Abstract:Online partial discharge (PD) measurements have long been used as an effective means to assess the condition of the stator windings of large generators. An increase in the use of PD online measurement systems during the last decade is evident. Improvements in the detection capabilities are partly the reason for the increased popularity. Another reason has been the development of digital signal processing techniques. In addition, rapid progress is being made in automated single PD source classification. However, there are still some factors hindering wider application of the system, such as the complex PD mechanism and PD pulse propagation in stator windings, the presence of detrimental noise and disturbances on-site, and multiple PD sources occurring simultaneously. To avoid repetition of past work and to provide an overview for fresh researchers in this area, this paper presents a comprehensive survey of the state-of-the-art knowledge on PD mechanism, PD pulse propagation in stator windings, PD signal detection methods and signal processing techniques. Areas for further research are also presented.
Runoff prediction is an important basis for rational allocation of basin water resources and plays a very important role in regional water resources management. In this study, a hybrid short-term runoff prediction model based on long short-term memory network (LSTM), improved Harris hawks optimization algorithm (IHHO) and optimal variational mode decomposition (OVMD) are proposed. Firstly, the original runoff data is decomposed into several sub-modes by OVMD, and then the sub-modes are reconstructed by phase space reconstruction (PSR). Secondly, the Harris hawks optimization algorithm is improved by the chaos map and the hill climbing algorithm. Then, the LSTM model is established for each sub-mode, and the improved Harris hawks optimization algorithm (IHHO) is used to optimize the number of hidden layer neurons and learning rate of the LSTM network. Finally, the results of all sub-modes are combined to obtain the finally runoff prediction result. In this study, seven control models are constructed and compared with the proposed model to verify the effectiveness of the proposed model in runoff prediction.
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