Multiple Current Sensors Research Articles

Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.

Fault diagnosis for multiple current sensors in grid‐connected inverter based on average modulation voltage

AbstractGrid‐connected inverters are the core equipment in the renewable power system. There are multiple current sensors which may affect the driving module of the switch in inverter. In multiple current sensor fault diagnosis, the coupling between fault components makes fault diagnosis difficult. This paper presents an offset fault diagnosis method of multiple current sensors based on the average modulation voltage model. Based on the influence of current sensor offset fault in the system, the average modulation voltage model is established in three‐phase stationary coordinates. The difference between the measured value of the current and the actual value is estimated through the model when the offset fault occurs. And then the fault is located. Experimental results show that the fault can be located accurately and fault tolerant control can be performed by this method when there are offset faults in multiple sensors simultaneously.

A Fault Tolerance Method for Multiple Current Sensor Offset Faults in Grid-Connected Inverters

Three-phase grid-connected inverters have been widely used in the distributed generation system, and the current sensor has been applied in closed-loop control in inverters. When the current sensor offset faults occurs, partial fault features of multiple current sensors disappear from the closed-loop control grid-connected system, which leads to difficulties for fault diagnostics and fault-tolerant control. This paper proposes a fault tolerance method based on average current compensation mode to eliminate these adverse effects of fault features. The average current compensation mode compensates the average of the three-phase current to the αβ axis current to realize the fault feature reconstruction of the current sensor. The mode does not affect the normal condition of the system. Then, the data-driven method is used for fault diagnosis, and the corresponding fault tolerant control model is selected according to the diagnosis results. Finally, the experimental results show that the proposed strategy has a good fault tolerance control performance and can improve the fault feature discrimination and diagnostic accuracy.

Vision-based fringe projection measurement system for radial vibration monitoring of rotating shafts

Shaft radial vibration signals of rotary machines are crucial dynamic information for condition monitoring and fault diagnosis. A novel vision-based fringe projection vibration measurement system is proposed to monitor the radial displacements of a rotating shaft. The fringe pattern was projected on the surface of the shaft and the deformed fringe was acquired by a high-speed camera. A novel fringe processing algorithm was proposed to extract the radial displacement with good accuracy and efficiency. The vertical displacement could be obtained by locating the symmetric center of the recorded fringe. Meanwhile, the horizontal displacement could be obtained from the shift of the fringe at the symmetric center. The influence factors affecting measurement performance were analyzed. Simulations and comparative experiments were carried out for the verification of the feasibility and reliability of the proposed system. Comparing with the multiple eddy current sensors based system, the proposed method can realize multi-dimensional vibration measurement using one camera. The merits of simple in system, easy in operation, high accuracy and effectiveness in measurement make it attractive for dynamic characterization, condition monitoring and faults diagnosis of rotating machineries in actual applications.

Self-Synchronizing Stationary Frame Inverter-Current-Feedback Control for LCL Grid-Connected Inverters

Current regulation of inductive–capacitive–inductive (LCL) grid-connected inverters usually requires multiple current sensors to actively damp the filter resonance. Inverter-side current regulation avoids this issue because of its inherent LCL filter damping properties but does not accurately control the grid-side current, particularly with distorted grids. Furthermore, both strategies typically require high-quality voltage sensors to maintain precise grid synchronization. This article proposes a novel current control strategy for LCL grid-connected inverters that only requires inverter-side current sensors. The strategy uses the resonator output states of a stationary reference frame (SRF) proportional resonant (PR) current regulator to estimate the grid voltage phase and filter capacitor current and, thus, achieve self-synchronized indirect regulation of the grid-side current. The effectiveness of the approach has been verified using simulation and matching experimental investigations.

An Online Data-Driven Method for Simultaneous Diagnosis of IGBT and Current Sensor Fault of Three-Phase PWM Inverter in Induction Motor Drives

This article presents an online data-driven diagnosis method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in the three-phase pulsewidth modulation inverter. The fast Fourier transform (FFT) algorithm is used to extract the fault frequency spectrum features of the three-phase currents. Then, a feature selection method named ReliefF is introduced to select the most critical features by removing redundant and irrelevant features. In addition, as novel fast learning technology, a random vector functional link network is applied to learn the faulty knowledge from the historical dataset. Based on the well-learned model, the fault type and location of the converter can be accurately identified as long as the three-phase current signals are measured. Offline test results verify that the proposed method can identify the IGBT and sensor faults with an accuracy of 98.83% and outperforms the state-of-the-art learning algorithms. Moreover, the real-time hardware-in-the-loop test results show that the proposed method can successfully identify the IGBT faults and current sensor faults within 22 ms. It is robust to the dc-link voltage fluctuations, model parameters, and speed or load variations. The extensibility of the proposed method is also validated based on the test results in terms of other fault modes and drive systems.

Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis

Gearboxes are widely used in rotating machinery and various industrial applications for transmission of power and torque. They operate for prolong hours and under different working conditions which may increase their probability of failure. Sudden failure of a gearbox may lead to significant downtime and increase maintenance costs. In industrial applications, usually fault detection and diagnosis techniques based on vibration signal are used for monitoring the health condition of gearboxes. In most of these techniques, time and frequency domain features are manually extracted from a vibration sensor and used for fault detection and diagnosis. In this research, a fault diagnosis methodology based on motor current signature analysis is proposed. The acquired data from multiple current sensors are fused by a novel 2-D convolutional neural network architecture and used for classification purpose directly without any need for manual feature extraction. Performance of the proposed method has been evaluated on the motor current data obtained from an industrial gearbox test rig in various health condition and with different working speeds. In comparison with classical machine learning (ML) algorithms, the presented methodology exhibits the best classification performance for gearbox fault detection and diagnosis.

A Current Decoupling Control Scheme for LCL-Type Single-Phase Grid-Connected Converter

For better performance, different current control schemes are used in LCL-type grid-connected converters. However, these schemes are mostly used in three-phase grid-connected converters and multiple current sensors are needed. To address these issues, a current decoupling control scheme for single-phase grid-connected converters in rotating reference frame is proposed in the paper. Because an extended state observer is adopted in the control scheme, only one current sensor is needed and the complicated coupling relationships between the $d$ axis components and $q$ axis components can be eliminated completely. Moreover, to improve the dynamic and steady-state performance of converters, a state feedback closed-loop system structure and nonlinear controllers are adopted. The current decoupling control scheme is validated in an LCL-type single-phase grid-connected converter. The experimental results show that a fast-dynamic response under load change and voltage sag can be achieved besides preferable static performances with the proposed control scheme.

Sensor fault-tolerant control of DFIG based wind energy conversion systems

This paper proposes a fault-tolerant control (FTC) strategy for a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS) with sensor faults. The DFIG system is decomposed into stator, rotor, grid current models, and the sensor fault observer (SFO) is presented to provide accurate estimations of sensor fault components in the presence of model uncertainties. Based on the independent current dynamics of DFIG system, three SFOs are designed in parallel to respectively estimate the stator, rotor, and grid current senor fault signals. During the occurrence of faults, the fault estimations derived from current observers are adopted to compensate the faulty signals, thereby achieving the system reconfiguration of DFIG without requiring any fault diagnosis schemes. Simulation studies are undertaken on a grid-connected WECS under the conditions of stepped and random wind speeds, and single and multiple current sensor faults. Simulation and experimental results reveal that the FTC-controlled DFIG system is resilient from the sensor faults and maintains continuous stable operation.

Detrimental Effect Elimination of Current Sensor Accuracy Uncertainty for High-Precision Position Sensorless Control of Interior Permanent Magnet Synchronous Motor Drives

The precision of position estimation is highly dependent on the accuracy of multiple current sensors, whereas the conventional error calibration strategies for current sensor accuracy uncertainty (SAU) problem have limitations regarding the position sensorless controlled drives. In order to solve this thorny problem, this article proposes a mutual calibration strategy for the multiple current sensors in an interior permanent magnet synchronous motor (IPMSM) drive. The proposed current sensor error calibration strategy takes full advantage of the correlation among the multiple current sensors themselves without using any complicated observers. Moreover, the traditional seven-segment space vector pulsewidth modulation technology is applied without modification. The only change is reflected in several sets of the artificially added current sampling points. The effectiveness of the proposed strategy is verified by experimental results on a 5-kW IPMSM motor prototype, which shows that the calibrated current sensors exhibit improved accuracy, and the position estimation accuracy is also guaranteed.

A Single Method for Multiple IGBT, Current, and Speed Sensor Faults Diagnosis in Regenerative PMSM Drives

This paper proposes a single fault diagnostic method for regenerative permanent magnet synchronous machine drives with the ability to handle multiple insulated gate bipolar transistor faults, single current sensor fault, and rotor-position disconnection. The considered faults are discriminated, and the faulty device is correctly identified in each case. The robustness against false alarms is improved by using adaptive thresholds and normalized diagnostic variables. The proposed algorithm requires the information given by current sensors and the rotor position sensor that are already available from the drive system, without requiring a high computational burden. Simulation and experimental results are presented to validate the effectiveness of the considered algorithm for both motoring and regenerative operations.

