In this article, a Kalman Filter-based disturbance observer is proposed for treating the fault detection and isolation problem of the VSC-HVDC transmission system. By proving differential flatness properties for the state-space model of the VSC-HVDC system which consists of an AC/DC converter-(rectifier), a high-voltage DC transmission line and a DC/AC inverter, it is confirmed that this system can be transformed into the input-output linearised form and in the canonical Brunovsky form. The effects of model uncertainty and external perturbations are modelled as additive disturbance inputs. Moreover, by considering the disturbance inputs as additional state variables, an extended state-space description is obtained, which is observable. For the latter state-space model, a Kalman Filter-based disturbance observer is designed. This is capable of identifying simultaneously the non-measurable state variables and the disturbance terms. Next, the residuals of the Kalman Filter undergo statistical signal processing, which finally enables fault detection and isolation. The sum of the squares of the residuals' sequence weighted by the inverse of the associated covariance matrix is a stochastic variable or ‘statistical test’, which follows the (Formula presented.) distribution. The confidence intervals of the (Formula presented.) distribution allow one to define fault thresholds.

Fault diagnosis for VSC-HVDC transmission systems using Kalman Filter-based disturbance observers

Siano, P.;
2026

Abstract

In this article, a Kalman Filter-based disturbance observer is proposed for treating the fault detection and isolation problem of the VSC-HVDC transmission system. By proving differential flatness properties for the state-space model of the VSC-HVDC system which consists of an AC/DC converter-(rectifier), a high-voltage DC transmission line and a DC/AC inverter, it is confirmed that this system can be transformed into the input-output linearised form and in the canonical Brunovsky form. The effects of model uncertainty and external perturbations are modelled as additive disturbance inputs. Moreover, by considering the disturbance inputs as additional state variables, an extended state-space description is obtained, which is observable. For the latter state-space model, a Kalman Filter-based disturbance observer is designed. This is capable of identifying simultaneously the non-measurable state variables and the disturbance terms. Next, the residuals of the Kalman Filter undergo statistical signal processing, which finally enables fault detection and isolation. The sum of the squares of the residuals' sequence weighted by the inverse of the associated covariance matrix is a stochastic variable or ‘statistical test’, which follows the (Formula presented.) distribution. The confidence intervals of the (Formula presented.) distribution allow one to define fault thresholds.
2026
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4937175
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