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Fuzzy Logic&

Its Control Applicationsin Power Plants

Presented By :

Aman Singhal (102028)

ET-09, PE-C&INTPC Ltd., EOC, Noida

20-Sep-2010 FTA, ET-09, Off-Campus Batch

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OUTLINE

Basic Concepts of Fuzzy Logic

Fuzzy Controller : Structure & Types

Applications of Fuzzy Logic Control in Power Plants

Fuzzy based Intelligent Soot Blowing System

Fuzzy Logic Controller for Industrial AC Systems

Self-Tuning Fuzzy PID Controller for Hydraulic Actuators Further Studies

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What is Fuzzy Logic ??

Fuzzy Logic is

A mathematical system, that

Analyzes analog input values in terms oflinguisticvariables, that

Take on continuous values between 0 and 1, incontrast to classical or digital logic, which operates on

discrete values of either 0 and 1 (true and false).

Fuzzy Logic is the theory of fuzzy sets, sets that calibratevagueness and uncertainty.

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Motivation : Why Fuzzy ??

Fuzzy logic is conceptually easy to understand. Implementing design objectives, that are difficult to express

mathematically, in Linguistic or descriptive rules.

No need for a mathematical model, based on natural language.

Relatively Simple, Adaptive and Flexible.

Less sensitive to system fluctuations.

Fuzzy logic can model nonlinear functions of arbitrary

complexity.

Fuzzy logic can be blended with conventional control

techniques.

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Fuzzy Set

A fuzzy set F in a universe of discourse U is characterizedby membership function F, which takes values in the

interval [0,1], i.e.,

F: U[0,1]

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Structure of a

Basic Fuzzy Controller

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Types of Fuzzy Controllers:

- Direct Controller -


- Direct Controller -

The Outputs of the Fuzzy Logic System Are the Command Variables of the Plant:The Outputs of the Fuzzy Logic System Are the Command Variables of the Plant:

Fuzzification Inference Defuzzification

IF temp=low

AND P=high

THEN A=med

IF ...

Variables

Measured Variables

Plant

Command

Fuzzy Rules OutputFuzzy Rules Output

Absolute Values !Absolute Values !

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- Supervisory Control -


- Supervisory Control -

Fuzzy Logic Controller Outputs Set Values for Underlying PID Controllers:Fuzzy Logic Controller Outputs Set Values for Underlying PID Controllers:


IF temp=low

AND P=highTHEN A=med

IF ...

Set Values

Measured Variables

Plant

PID

PID

PID

Human OperatorHuman Operator

Type Control !Type Control !

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- PID Adaptation -


- PID Adaptation -

Fuzzy Logic Controller Adapts the P, I, and D Parameter of a Conventional PID Controller:Fuzzy Logic Controller Adapts the P, I, and D Parameter of a Conventional PID Controller:


IF temp=low

AND P=high

THEN A=med

IF ...

P

Measured Variable

PlantPID

I

D

Set Point Variable

Command Variable

The Fuzzy Logic System Analyzes the PerformanceThe Fuzzy Logic System Analyzes the Performance

of the PID Controller and Optimizes It !of the PID Controller and Optimizes It !

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- Fuzzy Intervention -


- Fuzzy Intervention -

Fuzzy Logic Controller and PID Controller in Parallel:Fuzzy Logic Controller and PID Controller in Parallel:


IF temp=low

AND P=high

THEN A=med

IF ...

Measured Variable

PlantPID

Set Point Variable

Command Variable

Intervention of the Fuzzy LogicIntervention of the Fuzzy Logic

Controller into Large Disturbances !Controller into Large Disturbances !

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Fuzzy Control Applications

In Power Plants

Areas of application of fuzzy logic are:

Soot Blowing Optimization Evaporator Superheat Regulation

Control of Hydraulic Drives

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Fuzzy Logic Based

Intelligent Soot BlowingSystem

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Current Methodology / Practice

Traditionally been performed on a schedule basis.

Not based on actual fouling conditions in the boiler.

Amount of soot deposited is not measureable. Parameters, particularly, for the identification of soot

blowing regions are not defined.

Judgment of the engineer based on some parameters, but

no integrated system for soot blowing requirementmonitoring.

