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Intelligent and Integrated Condition Monitoring of Distributed Generation Systems

分布式发电系统的智能综合状态监测

基本信息

批准号：
EP/I037326/1
负责人：
Xiandong Ma
金额：
$ 12.62万
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FI037326%2F1
关键词：
Intelligent Integrated Condition Monitoring Distributed

项目摘要

Distributed electricity generation (DG) will play a significant role in future electric power system, as this type of power generation technology can provide electric power by utilising a wide range of renewable energy sources at a site close to end users. Considerable advances have been achieved during past decades in the capacity, scale and location of DG systems, e.g. from onshore to offshore. One of the most critical challenges for the deployment of DG systems relates specifically to availability and reliability in order to sustain energy generation and maximise a long service life of the energy systems unattended. This has, therefore, placed higher demand on predictive maintenance from innovative condition monitoring systems and solutions to tackle new arising challenges in this area.The research proposed in this first grant scheme application represents an effort to explore key issues of generic importance to condition monitoring techniques optimised for fault detection and diagnosis. The research is oriented towards DG systems with wind turbines being the DG sources as this particular application presents a number of realistic challenges. Firstly, measurement signals would exhibit strong non-stationary behaviour due to the intermittent nature of wind sources and fluctuations of grid system. Secondly, the signals of small magnitude may indicate a start of a significant failure, which are normally undetected by conventional methods particularly in a harsh environment. Thirdly, large volume of data needs to be processed and transmitted especially for continuous online monitoring. For example, if we assume that 250 points are required for a typical 2 MW wind turbine to monitor most subsystems of a turbine, this will give rise to 36 million data per day for a 1 GW wind farm under a sampling rate of 5 minutes. Furthermore, a critical issue needing urgent attention will be the health problems of the sensor system, which requires that the monitoring techniques should be assessing what is happening when some of the sensors read data incorrectly.In order to meet such diversified requirements, we plan to use and apply windowed transform, a technique well known for its ability to extract nonstationary components in the measurement data. By the optimal selection of a window shape, automatic windowed wavelet transforms can be achieved to accommodate different sensor data for better feature localisation, extraction and correlation. Although an incipient fault signal is usually of low magnitude and short duration, it would essentially carry the same features as the large ones, such as the regularity. If we can design a suitable algorithm to match the local regularity or singularity of a signal, any incipient faults, abnormalities and disorders can be detected irrespective of their magnitude and time duration. The project is also concerned with designing a hybrid neuro-fuzzy method for optimal sensor data fusion. The use of this artificial intelligence method can best correlate sensor data and predict the unknowns by systematic incorporation of priori information. Minimising the number of sensors whilst still maintaining a sufficient number to assess the system's conditions can not only minimise the complexity of sensor systems but it can also reduce data storage requirements. The final part of the project relates specially to the practical aspect, where the proposed algorithms are validated in real time for online monitoring purposes on a modular embedded system. The proposed condition monitoring system in this project would accommodate all monitoring techniques within one hardware module, which can be readily adapted to other applications. The project will provide better sensing techniques and improved algorithms towards real applications by improving our understanding of how to engineer them in order to aid the decision making process with respect to asset maintenance and management of existing and future DG systems.

分布式发电（DG）将在未来的电力系统中发挥重要作用，因为这种类型的发电技术可以在靠近最终用户的地点利用各种可再生能源提供电力。在过去的几十年中，DG系统的容量、规模和位置已经取得了相当大的进步，例如从陆上到海上。DG系统部署的最关键挑战之一具体涉及可用性和可靠性，以维持能源生产并最大限度地延长无人值守能源系统的使用寿命。因此，这对创新状态监测系统和解决方案的预测性维护提出了更高的要求，以应对这一领域出现的新挑战。这项首次资助计划申请的研究代表了对状态监测技术的普遍重要性进行探索的努力，以优化故障检测和诊断。该研究是面向DG系统与风力涡轮机的DG源，因为这个特定的应用提出了一些现实的挑战。首先，测量信号会表现出强烈的非平稳行为，由于间歇性的风源和电网系统的波动。其次，小幅度的信号可能指示重大故障的开始，这通常是传统方法检测不到的，特别是在恶劣的环境中。第三，需要处理和传输大量数据，特别是连续在线监测。例如，如果我们假设典型的2 MW风力涡轮机需要250个点来监测涡轮机的大多数子系统，则对于1 GW风电场，在5分钟的采样率下，这将产生每天3600万个数据。此外，一个关键的问题，需要迫切关注的是传感器系统的健康问题，这就要求监测技术应该是评估发生了什么，当一些传感器读取数据不正确，为了满足这样的多样化的要求，我们计划使用和应用窗口变换，一种众所周知的技术，其能力，以提取测量数据中的非平稳成分。通过对窗口形状的最佳选择，可以实现自动加窗小波变换，以适应不同的传感器数据，从而更好地进行特征定位、提取和相关。尽管早期故障信号通常幅度较低且持续时间较短，但它本质上具有与大故障信号相同的特征，例如规律性。如果我们能设计一个合适的算法来匹配信号的局部规律性或奇异性，那么任何早期的故障、异常和失调都可以被检测出来，而不管它们的大小和持续时间。该项目还涉及设计一个混合神经模糊方法的最佳传感器数据融合。使用这种人工智能方法可以最好地关联传感器数据，并通过系统地结合先验信息来预测未知数。最大限度地减少传感器的数量，同时仍然保持足够的数量来评估系统的状况，不仅可以最大限度地减少传感器系统的复杂性，而且还可以降低数据存储要求。该项目的最后一部分特别涉及到实际的方面，所提出的算法进行了验证，在真实的时间在线监测的目的，在一个模块化的嵌入式系统。本项目中的拟议状态监测系统将在一个硬件模块中容纳所有监测技术，该硬件模块可以很容易地适应其他应用。该项目将提供更好的传感技术和改进的算法对真实的应用程序，提高我们的理解如何工程，以帮助决策过程中的资产维护和管理现有的和未来的DG系统。