Large-scale Interconnected Systems Research Articles

Decentralised robust tracking and model following for uncertain large-scale interconnected systems with time-varying delays and dead-zone inputs

The problem of decentralised robust tracking and model following is investigated for a class of uncertain large-scale interconnected systems with both any time-varying delays and any dead-zone input constraints. By combining an adaptive approach with a multiply integral inequality proposed recently in the control literature, a new design method is developed so that (i) the designed decentralised robust tracking control schemes are direct, i.e. not the complicated ones composed of two parts, which can overwhelm the dead-zone input constraints; (ii) it is unnecessary to know any information on the characteristic parameters of dead-zone functions and the time-varying delays, which makes the decentralised robust tracking control rather simple. It is also proved that the local tracking error between the output of each actual subsystem with delays and input constraints and the dynamical signals of the corresponding local reference model is uniformly exponentially bounded. Finally, a numerical example is also given to describe the design procedure and the simulations of this numerical example are implemented to demonstrate the validity of the theoretical results.

Adaptive Fuzzy Decentralized Sampled-Data Control for Large-Scale Nonlinear Systems

This article proposes a sampled-data adaptive fuzzy decentralized output feedback control method for the uncertain nonstrict feedback large-scale interconnected systems whose nonlinear functions are completely unknown and whose states are unavailable for control design. With the help of fuzzy logical systems in identifying the unknown nonlinear functions, and by considering the sampling strategy, a novel sampled-data nonlinear state observer has been designed to approximate the immeasurable state variables, which only contains the output sampling information of the system to be controlled. Furthermore, to avoid the problem of “explosion of complexity” caused by the frequently using the derivative of the virtual controller, the nonlinear filter has been considered under the strategy of the sampled data. Based on Lyapunov theory, a sampled-data adaptive fuzzy decentralized output feedback backstepping control strategy was proposed under the framework of recursive design and sampled-data strategy. The designed sampled-data controller is able to guarantee that all closed-loop signals are semiglobally uniformly ultimately bounded. Two simulation examples are eventually provided to validate the effectiveness of the theoretical findings.

Neural-network-based adaptive secure control for nonstrict-feedback nonlinear interconnected systems under DoS attacks

This paper investigates the problems of decentralized adaptive neural network (NN) secure control for a class of nonstrict-feedback nonlinear interconnected large-scale systems (NILSs) under denial-of-service (DoS) attacks. The considered systems contain not only unknown interconnected terms but also general nonlinear functions that are not required to be globally Lipschitz, in contrast to most of the existing results in the area. When the system subject to the DoS attack, the sampled data can neither be sent by the sensors nor received by the actuators due to the appearance of DoS in the sensor-to-controller (S-C) and the controller-to-actuator (C-A) communication channels. The data packets sent from the sensors are blocked and the traditional backstepping technology cannot be adopted. In order to solve this difficulty, a novel dynamic gain observer is constructed, which can obtain the desired states from the NILSs under DoS attacks. Considering the energy limitation of attackers, two reasonable Assumptions of DoS attack intensity are given. The stability is proven based on the Lyapunov stability theory (LST) and the improved average dwell time (ADT) method. The proposed controller guarantees that all signals in closed-loop systems are bounded, while the tracking error signals converge to a small residual set. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed control method.

Online [formula omitted] control for continuous-time nonlinear large-scale systems via single echo state network

In this article, the H∞ optimal control for nonlinear large-scale interconnected systems is designed using novel adaptive/approximate dynamic programming (ADP) method and single echo state network (ESN). All subsystems of the large-scale systems are combined into an augmented system. Subsequently, the H∞ problem is transformed into a zero-sum game problem, where a nonlinear partial differential Hamilton-Jacobi-Isaacs (HJI) equation is required to be solved. However, the HJI equation has no analytically solution, so this paper develops a novel online ADP algorithm to approximatively obtain the index function and control policy simultaneously employing single ESN without requiring the initial admissible control. The stability of the closed-loop augmented system and the ESN output weights is analyzed using the Lyapunov theorem and guaranteed to be uniformly ultimate boundedness (UUB). A large-scale system example including three subsystems is simulated to show the feasibility of the suggested control scheme.

