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However, a transformer may include a tap changer on each winding if there are advantages to do so. For example, in power distribution networks, a large step-down transformer may have an off-load tap changer on the primary winding and an on load automatic tap changer on the secondary winding or windings. The high voltage tap is set to match long ...
Transformer architecture is now used in many generative models that contribute to the ongoing AI boom. In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer model. [35]
a load tap changer (LTC) at the substation transformer, which changes the turns ratio in response to load current and thereby adjusts the voltage supplied at the sending end of the feeder; voltage regulators , which are essentially transformers with tap changers to adjust the voltage along the feeder, so as to compensate for the voltage drop ...
Transformer architecture is now used in many generative models that contribute to the ongoing AI boom. In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer model. [33]
Data clustering techniques – such as the self-organizing map (SOM), have been applied to vibration monitoring and analysis of transformer on-load tap-changers (OLTCS). Using vibration monitoring, it can be observed that each tap change operation generates a signal that contains information about the condition of the tap changer contacts and ...
Both linear spacing and power-of-two-style Vernier spacing can be employed in establishing the tap positions, e.g., a four-tap transformer can provide taps at 0.25, 0.5, 0.75, 1.0 (linear) or 0.0625, 0.125, 0.25, and 0.5 (Vernier). (The limitations of the Vernier—in this case, that the maximum obtainable amplitude is 0.9375—are less ...
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This was optimized into the transformer architecture, published by Google researchers in Attention Is All You Need (2017). [27] That development led to the emergence of large language models such as BERT (2018) [28] which was a pre-trained transformer (PT) but not designed to be generative (BERT was an "encoder-only" model).