Multi-overlapping decentralized control design based on dynamic output feedback for large scale interconnected systems with radial structure

An overlapping robust decentralized connective stabilization method based on dynamic state feedback is proposed for a kind of large-scale interconnected systems with loop topological structure. Firstly, the state space of an interconnected system is expanded by using inclusion principle of large-scale systems and the system is divided into a series of pair-wise subsystems in the expanded space. Then, the robust decentralized controllers based on dynamic state feedback for each pair-wise subsystem are designed by using LMI method and Lyapunov theory. Next, the designed repeatedly controllers for every subsystem are contracted back to the original space. Coordination of the control laws can be achieved, so that the original interconnected system is robust connectively stable. Finally, the proposed method is applied to AGC control design of a three-area interconnected power system with loop structure. Simulation results show the feasibility and superiority of the proposed method.

This paper is concerned with the analysis of large-scale interconnected systems constructed from positive subsystems and a nonnegative interconnection matrix. We first show that the interconnected system is admissible and stable if and only if a Metzler matrix built from the coefficient matrices of the positive subsystems and the interconnection matrix is Hurwitz stable. By means of this key lemma, we further provide several results that characterize the admissibility and stability of interconnected systems in terms of the weighted L1-induced norm of each positive subsystem and the Frobenius eigenvalue of the interconnection matrix. Moreover, in the case where every subsystem is SISO, we provide explicit conditions under which the interconnected system has the property of persistence, i.e., the state of the interconnected system converges to a unique strictly positive vector (up to a strictly positive constant multiplicative factor) irrespective of nonnegative and nonzero initial states. We illustrate the effectiveness of the persistence results via formation control of multi-agent systems.

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

In this paper, the influence of a Superconducting Magnetic Energy Storage (SMES) unit on the performance of the Automatic Generation Control (AGC) is investigated. In which, the self-tuning Fuzzy PI Controller (FPIC) associated with the Automatic Generation Control (AGC) is proposed for the improvement of Load Frequency Control (LFC) in wind farm integrated large power system consisting of thermal and hydro power plants. The effects of the self-tuning FPIC in AGC on SMES control is compared with that of optimized fixed gain PI controlled AGC. The computer simulation of the two-area interconnected power system shows that the self-tuning fuzzy PI control scheme of AGC is very effective for SMES control to damp out of the oscillations caused by load disturbances in one or both of the areas and it is also seen that the proposed controlled AGC including SMES performs more effective primary frequency control compared to optimized fixed gain PI controlled AGC including SMES.

Delay-dependent robust and reliable [formula omitted] fuzzy hyperbolic decentralized control for uncertain nonlinear interconnected systems

Efficient decentralized iterative learning tracker for unknown sampled-data interconnected large-scale state-delay system with closed-loop decoupling property

In this paper, the problem of delay-dependent decentralized control for a class of nonlinear large-scale interconnected systems with similar structure is investigated, where the linear state vectors, the nonlinear state vectors and the nonlinear interconnections involve time delays. The new similar structure is presented via memory feedback. Based on which, by exploiting the structure of interconnected systems, the new integral inequalities, constrained Lyapunov equations and LMI method, a decentralized memory derivative feedback control strategy is proposed to stabilize the resulting interconnected systems uniformly asymptotically, in addition, the stability domain is estimated. Finally, simulation results show the effectiveness of the proposed approach.

In this paper, we propose a coordination scheme between balancing authority (BA) areas in an interconnected power system that decreases the regulation amount needed as well as the associated costs. Our approach aims at mimicking the behavior of the automatic generation control (AGC) system in a scenario where the whole interconnected system is assumed to be operated by a single BA area. To this end, we modify the area control error (ACE), which is fed into the AGC system of each BA area, and determine the AGC allocation based on a distributed algorithm that identifies the least expensive generators, with the mismatch of the total regulation needed being the only information exchanged between BA areas. We demonstrate the proposed ideas with the 3-machine 9-bus Western Electricity Coordinating Council (WECC) system, and compare the performance of our method with other three existing coordination approaches.

