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The paper presents a case study results emoting detection for a broken bar in an induction motor thermal power-plant. Two identical 3.15 MW motors are analyzed. The malfunctioning motor suffers from increased vibrations. A fault on the rotor is suspected. The induction motor phase current is analyzed for the healthy and the malfunctioning motor. The feature(More)
Paper deals with application of online rotor broken bar fault detection via artificial neural networks. Fault can be detected by monitoring abnormalities of the spectrum amplitudes at certain frequencies in the motor current spectrum. These discriminative features are used for training of feed-forward backpropagation artificial neural network. Trained(More)
Undesired low-frequency self-sustained speed oscillations are encountered in fan, compressor and pump drives utilizing open-loop frequency-controlled induction motor drives. Discontinuous rectifier current at light loads and the dead-time of the inverter switches are the main sources of such oscillations. This paper proposes a concise analytical method to(More)
This paper presents a novel method for short-term load forecasting (STLF), based on artificial neural network (ANN), targeted for use in large-scale systems such as distribution management system (DMS). The system comprises of a preprocessing unit (PPU) and a feed forward ANN ordered in a sequence. PPU prepares the data and feeds them as input to the ANN,(More)
This paper presents a novel hybrid method for short-term load forecasting. The system comprises of two artificial neural networks (ANN), assembled in a hierarchical order. The first ANN is a multilayer perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP,(More)
This paper proposes method for broken bar detection in induction motors at very low slip. The proposed method consists of extracting reliable discriminative feature from a steady state one-phase current signal and design of optimal classifier via a support vector machine. The fault related features are extracted from frequency spectra of a modulus of a(More)