Detection and identification of network parameter errors using conventional and synchronized phasor measurements

Abstract

The successful control and operation of the power system are performed based on the correct results of state estimation. The sources of errors of state estimation include the measurement noise and gross errors, circuit breaker status and switch information, and network parameter errors. Among them, the network parameter errors are the most challenging ones to be identified since generally network parameters are assumed to be known in state estimation. The inconsistency detected during state estimation may be blamed on measurement errors and the network parameter errors may affect the results of state estimation for a long time. The existing parameter error identification methods have the common limitations that they cannot perform correct identification with the presence of measurement error and a suspect parameter set is required a priori state estimation. This dissertation is dedicated to developing a network parameter error identification method to overcome the limitations of the existing methods. This method can be expanded by including PMUs in the power system to identify the errors appearing in the critical k-tuples of parameters. The existing parameter error identification methods are review first. Then an algorithm of network parameter error identification and correction is developed. This method can differentiate the measurement errors from the parameter errors even when they appear simultaneously. This method does not need to select a suspect parameters set a priori. It is illustrated that the parameters in some particular topology and measurement configuration form a critical k-tuple. The error of parameters in such critical k-tuple can be detected but cannot be identified. It is demonstrated that such critical k-tuples are caused by the possible multiple solutions of parameters and cannot be eliminated by only conventional measurements. After reviewing the state estimation algorithm with PMUs, the method proposed in the second part is expanded. By installing PMUs are some strategic locations, the method is able to identify the parameter errors in the critical k-tuples.