Distributed model predictive control for nonlinear large-scale systems based on reduced-order cooperative optimisation

In this paper, a novel cooperative constrained distributed model predictive control algorithm is proposed to control the nonlinear interconnected constrained large-scale systems. In this algorithm, a novel reduced-order cooperative optimisation approach is proposed which is its main contribution that reconstructs and improves the global cost function of any local controller. In proposed algorithm, each local controller computes its optimal control by minimising the corresponding global cost function which is a combination of its own and its neighbouring subsystems’ cost functions. The sufficient conditions are derived to guarantee the recursive feasibility and closed-loop stability specifications to ensure the convergence of the overall states into the positive region which is the neighbourhood of origin. The performance of the proposed algorithm is illustrated via simulation results of a nonlinear large-scale cart-spring-damper system.

Composite adaptive fuzzy decentralized tracking control for pure-feedback interconnected large-scale nonlinear systems

This paper focuses on a problem of composite adaptive fuzzy decentralized tracking control for a class of uncertain pure-feedback interconnected large-scale nonlinear systems with unmeasurable states. A fuzzy state observer is designed by using fuzzy logic systems; thus, the unmeasurable states of pure-feedback nonlinear systems are estimated based on the designed fuzzy state observer. A serial–parallel estimation model is designed by using the fuzzy state observer. To avoid the analytic computation, the command filters are employed to produce the command signals and their derivatives. The fuzzy adaptive laws are constructed by using prediction errors and compensating tracking errors. Finally, a controller is constructed by dynamic surface control technique. Through the proposed control method, all the signals in the closed-loop system are bounded, and the output of the system can track the given reference signal. The simulation studies verify the effectiveness of the proposed control method in this paper.

Novel Data-Driven Distributed Learning Framework for Solving AC Power Flow for Large Interconnected Systems

Recent advancement in power systems induces complexity in large-scale interconnected systems and poses challenges in performing security assessment studies at various operating conditions. Traditional model-based methods are computationally intensive and may not meet the requirements for real-time applications. This paper presents a novel data-driven framework for accelerating the process of obtaining multiple AC power flow (ACPF) solutions for large systems using deep convolutional neural networks (DCNN). DCNN models are designed and trained using various representative power flow cases from a system, whose outputs can be used to perform steady-state security assessment studies. Distributed training with multiple Graphical processing units (GPU)s is implemented using TensorFlow to reduce computation time. The proposed framework is implemented to identify critical buses and recognize the ACPF cases expected to cause steady-state bus voltage violations. The efficacy and feasibility of the proposed framework are evaluated on the Western Electricity Coordinating Council (WECC) 2028 system. Results demonstrate that the proposed framework is highly accurate and possess good interpretability in performing various transmission planning and operation assessment studies for large scale power networks.

Wide Area Measurement System in the IEEE-14 Bus System using Multiobjective Shortest Path Algorithm for Fault Analysis

In a large-scale interconnected power system network, few challenges exist in evaluating and maintaining the overall system stability. The power system’s ability to supply all types of loads during natural disasters or faults has yet to be addressed. This work focuses on developing a wide-area measurement system to manage and control the power system under all operating conditions. The IEEE-14 bus system was modeled in PSCAD software for simulating nineteen types of fault based on multi-objective shortest path algorithm. To manage the wide area measurement, the research must comprehend the working principle of the multi-objective shortest path algorithm, whereby the proposed method will determine the new path for the IEEE-14 bus system. To evaluate the performance of the multi-objective shortest path algorithm, all sections of the IEEE-14 bus system were simulated with faults. The distances of the normal path (without simulated fault) and the new path (with simulated fault) were recorded. Based on the recorded data, it was found that the location of the fault has significant influence on the shortest path of the buses connected to each other.

