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Interturn Fault Detection in IPMSMs: Two Adaptive Observer-based Solutions

Romeo Ortega, Alexey Bobtsov, Leyan Fang, Oscar Texis-Loaiza, Johannes Schiffer

TL;DR

This work tackles online detection of inter-turn short-circuit faults in interior PM synchronous motors by developing two observer-based solutions that exploit the ITSCF structure to yield an explicit fault current estimate. The first is a simple linear observer with exponential convergence, while the second is a generalized parameter estimation-based observer (GPEBO) that achieves finite convergence time under interval excitation. For isotropic PMSMs, the authors further introduce a parameter-estimation framework (LS+DREM) to adaptively estimate $R_s$ and $L$, enabling fully adaptive fault detection. Together, the approaches provide real-time fault detection with explicit fault-current information and robust adaptive capability, validated by realistic simulations. The work offers a practical path toward reliable ITSCF monitoring in IPMSMs and informs future comparisons with Kalman-filter-based methods and experimental validation.

Abstract

In this paper we address the problem of online detection of inter-turn short-circuit faults (ITSCFs) that occur in permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: (i) a very simple linear observer and (ii) a generalized parameter estimation based observer, that incorporates a high performance estimator -- with both observers detecting the short-circuit current and the fault intensity. Although the first solution guarantees the detection of the fault exponentially fast, the rate of convergence is fully determined by the motor parameters that, in some cases, may be too slow. The second observer, on the other hand, ensures finite convergence time under the weakest assumption of interval excitation. To make the observers adaptive, we develop a parameter estimator that, in the case of isotropic PMSMs, estimates on-line (exponentially fast) the resistance and inductance of the motor. It should be underscored that, in contrast with existing observers (including the widely popular Kalman filter) that provide indirect information of the fault current, our observers provide explicit one -- namely the amplitude of the fault current. The performance of both observers, in their linear and generalized parameter estimation-based versions, is illustrated with realistic simulation studies.

Interturn Fault Detection in IPMSMs: Two Adaptive Observer-based Solutions

TL;DR

This work tackles online detection of inter-turn short-circuit faults in interior PM synchronous motors by developing two observer-based solutions that exploit the ITSCF structure to yield an explicit fault current estimate. The first is a simple linear observer with exponential convergence, while the second is a generalized parameter estimation-based observer (GPEBO) that achieves finite convergence time under interval excitation. For isotropic PMSMs, the authors further introduce a parameter-estimation framework (LS+DREM) to adaptively estimate and , enabling fully adaptive fault detection. Together, the approaches provide real-time fault detection with explicit fault-current information and robust adaptive capability, validated by realistic simulations. The work offers a practical path toward reliable ITSCF monitoring in IPMSMs and informs future comparisons with Kalman-filter-based methods and experimental validation.

Abstract

In this paper we address the problem of online detection of inter-turn short-circuit faults (ITSCFs) that occur in permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: (i) a very simple linear observer and (ii) a generalized parameter estimation based observer, that incorporates a high performance estimator -- with both observers detecting the short-circuit current and the fault intensity. Although the first solution guarantees the detection of the fault exponentially fast, the rate of convergence is fully determined by the motor parameters that, in some cases, may be too slow. The second observer, on the other hand, ensures finite convergence time under the weakest assumption of interval excitation. To make the observers adaptive, we develop a parameter estimator that, in the case of isotropic PMSMs, estimates on-line (exponentially fast) the resistance and inductance of the motor. It should be underscored that, in contrast with existing observers (including the widely popular Kalman filter) that provide indirect information of the fault current, our observers provide explicit one -- namely the amplitude of the fault current. The performance of both observers, in their linear and generalized parameter estimation-based versions, is illustrated with realistic simulation studies.

Paper Structure

This paper contains 16 sections, 7 theorems, 65 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Fault Model and Problem Formulation
  3. Model of the faulty motor
  4. Fault detection procedure
  5. A Linear Observer-based Solution
  6. A Generalized Parameter Estimation-based Observer Solution
  7. Proposed GPEBO
  8. Discussion
  9. Estimation of $R_s$ and $L_d(=L_q)$ for Isotropic PMSMs
  10. Simulation Results
  11. Concluding Remarks
  12. List of Acronyms
  13. Mathematical Derivation of System (4)
  14. A Least Squares Estimator with Finite Convergence Time
  15. The LS+DREM Parameter Estimator of ORTROMARA
  16. ...and 1 more sections

Key Result

Lemma 1

Define the vector signal [ (i)] The signal $i_{\alpha\beta}(t) \in \mathbb{R}^2$ satisfies the differential equation where and $A(t)$ and $b(t)$ are known matrices given by [ (ii)] The following relation holds

Figures (6)

  • Figure 1: Inter-turn short circuit in phase A for a PMSM.
  • Figure 2: Comparison of the current $i_\alpha$ and its estimate $\hat{i}_\alpha$ using OLO and GPEBO
  • Figure 3: Comparison of the current $i_\beta$ and its estimate $\hat{i}_\beta$ using OLO and GPEBO
  • Figure 4: The norm of the estimated fault current $|\widehat{ i_{\alpha\beta}}(t)-i_{dq}(t)|$ and the norm of the actual fault current ${2 \eta\over 3} |i_f(t)|$.
  • Figure 5: The estimated value $\hat{\theta}_1$ and the actual one ${\theta}_1$ of GPEBO
  • ...and 1 more figures

Theorems & Definitions (10)

  • Remark 1
  • Lemma 1
  • Proposition 1
  • Proposition 2
  • Remark 2
  • Lemma 2
  • Proposition 3
  • Proposition 4
  • Definition 1
  • Proposition 5