Fuzzy Speed Controllers of Combined Cycle Power...

INTERNATIONAL JOURNAL OF INNOVATIONS IN ELECTRICAL POWER SYSTEMS

Vol. 3 • No. 2 • July-December 2011 • pp. 109-121 © International Science PressI J I E P S

Fuzzy Speed Controllers of Combined Cycle Power Plants

Magdy Aboeleela1, Abdulmonein Fetoh2 and Ahmed Bahgat Gamal3

1,3Department of Electrical Power and Machines, Faculty of Engineering,Cairo University, 1E-mail: Egypt [email protected], [email protected] of Electricity and Energy, Egypt, [email protected]

Abstract: This paper is focused on the implementation of fuzzy logic controllers anda combination between fuzzy logic control and proportional control to control thespeed of a combined cycle electric power plant (CCPP). The system is simulated usingMatlab/Simulink and the fuzzy controller is implemented as a box in the simulation.Different types of the fuzzy controller have been tried in order to obtain the requiredspeed response which achieves certain transient and steady state behavior.

1. INTRODUCTIONDuring the last decades there has been continuous development of combined cyclepower plants due to their increased efficiency and their low emissions.

Higher efficiency, greater flexibility, and lower emissions is obtained than manyconventional thermal generators, combined with progressively shorter installationtimes and reducing installation costs, are the basis for this move toward CCGTgeneration.

The basic controllers in the CCGT model are the inlet guide vane (IGV) control,the temperature control, and the frequency dependency of the gas turbine GToutput. This paper focuses on the speed control loop fuzzy logic control systems.

Fuzzy Logic ControlThe fuzzy logic controller is composed of the following elements:

1. A rule base (a set of if-then rules) which contains a fuzzy logic quantificationof the expert’s linguistic description of how to achieve good control.

2. An inference engine which emulates the expert’s decision making ininterpreting and applying knowledge about how best to control the plant.

3. A fuzzification interface, which converts controller input into informationthat the inference engine can easily use to activate and apply rules.

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110 INTERNATIONAL JOURNAL OF INNOVATIONS IN ELECTRICAL POWER SYSTEMS

4. A defuzzification interface, which converts the calculations of the inferenceengine in to actual inputs for the process.

For choosing the input and outputs, the controller is to be designed to automatehow a human expert who is successful at this task would control the system. Firstthe expert tells us what information the user will use as inputs to the decisionmaking process.

The following inputs are to be used for the controller inputs:

e(t) = r(t) – y(t) (1)

( )d e tdt (2)

Where r(t) and y(t) are the system input and output.

The controller will use these variables for decision making. Next the controlledvariable of fuzzy controller is incremental change of output as shown in figure 1.

Once the inputs and output are chosen, the membership functions of them arethen chosen. For a two input fuzzy controller the used numbers of membershipfunctions are 3, 5 and 7 are mostly used.

Figure 1: Block Diagram of Fuzzy Controller

Fuzzy logic controller input and output gains are chosen to normalize theuniverse of discourse and make the output suitable to the fuel demand signal.

To design the fuzzy controller, the control engineer must gather information

FUZZY SPEED CONTROLLERS OF COMBINED CYCLE POWER PLANTS 111

on how the artificial decision maker should act in the closed-loop system. Sometimesthis information can come from a human decision maker who performs the controltask, while at other times the control engineer can come to understand the plantdynamics and write down a set of rules about how to control the system withoutoutside help. These “rules” basically says, if the plant output and reference inputare behaving in a certain manner, then the plant input should be some value.

Combined Cycle Plant ModelDynamic Response of CCGTs

Combined cycle gas turbines integrate the technologies of both the gas turbine andthe steam turbine. The exhaust gases from the gas turbine are fed into the heatrecovery steam generator (HRSG), which produces a supply of steam to drive thesteam turbine.