Compositional (In)Finite Abstractions for Large-Scale Interconnected Stochastic Systems

This article is concerned with a compositional approach for constructing both infinite (reduced-order models) and finite abstractions [a.k.a. finite Markov decision processes (MDPs)] of large-scale interconnected discrete-time stochastic systems. The proposed framework is based on the notion of stochastic simulation functions enabling us to employ an abstract system as a substitution of the original one in the controller design process with guaranteed error bounds. In the first part of this article, we derive sufficient small-gain-type conditions for the compositional quantification of the probabilistic distance between the interconnection of stochastic control subsystems and that of their infinite abstractions. We then construct infinite abstractions together with their corresponding stochastic simulation functions for a particular class of discrete-time nonlinear stochastic control systems. In the second part of this article, we leverage small-gain-type conditions for the compositional construction of finite abstractions. We propose an approach to construct finite MDPs as finite abstractions of concrete models or their reduced-order versions satisfying an incremental input-to-state stability property. We also show that for the particular class of nonlinear stochastic control systems, the aforementioned property can be readily checked by matrix inequalities. We demonstrate the effectiveness of the proposed results by applying our approaches to a fully interconnected network of 20 nonlinear subsystems (totally 100 dimensions). We construct finite MDPs from their reduced-order versions (together 20 dimensions) with guaranteed error bounds on their output trajectories. We also apply the proposed results to a temperature regulation in a circular building and construct compositionally a finite abstraction of a network containing 1000 rooms. We employ the constructed finite abstractions as substitutes to compositionally synthesize policies regulating the temperature in each room for a bounded time horizon.

Distributed predictive control of interconnected systems based on disturbance observation

In this study, a distributed model predictive control (MPC) method based on extended state observer is proposed for a class of large-scale interconnected systems by using a reduced state-space decomposition strategy. Firstly, the full state of the system is divided into internal state and external state to decrease computation and communication burden, where the former one is used for local optimisation, and the latter one is used to exchange with its neighbours. Based on the receding optimisation strategy, a reduced state-space decomposition iterative algorithm is proposed, and the convergence of the proposed algorithm is given. To improve the disturbance rejection capability of the system, an extended state observer is designed to estimate the disturbance by using a pole assignment method. By applying a feedforward compensation strategy, a composite controller is derived by combining with local MPC controller and compensation controller, then the input-to-state stability is analysed for the closed-loop system. Finally, an example of a ship engine room power system is given to demonstrate the effectiveness of the proposed method.

Frequency Response Model and Its Closed-Form Solution of Two-Machine Equivalent Power System

For the purpose of considering the spatial-temporal characteristics (STC) of large-scale interconnected power system in the modeling, calculation, and analysis of dynamic frequency response (FR), this paper establishes a two-machine equivalent frequency response (TEFR) model and gets its closed-form solution (CFS), which can be used to analytically analysis and fast calculation. In the modeling, the network structure is idealized through simplified matrix of DC power flow and the system is reduced to a typical two-degree-of-freedom vibration system with viscous damping and excitation forces through breaking closed-loop and variable approximation. After that, modal analysis method is managed to obtain TEFR-CFS which represent the dynamic FR of TEFR model as the superposition of system FR and frequency oscillation. And the influence of parameters on frequency oscillation, such as electrical distance, inertial distribution, initial disturbance power distribution coefficient, etc., are analyzed. Furthermore, the analytical expressions of the key characteristics of dynamic FR, such as the initial decline slope, the nadir and the arrival time of nadir, etc., are derived. Numerical simulation proves that STC has significant influence on FR and can be described by TEFR model, TEFR-CFS and the expressions of its key characteristics with accuracy and efficiency.

Assessment of the Capacity Credit of Renewables and Storage in Multi-Area Power Systems

Capacity credit (CC) can be defined as the capacity of conventional generators that can be replaced by renewable energy sources (RES) and/or other resources such as energy storage without reducing system reliability. Conventional approaches for calculating CC typically treat the power system as a single area without considering transfer constraints and reliability of interconnectors. However, in multi-area power systems locational aspects are key to assess tradeoffs and synergies arising from transmission, storage and RES in providing adequacy of supply. In this work, we propose a new methodology to quantify the CC of RES and storage in a multi-area power system. Due to the large computational burden brought about by composite system adequacy evaluation, a new accelerated sequential Monte Carlo simulation strategy and a new adaptive sampling approach are specifically developed to achieve computation efficiency. The proposed methodology is tested by demonstrating the impact of interconnectors' transfer constraints and availability on RES-storage CC in the case of the Australian National Energy Market (NEM) multi-area power system, with applications to wind, solar, and pumped hydro storage plants. The results from several realistic NEM case studies highlight how the proposed model can inform strategic, reliability-aware integrated system planning of large-scale interconnected low-carbon power systems.

