With the breakthrough of electrooptical technology research on sensor application, the promulgation of IEC 61850, the application of Ethernet communication technology and the development of intelligent breaker, digital substation has become the mainstream of substation automation technology development in future. This paper briefly discusses issues related to electric energy measurement in digital substation, including the differences between electric energy measurement in digital substation and conventional substation, the impact to energy measurement of electronic transformer's application. Meanwhile, aiming at the present issues, potential research fields of electric energy measurement in future digital substation is proposed as well.

Aiming at the achievement of virtual power and electric energy measurement in digital substation, considering the unreasonable harmonic energy measurement problem in present power systems, according to the principle that the harmonics are produced by the phase -locking and frequency multiplication of first-harmonic, a kind of harmonic power computation based on the phase difference correcting method is introduced. The method corrects the harmonic frequency after correcting the first-harmonic frequency. The effectiveness of the method in normal and frequency deviation condition is simulated in the software LabVIEW and the results of the simulation are analyzed. Meanwhile, the method is tested in the existing virtual instrument software and hardware platforms. Both the simulation and the test results certify the real-timing and high accuracy of the virtual power measurement method. At last, in order to indicate the real-timing of the method in the practical electric energy measurement application, comparison experiments with watt-hour meter are performed. The results show the method still has good real-timing and high accuracy in the practical electric energy measurement.

Using the concepts of typical gas's concentration and cumulative frequency in analysis of the reliability data for dealing with the pretreatment of data of DGA, two new normalized methods which named characteristic normalization and mix normalization are presented in this paper. The Fisher rule to evaluate the results of the two pretreatment methods is also introduced. The evaluation of the results indicates that both of the two data pretreatment methods can achieve the purpose of big difference in the value of mean between classes and small difference in dispersion of a class. The DGA data of the failure transformers are treated by different normalization methods as the training samples, and then the samples are trained in the compound neural networks which use the CP algorithm. The diagnosis results of the test samples indicate that the new methods may help to improve the precision of network diagnosis.