UHF partial discharge localization algorithm based on reconstructed RSSI fingerprint

Summary

Partial discharge (PD) detection and localization is an effective way to monitor the power equipment malfunction in substation. Traditional time-difference based PD localization methods suffer to the high hardware cost especially the high-speed sampling rate which makes it hard to be applied in practice. This paper proposed a novel UHF PD localization algorithm based on received signal strength indication (RSSI) fingerprint map, which is suitable for PD localization in substation due to its advantage of low hardware cost and environmental adaptability. The proposed algorithm consists of two stages. In the offline stage, the RSSI fingerprint map is established by field test. In the online stage, the PD location is estimated by comparing online measured UHF PD signal strength with offline RSSI fingerprint map. The pattern recognition process in the online stage is realized by BP neural network. Since the UHF electromagnetic signal is sensitive to environmental change, the RSSI fingerprint map need to be updated often, which is a time-consuming process. The sparsity of the RSSI fingerprint map in frequency domain motivates us to use the compressive sensing (CS) algorithm to reduce the workload of reconstructing the RSSI fingerprint map. By this algorithm, the fingerprint map can be reconstructed at only a subset of the fingerprint data. A field experiment is performed in substation and the results show that, in the offline stage, only 50% of the original RSSI fingerprint data can recover the whole RSSI fingerprint map with the error rate about 17%, the workload is significantly reduced and the main information of the RSSI fingerprint map is recovered. In the online stage, with the use of the reconstructed RSSI fingerprint map, the average PD localization error is 4.08 meters and 42.4% localization errors are less than 3 meters. The results prove the accuracy and effectiveness of the proposed algorithm in this paper.

Additional informations

Authors

CAMPOS DE AMORIM FILHO, NASCIMENTO BARBOSA, MARVAL, ESMERALDO, LOPES CARVALHO BATISTA

Keywords

Partial discharge, localization algorithm, RSSI, fingerprint, compressive sensing