Large-scale Interconnected Systems Research Articles

Observer-Based Decentralized Predictor Control for Large-Scale Interconnected Systems With Large Delays

This article proposes a decentralized predictor-based output feedback to compensate for large delays in large-scale interconnected systems under the premise that the interconnections between subsystems are not strong. The full state of each subsystem is assumed to be unmeasurable and the observer-based output feedback is employed. Two methods are used to tackle the large delays: the backstepping-based partial differential equation (PDE) approach and the reduction-based ordinary differential equation (ODE) approach. The PDE method is used for continuous-time control and manages with larger delays, whereas the ODE method is applicable to sampled-data implementation under discrete-time measurement.

Adaptive Fuzzy Decentralized Tracking Control for Large-Scale Interconnected Nonlinear Networked Control Systems

This article investigates the problem of adaptive fuzzy decentralized tracking control design for a class of large-scale interconnected nonlinear systems with network-induced input time delay. First, fuzzy logic systems are used to approximate the unknown nonlinear functions. Then, a newly defined filtered error is introduced to compensate the network-induced delay, and a novel adaptive fuzzy decentralized control strategy is proposed to guarantee the uniform ultimate boundedness of the closed-loop nonlinear system. Finally, a simulation example is given to further demonstrate the effectiveness of the proposed method.

Wide-area power oscillation damper for DFIG-based wind farm with communication delay and packet dropout compensation

Data dropouts and time delays introduced in communication network would degrade the damping performance of the wide-area power oscillation dampers (WPODs) or may even cause instability of the closed-loop system. This paper establishes a data transmission model in the network control system (NCS) and proposes a networked predictive control (NPC) based WPOD of doubly-fed induction generator (DFIG)-based wind farm to damp inter-area oscillations in a large-scale interconnected power system. The major advantage of the proposed WPOD is that it can actively compensate packet dropouts and communication delays involved in wide-area control channels. Additionally, online model identification is employed to handle the parameter uncertainties and operating condition variations of the power system. Case studies are undertaken on the 16-machine 68-bus power system. Simulation results show that the proposed WPOD can provide better damping performances than those of the conventional WPOD over a wide range of operating conditions and different communication delays and packet dropouts.

A Laguerre spectral method for quadratic optimal control of nonlinear systems in a semi-infinite interval

This paper presents a Laguerre homotopy method for quadratic optimal control problems in semi-infinite intervals (LaHOC), with particular interests given to nonlinear interconnected large-scale dynamic systems. In LaHOC, the spectral homotopy analysis method is used to derive an iterative solver for the nonlinear two-point boundary value problem derived from Pontryagin's maximum principle. A proof of local convergence of the LaHOC is provided. Numerical comparisons are made between the LaHOC, Matlab BVP5C generated results and results from the literature for two nonlinear optimal control problems. The results show that LaHOC is superior in both accuracy and efficiency.

Detection of Covert Cyber-Attacks in Interconnected Systems: A Distributed Model-Based Approach

Distributed detection of covert attacks for linear large-scale interconnected systems is addressed in this article. Existing results consider the problem in centralized settings. This article focuses on large-scale systems subject to bounded process and measurement disturbances, where a single subsystem is under a covert attack. A detection methodology is proposed, where each subsystem can detect the presence of covert attacks in neighboring subsystems in a distributed manner. The detection strategy is based on the design of two model-based observers for each subsystem using only local information. An extensive detectability analysis is provided and simulation results on a power network benchmark are given, showing the effectiveness of the proposed methodology for the detection of covert cyber-attacks.

Dynamic output feedback control for nonlinear large-scale interconnected systems

Purpose This paper aims to focus on the design of a decentralized observation and control method for a class of large-scale systems characterized by nonlinear interconnected functions that are assumed to be uncertain but quadratically bounded. Design/methodology/approach Sufficient conditions, under which the designed control scheme can achieve the asymptotic stabilization of the augmented system, are developed within the Lyapunov theory in the framework of linear matrix inequalities (LMIs). Findings The derived LMIs are formulated under the form of an optimization problem whose resolution allows the concurrent computation of the decentralized control and observation gains and the maximization of the nonlinearity coverage tolerated by the system without becoming unstable. The reliable performances of the designed control scheme, compared to a distinguished decentralized guaranteed cost control strategy issued from the literature, are demonstrated by numerical simulations on an extensive application of a three-generator infinite bus power system. Originality/value The developed optimization problem subject to LMI constraints is efficiently solved by a one-step procedure to analyze the asymptotic stability and to synthesize all the control and observation parameters. Therefore, such a procedure enables to cope with the conservatism and suboptimal solutions procreated by optimization problems based on iterative algorithms with multi-step procedures usually used in the problem of dynamic output feedback decentralized control of nonlinear interconnected systems.

