The Comparison Study Among Optimization Techniques In Optimizing A Distribution System State Estimation

State estimation considered the main core of the Energy Management System and plays an important role in stability analysis, control and monitoring of electric power systems. The state estimator actually depends on many factors, such as data sensitive regarding the sensors accuracy, the availability of raw data, the network database accuracy, and the time skew of data. Many researchers already been studied multi-area power system state estimation and most of them investigation of state estimation schemes including different state estimators for each a central coordinator and control area. Therefore, accurate and timely efficient state estimation algorithm is a prerequisite for a stable operation of modern power grids. This thesis introduce an intelligent decentralized State Estimation method based on Firefly algorithm for distribution power systems. The mathematical procedure of distribution system state estimation which utilizing the information collected from available measurement devices in real-time. A consensus based static state estimation strategy for radial power distribution systems is proposed in this research. This thesis concentrates on the balanced systems. There are buses acting as agents using which we can evaluate the local estimates of the entire system. Therefore each measurement model reduces to an underdetermined nonlinear system and in radial distribution systems, the state elements associated with an agent may overlap with neighboring agents. The states of these systems are first estimated through centralized approach using the proposed algorithm to compare with weighted least squares technique. At the end, the result will presented the application of the developed approach to a network based on IEEE 13 bus, 14 bus and 33 bus test System. The result a proved to be computational efficient and accurately evaluated the impact of distributed generation on the power system. From the result, it can observe that for decentralized is faster and less error for both WLS and FA. In addition, FA show faster and less error than WLS for both centralized and decentralized. In addition, the proposed FA show faster with increasing the number of buses

Decentralized robust set-valued state estimation in networked multiple sensor systems

AbstractThis paper addresses a decentralized robust set-valued state estimation problem for a class of uncertain systems via a data-rate constrained sensor network. The uncertainties of the systems satisfy an energy-type constraint known as an integral quadratic constraint. The sensor network consists of spatially distributed sensors and a fusion center where set-valued state estimation is carried out. The communications from the sensors to the fusion center are through data-rate constrained communication channels. We propose a state estimation scheme which involves coders that are implemented in the sensors, and a decoder–estimator that is located at the fusion center. Their construction is based on the robust Kalman filtering techniques. The robust set-valued state estimation results of this paper involve the solution of a jump Riccati differential equation and the solution of a set of jump state equations

Integrated Fault-Tolerant Control for Close Formation Flight

This paper investigates the position-tracking and attitude-tracking control problem of close formation flight with vortex effects under simultaneous actuator and sensor faults. On the basis of the estimated state and fault information from unknown input observers and relative output information from neighbors, an integration of decentralized fault estimation and distributed fault-tolerant control is developed to deal with bidirectional interactions and to guarantee the asymptotic stability and H_\infty performance of close formations

Decentralized sliding mode control and estimation for large-scale systems

This thesis concerns the development of an approach of decentralised robust control and estimation for large scale systems (LSSs) using robust sliding mode control (SMC) and sliding mode observers (SMO) theory based on a linear matrix inequality (LMI) approach. A complete theory of decentralized first order sliding mode theory is developed. The main developments proposed in this thesis are: The novel development of an LMI approach to decentralized state feedback SMC. The proposed strategy has good ability in combination with other robust methods to fulfill specific performance and robustness requirements. The development of output based SMC for large scale systems (LSSs). Three types of novel decentralized output feedback SMC methods have been developed using LMI design tools. In contrast to more conventional approaches to SMC design the use of some complicated transformations have been obviated. A decentralized approach to SMO theory has been developed focused on the Walcott-Żak SMO combined with LMI tools. A derivation for bounds applicable to the estimation error for decentralized systems has been given that involves unknown subsystem interactions and modeling uncertainty. Strategies for both actuator and sensor fault estimation using decentralized SMO are discussed.The thesis also provides a case study of the SMC and SMO concepts applied to a non-linear annealing furnace system modelderived from a distributed parameter (partial differential equation) thermal system. The study commences with a lumped system decentralised representation of the furnace derived from the partial differential equations. The SMO and SMC methods derived in the thesis are applied to this lumped parameter furnace model. Results are given demonstrating the validity of the methods proposed and showing a good potential for a valuable practical implementation of fault tolerant control based on furnace temperature sensor faults