TY - GEN A1 - Würfel, Hans A1 - Lorenz-Meyer, Nicolai A1 - Schiffer, Johannes T1 - Experimentally Validated Reduced-Order Models for Grid-Connected Inverters Using Balanced Residualization T2 - 2023 8th IEEE Workshop on the Electronic Grid (eGRID), Karlsruhe, Germany, 16-18 October 2023 Y1 - 2023 SN - 979-8-3503-2700-7 U6 - https://doi.org/10.1109/eGrid58358.2023.10380931 CY - Karlsruhe ER - TY - GEN A1 - Lorenz-Meyer, Nicolai A1 - Würfel, Hans A1 - Schiffer, Johannes T1 - Consensus + Innovations approach for online distributed multi-area inertia estimation T2 - 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) N2 - The reduction of overall system inertia in modern power systems due to the increasing deployment of distributed energy resources is generally recognized as a major issue for system stability. Consequently, real-time monitoring of system inertia is critical to ensure a reliable and cost-effective system operation. Large-scale power systems are typically managed by multiple transmission system operators, making it difficult to have a central entity with access to global measurement data, which is usually required for estimating the overall system inertia. We address this problem by proposing a fully distributed inertia estimation algorithm with rigorous analytical convergence guarantees. This method requires only peer-to-peer sharing of local parameter estimates between neighboring control areas, eliminating the need for a centralized collection of real-time measurements. We robustify the algorithm in the presence of typical power system disturbances and demonstrate its performance in simulations based on the well-known New England IEEE 39-bus system. Y1 - 2024 U6 - https://doi.org/10.1109/ISGTEUROPE62998.2024.10863379 SP - 1 EP - 6 PB - IEEE CY - New York ER - TY - GEN A1 - Lorenz-Meyer, Nicolai A1 - Suchantke, René A1 - Schiffer, Johannes T1 - Dynamic state and parameter estimation in multi-machine power systems—Experimental demonstration using real-world PMU-measurements T2 - Control Engineering Practice N2 - 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 extra-high 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. This mapping considers the high-voltage transmission line, the tap-changing 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. Y1 - 2023 U6 - https://doi.org/10.1016/j.conengprac.2023.105491 SN - 0967-0661 VL - 135 ER - TY - GEN A1 - Lorenz-Meyer, Nicolai A1 - Rueda-Escobedo, Juan G. A1 - Moreno, Jaime A. A1 - Schiffer, Johannes T1 - A robust consensus + innovations-based distributed parameter estimator T2 - IEEE transactions on automatic control N2 - While distributed parameter estimation has been extensively studied in the literature, little has been achieved in terms of robust analysis and tuning methods in the presence of disturbances. However, disturbances such as measurement noise and model mismatches occur in any real-world setting. Therefore, providing tuning methods with specific robustness guarantees would greatly benefit the practical application. To address these issues, we recast the error dynamics of a continuous-time version of the widely used consensus + innovations-based distributed parameter estimator to reflect the error dynamics induced by the classical gradient descent algorithm. This paves the way for the construction of a strong Lyapunov function. Based on this result, we derive linear matrix inequality-based tools for tuning the algorithm gains such that a guaranteed upper bound on the L2-gain with respect to parameter variations, measurement noise, and disturbances in the communication channels is achieved. An application example illustrates the efficiency of the method. KW - Distributed parameter estimation KW - Sensor networks KW - System identification KW - Time-varying systems Y1 - 2025 U6 - https://doi.org/10.1109/TAC.2025.3597563 SN - 0018-9286 SN - 1558-2523 SP - 1 EP - 15 PB - IEEE CY - New York, NY ER - TY - GEN A1 - Lorenz-Meyer, Nicolai A1 - Bobtsov, Alexey A1 - Ortega, Romeo A1 - Nikolaev, Nikolay A1 - Schiffer, Johannes T1 - PMU-Based Decentralized Mixed Algebraic and Dynamic State Observation in Multi-Machine Power Systems T2 - IET Generation, Transmission & Distribution N2 - We propose a novel decentralized mixed algebraic and dynamic state observation method for multi-machine power systems with unknown inputs and equipped with Phasor Measurement Units (PMUs). More specifically, we prove that for the third-order flux-decay model of a synchronous generator, the local PMU measurements give enough information to reconstruct algebraically the load angle and the 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, we prove that the relative shaft speed can be globally estimated combining a classical Immersion and Invariance (I&I) observer with—the recently introduced—dynamic regressor and mixing (DREM) parameter estimator. This adaptive observer ensures global convergence under weak excitation assumptions that are verified in applications. The proposed method does not require the measurement of exogenous inputs signals such as the field voltage and the mechanical torque nor the knowledge of mechanical subsystem parameters. Y1 - 2020 UR - https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-gtd.2020.1275 U6 - https://doi.org/10.1049/iet-gtd.2020.1275 SN - 1751-8695 SN - 1751-8687 VL - 14 IS - 25 SP - 6267 EP - 6275 ER - TY - GEN A1 - Bobtsov, Alexey A1 - Ortega, Romeo A1 - Nikolaev, Nikolay A1 - Lorenz-Meyer, Nicolai A1 - Schiffer, Johannes T1 - State Observation of Power Systems Equipped with Phasor Measurement Units: The Case of Fourth Order Flux-Decay Model T2 - IEEE Transactions on Automatic Control N2 - The problem of effective use of phasor measurement units (PMUs) to enhance power systems awareness and security is a topic of key interest. The central question to solve is how to use these new measurements to reconstruct the state of the system. In this article, we provide the first solution to the problem of (globally convergent) state estimation of multimachine power systems equipped with PMUs and described by the fourth-order flux-decay model. This article is a significant extension of our previous result, where this problem was solved for the simpler third-order model, for which it is possible to recover algebraically part of the unknown state. Unfortunately, this property is lost in the more accurate fourth-order model, and we are confronted with the problem of estimating the full state vector. The design of the observer relies on two recent developments proposed by the authors, a parameter estimation based approach to the problem of state estimation and the use of the dynamic regressor extension and mixing (DREM) technique to estimate these parameters. The use of DREM allows us to overcome the problem of lack of persistent excitation that stymies the application of standard parameter estimation designs. Simulation results illustrate the latter fact and show the improved performance of the proposed observer with respect to a locally stable gradient-descent-based observer. Y1 - 2022 UR - https://doi.org/10.1109/TAC.2021.3073887 U6 - https://doi.org/10.1109/TAC.2021.3073887 SN - 0018-9286 SN - 1558-2523 VL - 67 IS - 4 SP - 2123 EP - 2130 ER -