Stability and Stabilization for T–S Fuzzy Large-Scale Interconnected Power System With Wind Farm via Sampled-Data Control

This article focuses on the stability and stabilization analysis of large-scale multiarea interconnected power systems (LSMAIPSs) involving the wind farm through the Lyapunov stability theory. Instead of linearizing the nonlinear model at a certain operating point the Takagi-Sugeno (T-S) fuzzy model is able to achieve better performance. In this article, a doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) are integrated into each area of the power system. The main reason behind the integration is because the power factor has the ability to destabilize the performance of the power supply. To ensure the stability of the LSMAIPS, a decentralized sampled-data feedback load frequency control is designed. The stability and stabilization conditions are derived through constructing suitable Lyapunov function which contains the sampling information and the solvable linear matrix inequalities (LMIs) along with an evaluation of H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance. To an evident, the simulation results are performed based on experimental values of two-area large-scale interconnected power system with DFIG-based wind farm, which guarantees the asymptotic stability of the proposed T-S fuzzy system under the sampled-data controller.

Adaptive Decentralized Output Feedback Tracking Control for Large-Scale Interconnected Systems with Time-Varying Output Constraints

This paper investigates a novel adaptive output feedback decentralized control scheme for nonstrict feedback large-scale interconnected systems with time-varying constraints. A decentralized linear state observer is designed to estimate the unmeasurable states of subsystems. Time-varying barrier Lyapunov functions are designed to ensure outputs are not violating constraints. A variable separation approach is applied to deal with the nonstrict feedback problem. Moreover, dynamic surface control and minimal parameter learning technologies are adopted to reduce the computation burden, and there are only two parameters for every subsystem to be updated online. The proof of stability is obtained by the Lyapunov method. Finally, simulation results are given to show the effectiveness of the proposed control scheme.

Observer-Based Adaptive Neural Networks Control for Large-Scale Interconnected Systems With Nonconstant Control Gains.

In this article, an adaptive neural network (NN) decentralized output-feedback control design is studied for the uncertain strict-feedback large-scale interconnected nonlinear systems with nonconstant virtual and control gains. NNs are utilized to approximate the unknown nonlinear functions, and the immeasurable states are estimated via designing an NN decentralized state observer. By constructing the logarithm Lyapunov functions, an observer-based NN adaptive decentralized backstepping output-feedback control is developed in the framework of the decentralized backstepping control. The proposed adaptive decentralized backstepping output-feedback control can make that the closed-loop system is semiglobally uniformly ultimately bounded (SGUUB) and that the tracking and observer errors converge to a small neighborhood of the origin. The most important contribution of this article is that it removes the restrictive assumption in the existing results that both virtual and control gain functions in each subsystem must be constants. A numerical simulation example is provided to validate the effectiveness of the proposed control method and theory.

Event-triggered distributed fault detection and control of multi-weighted and multi-delayed large-scale systems

This paper addresses the problem of distributed simultaneous fault detection and control (SFDC) of multi-weighted and multi-delayed (MWMD) large-scale interconnected systems which are subjected to event-triggered communication, nonlinear perturbations, measured output quantization, redundant channels, and stochastic deception attacks. The large-scale systems under consideration have multiple coupling links between neighboring subsystems, and all the links are considered to have different coupling weights and delays. A distributed fault detector and controller (FDC) module is designed to guarantee the exponential mean square stability of the overall closed-loop system along with a prescribed extended dissipative control performance and H∞ fault detection performance. The gain parameters of the distributed fault detector and controller module are determined by using the cone complementarity linearization (CCL) algorithm. In the end, a numerical example involving a continuous stirred tank reactor (CSTR) system is described to prove the validity of the presented results.

