Predictive maintenance for improving power system assets management

新的竞争性电力市场正在从根本上改变发电、输电和输送电能的游戏规则。 这些变化加上严格的监管要求、老化的电气设备以及人力和专业知识的减少，使得以根本不同的方式运行电力系统变得至关重要。电力系统和市场问题应该结合起来，经济和工程方面都要协调。在这种新的环境下，资产管理和预测性维护成为高效运营电力系统的关键业务工具。使用预测性维护可以帮助组织制定系统的维护计划，以经济有效的方式满足这些要求。预测性维护基本上将不同的技术和工具以系统的方式结合起来，以改善系统资产的管理，作为系统维护决策和有效运行的基础。 本研究的目标是双重的：第一个目标是开发动态维护模型的不同组成部分的电力系统，如变压器，断路器，电缆等，第二个目标是制定一项战略，将所有这些维护计划，以具有成本效益的管理资产的电力系统。 基于人工智能的状态监测技术将用于数据的收集、存储和调节。基于信号处理的技术将用于从数据中提取特征和分类信息。预测性维护技术的核心是健康评估单元和故障诊断单元。第一个单元的目的是生成诊断记录并提出故障可能性，而第二个单元的目的是计算资产的未来健康状况及其剩余使用寿命，同时考虑到未来的使用情况。

The new competitive deregulated electric power market is causing fundamental changes to the rules of the game of generating, transmitting and delivering electric energy.  These changes coupled with stringent regulatory demands, aging electrical equipment and reduction in manpower and expertise, make it crucial to operate the power system in a fundamentally different manner. The electrical power systems and market issues should be integrated, with both economical and engineering aspects coordinated. In this new environment asset management and predictive maintenance become key business instruments for efficiently operating the power system. The use of Predictive Maintenance can help organizations to develop a systematic maintenance program, meeting these requirements in a cost effective manner. Predictive maintenance basically combines different techniques and tools in a systematic approach to improve the managing of system assets as a basis for maintenance decisions and efficient operation of the system.   The objectives of this research are twofold: the first goal is to develop  dynamic maintenance models for the different components of the power system such as transformers, circuit breakers, cables, etc. and  the second goal is to develop a strategy to integrate all these maintenance schemes to cost-effectively manage the assets of the power systems.  Artificial intelligent-based condition monitoring techniques will be used in collecting, storing and conditioning of data. Signal processing -based technique will be used to extract features and classified information from the data. The heart of the predictive maintenance technique is the health assessment unit and the prognostics unit. The purpose of the first unit is to generate diagnostic records and propose fault possibilities, while the purpose of the second unit is to calculate the future health of an asset and the remaining useful life of it taking into account the future usage profiles.