Fuzzy Logic Based Intelligent Soot BlowingSystem

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Fuzzy-based

Intelligent Soot Blowing System

Fuzzy rule-based Expert system to estimate the

cleanliness factor (CF) of the furnace.

Calculates the heat absorbed and the degree of individualstage fouling in the form of CF.

No need of heat flux sensors.

Advises on 'When' and 'Where' to Soot Blow, depending

on a single index, CF.

Fuzzy Logic Based Intelligent Soot BlowingSystem

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Parameter Identification

Following input variables are identified for

fuzzification :

LTSH metal temperature

Total spray flow

Burner Tilt

Mill Combination

Load

Elapsed Time since last soot blowFuzzy Logic Based Intelligent Soot Blowing

System

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Block Diagram

(CF)

Fuzzy Logic Based Intelligent Soot Blowing

System

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Rule Base for Estimating

Furnace Dirtiness


System

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Commercial Aspects

Operationally, Soot blowing causes lowering of flue gas

temp & thus, increased fuel firing to maintain load, in

order to cope for the heat loss.

Fuzzy based Intelligent Soot Blowing System optimizesthe entire process, thereby having considerable cost

advantage.

Not possible to calculate the actual cost effect as no. of

factors are involved such as the running load, scheduled

generation and additional fuel/coal cost to maintain that

load & steam parameters.

Not to exclude the long term degradation effects.


System

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Advantages of Fuzzy-based

Intelligent Soot Blowing System

Implementation possible with the existing measurement

instrumentation.

Superior to direct measurement of heat flux as the heat flux

sensors are expensive and prone to single point failure. Improves boiler performance as heat absorption can be

maintained optimally.

Minimizes disturbances caused by soot blower activation.

Improves soot blower life and reduces maintenance cost. Reduces attemperation spray rates & soot blowing steam.

Reduces tube erosion & thus, reduces boiler tube leakage.


System

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Fuzzy Logic Controller

ForIndustrial Air-Conditioning

Systems

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Basic Air-Conditioning Cycle

Expansion Valve

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Fuzzy Controller Description

An electronic expansion valve coupled with fuzzy logic

control provides excellent operating efficiencies.

Monitors and controls an important refrigerant parametercalled superheat.

Optimizes the suction line superheat and the positioning

of the electronic expansion valve.

Compressor cycling is minimized, reducing wear on bothcompressor and starting components.

Fuzzy Logic Controller for Industrial AC Systems

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Principle

As the heat load increases,

evaporator superheat rises.

Controller senses this condition

and adjusts the expansion

valve aperture to allow more

refrigerant into the evaporator,

thus causing the superheat to

fall.

The result is optimized

evaporation within the

evaporator coil and maximum

heat absorption.Fuzzy Logic Controller for Industrial AC Systems

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Self-Tuning

Fuzzy PID ControllerFor

Hydraulic Actuators

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Electro-Hydraulic Actuator Model

Combination of a classical

PID and fuzzy controller.

Consists of a servo valveand a hydraulic cylinder.

When differences between

P2 and P1 exist, thehydraulic cylinder extends

or compresses. Electro-Hydraulic Actuator Model

Self-Tuning Fuzzy PID Controller for Hydraulic

Actuator

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Structure of

Self-Tuning Fuzzy PID Controller

Self-tuning fuzzy PID controller means that the three

parameters Kp, Ki and Kp ofPIDcontroller are

dynamically tuned by using fuzzy tuner.

Self-Tuning Fuzzy PID Controller for Hydraulic

Actuator

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Performance

Self-tuning Fuzzy PID

achieves better tracking

response than

conventional PID

controller.

It is indicated from :

Faster rise time

Faster settling time

Zero steady state

error.Self-Tuning Fuzzy PID Controller for Hydraulic

Actuator

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Advantages for NTPC

Fuzzy Logic Control philosophy is somewhat analogous toKaizen i.e. Small Continuous Improvement.

Fuzzy Control Integration could become a part of R&Mdrive.

Fuzzy Rule Base Design offers a highly intellectual yet a

very practical & feasible solution to conserve the richexperience bank that NTPC has built over the last 3decades.

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Further Studies

Speed Control of Chopper fed DC Motor

Voltage Stability Enhancement of AC Transmission

Fuel-Air Ratio Optimization

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THANK YOU

fta_20sep2010

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