Detection of induction motor load fluctuation with symmetrical components

International audience

The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

Three-phase induction motors are among the most frequently used motors in industrial areas due to their simple structure, low cost, power specifications, etc. Electric energy consumption from these motors accounts for 68% of the energy used by all motors. The faults that occur in these motors over time decrease motor efficiency and result in significant energy consumption. In this study, a new method based on spectral subtraction (SS) was suggested for determining broken rotor bar (BRB) faults in these motors. The traditional method of motor current signature analysis via Fast Fourier Transform (FFT) is hard to use to diagnose BRB faults because the sideband characteristics used as a fault indicator are also seen in the healthy state at low amplitude levels. In the proposed fault detection method, the FFT of both healthy case and faulty case current signals were calculated and then the SS signal is obtained by subtracting the FFT of the healthy motor from the faulty motor for each time step. In the residual SS signal, fault detection was performed by examining the amplitude levels of the harmonic component of the BRB fault. Experimental results indicate that BRB faults can be successfully detected in squirrel-cage rotor induction motors using the suggested method.

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The knowledge about fault mode behavior of an induction motor drive system is extremely important from the standpoint of improved system design, protection, and fault-tolerant control. This paper addresses the application of motor current spectral analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. Intensive research effort has been for some time focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult since distortions of the current waveform caused by the abnormalities in the induction motor are usually minute. This paper takes the initial step to investigate the efficiency of current monitoring for diagnostic purposes. The effects of stator current spectrum are described and the related frequencies determined. In the present investigation, the frequency signature of some asymmetrical motor faults are well identified using advanced signal processing techniques, such as high-resolution spectral analysis. This technique leads to a better interpretation of the motor current spectra. In fact, experimental results clearly illustrate that stator current high-resolution spectral analysis is very sensitive to induction motor faults modifying main spectral components, such as voltage unbalance and single-phasing effects.

This paper presents the analysis and development of a new method for electric fault detection in induction motor drives. The proposed method is based on extracting the magnitudes and phases of the high-frequency subband (HFSB) contents present in the d - q-axis components of the stator currents (i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> and i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> ) in an induction motor. The desired magnitudes and phases are extracted by processing id and iq using phaselet frames that are realized by a modulated filter bank. This filter bank is designed using six digital high-pass filters, whose coefficients are determined by biorthogonal phaselet basis functions. Extracted HFSB contents provide signature information that can offer accurate and fast detection of faults. The phaselet-based electric fault detection method has been made into a procedure for digital implementation. The performance of the proposed method is evaluated offline for collected stator currents from two different induction motor drives under different operating conditions. Offline test results show accurate, reliable, and fast detection of electric faults, with minor sensitivity to the ratings, loading levels, and type and location of faults.

This paper presents a simple fault detection method and fault-tolerant strategy based on the model predictive control theory. The proposed fault detection method can detect and locate a failed switch with current error signals dedicated to each switch, based on the position of space vectors. Following the fault detection, the proposed fault-tolerant control strategies are developed to produce three-phase balanced sinusoidal output voltages/currents with only normally functioning bidirectional switches. Therefore, the developed control technique for the matrix converter can preserve continuous operation even after the failure of switches and improve reliability of the drives. Simulation results are provided to validate the effectiveness of the applied detection and control strategy.

This paper proposes a new on-line fault detection method to detect open-switch faults in a Matrix Converter (MC). The proposed fault detection method is based on a two-step algorithm. The first step identifies the occurrence of the fault by monitoring the measured load currents. The second step locates the faulty switch by measuring the response of the MC to a set of predefined switching states. The proposed method allows fast detection and isolation of a faulty switch, independent of the modulation strategy, the voltage transfer ratio, or the output frequency, and without any requirement of additional voltage sensors. The paper also presents a Model Predictive Control (MPC)-based post-fault strategy that enables the MC to continue its operation during faulty conditions. Time-domain simulation studies are performed in the MATLAB/SIMULINK environment to evaluate the performance of the proposed fault detection method and the effectiveness of the MPC as a post-fault control strategy.

