Within energy management systems, state estimation is a key function for building a network model. The performance of most other application programs strongly depends on the accuracy of data provided by the state estimator. A network real-time model is built from a combination of snapshots of measurements and static network parameter data. The measured data are subject to well known errors. Network parameters, in general, may be erroneous as a result of inaccurately provided data, transmission line sags on hot days, error in calibration and calculation. The performance of a state estimator, therefore, depends on the accuracy of the measured data as well as the parameters of the network model. Bad data processing is now a standard subroutine in state estimation algorithm. However, definitive research results on the processing of parameter errors are rare. This book describes an approach to network parameter estimation. An appropriate method for network parameter error detection, identification, and correction is developed. The method shown is found to be highly accurate for the cases of single as well as multiple parameter errors.