Abstract
This article proposes a high-frequency injection based dq -inductance estimation technique for synchronous machines. The d- and q -axis inductance is estimated using a single HFI between d and q axes, i.e., 45 $^\circ$ injection angle. The d - and q -axis incremental inductance variation over four quadrants dq -plane operating condition is evaluated. The proposed technique can operate in real-time without a controlled position or velocity from the load side. Neither previous knowledge of machine parameters nor computationally expensive regression processes are required. The proposed technique can be used to evaluate the sensitivity of inductive saliency based self-sensing to determine preferable self-sensing operating conditions for permanent magnet synchronous machines.
Highlights
Accurate estimation of the inductance in permanent magnet synchronous machines (PMSMs) is critical for estimating the machine states, e.g. back-EMF [1], [2], High frequency (HF) current [3], [4], flux [5], [6], torque [7], and PM temperature [8], [9]
This paper focuses on dq-inductance estimation and inductive saliency based self-sensing condition monitoring using high-frequency injection (HFI)
We present the experimental results of the proposed incremental inductance estimation technique
Summary
Accurate estimation of the inductance in permanent magnet synchronous machines (PMSMs) is critical for estimating the machine states, e.g. back-EMF [1], [2], High frequency (HF) current [3], [4], flux [5], [6], torque [7], and PM temperature [8], [9]. MRAS techniques are closed-loop processes, which require an error vector formed from the output of two models, both dependent on different motor parameters; the target parameter estimation accuracy, in this case, inductance, depending on machine parameters accuracy (e.g., resistance, back-EMF, dq-transform induced coupled voltage). For these reasons, MRAS cannot adjust the model fast enough in dynamic load conditions [22].
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