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Using Machine Learning Tools to Predict Compressor Stall

机译:使用机器学习工具预测压缩机摊位

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Clean energy has become an increasingly important consideration in today's power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Results show that the tool is capable of predicting stalls 5-20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.
机译:清洁能源已成为当今电力系统中越来越重要的考虑因素。随着清洁能源的推动,许多燃煤发电厂正在退役,以便有利于风和太阳能的可再生能源。然而,可再生能源的间歇性意味着传统电力系统之后的动态负荷对栅极稳定性至关重要。在各种操作条件下具有高度的灵活性和快速响应,燃气轮机系统准备成为电网中部件后的主要负载。然而,载荷的快速变化可能导致燃气轮机动力系统中的流体流动稳定性。这些不稳定性往往导致压缩机浪涌和摊位,这是当今涡轮机械中压缩机安全有效运行的一些最关键的问题。虽然压缩机浪涌和摊位的话题已被广泛研究,但没有证明早期预测的方法是有效的。本研究探讨了机器学习工具的利用来预测压缩机摊位。长短期内存(LSTM)模型,一种经常性神经网络(RNN)的形式,使用来自设计用于混合配置的100kW恢复的燃气轮机电力系统的真实压缩机失速数据集进行培训。测试了LSTM模型,分类和回归的两个变体,以确定最佳性能。回归方案被确定为最准确的方法,使用该配置开发了一种用于预测压缩机失速的工具。结果表明,该工具能够在发生之前预测停顿5-20毫秒。利用能够进行5毫秒的高速控制器的时间步长,可以采取缓解作用以防止在发生之前的压缩机失速。

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