CCGT technologies have a maximum allowable temperature imposed by theturbine blade materials, any variation in the temperature of the exhaust gasesentering the HRSG will affect its efficiency and, thus, the efficiency of the steamturbine. Therefore, in order to achieve optimal efficiency in CCGTs, the exhaustgas temperature should be maintained at the maximum allowable level.

The exhaust temperature is maintained at this optimal level by controlling theair and fuel flows. Variable inlet guide vanes (IGV), which are fitted at the entranceto the compressor, control the incoming airflow. As the gas turbine runs up, theIGV are positioned to ensure a smooth run up of the air compressor (avoiding stallzones) until full speed at no load. Thereafter, the IGV move from their minimumon load position to their maximum opening in line with the admission of fuel tomaintain the programmed target exhaust gas temperature [1-7].

In order to maintain constant outlet temperature, it is necessary to adjust theairflow as the fuel flow changes.

The thermodynamic part giving the available thermal power to the gas turbineand the steam turbine is modeled by algebraic equations, corresponding to theadiabatic compression and expansion, as well as to the heat exchange in the recoveryboiler. These equations correspond to the block “Algebraic equations of energytransform” in Fig. 2. These algebraic equations are presented below: [8].

System Parameters

As an example, we consider a 1100 class single-shaft combined cycle plant. Itsrated power output is 160MW (gas turbine 106.7MW, steam turbine 53.3 MW).The model parameters are shown in the following table [8-10].


Figure 2: Single-shaft Combined Cycle Model

Figure 3: Algebric Equations Subsystem of Single-shaft Combined Cycle Model


Table 1 Model Parameters

Variable Description Value

oit Ambient temperature (K) 303

odt Nominal Compressors discharge temperature (C) 390

oft Nominal gas turbine inlet temperature (c) 1085

oet Nominal exhaust temperature (C) 532

opγ Nominal compressor pressure ratio 11.5γ Ratio of specific heat (Cp/Cv) 1.4

cη Compressor efficiency 0.85

tη Turbine efficiency 0.85Ko Gas turbine output coefficient (1/K) 0.00303K1 Steam turbine output coefficient (1/K) 0.000428R Speed governor regulation 0.04Tg Governor time constant (s) 0.05K4 Gain of radiation shielf (instantaneous) 0.8K5 Gain of radiation shield 0.2T3 Time constant of radiation shield (s) 15T4 Time constant of thermocouple (s) 2.5T5 Time constant of temperature control (overheat) 3.3Tt Temperature control (overheat) integeration rate (s) 0.4699Tcmax Temperature control upper limit 1.1Tcmin Temperature control lower limit 0Fdmax Fuel control upper limit 1.5Fdmin Fuel control lower limit 0K3 Ratio of fuel adjustment 0.77K6 Fuel valve lower limit 0.23Tv Valve positioner time constant (s) 0.05Tf Fuel system time constant (s) 0.4T6 Time constant of fuel system control (s) 60gmax Air valve upper limit 1.001gmin Air valve lower limit 0.73Tw Time constant of air control (s) 0.4699Tcd Gas turbine time constant (s) 0.2Tm Steam turbine time constant (s) 5Tb Heat recovery boiler time constant (s) 20Ti Turbine rotor inertia constant (s) 18.5Toff Temperature offset


Control LoopsTwo control loops are introduced so that the combined cycle unit functions properly.The first one is the frequency control loop, which includes the speed governor. Thesecond one is the overheat control loop [7].

Speed ControlThe first loop involves the speed governor, a speed governor is the main means ofcontrol on the gas turbine which detects frequency deviation from the nominalvalue and determines the fuel demand signal (Fd) so as to balance the differencebetween generation and load. Autonomous operation is assumed, so powerimbalances will cause electrical frequency deviations as shown in the rotor inertiablock of Figure 2.

Temperature ControlThe second loop is the temperature control and consists of two branches. The normaltemperature control branch acts through the air supply control. When thetemperature of the exhaust gases exceeds its reference value (Tr), this controlleracts on the air valves to increase the airflow, so as to decrease exhaust gastemperature (air control loop in Figure 2). In certain situations, however, this normaltemperature control is not enough to maintain safe temperatures. Thus, in cases ofa severe overheat, the fuel control signal is reduced through a low-value-selectfunction (LVS) that determines the actual fuel flow into the combustion chamber.

