A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC

For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

Coordination of PSS and TCSC controller using modified particle swarm optimization algorithm to improve power system dynamic performance

This paper develops a modified optimization procedure for coordination of a power system stabilizer (PSS) and a thyristor controlled series compensator (TCSC) controller to enhance the power system small signal stability.The new approach employs eigenvalue-based and time-domain simulation based objective functions simultaneously to improve the optimization convergence rate.A modified particle swarm optimization (MPSO) algorithm is used as the optimization algorithm.The results of simulations and eigenvalue analysis for a single machine infinite bus (SMIB) system equipped with the proposed PSS and TCSC controllers confirm that the new approach is effective in enhancing the system stability.

Demand response based congestion management in a mix of pool and bilateral electricity market model

The independent system operator （ISO） is a key element in the deregulated structure with one of the responsibilities of transmission congestion management （CM）. The ISO opts market based solutions to manage congestion receiving bids from generation companies （GENCOs） as well as distribution companies （DISCOMs） to reschedule their generation and relocate demand. The nodal prices increases during the congestion hours and the demand response to nodal prices will be an effective tool for the control of congestion. In this paper, demand response-based CM has been proposed for a mix of pool and bilateral electricity market model. The linear bid curves have been considered for demand bids to respond to the congestion in the network. The bilateral demand has been obtained with minimum deviations in their preferred schedule. The impact of flexible alternating current transmission system （FACTS） devices viz static var compensator （SVC） and thyristor controlled series compensator （TCSC） has also been considered for demand management during congestion. Multi-line congestion cases have been considered to study the impact on demand response without and with FACTS devices. The proposed approach has been tested on the IEEE 24 bus test system.