Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems.
Bottom Line:
The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple.The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop.The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method.
View Article:
PubMed Central - PubMed
Affiliation: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China. gangder@csu.edu.cn.
ABSTRACT
This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in αβ coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method. No MeSH data available. Related in: MedlinePlus |
Related In:
Results -
Collection
License getmorefigures.php?uid=PMC4481899&req=5
Mentions: That is, the current sensor faults are built by adding abrupt offset current of amplitude fb = −30 to the phase ‘b’ current sensor at t = 0.1 s and slow-variation offset current of amplitude fa = −1.5e7t to the phase ‘a’ current sensor at t = 0.2 s. The value of the speed is changed from 200 rad/s to 300 rad/s at 0.3 s. The observation of extended flux linkage and the torque calculation value are shown in Figure 16 and Figure 17, respectively. The phase currents, the actual value and the observation of the stator α and β axis currents are shown in Figure 18, Figure 19 and Figure 20, respectively. The current residuals in phase ‘a’ and in phase ‘b’ are respectively shown in Figure 21 and Figure 22. As shown in the following figures, when the abrupt offset fault is imposed at t = 0.1 s, the amplitude of the extended flux linkage is almost the same since the offset value is relatively small, but it increases slowly after the slow-variation offset fault is imposed at t = 0.2 s. The constant offset fault produces an electromagnetic torque equiamplitude oscillation at t = 0.1‒0.2 s, but the amplitude of oscillation increases gradually after t = 0.2 s. The measured current in b-phase current sensor and the observation of β axis stator current is produced constant offset at t = 0.1‒0.2 s, and after t = 0.2 s the offset increases gradually. The measured current in a-phase current sensor and the observation of α axis stator current produce a slow-variation offset at t = 0.2 s. When the speed increases, the frequency of the current increases at 0.3 s. The residual ra mutates from zero to 6.0828 at t = 0.2 s and then gradually increases exponentially. The residual rb mutates to 30 at t = 0.1 s and then remains constant. This illustrates that the phase ‘a’ current sensor occurs slow-variation fault at t = 0.2 s and the phase ‘b’ current sensor occurs abrupt fault at t = 0.1 s. |
View Article: PubMed Central - PubMed
Affiliation: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China. gangder@csu.edu.cn.
No MeSH data available.