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BTU
The availability of excitation controllers to enhance transient stability has regained significant relevance in recent years, due to the unprecedented ongoing changes in power systems. Yet, the practical deployment of many reported control schemes is hampered by the fact that their implementation requires the measurement of the full state vector. Our main contribution is to address this fundamental obstacle by proposing an observer-based excitation controller using modern phasor measurement technology. For this purpose, a linear time-varying observer scheme for the generator frequency and the internal voltage is derived. This observer is then combined with a classical passivity-based excitation controller. Stability of the resulting nonlinear observer-based closed-loop system is shown by deriving an ISS-based separation principle. The performance of the proposed approach is demonstrated via simulation example.
Due to its simplicity and inexpensive computation, the gradient descent algorithm is one of the most used tools in adaptive control and system identification. Although it has been studied for decades, little has been achieved in terms of tuning methods in the presence of disturbances. One of the main difficulties in its analysis is the time-varying nature of the algorithm. In this work, we contribute in such direction by providing LMI tools for tuning the gradient descent algorithm gain such that a guaranteed upper bound on the L2 -gain with respect to parameter variations and measurement noise is achieved. Two academic examples are provided to illustrate the efficient application of the method.
The increasing ease of obtaining and processing data together with the growth in system complexity has sparked the interest in moving from conventional model-based control design toward data-driven concepts. Since in many engineering applications time delays naturally arise and are often a source of instability, we contribute to the data-driven control field by introducing data-based formulas for state feedback control design in linear discrete-time time-delay systems with uncertain delays. With the proposed approach, the problems of system stabilization as well as of guaranteed cost and H∞ control design are treated in a unified manner. Extensions to determine the system delays and to ensure robustness in the event of noisy data are also provided
In this work, an observer for a linear time-varying system with delayed measurements is developed. The delay is assumed to be unknown, bounded, and it can be time-varying with no restriction on its rate of change. The observer uses auxiliary signals related to the constructibility Gramian of the system and it contains nonlinearities that provide a uniform fixed-time convergence to a bounded region in the estimation error coordinates. This means that the convergence time can be bounded by a positive constant which is independent from the initial conditions and the initial time. This property is new for the addressed class of systems. The ultimate bound of the estimation error depends on the maximum difference between the nominal output and the delayed one, and not directly on the delay size or its time derivative. These properties are illustrated in a numerical simulation.
Strong Lyapunov functions for two classical problems in adaptive control and parameter identification are presented. These Lyapunov functions incorporate in their structure the classical persistency of excitation conditions, allowing to show global uniform asymptotic stability of the associated adaptive systems under sufficient and necessary conditions.
The increase in system complexity paired with a growing availability of operational data has motivated a change in the traditional control design paradigm. Instead of modeling the system by first principles and then proceeding with a (model-based) control design, the data-driven control paradigm proposes to directly characterize the controller from data. By exploiting a fundamental result of Willems and collaborators, this approach has been successfully applied to linear systems, yielding data-based formulas for many classical linear controllers. In the present paper, the data-driven approach is extended to a class of nonlinear systems, namely second-order discrete Volterra systems. Two main contributions are made for this class of systems. At first, we show that - under a necessary and sufficient condition on the input data excitation - a data-based system representation can be derived from input-output data and used to replace an explicit system model. That is, the fundamental result of Willems et al.is extended to this class of systems. Subsequently a data-driven internal model control formula for output-tracking is derived. The approach is illustrated via two simulation examples
The worldwide transition to climate-friendly energy systems entails the substitution of conventional energy generation based on synchronous generators by renewable energy sources based on power electronics. As a consequence, the overall inertia of the grid decreases, resulting in high volatility of the frequency and posing new challenges for the estimation of the latter quantity. To address this problematic, in the present paper a phase-locked-loop (PLL) based on the super-twisting algorithm is considered for the estimation of the phase angle and time-varying frequency of a symmetric three-phase signal. A rigorous proof of the algorithm’s exact convergence in the presence of a fast-varying frequency together with tuning rules for its gains are derived by means of Lyapunov theory. Additionally, an estimate of the region of attraction is provided. The effectiveness of the proposed tuning method is illustrated in numerical simulations, while comparing its performance against a standard synchronous reference frame PLL.
Phase-locked loop (PLL) implementations are critical components for the control and operation of grid-connected converters. Hence, they have to exhibit a highly reliable behavior under a wide range of operating conditions. Available implementations and performance analyses mainly focus on the impact of unbalances and harmonics. However, in converter-dominated low-inertia power systems an additional important type of perturbation will arise from fast variations in the grid frequency. Motivated by this, we show that the structure of several popular PLL implementations is closely related to that of high-gain observers and, by using this framework, provide a tuning criterion for the PLL gains that mitigates the impact of the rate of change of the frequency (RoCoF) on the estimation performance. This criterion is then used to conduct a numerical comparison of four popular PLL implementations under three distorted conditions: unbalances, harmonics and frequency variations.
Interior permanent magnet synchronous machines (IPMSMs) are well-suited for high-performance applications, such as traction drives in hybrid and electric vehicles. Yet a major challenge to fully exploit their potential is the fact that their self and cross-coupling inductances vary significantly across the operation range. In addition, this variation is difficult to characterize and complicates the design of provably stabilizing and robust controls. Motivated by this, by using an IPMSM model with current dependant inductances together with the internal model principle, a nonlinear current control scheme is derived that renders the equilibrium point of the closed-loop system exponentially stable. Both the control and the stability result only require the knowledge of an upper bound of the gradient of the inductances as well as lower and upper bounds on the inductance values themselves, while their actual evolution can be completely unknown. This is a major advantage compared to existing (PI-based) current control approaches, as it makes costly practices to determine the inductance variations unnecessary. The efficacy of the proposed control scheme is demonstrated in a simulation example.
Robust State and Input Estimation with Enhanced Convergence Rate for Monitoring Anaerobic Digestion
(2022)
In this study, an observer for estimating the state and unknown inputs is proposed for monitoring anaerobic digestion processes. This estimator is based on a dynamic model considering acidogenesis and methanogenesis, and consists of three sub-observers: (a) a gramian-based fixed-time convergent observer for the inlet chemical oxygen demand (COD) and the acidogenic bacteria population, (b) an asymptotic observer for the methanogenic bacteria population, and (c) a super-twisting observer for systems with time-varying parameters to estimate the inlet volatile fatty acid (VFA) concentration. These sub-observers can be designed independently, which greatly simplifies the tuning process. Proofs of convergence are developed and simulation tests show the performance of the estimation scheme as compared to classical extended Kalman filtering.