, and normal operation. The proposed algorithm is implemented by using MatlabTM and C++ with a NI-DAQ data acquisition board.
Özet
Protection of an induction motor (IM) against possible faults, such as a stator winding fault, due to thermal deterioration, rotor bar and bearing failures, is very important in environments in which it is used intensively, as in industry as an actuator. In this work, a real time digital protection algorithm based on principal component analysis (PCA) and neural network method is presented for induction motors. The proposed protection algorithm covers internal winding faults (also known as stator faults), broken rotor bar faults, and bearing faults. Many laboratory experiments have been performed on a specially designed induction motor to evaluate the performance of the suggested protection algorithm. The hybrid protection algorithm described in this paper uses is based on a three phase rms supply. These currents are first preprocessed by PCA to extract distinctive features called residuals. The calculated residuals are applied to a feed-forward back-propagation neural network as input vectors for decision making. Outputs of the network are signals denoting winding fault, rotor bar fault, bearing fault, and normal operation. The proposed algorithm is implemented by using MatlabTM and C++ with a NI-DAQ data acquisition board.