28
Eduardo Perez, Ph.D. Industrial Engineering Department Texas State University San Marcos Texas
Eduardo Perez, Ph.D.
Industrial Engineering Department
Texas State University
San Marcos Texas
AGENDA
Research Motivation
Problem Statemento State of the Art
o Research Objective
o Related Work
Solution Approacho Simulation
o Online Scheduling
o Stochastic Online Scheduling
Summary and Ongoing Work
2Air Force DDDAS Program PI Meeting, 01/28/2016
RESEARCH MOTIVATION
Wind farms provide a source of clean energy
Wind power capacity has increased in the U.S.o 27,176 MW in 2008, 50% increase
from 2007
o Over 65,000 MW in 2014
3
Wind power is expected to produce 20% of the total power capacity by 2030
Air Force DDDAS Program PI Meeting, 01/28/2016
RESEARCH MOTIVATION
Operations and maintenance (O&M) costs are asignificant component of the overall cost
O&M costs account for 20% - 47.5% of the wholesalemarket price• Heavy-duty equipment involve for maintenance
• Operated under non-stationary weather conditions
• Long lead time for repair resources
4Air Force DDDAS Program PI Meeting, 01/28/2016
PROBLEM STATEMENTCHALLENGES
The decision-making process of when and what type ofmaintenance action to under take is very challenging.
Wind turbines suffer from stochastic loading
Stochastic loading makes turbine degradation or failureprediction rather complex
New models are needed to assist in wind farmoperations and maintenance decisions
5Air Force DDDAS Program PI Meeting, 01/28/2016
STATE OF THE ART
Current maintenance practice for wind farms mainly consists ofscheduled maintenance (SM) and corrective (or breakdown)maintenance.
Scheduled maintenance is carried out usually twice a year
Corrective maintenance is performed when unexpected breakdowns occur
The cost of maintenance visits is substantial in large-scale wind farms
Wind farm operators want to avoid unnecessary visits bydetecting and fixing the problems before any failure occurs
Most modern turbines are equipped with condition monitoringequipment (sensors)
Enables more effective maintenance planning
6
PROBLEM STATEMENT
Air Force DDDAS Program PI Meeting, 01/28/2016
RESEARCH OBJECTIVE
To establish how stochastic online data-enabled models and algorithmscan lead to wind turbine rapid damage detection and failure reduction.
To accomplish this objective, the PI proposes:
A data-driven stochastic online optimization algorithm that takes into account data uncertainties in turbines status, weather conditions, and resources availability in scheduling maintenance and resources.
The research plan involves:
stochastic programming models,
stochastic online scheduling algorithm,
integrated stochastic online optimization and simulation framework for assessment purposes
7Air Force DDDAS Program PI Meeting, 01/28/2016
METHODOLOGY
8
Stochastic Online
Scheduling
SimulationDEVS
Real System
Sensor Database
Weather Database
Resource Database
Input data
current schedule
schedulerevisions
Air Force DDDAS Program PI Meeting, 01/28/2016
Fig.1 Simulation – optimization framework
DISCRETE EVENT SYSTEM SPECIFICATION
Formal modeling and simulation framework
Based on generic dynamical systems concepts
Provides well-defined separation of concerns
Supports distinct modeling and simulation layers
Simplifies and accelerates model development
Enables hierarchical model composition construct
9Air Force DDDAS Program PI Meeting, 01/28/2016
MODEL ABSTRACTION
The simulation model is composed of all critical component models for a wind farm.
Model will account for the stochastic nature of the system.
The model integrates with scheduling methods to track operation levels and managers’ objectives.
10Air Force DDDAS Program PI Meeting, 01/28/2016
WIND FARM SPEED MODEL
Uses wind speeds data measured from west Texas MESONET
Generates sequences of wind speeds at turbine locations.
Reflects diurnal cycle and yearly seasonality using time series model with Fourier series
Adjusts wind speeds to turbine hub height level
11Air Force DDDAS Program PI Meeting, 01/28/2016
WIND TURBINE POWER GENERATION MODEL
Power generated by wind turbines mainly depends on wind speed
Power can be calculated using a power curve
• Designed to start generating power at the cut-in wind speed 𝑉𝑐𝑖
• Increases nonlinearly as the wind speed increases
• At higher wind speeds than the cut-out wind speed 𝑉𝑐𝑜 turbines are shut down to avoid damage to the structure
12Air Force DDDAS Program PI Meeting, 01/28/2016
Fig. Wind turbine power curve
WIND TURBINE COMPONENTS WITH DEGRADATION MODEL
A Markov model is used to represent component degradation.
