电力系统和电动汽车的故障诊断

English | 2024 | ISBN: 9781003527657 | 251 pages | True PDF | 18.31 MB

The present monograph offers a detailed and in-depth analysis of the topic of fault diagnosis for electric power systems and electric vehicles. First, the monograph treats the problem of Fault diagnosis with model-based and model-free techniques (Model-based fault diagnosis techniques and Model-free fault diagnosis techniques). Next, the monograph provides a solution for the problem of Control and fault diagnosis for Synchronous Generator-based renewable energy systems (Control of the marine-turbine and synchronous-generator unit and Fault diagnosis of the marine turbine and synchronous-generator unit. Additionally, the monograph introduces novel solutions for the problem of Fault diagnosis for electricity microgrids and gas processing units (Fault diagnosis for electric power DC microgrids and Fault diagnosis for electrically actuated gas compressors). Furthermore, the monograph analyzes and solves the problem of Fault diagnosis for gas and steam-turbine power generation units (Fault diagnosis for the gas-turbine and Synchronous Generator electric power unit and for the steam-turbine and synchronous generator power unit). Finally, the monograph provides a solution for the problem of Fault diagnosis for wind power units and for the distribution grid (Fault diagnosis for wind power generators and Fault diagnosis for the electric power distribution grid).

The new fault detection and isolation methods that the monograph develops are of generic use and are addressed to a wide class of nonlinear dynamical systems, with emphasis on electric power systems and electric vehicles.

On the one side, model-based fault detection and isolation methods are analyzed. In this case, known models about the dynamics of the monitored system are used by nonlinear state observers and Kalman Filters, which emulate the system’s fault-free condition.

On the other side, model-free fault detection and isolation methods are analyzed. In this case, raw data are processed by neural networks and nonlinear regressors to generate models that emulate the fault-free condition of the monitored system.

Statistical tests based on the processing of the residuals, which are formed between the outputs of the monitored system and the outputs of the fault-free model provide objective and almost infallible criteria about the occurrence of failures.

The new fault detection and isolation methods with statistical procedures for defining fault thresholds enable early fault diagnosis and reveal incipient changes in the parameters of the monitored systems.

该专著对电力系统和电动汽车的故障诊断进行了详细的深入分析。首先，专著探讨了基于模型和无模型的方法（基于模型的故障诊断技术和无模型的故障诊断技术）来处理故障诊断问题。其次，专著提供了解决同步发电机为基础的可再生能源系统的控制与故障诊断问题的解决方案（包括海上涡轮机和同步发电机单元的控制以及海上涡轮机和同步发电机单元的故障诊断）。此外，专著介绍了适用于电力微电网和气体处理单元的新型故障诊断方法（针对直流电力微电网的故障诊断和电驱动气体压缩机的故障诊断）。进一步地，该专著分析并解决燃气和蒸汽涡轮发电机组的故障诊断问题（包括燃气涡轮和同步发电机的电力系统以及蒸汽涡轮和同步发电机的电力系统）。最后，该专著提供了对风力发电机组和配电网进行故障诊断的方法（针对风力发电机的故障诊断及电力配电网的故障诊断）。 新开发的故障检测与隔离方法具有通用性，并应用于广泛的非线性动态系统，重点在于电力系统和电动汽车领域。 一方面，基于模型的故障检测与隔离技术进行了分析。在这种情况下，监控系统的动力学已知模型由非线性状态观测器和卡尔曼滤波器使用，以模拟系统的无故障状态。 另一方面，针对未知或自建模型的新一代故障诊断方法进行了分析。在这种情况下，原始数据被传递给神经网络和非线性回归器来生成用于模仿监控系统无故障状态的模型。 基于残差处理后的统计测试提供了客观且几乎无可置疑的标准，以确定故障的发生。 具有统计程序定义故障阈值的新一代故障检测与隔离方法使得早期故障诊断成为可能，并揭示了被监控系统的参数发生初期变化的情况。

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