The authors propose a novel decentralised mixed algebraic and dynamic state observation method for multi‐machine power systems with unknown inputs and equipped with phasor measurement units (PMUs). More specifically, they prove that for the third‐order flux‐decay model of a synchronous generator, the local PMU measurements provide enough information to reconstruct algebraically the load angle and quadrature‐axis internal voltage. Due to the algebraic structure, a high numerical efficiency is achieved, which makes the method applicable to large‐scale power systems. Also, they prove that the relative shaft speed can be globally estimated combining a classical immersion and invariance observer with – the recently introduced – dynamic regressor extension and mixing parameter estimator. This adaptive observer ensures global convergence under weak excitation assumptions that are verified in applications. The proposed method neither requires the measurement of exogenous input signals such as the field voltage and the mechanical torque nor the knowledge of mechanical subsystem parameters.

No abstract

No abstract

Dynamic state and parameter estimation (DSE) plays a key role for reliably monitoring and operating future, power-electronics-dominated power systems. While DSE is a very active research field, experimental applications of proposed algorithms to real-world systems remain scarce. This motivates the present paper, in which we demonstrate the effectiveness of a DSE algorithm previously presented by parts of the authors with real-world data collected by a Phasor Measurement Unit (PMU) at a substation close to a power plant within the extrahigh voltage grid of Germany. To this end, at first we derive a suitable mapping of the real-world PMU-measurements recorded at a substation close to the power plant to the terminal bus of the power plants' synchronous generator (SG). This mapping considers the high-voltage (HV) transmission line, the tapchanging transformer and the auxiliary system of the power plant. Next, we introduce several practically motivated extensions to the estimation algorithm, which significantly improve its practical performance with real-world measurements. Finally, we successfully validate the algorithm experimentally in an auto-as well as a cross-validation.