Towards Autonomous Learning Wind Turbines

Transcription

1 Towards Autonomous Learning Wind Turbines FH Flensburg 2016 Volkmar Sterzing, Siemens, Corporate Technology, Learning Systems In collaboration with Unrestricted Siemens AG 2016, all rights reserved

2 Artificial Intelligence and Machine Learning Machine Learning (Informal Definition): Ability of a computer to learn to perform a task without being explicitly programmed to do so Examples: Google s self-driving car DeepMind s Alpha Go Apple s Siri voice recognition software Amazon s Echo or Google s Home Picture: Google, ww.google.com/selfdrivingcar/ Google s Image Search Recommendation systems: You may also like Spam filters Fraud and intrusion detection software Picture: openclipart.org Picture: Google Picture: Amazon, ww.amazon.com Page 1

3 Developmental state of Artificial Intelligence Biological transfer of behavioral information from one generation to the next Stephen Hawking [quote]: Success in creating AI would be the biggest event in human history. [ ] Unfortunately, it might also be the last, unless we learn how to avoid the risks. The Independent, 2014 through genes, i.e. programming through teaching [1] Artificial Intelligence Artificial Intelligence: Adaption to new information is defined by fixed program code. Developmental state of AI: on level between insects and simple mammal [Presenter s opinion] Hardware [1] Galef, Bennett G. Culture in Animals? The Question of Animal Culture. Ed. Kevin N. Laland and Bennett G. Galef. Cambridge, Mass: Harvard UP, Page 2

4 Business Applications Analyze Inform Diagnostic Analytics Descriptive Analytics Prescriptive Analytics Predictive Analytics What happened? Why did it happen? What will / should happen? What shall we do? Act Business Applications Service statistics Sales reports Root cause identification Fault analysis Condition monitoring Fault prediction Power optimization Load balancing Data Mining Machine Learning Page 3

5 Neural Siemens: 29 Years of Research, Development, Innovation Demand Forecasting Global Footprint Simulator Wind Turbines Soft Sensors Forecasting Transfer Losses Load Forecasts for DB Energie Solar Power Forecasting Smart Meters Price Forecasting Software Environment Energy Price Forecasts LME Copper Forecasts Graphical User High Performance Interface State Space Computing Prior Rule Modeling Demand Forecasting Customer Forecasting Information Specification & Diagnosis Scripted Relation Mgt. Languages Modeling Function Complex Systems Uncertainty Approximation Analysis Mathematics of Feedforward Decision Neural Nets Neural Networks Learning Data Support Process Algorithms processing Modeling Neuro-Fuzzy Optimal Process Networks Recurrent Control Surveillance Neural Nets Fuzzy Topology Rules Design Forecasting Renewables Economical Applications for Neural Networks Technical Applications Risk Analysis Process Control Gas Turbine Analysis Traffic Forecasting Clinical Trial Analysis Quality Surveillan Forecasting Emissions Failure Detection

6 Areas of Machine Learning: Supervised Learning Supervised Learning: Given: input and desired output (target), i.e. training data points: Solar Radiation Ambient Temp Goal: learn model which predicts target 5am 9am 12pm Training error: Nacelle Temp Model-predicted measured SIEMENS Wind Power s Remote Diagnostic Center Potential application examples: Model-based diagnostics o Identify problem without inspection Condition-based maintenance o detect early warning signs o replace worn out components when convenient Page 4

7 Modeling of Open Dynamical Systems with Recurrent Neural Networks (RNN) y ( s u ) s f, u t+1 = y = g t ( ) t s t t s t T d 2 ( y t y t ) min A, B, C t= 1 ( As Bu ) s + 1 = tanh + y t = Cs t t t state transition output equation identification Finite unfolding in time transforms time into a spatial architecture. We assume, that future inputs are const. The analysis of open systems by RNNs allows a decomposition of its autonomous & external driven subsystems. Long-term predictability depends on a strong autonomous subsystem. y t 2 C s t 2 B u t 3 A y t 1 C st 1 B u t 2 A y t s t C B u t 1 A y t+1 C s t+1 u t B A y t+2 C s t+2 A y t+3 C s t+3 A y t+4 C s t+4

8 Areas of Machine Learning: Unsupervised Learning Unsupervised Learning: Given: only data points Goal: find structure in data, e.g. detect the presence of anomalies in materials Potential application examples: Fatigue and crack detection through non-invasive methods o Structural damage detection for turbine towers IT security o Detection of security breach of data systems Page 5

9 Areas of Machine Learning: Reinforcement Learning Environment State Action The Controler Reward Controler (Decision) Knows the situation (state) Makes a decision Changes therefore the situation (moves to a new state) Receives a reward as feedback Goal is to maximize the discounted sum of all future rewards

10 Elements of Reinforcement Learning Policy Reward Value Model of environment Policy: Reward: Value: Model of environment: taken? What to do? What is good? Which state is good by predicting rewards What will be the new state due to the action

11 Areas of Machine Learning: Reinforcement Learning Reinforcement Learning: Setup: Interact with environment in discrete time steps time At each time step Observe system state is set of all possible system states Apply action is set of all possible actions System transitions into next state Receive reward Goal: learn policy (action strategy) Sensor readings constitute state action Sensor readings constitute state Action duration State transition Energy production constitutes reward that maximizes cumulative reward, called return Potential application examples: ALICE: Power optimization of Wind Turbines Balancing of Lifetime consumption across Wind Park Wake optimization in wind park Page 6

12 Motivation A L I C E Autonomous Learning In Complex Environments Optimize level of capacity utilization (current: % onshore, % offshore) ALICE offers additional energy at low and medium wind speeds (e.g. onshore) Minimize Levelized Costs of Energy (LCoE) and costs per MWh ALICE offers increase of Annual Energy Production (AEP) without hardware modifications Page 7 Public Siemens AG, 2016, All rights reserved

13 Idea Behind ALICE Page 8

14 Technological Overview Data Base 2 nd level control loop Machine learning loop ALICE adapts setpoints of low-level turbine controllers Machine learning exploits available data to create ALICE policy Use available sensors to learn and optimize ALICE policy Page 9 Public Siemens AG, 2016, All rights reserved

15 Limits of Authority Optimization Constraints Limits of authority have to be defined for control parameters, e.g. limit pitch angle to avoid excessive loads Constraints for the control policy have to be identified, e.g., allowed parameter change rates Parameters for Control Policy The control policy optimizes turbines parameters by using scalar meta parameters: Complex parameters, e.g. characteristic lines, are mapped to scalar parameter(s) for optimization Constraints and limits of authority are translated into bounds for these parameters Control parameter ALICE Output Multiplicative factor α Controller s default characteristic map Upper bound: Default map * 1.2 Lower bound: Default map * 0.8 Wind Speed Scaling factor α = Time Page 10 Public Siemens AG, 2016, All rights reserved

16 ALICE Policy Training and Deployment 1. Machine Learning: Model training Turbine-individual ALICE policy training 2. Model Deployment: Policy frozen and saved as xml file Uploaded to turbine and evaluated POLICY Database ¼ to 1 year past archived data Cloud-based training service Page 12 Public Siemens AG, 2016, All rights reserved

17 Conclusions In collaboration with Siemens Wind Power Service Gained by Machine Learning Increased annual energy production Potential Reduced Loads Extended Lifetime Active Wake Control without physical models Page 14 Public Siemens AG, 2016, All rights reserved