Distributed Fault-Tolerant Control of Large-Scale Systems: An Active Fault Diagnosis Approach

The paper proposes a methodology to effectively address the increasingly important problem of distributed fault-tolerant control for large-scale interconnected systems. The approach dealt with combines, in a holistic way, a distributed fault detection and isolation algorithm with a specific tube-based model predictive control scheme. A distributed fault-tolerant control strategy is illustrated to guarantee overall stability and constraint satisfaction even after the occurrence of a fault. In particular, each subsystem is controlled and monitored by a local unit. The fault diagnosis component consists of a passive set-based fault detection algorithm and an active fault isolation one, yielding fault-isolability subject to local input and state constraints. The distributed active fault isolation module–thanks to a modification of the local inputs–allows to isolate the fault that has occurred, avoiding the usual drawback of controllers that possibly hide the effect of the faults. The Active Fault Isolation method is used as a decision support tool for the fault-tolerant control strategy after fault detection. The distributed design of the tube-based model predictive control allows the possible disconnection of faulty subsystems or the reconfiguration of local controllers after fault isolation. Simulation results on a well-known power network benchmark show the effectiveness of the proposed methodology.

Echo State Network-Based Decentralized Control of Continuous-Time Nonlinear Large-Scale Interconnected Systems

This article addresses the decentralized control problem of continuous-time nonlinear large-scale interconnected systems by the means of echo state network (ESN). The interconnected terms between the subsystems are treated as the disturbances added to the system dynamics, then the control problem is solved by the proposed robust controllers. By designing a cost function for the nominal subsystems, the robust controller is obtained by solving optimal controllers. The stability of the interconnected systems is proved by a composite Lyapunov function. The online adaptive dynamic program (ADP) method is employed to solve the transformed optimal problem, where a single ESN is utilized to approximate the critic cost and control policy. The optimal control policies of all the isolated subsystems are obtained simultaneously. The closed-loop stability of the feedback nominal systems is also proved, in a uniformly ultimately boundedness (UUB) manner. In the simulation, a large-scale interconnected system with four subsystems is provided to verify the effectiveness of the proposed decentralized controllers.

Decentralized observer-based optimal control using two-point boundary value successive approximation approach application for a multimachine power system

In this paper, we present a new decentralized observer for a class of nonlinear interconnected systems, based on an optimal control law using the two-point boundary value (TPBV) successive approximation approach. This technique, used previously to develop a nonlinear decentralized suboptimal control for a multimachine power system, inspired us to develop a new method to design a decentralized observer-based suboptimal control law for the same system. The TPBV approach is characterized by the transformation of each high order coupling nonlinear TPBV problem into a sequence of linear decoupling TPBV problems that uniformly converge to the optimal control for nonlinear interconnected large-scale systems. Sufficient conditions for the asymptotic stability of the developed feedback control scheme are proposed in terms of linear matrix inequalities (LMIs) constraints, which can be efficiently solved by the LMI optimization techniques, to compute the control and the observation gains of the overall system. We applied this approach to a three-machine power system which generators are nonlinear systems strongly interconnected. We demonstrated clearly, via advanced simulations, that this approach was able to bring good performances, improving effectively transient stability of this power system in few iterative sequences while allowing the reconstruction of its non-measurable state variables.

Functional reduced order H∞ decentralized observer based control for large scale interconnected nonlinear stochastic systems

In this work, the H∞ decentralized reduced order observer based control for a class of large scale nonlinear stochastic systems is concerned. In this context we consider subsystems which are interconnected by some nonlinear interconnections under quadratic boundedness and Lipschitz property of the system. The proposed control law is based on the resolution of some LMI.

On detectability of cyber-attacks for large-scale interconnected systems

The paper deals with the key problem of detecting cyber-attacks in the context of large-scale systems (LSS). As these systems grow in size and complexity, cyber-attacks may target limited parts of the LSS, leading to tackling the problem in a decentralized way. We analyze the properties of distributed detection schemes under both local and interconnection attacks, and show that they are vulnerable to attacks that exploit the structure of the interconnections between subsystems. We also provide conditions and strategies that may be adopted to make the distributed control architecture regulating the LSS robust to this class of cyber-attacks.

