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A Hybrid Multi-Objective Optimization Model for Vibration Tendency Prediction of Hydropower Generators

机译：水轮发电机振动趋势预测的混合多目标优化模型

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摘要

The hydropower generator unit (HGU) is a vital piece of equipment for frequency and peaking modulation in the power grid. Its vibration signal contains a wealth of information and status characteristics. Therefore, it is important to predict the vibration tendency of HGUs using collected real-time data, and achieve predictive maintenance as well. In previous studies, most prediction methods have only focused on enhancing the stability or accuracy. However, it is insufficient to consider only one criterion (stability or accuracy) in vibration tendency prediction. In this paper, an intelligence vibration tendency prediction method is proposed to simultaneously achieve strong stability and high accuracy, where vibration signal preprocessing, feature selection and prediction methods are integrated in a multi-objective optimization framework. Firstly, raw sensor signals are decomposed into several modes by empirical wavelet transform (EWT). Subsequently, the refactored modes can be obtained by the sample entropy-based reconstruction strategy. Then, important input features are selected using the Gram-Schmidt orthogonal (GSO) process. Later, the refactored modes are predicted through kernel extreme learning machine (KELM). Finally, the parameters of GSO and KELM are synchronously optimized by the multi-objective salp swarm algorithm. A case study and analysis of the mixed-flow HGU data in China was conducted, and the results show that the proposed model performs better in terms of predicting stability and accuracy.
机译：水力发电机组（HGU）是电网中频率和峰值调制的重要设备。其振动信号包含大量信息和状态特征。因此，使用收集到的实时数据预测HGU的振动趋势并实现预测性维护非常重要。在以前的研究中，大多数预测方法仅集中于提高稳定性或准确性。然而，在振动趋势预测中仅考虑一个标准（稳定性或准确性）是不够的。本文提出了一种智能振动趋势预测方法，该方法同时实现了强大的稳定性和高精度，将振动信号的预处理，特征选择和预测方法集成在一个多目标优化框架中。首先，原始传感器信号通过经验小波变换（EWT）分解为几种模式。随后，可以通过基于样本熵的重构策略获得重构模式。然后，使用Gram-Schmidt正交（GSO）过程选择重要的输入特征。后来，通过内核极限学习机（KELM）预测了重构模式。最后，通过多目标salp算法对GSO和KELM的参数进行同步优化。通过对中国混合流HGU数据进行案例分析，结果表明，该模型在预测稳定性和准确性方面表现更好。

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