adaptive control for power systems challenges and directions
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Samad/Confs/NSF-EPRI-2002/
Adaptive Control for Power SystemsChallenges and Directions
Tariq Samad
Honeywell Automation and Control Solutions
samad@ieee.org
Complex Engineering
Samad/Confs/NSF-EPRI-2002/
Why Adaptation?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
Samad/Confs/NSF-EPRI-2002/
Adaptive Control: ComplicationsAdaptive 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
Samad/Confs/NSF-EPRI-2002/
Increasing Automation in Industrial ProcessesIncreasing 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!)
Samad/Confs/NSF-EPRI-2002/
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
Samad/Confs/NSF-EPRI-2002/
Promising Directions for ResearchPromising 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
Samad/Confs/NSF-EPRI-2002/
Data-Centric Modeling Data-Centric Modeling
Alldata
Estimation
Globalmodel
Complex, nonlinear/non-Gaussian behavior fit
globally witha single model
Adaptation
Recentdata
Localmodel
Complex, nonlinear/non-Gaussian behavior fit locally in time with
a simple model
Querydriven
retrieval
Relevantdata
Localmodel
Complex, nonlinear/non-Gaussian behavior fit
locally in data cubewith a simple model
Data-Centric Technology
Samad/Confs/NSF-EPRI-2002/
Target variable(product demand,product property,perform. measure)
ALGORITHM
• Assess the conditions at the query point.
• Search database for similar conditions.
• Extrapolate from the past values.
• Estimate precision of the forecast.
Data-Centric ForecastingData-Centric Forecasting
State and/or action variables
Neighborhood
Query point
Real-time analysis of enterprise-wide data
Samad/Confs/NSF-EPRI-2002/
Process knowledge can be fed into the system by storing
model-based and/or expert data along with
the actual data
Data repository
Virtualdata
Actualdata
Mechanisticmodel
Expert
A weighted mixtureof virtual & actual data
Data-Centric Knowledge IntegrationData-Centric Knowledge Integration
Model Expert
Heatdemand
Outdoortemperature
actual
simulated
Outdoortemperature
actual
expert knowledge
Heatdemand
In operation at municipal power/district heating network in Czech Republic (five plants, 100+ miles of steam/water pipes)
Samad/Confs/NSF-EPRI-2002/
Coordination of Networked SystemsCoordination 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
Samad/Confs/NSF-EPRI-2002/
Refinery-wide Dynamic Coordination
• Additional structure required for capturing dynamic interactions
• Steady state detection unnecessary, unlike conventional real-time optimization
o
o
Slow Path
Fast Path
Coordination Target
CV2
CV3
CV1(regulate
d)
Start
• 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.
Plantwide Objective Function J = f(x)
MPC 1 MPC 2 MPC 3 MPC n
Plantwide optimizer based on predicted constraints
CoordinationCollar . . .
Samad/Confs/NSF-EPRI-2002/
Statistical AssuranceStatistical 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
Samad/Confs/NSF-EPRI-2002/
Statistical Approaches for Controller VerificationStatistical 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
Samad/Confs/NSF-EPRI-2002/
“Probably Approximately Correct” Assurance“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
13
log82
log41
22 hm
1)(errorPr F
m: number of i.i.d. samplesh: VC dimension of hypothesis space: confidence: error tolerance
Scalable solutions—escape from the curse of dimensionality
Samad/Confs/NSF-EPRI-2002/
Adaptive Resource ManagementAdaptive 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
Research supported by DARPA “Software-Enabled Control” projects
Adaptive control solutions require adaptive resource management
Samad/Confs/NSF-EPRI-2002/
Adaptation of Task Computing ResourcesAdaptation 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).
task criticality
task execution
plantstate
Samad/Confs/NSF-EPRI-2002/
Open Control Platform (OCP) ImplementationOpen Control Platform (OCP) Implementation
RTARM
AnytimeScheduler
Anytime CPUAssignmentController
AnytimeTasks
AnytimeTask Alloc
Table
• Allocate
• Set• Clear• Lookup
• Create_x
Task Descriptors forPeriodics,
Aperiodics,and Anytimes
RTARM API
OCP Controls API
Mapping
ConfiguratorP AP
PP
APAP
APAP
AT
PeriodicOperations
AperiodicOperations
Adaptation
RateSelection
TAO
OrbInteractions
Mapping
CPUScheduler
EventChannel
• Allocation
FaultManagement
Other Services...
….
Mapping
Integration of “anytime” and conventional processes in UAV OCP
Samad/Confs/NSF-EPRI-2002/
Adaptive Agents: The SEPIA Simulator
• Allow optimized control and decision strategies to be explored
• Study strategic effects of different
– regulations – system configurations– competitor strategies– disaster scenarios
Adaptive agents encapsulate models for business and
physical entities
Proof-of-concept simulation and optimization tool for the electricity enterprise
Research supported by Electric Power Research Institute http://www.htc.honeywell.com/projects/sepiaJoint work with Univ. of Minn. (Wollenberg, Brignone)
Samad/Confs/NSF-EPRI-2002/
SEPIA Insights...
• Let agents interact– autonomously pursue their
own objectives
• Examine: – learned strategies– system statistics
Samad/Confs/NSF-EPRI-2002/
Intelligent Control: A Partial SuccessIntelligent 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
Samad/Confs/NSF-EPRI-2002/
Architecture for Adaptation (1) ?Architecture for Adaptation (1) ?
Brain
StomatogastricGanglionCommisural
Ganglion
SubesophagealGanglion
CommisuralGanglion
Thoracic Ganglia
Abdominal Ganglia
Telson Ganglion
Crustacean Central Nervous System Architecture
Samad/Confs/NSF-EPRI-2002/
Architecture for Adaptation (2) ?Architecture for Adaptation (2) ?
Architecture of primate CNS (simplified!)
Limbic System(Motivation)
Cerebrum(Cognitive Processes)
Basal Ganglia(Coordination) Thalamus
(Data Concentrator)
Cerebellum(Proprioception)
Brainstem
Spinal Column
A
B
From bio-inspired algorithms to bio-inspired architectures…
Samad/Confs/NSF-EPRI-2002/
ConclusionsConclusions
• 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
Tech
nica
l
Cultu
re
Computing
Platform
Architectural
Perspective
Economic
Value
ClassicalControl
Algorithms
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