Parallel State Estimation With Confidence Limit Analysis

This paper describes the parallelisation of a state estimator with confidence limit analysis. State estimation involves the optimal fitting of an overdetermined set of measurements to the corresponding values calculated from the mathematical model of the system. The inaccuracies associated with measurements lead to discrepancies within the state estimate. Consequently for the state estimation algorithm to be of practical use it needs to quantify the effect of these discrepancies in the form of state confidence limits [2]. However, the quasi-quadratic numerical complexity of the state estimation algorithms suggests a need for parallel implementation of the probabilistic state estimation, so that the real-time performance may be maintained also for large-scale systems. The algorithm is based on the idea of `tearing' the original system into subsystems and then coordination of the resulting subsystem solutions. The algorithm has been tested in the context of water distribution systems state estimation. KEY WORDS: Distributed systems, state estimation, confidence limit analysis, transputers, parallel algorithms. 1. INTRODUCTION The basic role of state estimation is the generation of a coherent and reliable state vector based on the measurements and the mathematical model of the system. State estimation involves minimising the difference between the actual measurements and the corresponding values that are calculated using the current estimate of the state vector. The way in which this optimisation problem is posed, regarding the definition of the cost function, characterises different state estimators [4,7,8,9,12]. In general, state estimation procedures are approximately quadratically dependent on the size of the physical network. Consequently, for a large scale system, it..