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Determining the voltage ratio change is one of the core issues in the traceability of the DC voltage divider. Basing on the previous research results, this study proposes an improved DC voltage summation method to evaluate the voltage division ratio error of 1000 kV DC resistance divider. The principle of the method is to calibrate the voltage divider with rated voltage 2U by using two auxiliary voltage dividers which are with rated voltage U, wherein the high-voltage (HV) arm and the low-voltage arm of the auxiliary voltage divider can be separated. Research results show that compared with the conventional method, the method can reduce one measurement variable when determining the divider's ratio change, thus simplifying the calibration process. The voltage ratio of 100 kV measured by the method of this study was well-verified by the calibration results from the National Institute of Metrology (NIM, China) and Physikalisch-Technische Bundesanstalt (PTB, Germany). Using the proposed method, the ratio change of DC voltage divider at an applied voltage of 1000 kV was effectively obtained and the uncertainty of 2.5 μV/V was achieved. Research results can provide technical guarantee for the accurate measurement of HVDC magnitude.

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Many electrical equipment malfunction text messages are collected during power system operation and maintenance procedures. These texts usually contain crucial information for maintenance and condition monitoring. Because these power system malfunction texts are characterized by multidomain vocabularies, complex-syntactic structures, and long sentences, it is challenging to for automated systems to capture their semantic meaning and essential information. To address this issue, we propose a hybrid natural language processing (hybrid-NLP) algorithm to extract entities that represent electrical equipment. This algorithm is composed of a dictionary-based method, a language technology platform (LTP) tool, and the bidirectional encoder representations from a transformers-conditional random field (BERT-CRF) model. Significantly, the softmax output layer of the bidirectional encoder representations from the transformers (BERT) model is replaced by the conditional random field (CRF) algorithm to strengthen the contextual relationships between words and thus solve the local optimization of the preferred word label. The effectiveness of the proposed hybrid-NLP method is verified on a realistic dataset. Moreover, a statistical analysis is conducted to provide a reference for the operation and maintenance of power systems. INDEX TERMS electrical equipment malfunction text, natural language processing, entity extraction, BERT-CRF model.

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