Adaptive Control for Power Systems Challenges and Directions

Samad/Confs/NSF-EPRI-2002/

Adaptive Control for Power SystemsChallenges and Directions

Tariq Samad

Honeywell Automation and Control Solutions

[email protected]

Complex Engineering


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


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


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!)


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


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


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


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


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)


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


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 . . .


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


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


“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


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


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


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


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)


SEPIA Insights...

• Let agents interact– autonomously pursue their

own objectives

• Examine: – learned strategies– system statistics


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


Architecture for Adaptation (1) ?Architecture for Adaptation (1) ?

Brain

StomatogastricGanglionCommisural

Ganglion

SubesophagealGanglion

CommisuralGanglion

Thoracic Ganglia

Abdominal Ganglia

Telson Ganglion

Crustacean Central Nervous System Architecture


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…


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

Adaptive Control for Power Systems Challenges and Directions

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