State estimation plays a vital role in the security analysis of a power system. The weighted least squares method is one of the conventional techniques used to estimate the unknown state vector of the power system. The existence of bad data can distort the reliability of the estimated state vector. A new algorithm based on the technique of quality control charts is developed in this paper for detection of bad data. The IEEE 6-bus power system data are utilised for the implementation of the proposed algorithm. The output of the study shows that this method is practically applicable for the separation of bad data in the problem of power system state estimation.
This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.