Advanced Methods of Analyzing Operational Data to Provide Valuable Feedback to Operators and Resource Scheduling (HQ-KPI, BigData/Anomaly Detection, Predictive Maintenance)
(Room Lehar 3)
20 Jun 18
2:00 PM
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3:30 PM
Tracks:
Track 3: Big Data, Big Opportunity.
Whilst the already high stress from markets and political influences on utilities remains on a high level, the demands for intelligent support for operators and strategy are increasing. The paper will cover the experiences STEAG Energy Services (SES) has made in recent internal and external projects, discussing the practical consequences of "worlds colliding" when powerful IT approaches meet high expectations from the power plant managers, operators, and trading departments. SES is by its business model exactly at this interface of IT and real power plant world since STEAG - the parent company - owns and operates a variety of power plants worldwide (conventional + renewable, 10 GW+ owned and ~ 200 GW operating experience). In-house engineering experts develop and maintain modules to answer issues in power production that cover the whole scope. For early and reliable warnings to tackle developing problems, advanced approaches for Key Performance Indicators with a high information content are available and well established. Recently, STEAGs projects, as well as customers/partners from the wind power industry, refineries, cement plants and coal/gas-fired power plants have been increasingly demanding a fast and extensive approach that helps to efficiently prioritize and focus on the developing problems. Differences between "traditional" approaches of monitoring plant operation and distill early warning for predictive maintenance as well as "big data/anomaly detection" approaches, which are often based on fully automated learning will be discussed and experiences and conclusions from recent projects with both approaches will be shared from the perspective of an owner & operator of power plants. Finally, as a case study the paper will share the implementation of new ideas of using often already existing models of the plant to gain a deeper insight in degrees of freedom of the operation in terms of balancing heat and power production and avoiding unnecessary losses.