Stochastic Hybrid System Modeling and State Estimation of Modern Power Systems under Contingency

TL;DR

This paper develops a stochastic hybrid system framework for modern power systems to improve state estimation accuracy and reliability amid sensor noise and communication disruptions, using new modeling and observer algorithms.

Contribution

It introduces a novel stochastic hybrid system model for power systems and proposes coordinated observer algorithms with proven convergence and reliability.

Findings

Effective state estimation under random sensor noise and packet loss.

Tradeoff between convergence speed and steady-state error.

Simulation results demonstrate improved estimation performance.

Abstract

This paper introduces a stochastic hybrid system (SHS) framework in state space model to capture sensor, communication, and system contingencies in modern power systems (MPS). Within this new framework, the paper concentrates on the development of state estimation methods and algorithms to provide reliable state estimation under randomly intermittent and noisy sensor data. MPSs employ diversified measurement devices for monitoring system operations that are subject to random measurement errors and rely on communication networks to transmit data whose channels encounter random packet loss and interruptions. The contingency and noise form two distinct and interacting stochastic processes that have a significant impact on state estimation accuracy and reliability. This paper formulates stochastic hybrid system models for MPSs, introduces coordinated observer design algorithms for state estimation, and establishes their convergence and reliability properties. A further study reveals a fundamental design tradeoff between convergence rates and steady-state error variances. Simulation studies on the IEEE 5-bus system and IEEE 33-bus system are used to illustrate the modeling methods, observer design algorithms, convergence properties, performance evaluations, and impact sensor system selections.