Digital twin approach for on-load tap changers using data-driven dynamic model updating and optimization-based operating condition estimation

Abstract

On-load tap changers (OLTCs), which are found in power transformers, are mechanically operating components. The vibration signal of an OLTC can provide effective observed data for estimation of the mechanical state transition and a faulty operating condition. Data-driven methods (e.g., deep learning, machine learning) and physics-based methods (e.g., the finite element method, the lumped parameter model) for health-state estimation require sufficient prior knowledge – such as various observed data about fault states, modeling information (including geometry, material properties), and operating conditions – to build a valid digital twin approach. However, prior knowledge for various OLTCs and transformer models is hard to obtain. To begin to address the shortcomings of existing methods, this study proposes a digital twin approach for OLTCs using 1) pre-processing of the vibration signal, 2) data-driven dynamic model updating, and 3) optimization-based operating condition estimation. First, the time–frequency domain features are extracted from the reference signal using a minimum entropy deconvolution (MED) filter, to extract the impulsive vibration signal from OLTC operation. The initial operating conditions that arise from tap changing and diverter switching are assumed as impulsive force using extracted features. The dynamic model is driven by the numerical algorithm for subspace state-space system identification (N4SID), the reference signal, and the excitation impulse force. Next, the phase and magnitude modulation are updated to estimate uncertain operating condition and refine the dynamic model using optimization-based parameter tuning. Analysis results from the proposed digital twin approach are demonstrated for both a numerical and experimental example to verify the effectiveness of the proposed approach. In the numerical case study, i) the simplified physics-based modelling and ii) the joint-input state estimation method were compared with the proposed method. In the experimental case study, the proposed method was applied to an OLTC vibration signal of both inactive and active power transformers. The inflection points in the Dynamic Resistance Measurement (DRM) graph show synchronization with the experimental results of the estimated excitation forces derived using the proposed method.