Mutual Calibration of Multiple Current Sensors With Accuracy Uncertainties in IPMSM Drives for Electric Vehicles

This paper proposes a mutual calibration strategy for multiple current sensors in an electric vehicle motor drive. The motor drive usually consists of three current sensors, i.e., a dc-bus current sensor and two phase current sensors. Due to the aging effect and harsh operating environment, the accuracy uncertainty issue is inevitable in these crucial sensors, which results in poor driving performance. In this paper, the detection voltage injection (DV-Injection) method is proposed for mutual calibration of the aforementioned current sensors. Two opposite basic vectors are set together to detect and eliminate the offset error of the dc-bus current sensor. Then, both the directly measured phase-current values by the phase-current sensors and the indirectly measured values by the dc-bus current sensor are sampled. These values are utilized for mutual calibration of the phase-current sensor offset errors and scaling error differences among all the current sensors. Meanwhile, the DV-Injection process is only applied in the period of calibration process, whereas in the remaining intervals the space vector pulsewidth modulation technology is utilized. Finally, the effectiveness of the proposed scheme is verified by simulation study in MATLAB/Simulink and experimental results on a 5-kW interior permanent magnet synchronous motor motor prototype.

Machine Current Sensor FDI Strategy in PMSMs

This work proposes a machine current sensor fault detection and isolation (FDI) strategy in permanent magnet synchronous machines (PMSMs) resilient to multiple faults. The fault detection is performed by comparing the measured and estimated DC link currents. The fault isolation is achieved according to machine phase signal estimation and the corresponding residual examination. Single sensor fault, multiple sensor faults and non-sensor fault are covered by the proposed FDI method. The proposed sensor FDI method is not influenced by machine imbalance, feasible for FDI of both single and multiple machine current sensor faults, and capable of distinguishing between machine current sensor fault and non-sensor fault. The proposed method is validated with simulation studies in MATLAB, and the studies demonstrate that the proposed machine current sensor FDI method, different from existing methods, has a unique feature of being able to handle multiple sensor faults with the consideration of non-sensor fault disturbance.

Two current sensor fault detection and isolation schemes for induction motor drives using algebraic estimation approach

The paper focuses on single and multiple current sensors fault detection and isolation (FDI) for induction motor (IM) drives using only two current sensors. Two model based diagnosis methods are proposed to estimate the current residuals dynamics in the stationary reference frame: The first one is a differential algebraic estimation of fault dynamics and the second one is based on a combination of the Robust Integral Sign of the Error (RISE) observer with the algebraic approach. After detecting the fault occurrence, a logic based decision unit is then built to identify the faulty sensors. The resulting residuals are robust to load torque disturbances. Moreover, the developed fault estimators are simple with a 1-D model, based only on the input–output measurements and their derivatives and do not require a bank of observers. Furthermore, the used residual threshold is well defined and is suitable for the whole operating range. The ability of diagnosing the recovery of a sensor from a fault is also demonstrated. The validity of the developed approaches is analytically proved and a comparative study is carried out between the two FDI schemes.

A modular transformerless DC-DC step-up converter with very high voltage gain and adjustable switch stress

According to limited voltage gain of the standard step-up converters, cascade connection of the boost choppers can be employed to increase voltage gain. However, negative impedance instability problem due to converters constant power characteristic will complicate closed loop control of the cascaded converters. To cope with the problem, switched capacitor/switched inductor (SC/SI) circuits can be employed for voltage gain increment in transformerless DC–DC converters. High gain DC–DC converter model includes several state variables; hence multiple voltage and current sensors are required in closed loop applications. In this paper, a novel transformerless DC–DC step-up converter with very high voltage gain is proposed using SC/SI circuits which mitigates negative impedance instability problem. By modular extension, more improvements in converter gain and switch voltage stress can be obtained. In the developed circuit, inductors currents (and voltages of the capacitors) are equal during transient and steady-state conditions. Thus, model complexity is similar to standard DC–DC converters. Experimental response of the designed system is investigated in both continuous and discontinuous conduction modes of operation. Considering different performance parameters, proposed DC–DC converter is compared with recently reported methods. Closed loop response of the presented regulator is studied using two-loop control strategy under load and line changes.