Extreme UltraViolet (EUV) lithography is a new technology for production of integrated circuits. In EUV lithographic machines, optical elements are heated by absorption of exposure energy. Heating induces thermoelastic deformations of optical elements and consequently, it creates wavefront aberrations. These Thermally Induced Wavefront Aberrations (TIWA) can significantly degrade the resolution of EUV lithographic machines. One of the ways to correct TIWA and consequently, to improve the resolution of EUV lithographic machines, is to use the Adaptive Optics (AO) technique and predictive control algorithms. However, the predictive control of TIWA is a challenging problem, mainly because the dynamical behavior of TIWA is described by thermoelastic Partial Differential Equations (PDEs). By discretizing the thermoelastic equations using the finite difference or finite element methods, large-scale state-space models can be obtained. A striking feature of these state-space models is that they have sparse (multi) banded matrices. Consequently, these state-space models can be interpreted as large-scale networks of local subsystems. All this implies that the problem of correcting TIWA can be placed in a much more general context of identifying, estimating and controlling large-scale interconnected (distributed) systems. However, currently used estimation and control algorithms are not computationally feasible for large-scale systems. For this reason, this thesis focuses on the development of computationally efficient identification, estimation and control algorithms for large-scale interconnected systems. In this thesis, we prove that the inverses of (finite-time) Gramians of large-scale interconnected systems, obtained by discretizing PDEs, belong to a class of off-diagonally decaying matrices. Consequently, these inverses can be approximated by sparse (multi) banded matrices with O(N) complexity, where N is the number of local subsystems. To compute the approximate inverses, the Chebyshev approximation method and the Newton iteration are used. On the basis of these theoretical results, we develop novel estimation and identification algorithms for large-scale interconnected systems. The computational and memory complexity of these methods scale with O(N). This is the main advantage over the traditional estimation and identification methods, which complexity is O(N^3). Furthermore, in this thesis we develop and experimentally verify AO control algorithms for predictive correction of TIWA. The experimental results give a good first indication for a possible implementation of the developed predictive control algorithms in EUV lithographic machines. Finally, we develop and experimentally test an iterative learning control algorithm for high performance correction of wavefront aberrations.

Dynamic output-feedback wide area damping control of HVDC transmission considering signal time-varying delay for stability enhancement of interconnected power systems

This paper focuses on the adaptive control problems for a class of interconnected systems. The nonLinear uncertainties of the systems under study are bounded by polynomial functions with unknown gains. Firstly, the adaptive feedback controller is proposed to guarantee the stabiLity of the closed-loop system. Then the proposed adaptive idea is extended to robust stabiLizing designing method for a class of large-scale interconnected systems. Based on Lyapunov's theory and LMI method, we can deal with the time-delay term in the system and construct the corresponding adaptive control and state feedback controller. Finally, an illustrative example is given to show the vaLidity of the proposed approach.

Decentralized optimal controller design for multimachine power systems using successive approximation approach

In this paper we present a new general stochastic model for the reliability dynamics of large scale interconnected power systems. We show that this model can be used for optimum operation and management of any power pool. This is illustrated by a flow diagram showing how an on-line computer determines the optimum power dispatch policy for maximum reliability. For convenience (of users) this is also presented in the form of an algorithm. In order to illustrate the model and the proposed algorithm, a simple example of a power pool, consisting of three interconnected utilities, is considered and the effect of the proposed control scheme on the reliability of the entire system is discussed. The general model can also be used for economic dispatch and generation expansion planning.

In industry or any area increasing load is a vast problem for power generation plants due to increase in demand for power. So making balance between generation and demand is the operating principle of load frequency control (LFC). The reliable operation of a large interconnected power system necessarily requires an Automatic Generation Control (AGC). The objective of AGC is to regulate the power output of Generators within a specified area in response to change in the system frequency, tie line power or relation of the two to each other, so as to maintain the scheduled system frequency and power interchange in the other are within the prescribed limits. This paper presents the use of conventional PI controller and artificial intelligence to study the load frequency control of interconnected power system. In the proposed scheme, a control methodology is developed using conventional PI controller and Fuzzy Logic controller (FLC) for interconnected hydro-thermal power system. The control strategies guarantees that the steady state error of frequencies and inadvertent interchange of tie-lines power are maintained in a given tolerance limitations. The performances of the controllers are simulated using MATLAB/SIMULINK package. A comparison of Fuzzy controller and PI controller based approaches shows the superiority of proposed Fuzzy logic controller for step change in loading conditions. The simulation results also tabulated as a comparative performance in view of settling time and peak over shoot.

Automatic generation control: Decentralized area-wise optimal solution