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Item type:Publication, Access status: without files , Approximations for optimal experimental design in power system parameter estimationThis paper is about computationally tractable methods for power system parameter estimation and Optimal Experiment Design (OED). The main motivation of OED is to increase the accuracy of power system parameter estimates for a given number of batches. One issue in OED, however, is that solving the OED problem for larger power grids turns out to be computationally expensive and, in many cases, computationally intractable. Therefore, the present paper proposes three numerical approximation techniques, which increase the computational tractability of OED for power systems. These approximation techniques are benchmarked on a 5-bus and a 14-bus case study.Publicationtype: Conference Paper4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Event-Triggered ℓ2-Optimal Formation Control with State-Estimation for Agents Modeled as LPV Systems(2022-12); ; This paper proposes a distributed scheme with different estimators for the event-triggered formation control of polytopic homogeneously scheduled linear parameter-varying (LPV) multi-agent systems (MAS). Each agent consists of a time-triggered inner feedback loop and a larger event-triggered outer feedback loop to track a formation reference signal and reject input and output noise. If a local event-trigger condition is violated, the event-triggered outer feedback loop is closed through the communication network. The event-trigger condition is only based on locally available information. To design the controller, a synthesis problem is formulated as a linear matrix inequality of the size of a single agent under the assumption, that local estimators trigger intercommunication events with neighboring agents if the event-trigger condition is violated. The design procedure guarantees stability and bounded l 2 - performance. Furthermore, the estimators are interchangeable for a given controller. We compare in simulation zero-order hold, open-loop estimation, and closed-loop estimation strategies. Simulation trials are carried out with non-holonomic dynamic unicycles modeled as polytopic LPV systems.Publicationtype: Conference Paper28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Distributed Linear-Quadratic Discrete-Time Game Approach to Multi-Agent Consensus(2022-12); In this paper we propose a distributed game theoretic approach to the multi-agent consensus problem, where we represent the consensus problem with multiple decision-makers as a Linear Quadratic Discrete-Time Game (LQDTG) over a connected communication graph. The players - taken here as double integrators - minimize individual finite-horizon cost functions (which depend on decisions by other players) and aim at reaching a Nash equilibrium. This involves a set of coupled Riccati difference equations, which cannot be solved in a distributed manner. Here we propose a distributed strategy for reaching a Nash equilibrium that is based on an associated multi-agent system evolving not on the vertices but on the edges of the underlying communication graph. This makes it possible to move the coupling between agents from the cost function to the agent dynamics, and to formulate an LQR problem with a decoupled cost that allows a distributed solution on the edges of the graph. Mapping this solution to local control inputs for the real agents (the nodes of the graph) requires however knowledge of the whole network. To arrive at a distributed control strategy, we propose an iterative, gradient-based construction of the individual control inputs that is based only on locally available information, but requires repeated exchange of information between neighboring agents within a single sampling interval. The resulting distributed strategy can be implemented in a receding horizon manner, and the optimal first-step state feedback gain matrices for the edge system can be calculated offline and stored by each agent. Consensus will be reached iff the edge system is stable under this state feedback law.Publicationtype: Conference Paper30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven Adaptive Model Predictive Control for Wind Farms: A Koopman-Based Online Learning Approach(2022-12); ; A novel adaptive Koopman based model predictive control (MPC) algorithm for wind farm control is presented. Using a data-driven Koopman approach the highly non-linear wake effects governing wind farm dynamics can be efficiently modelled. An update rule is presented to enable online learning only when new information is available. Moreover, to provide sufficient excitation of all relevant model frequencies in closed loop, a small test signal is superimposed on the control input while the Koopman model is updated. Simulation studies in the WFSim environment illustrate excellent accuracy for wind speed estimation with changing wind speed. In closed loop, the adaptive online update strategy tracks reference farm yield well, considerably outperforming recently presented non-adaptive schemes.Publicationtype: Conference Paper48 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Decentralized non-convex optimization via bi-level SQP and ADMMDecentralized non-convex optimization is important in many problems of practical relevance. Existing decentralized methods, however, typically either lack convergence guarantees for general non-convex problems, or they suffer from a high subproblem complexity. We present a novel bilevel SQP method, where the inner quadratic problems are solved via ADMM. A decentralized stopping criterion from inexact Newton methods allows the early termination of ADMM as an inner algorithm to improve computational efficiency. The method has local convergence guarantees for non-convex problems. Moreover, it only solves sequences of Quadratic Programs, whereas many existing algorithms solve sequences of Nonlinear Programs. The method shows competitive numerical performance for an optimal power flow problem.Publicationtype: Conference Paper17