Decentralized Event-Triggered Online Adaptive Control of Unknown Large-Scale Systems Over Wireless Communication Networks.

In this article, a novel online decentralized event-triggered control scheme is proposed for a class of nonlinear interconnected large-scale systems subject to unknown internal system dynamics and interconnected terms. First, by designing a neural network-based identifier, the unknown internal dynamics of the interconnected systems is reconstructed. Then, the adaptive critic design method is used to learn the approximate optimal control policies in the context of event-triggered mechanism. Specifically, the event-based control processes of different subsystems are independent, asynchronous, and decentralized. That is, the decentralized event-triggering conditions and the controllers only rely on the local state information of the corresponding subsystems, which avoids the transmissions of the state information between the subsystems over the wireless communication networks. Then, with the help of Lyapunov's theorem, the states of the developed closed-loop control system and the critic weight estimation errors are proved to be uniformly ultimately bounded. Finally, the effectiveness and applicability of the event-based control method are verified by an illustrative numerical example and a practical example.

Real-time dynamic security analysis of power systems using strategic PMU measurements and decision tree classification

Fast and accurate online dynamic security analysis (DSA) is the key enabler for secure operation of modern power systems. Real-time assessment of the current power system operating state and increased awareness about plausible future insecurity can enable necessary operational and control measures to ensure secure operation. This paper proposes an ensemble decision tree (DT)-based online DSA method for large-scale interconnected power system networks using wide area measurement (WAMS) with phasor measurement units (PMU). A novel attribute selection method has been demonstrated for optimizing PMU installation at strategic buses in large-scale power networks. Multi-stage screening of the initial measurements has been done to minimize the data acquisition cost and computation overhead, which are the key challenges in real-time DSA. The ensemble DT classifier was trained offline using data from the operational model of the power system under different system loading and contingency conditions. The trained classifier provides online security assessment and classifies the power system’s current operating state as secure or insecure based on real-time measurements of the key attributes by selective PMUs. The proposed scheme was tested on IEEE 118-bus system, and the results demonstrate that it has the potential to be used as a reliable online DSA method.

Decentralized Adaptive Fuzzy Tracking Control for a Class of Nonlinear Uncertain Interconnected Systems With Multiple Faults and Denial-of-Service Attack

This article studies the decentralized adaptive fuzzy tracking control problem for a class of large-scale interconnected uncertain systems while considering faults and denial-of-service attack. It is assumed that nonlinear term, interconnected term, actuator faults coefficients, and sensor faults coefficients are unknown. Thus, fuzzy logic system and adaptive method are utilized to deal with these unknown terms. Backstepping and dynamic surface control technique are used in control design procedure and a new algorithm is proposed to obtain a switching observer gain for tracking problems with faults and cyber-attack. It turns out that the designed controller can compensate the effect of faults and uncertainties, all the signals in close-loop systems are bounded, and the tracking error converges to a small neighborhood of origin. Finally, a simulation example is given to prove the feasibility and effectiveness of the theoretical findings in this article.

A decentralized observer-based optimal control for interconnected systems using the block pulse functions

The paper proposes a method to integrate numerically an interconnected system, based on an idea of orthogonal approximation of functions. Here, block pulse functions (BPFs) are chosen as the orthogonal set. The main advantage of using this technique is its ability to transform the original optimal control problem to a mathematical programming problem relatively easier to solve. The primary focus of this paper is to exploit and rigorously develop the BPFs parametrization technique for the synthesis of a decentralized observer-based optimal control for large-scale interconnected systems. In addition, we develop a mathematical model of a double-parallel inverted pendulum coupled by a spring, taking into account all possible changes of the connecting position of the elastic spring. In so doing, we conducted advanced simulations applying the new optimal control method to the studied interconnected system. Our results demonstrate the validity and the effectiveness of the developed decentralized observer-based optimal control approach.

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.