1. Fuzzy logic controller with 5 membership function

The error and change of error are the inputs of this controller. The usedmembership functions are as follows [11-16]:

NB : Negative BigNM : Negative MediumZE : Zero ErrorPM : Positive MediumPB : Positive Big

Table 2Rule Base for 5 Membership Functions Controller

∆e/e NB NM ZE PM PBNB NB NB NB NM ZENM NB NB NM ZE PMZE NB NM ZE PM PBPM NM ZE PM PB PBPB ZE PM PB PB PB


The followings are the responses of CCGT using fuzzy logic controller with5membership functions for the inputs and the output of the controller atdifferent powers:

Figure 4: System Response using Fuzzy Logic Controller with 5 MembershipFuncitons at P = 0.4

Figure 5: System Response using Fuzzy Logic Controller with 5 Membership Functions at P = 0.7.


We notice that the system is stable till the power reaches 0.85 the system becomesunstable.

2. An intelligent hybrid fuzzy proportional controller and optimizing its Pconstant using GA [17-18].




If we considered the fuzzy logic controller as an integral controller and addingin parallel a constant gain P to have PI controller.

Using the GA with maximum limit = 40 and power of the system is 0.9 andfuzzy logic controller of 5 membership functions to optimize the constant gain KPgives the following result:

KP = 39.797971

The model responses with fuzzy logic controller with 5 membership functionsat different powers:

Figure 8: System Response using an Intelligent Hybrid Fuzzy ProportionalController at P = 0.4.

Figure 9: System Response using an Intelligent Hybrid Fuzzy ProportionalController at P = 0.7.


Figure 10: System Response using an Intelligent Hybrid Fuzzy ProportionalController at P = 0.9

For the above case the system still stable, fast, settling time is small and overshoot is small till the power reaches 0.9.

3. An intelligent fuzzy controller and optimizing its gain constants using GA[19].

Using GA to find fuzzy logic controller gains shown in figure 6.17 gives thefollowing results:

Gain 1 = 15.613055Gain 2 = 16.132798Gain 3 = 4.073207

The system response using these values at different powers is as follows:

Figure 11: System Response using Fuzzy Logic Controller with Its Gains Optimized using GAat P = 0.4.




Comparison Between the Different Controllers SpeedFigure 14 shows a plot that contains the speed response for the different

controllers. The best response resulted using the hybrid fuzzy P controller andoptimizing its P constant using GA.

Fuzzy logic controller response is fast but its overshoot is the highest.

PID controller response reaches its steady state but with high settling time.


Where:

Npi is the speed using PI controller

Npid is the speed using PID controller

Ngap is the speed using fuzzy logic controller parallel with constant gain Nf5 isthe speed using fuzzy logic controller with 5 membership functions Tpi, Tpid, Tgapand Tf5 are time in each case

CONCLUSIONThe outcomes of this work can be summarized as follows

1. For fuzzy logic controller, the best result was obtained using fivemembership functions. The three membership function controller responseis satisfactory till 0.65 p.u of power. The controller with seven membershipfunctions failed to give any good response with any value of power and theresponse is oscillating and didn’t reach a steady value. The five membershipfunctions controller is the better response among fuzzy controllers. Theresponse is stable till power of 0.75 p.u. The system loses its stability for thepowers above 0.75 p.u.

2. The intelligent hybrid fuzzy proportional controller uses the fuzzy controllerin parallel with proportional controller. The gain of the proportionalcontroller is optimized using GA. The system response in this case is thebest among all the controllers used in this paper. The settling time is theminimum and also the overshoot.

Figure 14: Comparison Between the Different Controllers Speed


3. Using GA to optimize fuzzy logic controller gains failed to get values for thesegains to get satisfactory response. The obtained response is oscillating.

REFERENCES

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