Robust nonlinear adaptive backstepping controller design for power system applications including renewable energy systems

This thesis has developed robust nonlinear backstepping and adaptive backstepping controllers for power networks. Comprehensive control solutions are provided for conventional and modern power generation systems including DC microgrids. One of the excellent features of these controllers is the robustness against parameter uncertainties and measurement noises

A non‐linear adaptive excitation control scheme for feedback linearized synchronous generations in multimachine power systems

A new adaptive scheme is proposed in this paper to design excitation controllers for feedback linearized models of synchronous generators in multimachine power systems in order to ensure the stability during large disturbances. The proposed scheme uses speed deviations of synchronous generators, readily available measured physical properties of multimachine power systems, to make all generators within a power network as partially linearized as well as to provide more damping. An adaptive scheme is then used to estimate all unknown parameters which appear in the partial feedback linearizing excitation controllers in order to avoid parameter sensitivities of existing feedback linearization techniques. The overall stability of multimachine power systems is ensured through the excitation control and parameter adaptation laws. The Lyapunov stability theory is used to theoretically analyse the stability of multimachine power systems with the proposed scheme. Simulation studies are presented to evaluate the performance of the proposed excitation control scheme for two different test systems by different operating conditions including short-circuit faults on key locations along with variations in parameters for a large duration. Furthermore, comparative results are presented to highlight the superiority of the proposed adaptive partial feedback linearizing excitation control scheme over an existing partial feedback linearizing excitation controllers

Feedback linearizing model predictive excitation controller design for multimachine power systems

In this paper, a nonlinear excitation controller is designed for multimachine power systems in order to enhance the transient stability under different operating conditions. The two-axis models of synchronous generators in multimachine power systems along with the dynamics of IEEE Type & #x2013;II excitation systems, are considered to design the proposed controller. The partial feedback linearization scheme is used to simplify the multimachine power system as it allows to decouple a multimachine power system based on the excitation control inputs of synchronous generators. A receding horizon-based continuous-time model predictive control scheme is used for partially linearized power systems to obtain linear control inputs. Finally, the nonlinear control laws, which also include receding horizon-based control inputs, are implemented on an IEEE 10-machine, 39-bus New England power system. The superiority of the proposed scheme is evaluated by providing comparisons with a similar existing nonlinear excitation controller where the control input for the feedback linearized model is obtained using the linear quadratic regulator (LQR) approach. The simulation results demonstrate that the proposed scheme performs better as compared to the LQR-based partial feedback linearizing excitation controller in terms of enhancing the stability margin

Distributed Fault Diagnosis of Interconnected Nonlinear Uncertain Systems

Fault diagnosis is crucial in achieving safe and reliable operations of interconnected control systems. This dissertation presents a distributed fault detection and isolation (FDI) method for interconnected nonlinear uncertain systems. The contributions of this dissertation include the following: First, the detection and isolation problem of process faults in a class of interconnected input-output nonlinear uncertain systems is investigated. A novel fault detection and isolation scheme is devised, and the fault detectability and isolability conditions are rigorously investigated, characterizing the class of faults in each subsystem that are detectable and isolable by the proposed distributed FDI method. Second, a distributed sensor fault FDI scheme is developed in a class of interconnected input-output nonlinear systems where only the measurable part of state variables are directly affected by the interconnections between subsystems. A class of multimachine power systems is used as an application example to illustrate the effectiveness of the proposed approach. Third, the previous results are extended to a class of interconnected input-output nonlinear systems where both the unknown and the measurable part of system states of each subsystem are directly affected by the interconnections between subsystems. In this case, the fault propagation effect among subsystems directly affects the unknown part of state variables of each subsystem. Thus, the problem considered is more challenging than what is described above. Finally, a fault detection scheme is presented for a more general distributed nonlinear systems. With a removal of a restrictive limitation on the system model structure, the results described above are extended to a class of interconnected nonlinear uncertain systems with a more general structure. In addition, the effectiveness of the above fault diagnosis schemes is illustrated by using simulations of interconnected inverted pendulums mounted on carts and multi-machine power systems. Different fault scenarios are considered to verify the diagnosis performances, and the satisfactory performances of the proposed diagnosis scheme are validated by the good simulation results. Some interesting future research work is also discussed

Systems Structure and Control

The title of the book System, Structure and Control encompasses broad field of theory and applications of many different control approaches applied on different classes of dynamic systems. Output and state feedback control include among others robust control, optimal control or intelligent control methods such as fuzzy or neural network approach, dynamic systems are e.g. linear or nonlinear with or without time delay, fixed or uncertain, onedimensional or multidimensional. The applications cover all branches of human activities including any kind of industry, economics, biology, social sciences etc

A functional link network based adaptive power system stabilizer

An on-line identifier using Functional Link Network (FLN) and Pole-shift (PS) controller for power system stabilizer (PSS) application are presented in this thesis. To have the satisfactory performance of the PSS controller, over a wide range of operating conditions, it is desirable to adapt PSS parameters in real time. Artificial Neural Networks (ANNs) transform the inputs in a low-dimensional space to high-dimensional nonlinear hidden unit space and they have the ability to model the nonlinear characteristics of the power system. The ability of ANNs to learn makes them more suitable for use in adaptive control techniques. On-line identification obtains a mathematical model at each sampling period to track the dynamic behavior of the plant. The ANN identifier consisting of a Functional link Network (FLN) is used for identifying the model parameters. A FLN model eliminates the need of hidden layer while retaining the nonlinear mapping capability of the neural network by using enhanced inputs. This network may be conveniently used for function approximation with faster convergence rate and lesser computational load. The most commonly used Pole Assignment (PA) algorithm for adaptive control purposes assign the pole locations to fixed locations within the unit circle in the z-plane. It may not be optimum for different operating conditions. In this thesis, PS type of adaptive control algorithm is used. This algorithm, instead of assigning the closed-loop poles to fixed locations within the unit circle in the z-plane, this algorithm assumes that the pole characteristic polynomial of the closed-loop system has the same form as the pole characteristic of the open-loop system and shifts the open-loop poles radially towards the centre of the unit circle in the z-plane by a shifting factor α according to some rules. In this control algorithm, no coefficients need to be tuned manually, so manual parameter tuning (which is a drawback in conventional power system stabilizer) is minimized. The PS control algorithm uses the on-line updated ARMA parameters to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane. Simulation studies on a single-machine infinite bus and on a multi-machine power system for various operating condition changes, verify the effectiveness of the combined model of FLN identifier and PS control in damping the local and multi-mode oscillations occurring in the system. Simulation studies prove that the APSSs have significant benefits over conventional PSSs: performance improvement and no requirement for parameter tuning