Adaptive Control for Power Systems Challenges and Directions

1. Complex Engineering Adaptive Control for Power SystemsChallenges and Directions Tariq Samad Honeywell Automation and Control Solutions samad@ieee.org

2. Why Adaptation? • Complex engineering systems aren’t stationary • equipment degradation, repair, upgrades • raw material and environmental variations • revisions of operational objectives • … to say nothing of deliberate attacks • Consequences: manual intervention, failures • staffing costs • long reaction times • suboptimal decision-making • economic and societal costs of downtime and damage

3. Adaptive Control: Complications • Investment in automation focused on higher levels of automation • “adaptive PIDs” won’t create economic impact • Consequences of “failure” can be catastrophic • a boiler installation is not an inverted pendulum experiment! • Assumptions of linearity, convexity, etc., untenable • usual theory of limited use • …many others

4. Increasing Automation in Industrial Processes • For “nominal” operations, human operators are not needed today for most complex engineering systems • manual intervention required for abnormal and changing conditions • Industry push to reduce operational staff • a diligently tracked metric: “loops per operator” in process plants • United States refining industry data: • 1980: 93,000 operators, 5.3 bbl production • 1998: 60,000 operators, 6.2 bbl production (U.S. Bureau of the Census, 1999) (Lights off operation in some plants already!)

5. Automation in Commercial Aviation • Lockheed L-749 Constellation (1945) • 5 man crew: pilot, copilot, flight engineer, navigator, radio operator • Boeing 777 (1995) • Two person crew: pilot, copilot ? What will air transport be like in 2045? With today’s technology, pilots are needed to deal with unforeseen situations

6. Promising Directions for Research • Adaptive controls are necessary if demands for autonomous and optimized operations are to be met • Selected technologies of interest • Data-centric modeling • Coordination of networked systems • Statistical assurance for control systems • Adaptive resource management • Adaptive software agents • Intelligent control architectures • Examples presented just scratch the surface of the research and application possibilities

7. Data-Centric Modeling Complex, nonlinear/ non-Gaussian behavior fit globally with a single model Estimation All data Global model Complex, nonlinear/ non-Gaussian behavior fit locallyin time with a simple model Adaptation Recent data Local model Query driven retrieval Complex, nonlinear/ non-Gaussian behavior fit locallyin data cube with a simple model Local model Relevant data Data-Centric Technology

8. Data-Centric Forecasting State and/or action variables ALGORITHM • Assess the conditions at the query point. • Search database for similar conditions. • Extrapolate from the past values. • Estimate precision of the forecast. Target variable (product demand, product property, perform. measure) Query point Neighborhood Real-time analysis of enterprise-wide data

9. Model Expert Heat demand Heat demand expert knowledge simulated actual actual Outdoor temperature Outdoor temperature In operation at municipal power/district heating network in Czech Republic (five plants, 100+ miles of steam/water pipes) Data-Centric Knowledge Integration Mechanistic model Expert Process knowledge can be fed into the system by storing model-based and/or expert data along with the actual data Virtual data Actual data Data repository A weighted mixture of virtual & actual data

10. Coordination of Networked Systems • Networks: a metaphor for complex systems—in nature and engineering • New discipline emerging that encompasses networks in the abstract, general sense • small world networks • phase transitions in network dynamics • power law distributions • network robustness and attack tolerance • Emerging topic: control of complex networks • decentralized solutions necessary for efficiency and robustness • but subsystems cannot be assumed dynamically isolated

11. CV1 (regulated) CV2 Coordination Target o Slow Path Fast Path o Start CV3 Refinery-wide Dynamic Coordination • Additional structure required for capturing dynamic interactions • Steady state detection unnecessary, unlike conventional real-time optimization • Coordinator specifies target and desired speed—runs in synchrony with MPC controllers • Linear MPC solution via range control algorithm In operation in several refineries and ethylene plants. Largest implementation coordinates 40 MPCs.

12. Statistical Assurance • Automation and control systems taking on increasingly critical roles • human lives • environment • economics • Current methods for ensuring reliability cannot accommodate variation in online processing How can we trust automation to do the right thing? Research supported by DARPA “Software-Enabled Control” project

13. Statistical Approaches for Controller Verification • Verification, validation, and certification today focus on worst-case, deterministic guarantees • intractable (or undecidable) for complex controllers • unacceptably conservative for many applications • High-performance and/or adaptive algorithms rarely used in critical applications (!) • fast dynamics, unstable, nonlinear systems • “advanced control” turns into PIDs at implementation time! • Alternative: statistical characterization • e.g., n nines stability likelihood • rigorous, not anecdotal, confidence measures are desirable

14. “Probably Approximately Correct” Assurance • Given (random) system state x, will controller C meet specs? • Classification perspective: F(x) indicates yes/no prediction • Classifier designed with machine learning techniques, exploiting results from statistical learning theory What confidence can we have in observed classifier accuracy, error(F)? Result assured under fairly general conditions given a training set such that m: number of i.i.d. samples h: VC dimension of hypothesis space 1-d: confidence e: error tolerance Scalable solutions—escape from the curse of dimensionality

15. Adaptive Resource Management • Real-time systems must execute several processes under timing and resource constraints • Today’s solution: real-time tasks limited to computationally simple, deterministic processes • task schedules generated offline • Infrastructure and middleware support needed for online-reconfigurable processes Adaptive control solutions require adaptive resource management Research supported by DARPA “Software-Enabled Control” projects

16. Adaptation of Task Computing Resources • How is adaptation enabled? • Based on computed / observed state, set task criticality and computing requirements. • CPU resource (rate x load) is made available to tasks based on criticality, requests, and schedulability analysis. • Control tasks execute with allotted time. Adapt to meet application constraints (deadlines, accuracy). Adaptation: New algorithms plant state task criticality System effects: Operational changes Computing models: Scheduling task execution

17. Open Control Platform (OCP) Implementation Integration of “anytime” and conventional processes in UAV OCP

18. Allow optimized control and decision strategies to be explored Study strategic effects of different regulations system configurations competitor strategies disaster scenarios Adaptive Agents: The SEPIA Simulator Proof-of-concept simulation and optimization tool for the electricity enterprise Adaptive agents encapsulate models for business and physical entities Research supported by Electric Power Research Institute http://www.htc.honeywell.com/projects/sepia Joint work with Univ. of Minn. (Wollenberg, Brignone)

19. Let agents interact autonomously pursue their own objectives Examine: learned strategies system statistics SEPIA Insights...

20. Intelligent Control: A Partial Success • Computational intelligence techniques now well-established as part of the control engineer’s toolbox • neural networks (PID tuning, nonlinear control, model-predictive control) • fuzzy logic (feedback and feedforward controllers) • genetic algorithms (system identification, control design) • Yet original grand visions for the field remain unfulfilled • we still cannot engineer the sophisticated examples of control we see in nature • scalable methods needed • Inspiration from nature need not stop at algorithms • adaptation and learning require architectural support as well

21. Architecture for Adaptation (1) ? Crustacean Central Nervous System Architecture

22. Architecture for Adaptation (2) ? Architecture of primate CNS (simplified!) From bio-inspired algorithms to bio-inspired architectures…

23. Conclusions • Lack of impact with adaptive control hasn’t lessened interest within industry and government! • Research so far has focused on one piece of the overall problem • Broader-based agenda is needed P o w e r A d a p t i v e Computing Platform Technical Culture Classical Control Algorithms S y s t e m s Architectural Perspective Economic Value