An increased integration of distributed generators into microgrids presents a technical challenge to maintain voltage and frequency stability, especially in islanded or autonomous operation. For stability assessment of the microgrid, a proper initialization is particularly needed to get the right steady-state operating point when the widely used induction motor (IM) loads are mainly considered. Since a conventional power-flow approach is not straightforward and suitable for initializing the IM loads in the isolated microgrid network due to an unavoidable discrepancy between initial-bus scheduled and actual values of IM’s reactive powers. To eliminate this mismatch, this paper presents a precise power-flow initialization using a unified Newton–Raphson approach. The correct steady-state initializations are demonstrated using 6bus and 13bus microgrid systems. IEEE standard 33, 38, and 69bus distribution networks are employed to explore computational performances of the proposed algorithm for the case where a large number of IM loads are incorporated via distribution feeder loads.

Fault detection plays an important role in providing reliable operation for permanent‐magnet synchronous machine (PMSM). The inter‐turn fault is one of the most common faults for the PMSM. Hence, this study focuses on the incipient stage inter‐turn fault detection. An improved zero‐sequence voltage component‐based (ZSVC) inter‐turn fault detection method is proposed. In the proposed method, discrete wavelet transform is first applied to remove the noise and harmonic components in the ZSVC for highlighting the fault characteristic component. Then, fast Fourier transform is used to analyse the obtained signal for the inter‐turn fault detection. In addition, to show the performance of the proposed method, the commonly used fault detection based on stator current is studied. The effectiveness of the proposed fault diagnosis method is validated by the simulation and experimental results, showing that the proposed method has good performance for the incipient stage inter‐turn fault diagnosis.

These days, electrical drives generally associate inverter and induction machine. Thus, these two elements must be taken into account in order to provide a relevant diagnosis of these electrical systems. The aim of this paper is to study the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. The proposed approach is a neural network classification applied to the fault diagnosis of a field oriented drive of induction motor. Multilayer perception (MLP) networks are used to identify the type and location of occurring fault using the stator Concordia mean current vector. In the case of a single fault occurrence, a localization domain made with seven patterns is built. With the possibility of occurrence of two faults simultaneously, there are twenty-two different patterns. Simulated experimental results on 1.5-kW induction motor drives show the effectiveness of the proposed approach with a classification performance over than 95%.

Software fault detection is crucial for ensuring reliable and high-quality software systems. However, traditional fault detection methods often rely on manual inspection or rule-based techniques, which are time-consuming and prone to human errors. In this research, the researchers propose an enhanced fault detection approach using an adaptive neural transfer learning algorithm. The goal is to leverage the power of neural networks and adaptability to improve fault detection accuracy and classification performance. The problem addressed in this research is the need for more effective fault detection methods that can handle the complexities of modern software systems. Existing fault detection techniques lack adaptability and struggle to cope with diverse software scenarios. Neural networks have shown promise in pattern recognition and classification tasks, making them suitable for fault detection. However, fixed architectures and training strategy limit their performance in different software contexts. To address this problem, the research proposes an adaptive neural transfer learning algorithm for fault detection. The algorithm dynamically adjusts its neural network architecture and training process based on the characteristics of the software under test. It incorporates adaptive mechanisms, such as adjusting learning rates and regularization techniques, to optimize performance. Real-time feedback and performance evaluation during the training process drive the adaptive mechanisms. To evaluate the proposed approach, the researchers conducted a series of experiments using diverse software systems and fault scenarios. The research compared the performance of the adaptive algorithm with traditional fault detection methods, including rule-based techniques and fixed neural network architectures. Evaluation metrics such as accuracy, precision, recall, and F1 score were used. The results consistently show that the adaptive neural transfer learning algorithm outperforms existing methods, achieving higher fault detection accuracy and improved classification performance.

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