Simulation, Modeling, & Decision Science AMES LABORATORY

Dr. Paolo Pezzini Mr. Peter FinzellDr. Mark Bryden

Building an Extensible Framework for 

Testing New Engineering Concepts

Current challenge

Advanced concept power systems are oftentightly coupled and difficult to control

• Protect equipment

• Enable coupling of disparate thermal cycle and components

• Larger optimum control envelope

• Support transient operation

Cyber‐Physical System

ModelsCyber-

PhysicalSystem

Hardware

Increasing  Accuracy of Exploration

Increasing Flexibility of Exploration

Goal – develop advanced concept power plants

• Development and validation of novel sensor control strategies

• Testing of sensor hardware

• Low cost investigation for new engineering concepts

• Exploring other advanced computational algorithms

Today’s discussion

• Development and validation of novel sensor control strategies

• Testing of sensor hardware

• Low cost investigation for new engineering concepts

• Exploring other advanced computational algorithms

Multi‐agents control solutions Multivariable control schema

Motivation

Air

Exhaust

Air PlenumV-301

MixingVolumeV-304

Load BankE-105

F, T, P

Q

FuelValveModel

NaturalGas

C-100 T-101GeneratorG-102

TE-326

Fuel CellSimulator

V-302

FE-380

PT-305

FV-432

FV-170

FV-162

FV-380

%

Real-TimeSOFC

SystemModels

LHVNatural GasPressure

Load

% Recycle

Real-TimeGasifierSystemModel

Biomass

Oxidant

Flow

Composition

Temperature

SyngasOutput

Hot AirBypas

s

Cold Air Bypass

Bleed AirBypass

Motivation

Air

Exhaust

MixingVolume

Load Bank

Generator

Cyber-physical Devices

Hot AirBypass

Cold Air Bypass

Bleed AirBypass

Anode

CathodeCathode

Electrolyte

Fuel

Fuel Cell Simulator

Traditional Single‐input Single‐output PIDs failed 

PID HyperFuel Valve20 % ‐ 80% 

Turbine SpeedSet Point

Turbine Speed [rpm]

PID Hyper0‐200%

FV‐170 =  0‐100%

FV‐380 =  100‐200%

Fuel FlowCathode Mass Flow

Equivalence Ratio Set Point

Cathode Airflow [kg/s]

Multi‐agent Control Strategy

Sensors

Control Action

Local Decision

Actuator

Local Agent N

Sensors

Control Action

Local Decision

Actuator

Local Agent 1

…………… .

Multi‐agent Control Strategy

Cathode Airflow

Control Action

Local Decision

Cold‐air Bypass

Cold‐air Bypass Agent

Turbine Speed

Control Action

Local Decision

Electric Load

Electric Load Agent

Multi‐agent Control Schema

Air

Exhaust

MixingVolume

Load Bank

Generator

Cyber-physical System

Hot AirBypass

Cold Air Bypass

Bleed AirBypass

Fuel

Fuel Cell Simulator

AnodeAnode

Cathode

Electrolyte

Block Repository

Cathode Airflow

Control Action

Local Decision

Cold‐air Bypass

Cold‐air Bypass AgentElectric Load  Agent

Global Goal

Turbine Speed

Control Action

Local Decision

Electric Load

Less Complexity• No System Modeling• Easy Tuning• Easy Adaptability

Multi‐agent Steady State Results

Stable control at steady state

Multi‐agent Transient Results

Slow Response during tracking

Multi‐agent PID and Transients

Multi-agent transient results PIDs transient results

Developing a Multivariable Control Schema

State Space System Modelling

uInput

yOutput

2

1

4241

3231

2221

1211

4

3

2

1

44434241

34333231

24232221

14131211

4

3

2

1

uu

bbbbbbbb

xxxx

aaaaaaaaaaaaaaaa

xxxx

4

3

2

1

2

1

10000100

xxxx

yy

High Complexity in• System Modeling• Control Law• Tuning• Adaptability

Mitigation of Centralized and Decentralized Interactions

Air

Exhaust

MixingVolume

Load Bank

Generator

Cyber-physical System

Hot AirBypass

Cold Air Bypass

Bleed AirBypass

Fuel

Fuel Cell Simulator

AnodeAnode

Cathode

Electrolyte

Multivariable Steady State Results

Stable control at steady state

Multivariable Transient Results

Multivariable PID and Transients

Multivariable transient results PIDs transient results

Summary

Multi-Agent Control Schema

• Stable control at nominal condition during the first implementation on a hardware system

• Slow response

Multivariable Control Strategy

• Good performance under disturbance rejection and tracking

• Simultaneous control of actuators causes oscillations in advanced power systems

Future Work

• Optimization for the multi‐agent schema

• Reproducibility in the results

• Increase of the size for the multivariable controller

Acknowledgements

MESA Team in Ames

Dan Bell (PhD grad, Iowa State U)

Zach Reinhard (PhD grad, Iowa State U)

Hyper Team in NETL 

Dr. David Tucker 

Farida Nor Harun (PhD grad, McMaster U)

Valentina Zaccaria (PhD grad, U of Genoa)