decentralized interaction estimators in large scale power systems with neural networks.

Decentralized Interaction Estimators in Large Scale Power Systems with Neural Networks.

Decentralized Adaptive Control of Large-Scale Non-affine Nonlinear Time-Delay Systems Using Neural Networks

In this paper, a decentralized adaptive neural controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, non-affine subsystems and unknown nonlinear time-delay interconnections. The stability of the closed loop system is guaranteed through Lyapunov-Krasovskii stability analysis. Simulation results are provided to show the effectiveness of the proposed approache...

A New Class of Decentralized Interaction Estimators for Load Frequency Control in Multi-Area Power Systems

Load Frequency Control (LFC) has received considerable attention during last decades. This paper proposes a new method for designing decentralized interaction estimators for interconnected large-scale systems and utilizes it to multi-area power systems. For each local area, a local estimator is designed to estimate the interactions of this area using only the local output measurements. In fact,...

Generation Scheduling in Large-Scale Power Systems with Wind Farms Using MICA

The growth in demand for electric power and the rapid increase in fuel costs, in whole of theworld need to discover new energy resources for electricity production. Among of the nonconventionalresources, wind and solar energy, is known as the most promising deviceselectricity production in the future. In this thesis, we study follows to long-term generationscheduling of power systems in the pre...

Nonlinear decentralized control of large-scale power systems

This paper describes an application of nonlinear decentralized robust control (Guo, Jiang & Hill, 1998) to large-scale power systems. Decentralized power controllers are designed explicitly to maintain transient stable closed-loop systems. For the "rst time, nonlinear bounds of generator interconnections are used which achieves less-conservative control gains. The proposed controllers are robus...

Decentralized Neural Network-based Excitation Control of Large-scale Power Systems

This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activiti...