Fault detection methods for frequency converters fed induction machines

Mihet‐Popa, Lucian; Proştean, Octavian; Filip, Ioan; Szeidert, Iosif; Vasar, Cristian

doi:10.1109/efta.2007.4416767

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…The use of GA helps achieve an optimized weight value for BPN to obtain the desired output for the fault situation. Thus, the neuro-genetic technique based on a FPGA when implemented functions in such a way that the control scheme is capable of fast processing to achieve fault diagnosis almost instantly [ 12 , 13 ]. Here, the mixed design of neural networks and genetic algorithms is developed and implemented in the FPGA process as given by the flow diagram in Figure 5 .…”

Section: Neuro-genetic Approach For Fault Classificationmentioning

confidence: 99%

“…The CM system obtains basic data information from the motor via the use of signal processing or data analysis methods as described before. Although the method does not need human interpretation, it does have a fundamental downside [ 11 , 12 , 13 ]. The automation of the fault detection and diagnosis process is a natural evolution in the development of CM technologies [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

et al. 2022

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In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated.

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Section: Neuro-genetic Approach For Fault Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

et al. 2022

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“…Finally, the third condition ( . V < 0) can be satisfied by inserting Equation (8) into the time derivative of the Lyapunov candidate function.…”

Section: Dynamic Modelmentioning

confidence: 99%

“…Doubly fed induction generator (DFIG)-based wind turbines are also very fragile under grid voltage problems [7,8,[35][36][37][38], and it can be considered that problem solution techniques applied to DFIG applications can be utilized in GCI applications. DOb-based current controllers are applied to DFIGs and GCIs in [39,40] by considering robustness against parameter variations under balanced voltage sets.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the performance and reliability of the system under power electronic circuits in normal conditions, stability and grid support under grid faults are crucial due to restrictive grid code requirements [1,2]. Moreover, stability and reliability of the grid-connected inverter (GCI) under grid voltage faults must be considered for microgrid applications [3][4][5][6][7][8] with battery storage systems [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Control Strategy for a Grid-Connected Inverter under Unbalanced Network Conditions—A Disturbance Observer-Based Decoupled Current Approach

et al. 2017

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This paper proposes a new approach on the novel current control strategy for grid-tied voltage-source inverters (VSIs) with circumstances of asymmetrical voltage conditions. A standard grid-connected inverter (GCI) allows the degree of freedom to integrate the renewable energy system to enhance the penetration of total utility power. However, restrictive grid codes require that renewable sources connected to the grid must support stability of the grid under grid faults. Conventional synchronously rotating frame dq current controllers are insufficient under grid faults due to the low bandwidth of proportional-integral (PI) controllers. Hence, this work proposes a proportional current controller with a first-order low-pass filter disturbance observer (DOb). The proposed controller establishes independent control on positive, as well as negative, sequence current components under asymmetrical grid voltage conditions. The approach is independent of parametric component values, as it estimates nonlinear feed-forward terms with the low-pass filter DOb. A numerical simulation model of the overall power system was implemented in a MATLAB/Simulink (2014B, MathWorks, Natick, MA, USA). Further, particular results show that double-frequency active power oscillations are suppressed by injecting appropriate negative-sequence currents. Moreover, a set of simulation results provided in the article matches the developed theoretical background for its feasibility.

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Fault detection methods for frequency converters fed induction machines

Cited by 2 publications

References 12 publications

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

Control Strategy for a Grid-Connected Inverter under Unbalanced Network Conditions—A Disturbance Observer-Based Decoupled Current Approach

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