Distribution network state estimation data identification and correction method based on quality evaluation

Abstract

The invention relates to the field of power distribution network state estimation data identification and correction, and in particular relates to a distribution network state estimation data identification and correction method based on quality evaluation. Methods for correcting and supplementing remote sensing quality evaluation indexes, switch remote signaling quality evaluation indexes and bus voltage quality evaluation indexes under various conditions are provided according to a switch remote sensing quality evaluation index and a power distribution network parent-child layering model, and the switch remote sensing value, the remote signaling state and the bus voltage are corrected and supplemented according to the corrected quality evaluation indexes. According to the distribution network state estimation data identification and correction method, the identification and correction of power distribution network state estimation data under the conditions of complete measurement data and less bad data in a cable and overhead feeder mixed network can be effectively realized. The corrected measurement value and the measurement quality evaluation indexes thereof can be used as source data of advanced application, and can also be used as input values and weights of weighted least square state estimation.

Description

technical field [0001] The invention relates to the field of distribution network state estimation data identification and correction, in particular to a distribution network state estimation data identification and correction method based on quality evaluation. Background technique [0002] In recent years, with the country's vigorous investment in distribution network infrastructure, a number of distribution network automation pilot projects have successfully passed the acceptance test. The continuous expansion of the scope and scale of distribution network data collection, coupled with the access of quasi-real-time systems such as load management systems (CIS) and geographic information systems (AM\FM\GIS), has made it possible to implement advanced applications in distribution networks. [0003] Distribution network state estimation, as the basis of advanced distribution network applications, has the functions of data completion and filtering, and provides mature data so...

AI Technical Summary

Problems solved by technology

However, due to insufficient measurement data, the traditional distribution network state estimation algorithm is mostly completed by the power flow calculation with additional conditions, the calculation speed is slow, it takes up a lot of resources, and a small amount of bad data may increase the calculation error due to the conductivity of the algorithm. Therefore, The effective identification and correction of bad data has important practical significance

[0004] In addition, the current distribution network automation construction is transitioning from the traditional type to the intensive type and even the intelligent type. The grid model is mostly derived by GIS, and it is difficult to guarantee the parameters of the feeder section.

Moreover, most of the distribution networks in various places are hybrid distribution networks that combine traditional overhead feeders and cables. The network structure is complex, and most of the overhead feeders are segmented by load switches. estimated to be difficult

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