Big Data for Asset Management in Power Generation. Dr. Tomasz Kaminski / Dr. Peter Deeskow January 2019

Transcription

1 Big Data for Asset Management in Power Generation Dr. Tomasz Kaminski / Dr. Peter Deeskow January 2019

2 Big Data in Power Generation Big Data - new ways of improving the operation of power plants with respect to: Efficiency Availability Early detection of failures and losses Planning of maintenance Optimization of staff deployment 2

3 Predictive Maintenance Acting at the right moment, decreasing costs, and raising the plant s availability owing to predictive maintenance Current sensor-based data Compare Digital Replica / Digital Twin Detecting anomalies Informing early on Avoiding false alarms 3

4 Digital Twin Physical Models: Fatigue / Creep Model, Thermodynamic Model 4

5 Digital Twin Data-Based Models HQ KPI / Big Data Methods HQ KPI Training Sensor-based data Big Data methods 5

6 Data Based Models - Supervised and Unsupervised Learning Supervised learning Expert knowledge on cause and effect is displayed. Engineering in the modeling but simplified evaluation Training data thorougly selected Experts HQ-KPI Unsupervised learning Correlations between the measured values are detected and independent key variables are identified (Deep Autoencoder). Automatic modeling but engineers analysis in the evaluation Training data taken as it is Big Data ML-KPI 6

7 Machine Learning for High Quality KPIs 1. Sensor data 2. Learning process 3. Actual and reference data Data set for training Artificial neural network 4. KPI = (actual/reference) 5. Significance test 6. Root cause analysis 7

8 Big Data for a 1,100 MW Unit Turbo Set Heat Map >1,200 channels 60 months (Jan. 1, 2012 Jan. 1, 2017) Evaluated data record: >1,200 sensors, 43,800 hourly values 52,560,000 hourly mean values from 630,072,000 5-minute values 8

9 Big Data for a 1,100 MW Unit Turbine Damage: A Posteriori Analysis June 1, April 15, The system identifies abnormal running behavior of the turbine on June 2 nd at 7:55 a.m., several hours before the first alarms in the DCS. 9

10 Big Data for a 1,100 MW Unit First Online Results Heat map all channels (not clustered) Heat map only channels with anomalies (hierarchically clustered) Status: Sep. 24 th Status: Sep. 26 th Status: Sep. 28 th Online operation of the software since 9/2018 Output of the heat map every 24h Operational assessment - abnormalities, creating a statistic Already two defect pressure sensors identified and rectified 10

11 Holisitic approach to Maintenance - Predictive Maintenance Integration Scenarios with SAP Integration with larger Platforms offers potential for future developments Closed Loop Alert Processing for Power Plants Forward solutions supporting RUL applying toolboxes for AI/ML Reliability and Flexibility Centric Operations of Power Plants 11

12 Digital Twins for Predictive Maintenance Optimal Support in Daily Business Advantages Limitations a) Experts HQ-KPI Targeted configuration for each KPI and individual training periods ensure high sensitivity. The initial engineering effort is bigger than with the Big Data approach. Digital Twin Easy interpretation of the results Errors/faults for which no KPI has been defined are not detected. b) Big Data ML Lower costs when creating the system Anomalies do not go unnoticed even if no KPI has been defined. The attribution of detected anomalies to possible errors/faults requires a technical analysis. The selection of the learning phase influences the result. More data / longer periods are required. 12

13 Please feel free to contact me if you have any questions or require further information: Dr. Peter Deeskow Phone Thank you for your attention! STEAG Energy Services GmbH Rüttenscheider Str Essen Germany Phone Fax