KEYWORDS: Transformers, Energy efficiency, Standards development, Sensors, Power grids, Equipment, Inspection equipment, Resistance, Power consumption, Iron
Reducing consumption and increasing efficiency is an important task in building a new type of power system to achieve the target of dual carbon. 40% of losses in the power system come from transformers. However, due to limitations in current testing techniques, lack of regulatory measures, and human factors, there is still a certain distance to achieve the expected goals. Clarifying the relationship between offline measurement values of transformer losses and actual operating losses, controlling transformer loss indicators well, and researching new technologies for improving transformer energy efficiency urgently require new breakthroughs in online testing technology for transformer losses. This article summarizes the mechanism of transformer losses and related technical parameter indicators, proposes online detection technology for transformer losses and methods to improve measurement accuracy. The practical application cases have verified that this method can achieve good results, laying the foundation for improving the energy efficiency of transformer operation in the future.
Aiming at the problem that it is difficult to obtain the core parameters of the current transformer simulation model, a genetic simulated annealing algorithm is proposed in this paper. This method combines simulated annealing algorithm with genetic algorithm to overcome the premature phenomenon of traditional genetic algorithm. It realizes the fast fitting of core specific parameters of current transformer J-A model, and can quickly build the current transformer simulation model. The effectiveness of the algorithm is verified by example simulation.
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