FG Regelungssysteme und Netzleittechnik
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Borehole thermal energy storage (BTES) systems are critical components in the decarbonization of district heating networks as they enhance operational flexibility by seasonally storing thermal energy. While various modeling approaches for BTES exist, they are typically unsuitable for optimization problems and model-based control design. In this work, we propose a novel modeling approach that leads to a physics-based neural network surrogate model of the BTES temperature dynamics, capturing the fundamental dynamics with sufficient accuracy, while maintaining a relatively low complexity that makes it suitable for deployment in operational optimization or control algorithms. Specifically, we examine a standard BTES system that combines multiple heat sources, heat pumps, and storage buffers, detailing the associated mass flows and temperatures. We utilize a Python-based operational optimization process for the theoretical system setup using Pyomo and demonstrate that our modeling approach enables accurate optimizations over planning horizons of up to one year with a sample time of one hour. The new modeling approach significantly improves prediction accuracy across the relevant system states, with mean absolute errors reduced by approximately one-third compared to a single-capacitance model identified with the sparse identification framework SINDy.
The Kuramoto model is essential for studying synchronization. In this work, we present sufficient conditions for global synchronization in networks of heterogeneous Kuramoto oscillators in the absence of homoclinic and heteroclinic cycles. The result is established by constructing a suitable Leonov function candidate for the Kuramoto model, which provides sufficient conditions for almost global synchronization in networks with acyclic and meshed topologies. The synchronization property is accompanied by necessary and sufficient conditions to guarantee the existence of equilibria, which are satisfied if the conditions for synchronization hold. The implications of the main conditions and their relationship with the network topology and parameters are discussed. Finally, the results are illustrated via a numerical example.
This paper continues with the development of the input‐to‐state stability (ISS)‐control Leonov function (CLeF) approach. The definitions of practical ISS and integral ISS (iISS)–CLeFs are refined, and the proposed methodology for control synthesis is improved to simplify the final control law. Then, it is shown that the existence of practical ISS‐ and iISS–CLeFs is a sufficient condition to guarantee the existence of a controller that endows multistable state periodic systems with the ISS and iISS properties, respectively. Furthermore, a methodology for the design of such a controller is provided via the well‐known Sontag's universal formula. Besides, an extension of the main result is presented to connect the ISS–CLeF approach with the standard Leonov function method such that the maximal invariant set of the closed‐loop system is compact on a manifold. Finally, the proposed approach is applied to the design of an excitation controller for a synchronous generator, which guarantees global ISS properties for the closed‐loop system, unlike the usual local results reported in the literature. The obtained control is also independent of the load angle. The effectiveness of the designed controller is demonstrated in simulations.
In this work we present a cost-optimal energy management scheme for sector-coupled energy plants, focusing on systems that enable gas-to-electricity, gas-to-heat, and power-to-heat conversions. To capture realistic operational challenges, the considered system model incorporates dynamic constraints, including start-up delays and off-time-dependent start-up costs. By accounting for the impact of these factors, we demonstrate on a case study that they can significantly reduce overall operational costs. The energy management problem is cast as a mixed-integer nonlinear programming (MINLP) problem, which is then solved within a model predictive control (MPC) framework. The proposed approach provides a structured methodology for real-time energy management, enhancing energy efficiency while minimizing costs.
District heating networks (DHNs) are crucial for decarbonizing the heating sector. Yet, their efficient and reliable operation requires the coordination of multiple heat producers and the consideration of future demands. Predictive and optimization-based control is commonly used to address this task, but existing results for DHNs do not account for time-varying problem aspects. Since the turnpike phenomenon can serve as a basis for model predictive control design and analysis, this letter examines its role in DHN optimization by analyzing the underlying optimal control problem with time-varying prices and demands. That is, we derive conditions for the existence of a unique time-varying singular arc, which constitutes the time varying turnpike, and we provide its closed-form expression. Additionally, we present converse turnpike results showing a exact time-varying case implies strict dissipativity of the optimal control problem. A numerical example illustrates our findings.
The increasing integration of renewable energy sources (RESs) into power systems requires the deployment of grid-forming inverters to ensure a stable operation. Accurate modeling of these devices is necessary. In this paper, a system identification approach to obtain low-dimensional models of gridforming inverters is presented. The proposed approach is based on a Hammerstein-Wiener parametrization of the normal-form model. The normal-form is a gray-box model that utilizes complex frequency and phase to capture non-linear inverter dynamics. The model is validated on two well-known control strategies: droop-control and dispatchable virtual oscillators. Simulations and hardware-in-the-loop experiments demonstrate that the normalform accurately models inverter dynamics across various operating conditions. The approach shows great potential for enhancing the modeling of RES-dominated power systems, especially when component models are unavailable or computationally expensive.
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.
Systematic and robust tuning of proportional-resonant controllers for current and voltage tracking
(2025)
In the control and operation of power inverters, proportional-resonant (PR) controllers are used to track references and reject disturbances that can be described by a sum of sinusoidal signals of known frequency. The relevance of these controllers is increasing due to the increase in distortion and volatility of three-phase signals in the grid. However, as reported in the literature, the tuning of PR controllers is far from trivial due to their large number of parameters and the presence of pure imaginary poles in the associated transfer function. To address these issues, a time-domain framework based on linear matrix inequalities (LMIs) is presented for the tuning of PR controllers with applications to voltage and current tracking. The advantage of this approach is the straightforward combination with other techniques such as H∞ control. The effectiveness of the approach is illustrated through numerical simulations, where the injection of a constant active power is achieved in the presence of distorted voltages.
In this article, we address the problem of online detection of interturn short-circuit faults (ITSCFs) that occur in interior- and surface-mounted permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: 1)a very simple linear observer and 2) a generalized parameter estimation-based observer, that incorporates a high performance estimator—with both observers detecting the short-circuit current and the fault intensity. Although the first solution guarantees the detection of the fault exponentially fast, the rate of convergence is fully determined by the motor parameters that, in some cases, may be too slow. The second observer, on the other hand, ensures finite convergence time (FCT) under the weakest assumption of interval excitation (IE). To make the observers adaptive, we develop a parameter estimator that, in the case of surface-mounted motors, estimates online (exponentially fast) the resistance and inductance of the motor. It should be underscored that, in contrast with existing observers (including the widely popular Kalman filter) that provide indirect information of the fault current, our observers provide an explicit one—namely the amplitude of the fault current. An additional advantage of the observers is that they do not require the knowledge of the motor currents, making them insensitive to current measurement noise. The performance of both observers, in their linear and generalized parameter estimation-based versions, is illustrated with realistic simulation studies.
Collision avoidance is a problem largely studied in robotics, particularly in uncrewed aerial vehicle (UAV) applications. The main challenges in this area are hardware limitations, the need for rapid response, and the uncertainty associated with obstacle detection. Artificial potential functions (APOFs) are a prominent method to address these challenges. However, existing solutions lack assurances regarding closed-loop stability and may result in chattering effects. Hence, we propose a high-level control method for static obstacle avoidance based on multiple artificial potential functions (MAPOFs), with a set of switching rules with conditions on the parameter tuning ensuring the stability of the final position. The stability proof is established by analyzing the closed-loop system using tools from hybrid systems theory. Furthermore, we validate the performance of the MAPOF control through simulations and real-life experiments, showcasing its effectiveness in avoiding static obstacles.