Bad Data Detection and Identification Techniques Using Estimation Orthogonal Methods

This paper presents methods for bad data detection, identification and elimination, which can be used in connection with orthogonal state estimation techniques. Full advantage is taken of the properties and characteristics of the Golub´s and Givens´ methods. A basic review of the foundations of detection and identification techniques is firstly presented. Also, it is shown how easy it is to eliminate the effects of bad data already processed when using the orthogonal Givens rotations. Several numerical test examples are presented.