In order to achieve multi-objective optimization for a permanent magnet water pump motor in heavy commercial vehicles, we propose a strategy based on response-surface methodology and the improved sparrow algorithm (CG...
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In order to achieve multi-objective optimization for a permanent magnet water pump motor in heavy commercial vehicles, we propose a strategy based on response-surface methodology and the improved sparrow algorithm (CGE-SSA). Firstly, the output capacity of the pump during actual operation was tested with an experimental bench to determine the design parameters of the motor, and then its modeling was completed using Ansys Maxwell 2022r2 software. Secondly, the response-surface model was established by taking the parameters of permanent magnet width, rib width, and slot width as optimization parameters and the output torque (Ta), torque ripple (Tr), and back electromotive force (EMF) amplitude as optimization objectives. Meanwhile, three methods-namely, circular sinusoidal chaotic mapping, improved golden sinusoidal strategy, and adaptive weight coefficients-were used to improve the convergence speed and accuracy of the sparrow search algorithm (SSA). Finally, the multi-objective optimization of the permanent magnet synchronous motor was completed using the improved sparrow algorithm. A comparative analysis of the motor's output before and after optimization showed that the torque pulsation and reverse electromotive force of the motor were significantly improved after optimization.
Wind power generation technology has attracted worldwide attention. However, its inherent nonlinearity and uncertainty make itself hard to be accurately predicted. As a result, exploring the ways to remedy these defec...
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Wind power generation technology has attracted worldwide attention. However, its inherent nonlinearity and uncertainty make itself hard to be accurately predicted. As a result, exploring the ways to remedy these defects become the key to the stable operation of power grid. This paper proposed a wind power prediction model based on the improved Long Short-Term Memory (LSTM) network to fit the nonlinearity between data variables and wind power. The chaotic sequence and Gaussian mutation strategy are introduced into the original sparrowalgorithm, so as to improve its stability and search performance. Then, the modified sparrowalgorithm is implemented to adjust the LSTM network's hyperparameters like batch size, cell number and learning rate;and therefore the prediction accuracy is increased. After that, the improved model is applied to the data sets of a wind farm in Hunan province during the four seasons of 2020. And then it is compared with other four combined models. The experimental results show that, the RMSE of the proposed prediction method is reduced respectively by 37.37%, 13.44%, 10.64% and 20.78% in four seasons. It is proved that the proposed method improves the accuracy for wind power prediction and the effectiveness for power dispatching.
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