Four different states: normal, alert, alarm, and failed.
13Air Force DDDAS Program PI Meeting, 01/28/2016
WIND TURBINE
14Air Force DDDAS Program PI Meeting, 01/28/2016
Fig. Wind turbine block diagram
WIND FARM MODEL
Air Force DDDAS Program PI Meeting, 01/28/2016 15
Fig. Wind farm block diagram
CASE STUDY Wind turbine configuration: GE 1.5 sle turbine
O&M Costs: $8,632 for PM, $17,264 for CM
10 replications for each O&M Strategy for 20-year turbine lifetime with 100 turbines
Implement two maintenance strategies
Corrective maintenance is carried out in both strategies when a component fails
16Air Force DDDAS Program PI Meeting, 01/28/2016
POWER GENERATED
17Air Force DDDAS Program PI Meeting, 01/28/2016
Fig. Average power generated by wind farm annually
AVERAGE NUMBER OF FAILURES
18Air Force DDDAS Program PI Meeting, 01/28/2016
Fig. Average number of failures per turbine in each year
RESULTS
19Air Force DDDAS Program PI Meeting, 01/28/2016
Table: Figures in industry
Table: Simulation results for power generation and capacity factor
ONLINE SCHEDULING Some relevant data arrives in the future and is not accessible at
present
Online algorithms generate output without the knowledge of the entire set of inputs
Competitive analysis: compares the output generated by an online algorithm to the output produced by the offline problem solution
Mathematical description
An online algorithm A is presented with a request sequence 𝜃 =𝜃 1 , 𝜃 2 ,… , 𝜃 𝑚 .
The request 𝜃 𝑡 , 1 ≤ 𝑡 ≤ 𝑚, must be served in order of occurrence
When serving request 𝜃 𝑡 , algorithm A does not know any request 𝜃 𝑡′ with 𝑡′ > 𝑡.
20Air Force DDDAS Program PI Meeting, 01/28/2016
STOCHASTIC ONLINE SCHEDULING
Extension of the online problem
Use stochastic information to improve solution quality
Stochastic Online Scheduling Algorithm
Use stochastic information about weather conditions, resource availability, and component conditions to improve solution quality
Within the stochastic online scheduling framework the problem is formulated as a stochastic programming model
The algorithm provides maintenance schedule for the turbine by taking into consideration stochastic information
21Air Force DDDAS Program PI Meeting, 01/28/2016
TWO-STAGE STOCHASTIC PROGRAMMING MODEL
Air Force DDDAS Program PI Meeting, 01/28/2016 22
First-Stage Decisions• Turbine maintenance schedule• Resources scheduled to complete maintenance
Uncertainty• Weather conditions• Resource availability• Wind turbines conditions
Recourse Decisions• Maintenance rescheduling
TWO-STAGE STOCHASTIC PROGRAMMING MODEL
Air Force DDDAS Program PI Meeting, 01/28/2016 23
First Stage
Second Stage
• Allow decisions to be evaluated against possible future scenarios
• In the first-stage, the model decides when to schedule the
maintenance and which resources to use.
• In the second stage, the model makes corrective actions on the
maintenance schedule.
Air Force DDDAS Program PI Meeting, 01/28/2016 24
TWO-STAGE STOCHASTIC PROGRAMMING MODEL
TWO STAGE STOCHASTIC PROGRAMMING MODEL
Air Force DDDAS Program PI Meeting, 01/28/2016 25
SIP model
0001110110000000000110000000
Resource 1
Online Scheduler
maintenancerequest
Create model
Update schedules
maintenanceschedule
SIP modelsolver
Data
Master Schedule
0001110110000000000110000000
Resource 2
0001110110000000000110000000
Team 1
0001110110000000000110000000
Team 2
STOCHASTIC ONLINE SCHEDULING
SUMMARY AND ONGOING WORK
The stochastic loading of wind turbines makes their degradation and failure prediction rather complex.
The decision making process of when and what type of maintenance action to undertake is very challenging.
A well-planned maintenance strategy is needed to reduce costs and increase the availability of wind turbines.
Integration and testing of: stochastic programming models,
stochastic online scheduling algorithm, and
stochastic online optimization and simulation framework for assessment purposes.
27Air Force DDDAS Program PI Meeting, 01/28/2016
Eduardo Perez, Ph.D.
Director of the iMOSS Lab
Assistant Professor
Industrial Engineering Department
Texas State University
San Marcos Texas
Email: [email protected]
28Air Force DDDAS Program PI Meeting, 01/28/2016
QUESTIONS
