A Modular Measurement Integrity Verification for Transformer Currents with Applications in Hardware in-the-Loop Digital Twin

Abstract: This paper proposes a recursive method for integrity verification of measured transformer currents, which is suitable for the modular development of Hardware-In-the-Loop Digital Twins (HIL DTs). The Differential Equations (DEs) describing transformer transients are relatively complex, requiring the use of numerical DE solvers with small time steps. This implies that replicating transformers with HIL DTs requires a continuous flow of accurate measurement samples with a high sampling rate. The proposed method utilizes the Adaptive Extended Kalman Filter to estimate the parameters of transformer currents which can be non-sinusoidal during transients such as transformer energization. Then, after evaluating the validity of estimations, the proposed method utilizes the estimated parameters to reconstruct noiseless measurements with desirable sampling rates. The performance of the proposed method is evaluated using EMTP-RV for simulating transformers and Matlab for executing the proposed method. The simulation results demonstrate that the method is able to accurately estimate and closely track the current measurements.

• The paper presents a modular method using an Adaptive Extended Kalman Filter (AEKF) to verify and enhance the integrity of transformer current measurements in Hardware-in-the-Loop (HIL) Digital Twins.
• The methodology involves a recursive framework that estimates parameters and dynamically validates measurements, adapting to noise and transient conditions while balancing computational limits.
• Validated through simulations, the method significantly reduces estimation errors, improving accuracy during transient states and enhancing digital twin reliability for grid monitoring and diagnostics.

Insights on Modular Measurement Integrity Verification for Transformer Currents

The discussed paper presents an innovative approach for ensuring measurement integrity in replicating transformer transients for Hardware-in-the-Loop Digital Twins (HIL DTs). The core of this research lies in addressing uncertainties and noise within the dielectric equations (DEs) that elaborate transformer dynamics, particularly during transients like energization. Given the need for highly accurate and high-frequency sampling in HIL DTs, the paper introduces a modular integrity verification method utilizing an Adaptive Extended Kalman Filter (AEKF).

Methodology and Execution

The method proposes a recursive evaluation framework that estimates transformer current parameters and dynamically validates the integrity of these measurements. Key to the application of these DEs is the need for small, frequent time steps due to their inherent complexity and the real-time accuracy demands of DTs.

The recursive method is anchored by the AEKF, which provides a robust filtering solution capable of dealing with non-sinusoidal transients and the non-linear nature of inrush currents. It adapts to mitigate measurement noise, offering a computationally effective solution that balances real-time evaluation needs with practical computational limits. The transformation to noiseless high-frequency data streams is crucial, especially when communication capabilities are constricted by bandwidth limitations.

Results and Simulated Evaluations

The efficacy of the method was validated using EMTP-RV for transformer simulations and Matlab for running the proposed estimations. The paper details tests on both single-phase and core-type three-phase transformers. The simulations appraise the proposed method's precision in estimating and reconstructing current measurements under various operational scenarios including no-load, inrush current during energization, and underload switching. The results establish the method’s capability to closely track transformer currents, even under conditions of high core saturation.

Numerical results demonstrate minimal estimation errors with Mean Squared Errors (MSEs) showing marked reduction when utilizing the proposed method, particularly enhancing measurement integrity during significant transient states. For example, a switching angle near 45° yields improved estimation accuracy, highlighting the interrelation between measurement strategies and their temporal characteristics on RT DT performance.

Implications and Future Considerations

This work significantly contributes to DT applications in electrical grid monitoring, making strides toward increased reliability and precision in transformer simulation and digital twin applications. The practical significance of this is notable in scenarios where accurate grid diagnostics and transformer health monitoring are fundamental, facilitating applications like fault detection, cyber security frameworks, and predictive maintenance.

Theoretical extensions of this work could explore the integration of more complex transformer models and differential equations, potentially guided by advancements in machine learning techniques to refine the reconstruction and resilience of real-time estimations against unforeseen anomalies. Considerations on the scalability of this method across varying grid architectures and different transformer designs are subjects for future exploration.

This method is a step forward in the modular development of digital twins, offering a framework that practitioners and researchers could build upon to tackle the multi-faceted challenges of transformer monitoring and diagnostics. The proposed adaptive approach sets a foundation for enhancing the robustness of DTs in increasingly complex and distributed electrical systems.