A Robust Observer-Based Method for IGBTs and Current Sensors Fault Diagnosis in Voltage-Source Inverters of PMSM Drives

Permanent magnet synchronous motors (PMSMs) drives using three-phase voltage-source inverters (VSIs) are currently used in many industrial applications. The reliability of VSIs is one of the most important factors to improve the reliability and availability levels of the drive. Accordingly, this paper presents a robust fault diagnostic method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in three-phase PMSM drives. The proposed observer-based algorithm relies on an adaptive threshold for fault diagnosis. Current sensor and open-circuit faults can be distinguished and the faulty sensors and/or power semiconductors are effectively isolated. The proposed technique is robust to machine parameters and load variations. Several simulation and experimental results using a vector-controlled PMSM drive are presented, showing the diagnostic algorithm robustness against false alarms and its effectiveness in both IGBTs and current sensors fault diagnosis.

Detection and Estimation of Extremely Small Fault Signature by Utilizing Multiple Current Sensor Signals in Electric Machines

This paper presents a novel motor current signature analysis algorithm for accurate detection and precise estimation of extremely small fault signature by utilizing multiple–phase-current signals in an electric machine. The two most critical challenges in developing a reliable diagnosis method are the detection of extreme small signals under extremely harsh environment, and the formulation of adaptive threshold under dynamic operation and noise variation of a motor. These challenges are addressed in this paper by logically and statistically utilizing multiple existing current sensors and current signals to make an accurate detection, and statistical/adaptive threshold for precise detection and decision making. The performance of the proposed algorithm has been mathematically and theoretically proved under modeled Gaussian noise conditions. Proposed theory is also experimentally verified on a 3-kW five-phase permanent-magnet-assisted synchronous reluctance motor.

Robust Fault Detection, Isolation, and Accommodation of Current Sensors in Grid Side Converters

The integration of modern renewable energy sources is enabled by the grid side converter (GSC) based on power electronic technology. The operation of the GSC is properly regulated by the GSC controller according to sensor measurements (i.e., of the grid voltage, of the line currents, and of the voltage at the dc-link). However, in case of current sensor faults, the operation of the GSC and of the entire renewable system can be critically affected and catastrophic failures may occur if the sensor fault is not accommodated on time. This paper proposes a model-based Fault Detection, Isolation, and Accommodation (FDIA) scheme, which enables the GSC to overcome faults that may occur on the associated current sensors. The sensor fault detection and isolation scheme has been designed based on analytical redundancy relations, while the accommodation of the faults is based on an adaptive estimation scheme. The proposed FDIA scheme has been applied on a modern GSC and the effectiveness of the scheme has been tested under several multiple current sensor faults and under several grid conditions. Furthermore, the FDIA scheme enables the GSC to operate properly (without causing failures or damages to its components) under current sensor faults and thus, the reliability of the GSC is enhanced.

Current Sensorless Equalization Strategy for a Single-Switch Voltage Equalizer Using Multistacked Buck–Boost Converters for Photovoltaic Modules Under Partial Shading

Differential power processing converters and voltage equalizers have been proposed and used for photovoltaic strings comprising multiple modules/substrings connected in series in order to preclude negative influences of partial shading. The single-switch voltage equalizer using multistacked buck–boost converters can significantly reduce the necessary switch count compared to that of conventional topologies, achieving simplified circuitry. However, multiple current sensors are necessary for this single-switch equalizer to effectively perform equalization. In this paper, a current sensorless equalization technique ( ΔV -controlled equalization) is presented. The equalization strategy using the ΔV -controlled equalization is explained and discussed on the basis of comparison with current-controlled equalization strategies. Experimental equalization tests emulating partial-shading conditions were performed using the single-switch equalizer employing the ΔV -controlled equalization. Negative impacts of partial shading were successfully precluded, demonstrating the efficacy of the proposed ΔV -controlled equalization strategy.

A Phase Current Reconstruction Technique Using a Single Current Sensor for Interleaved Three-phase Bidirectional Converters

This paper proposes a new phase current reconstruction technique for interleaved threephase bidirectional dc-dc converters using a single current sensor. In the proposed current reconstruction algorithm, a single current sensor is employed at the dc-link, and the dc-link current information is sampled at either the peak or valley point of the pulse-width modulation (PWM) carriers regularly. From the obtained current information, all phase currents are reconstructed in a single PWM cycle. After that, the digital current controller is applied to achieve current balancing in each phase. Compare to the previous multiple current sensor method, the proposed strategy reduces the number of the current sensors in the interleaved three-phase bidirectional converter as well as reducing potential current sensing error caused by non-ideal characteristics of the multiple current sensors. The effectiveness of the proposed method is verified from the experiments based on a 3kW three-phase bidirectional converter prototype for the automotive battery charging application.