Multiple integral inequality and its applications to decentralised control of large‐scale systems with time‐varying delays and unknown saturated inputs

As the main objective of this study, a new multiple integral inequality is proposed. By combining this multiple integral inequality with adaptive approach, some simple design methods can be developed to synthesise the decentralised adaptive robust control schemes for a rather large class of uncertain large-scale interconnected systems. As an application of the proposed multiple integral inequality to decentralised control theory, the problem of decentralised robust stabilisation is also considered for a class of uncertain large-scale interconnected systems with both time-varying delays and saturated input non-linearities. It is shown that this multiple integral inequality can well deal with the interconnection terms of large-scale systems. In particular, based on the proposed multiple integral inequality, the time-varying delays can be assumed to be any continuous and bounded non-negative functions in time which are not required that their derivatives have to be less than one, and it is also not required for any information on the saturated input non-linearities to be known. A numerical example and the corresponding simulations are also given to illustrate the validity of the theoretical results.

Decentralized Predictor Output Feedback for Large-scale Systems with Large Delays

A majority of existing literature on time-delay systems focus on the robust stability of a single plant with respect to a “small” delay. This paper proposes a decentralized predictor-based feedback to compensate large delays for large-scale interconnected systems. The full-state of each subsystem is assumed to be unmeasurable and the observer-based output feedback is employed. Two methods are used to tackle the large delays: the backstepping-based partial differential equation (PDE) method is employed for continuous-time control, which derives simpler linear matrix inequality (LMI) conditions and manages with larger delays, whereas the reduction-based ordinary differential equation (ODE) method is applied to sampled-data implementation under continuous-time measurement. Instead of treating the large-scale systems as a whole, a decentralized Lyapunov-Krasovskii method is presented to guarantee the exponential stability of the large-scale systems under decentralized predictors.

Scalable robustness of interconnected systems subject to structural changes

This paper studies the robustness of large-scale interconnected systems with respect to external disturbances, focussing on their scalability properties. Specifically, a notion of scalability is introduced that asks for these robustness properties to remain unchanged under a structural change of the system, such as the addition/removal of a subsystem or a change in the interconnection structure. Both necessary and sufficient conditions, in terms of the interconnection structure and edge weights, are given under which elementary structural changes are scalable. The results are illustrated through a simple example.

Self-adaptive fuzzy-PID controller for AGC study in deregulated Power System

The aim of this paper elucidates the AGC issues in a large scale interconnected power system incorporating HVDC link under the deregulated environment. The performance of the system is degraded under the influence of abrupt load change, and parameter variation. To perceive a reliable and quality power supply, secondary robust controllers are essential. A novel self-adaptive Fuzzy-PID controller is proposed to ameliorate the dynamic performance of both the conventional PID and Fuzzy-PID controller, employed in the restructured power system. In self-adaptive Fuzzy-PID controller unlike the Fuzzy-PID controller, the output scaling factors are tuned dynamically while the controller is functioning. These three controllers are designed by enumerating different gains and scaling factors, applying a budding nature-inspired algorithm known as Wild Goat Algorithm (WGA). The superior dynamic performance of frequency and tie-line power deviation under self-adaptive Fuzzy-PID controller in comparison to its' counterparts is investigated by dispatching the scheduled and unscheduled power under different contracts such as poolco based transaction, bilateral transaction and contract violation based transaction through different tie-lines. The dynamic response under parameter variation and random load perturbation confers the robustness of the proposed controller.

Self-adaptive fuzzy-PID controller for AGC study in deregulated Power System

The aim of this paper elucidates the AGC issues in a large scale interconnected power system incorporating HVDC link under the deregulated environment. The performance of the system is degraded under the influence of abrupt load change, and parameter variation. To perceive a reliable and quality power supply, secondary robust controllers are essential. A novel self-adaptive Fuzzy-PID controller is proposed to ameliorate the dynamic performance of both the conventional PID and Fuzzy-PID controller, employed in the restructured power system. In self-adaptive Fuzzy-PID controller unlike the Fuzzy-PID controller, the output scaling factors are tuned dynamically while the controller is functioning. These three controllers are designed by enumerating different gains and scaling factors, applying a budding nature-inspired algorithm known as Wild Goat Algorithm (WGA). The superior dynamic performance of frequency and tie-line power deviation under self-adaptive Fuzzy-PID controller in comparison to its' counterparts is investigated by dispatching the scheduled and unscheduled power under different contracts such as poolco based transaction, bilateral transaction and contract violation based transaction through different tie-lines. The dynamic response under parameter variation and random load perturbation confers the robustness of the proposed controller.