09-05-2012, 11:53 AM
Fuzzy Logic Controlled Microturbine for Distributed Generation Systems Application
Fuzzy Logic Controlled Microturbine for Distributed Generation Systems Application.pdf (Size: 152.24 KB / Downloads: 40)
Abstract
The advance development and successive operation of
microturbine as distributed generation resulting in commercial
and scientific approach is due to its flexible control and reliable
operation. There are several advance methods to control the
speed of microturbine, but still lack of in the research field of
microturbine anti interference problem has been not solved due
to very high speed. To interface the microturbine model, this
paper investigates the fuzzy logic based speed governor of the
microturbine as an alternative to nominal PI controller or lead
lag based transfer function system. The development of fuzzy
logic based speed governor includes the membership function
with input and output relations, which improves the stability of
the microturbine system through load change and robust
simulation. The superior performance of this type of governor is
compared with lead lag base transfer function governor. This
model is developed in the MATLAB/Simulink system library.
Keywords, Microturbine, Fuzzy Logic Controller, Speed Governor.
I. INTRODUCTION
he control algorithms based on fuzzy logic have been
implemented in many processes. The application of such
control technique have been encouraged because of
several reason such as it improves the robustness over the
conventional linear control algorithm, simplified control
design of a difficult system module into simplified
implementation [1].
The microturbine is one of the most promising alternative
sources that demonstrate high potential to meet the user need
as a distributed generation and delivering the quality power.
They are one part of general evolution in gas turbine
technology, techniques incorporated into the larger machine to
improve the performance can typically found in microturbines
as well. The recuperation, low NOx technologies, and the
potential use of advanced material such as ceramics for hot
section parts [2].
Sanjeev K Nayak is a full time research scholar in the Department of
Electrical and Electronics Engineering, at National Institute of Technology
Karnataka Surathkal, Mangalore-575025 Karnataka, India (e-mail:
saneevnayak_82[at]yahoo.co.in).
D. N. Gaonkar, is an Asst. Professor in the Department of Electrical and
Electronics Engineering, at National Institute of Technology Karnataka
Surathkal, Mangalore-575025 Karnataka, India (e-mail:
dngaonkar[at]ieee.org).
The microturbine has many advantages like durability, greater
efficiency, power quality, reliable operation, low emission and
fuel flexibility. The microturbines are developed with
advanced technology of turbo-chargers, gas turbine and
auxiliary system. Microturbines are simple and small gas
turbine which consists of components like compressor,
combustor, turbine and recuperator to increase the efficiency.
The atmospheric air is compressed with high pressure by the
compressor, thus compressed air mixes with the fuel and this
mixture is ignited in combustor to increase the pressure of gas
which in turn is utilized to run the turbine.
The mathematical model of microturbine generator is complex
and very strong nonlinear, and there is strong interference due
its speed, so it difficult to control it. The nonlinear control
methods based on model cannot solves various control
problem in microturbine, some of the advance control have
been applied to control the microturbine such as lead lag
transfer function controller(governor) PI controller or PID
controller [3].
In conventional control, the amount of control is determined
in relation to number of data inputs using a set of equation to
expresses the entire control processes. Conveying this to
human knowledge in the form of mathematical equation is a
bit difficult task otherwise may not be possible, fuzzy logic
provides a simple tool to interpret into the reality.
This paper presents the fuzzy logic based microturbine speed
governor for various loading and unloading operations. A
comparative study has been made between the conversional
lead lag transfer function governor and fuzzy logic based
speed governor. The simulation results clearly demonstrate
the superiority of fuzzy control over the lead lag transfer
function. The functioning of fuzzy logic based speed
governors and transfer function generates transients on
loading and unloading.
II. MICROTURBINE
There are basically two types of microturbine designs, one is
high speed single shaft microturbine where the compressor
and turbine are mounted on the same shaft and rotates at a
speed of 50,000 to 120,000 rpm. The frequency of generated
voltage is around 1.5 to 4 KHz. However, to reduce the
frequency to 60Hz or grid frequency the power electronic
converters and inverters are used towards the load side.
Another type of microturbine design is split shaft microturbine
which uses the rotating power at 3600 rpm and the
Fuzzy Logic Controlled Microturbine for
Distributed Generation Systems Application
Sanjeev K Nayak and D. N. Gaonkar, Member, IEEE
T
34th National Systems Conference, 10-12th December, 2010
National Institute of Technology Karnataka Surathkal, Mngalore-575025
conventional generator is connected through a gear box. These
two are small scale generation turbine [4] [5]
The simplified single shaft microturbines with control system
is show in fig.1, The model consists of temperature control,
fuel control, turbine dynamics, speed governor, and
acceleration control blocks. This model mostly works for the
control of slow dynamics. It is presumed that all other
operations are under normal condition excluding the fast
dynamics.
Temperature
Control
Fuel
Control
Lead lag Tran,
function
L O W
Acceleration
Control
Combustion
Exhaust
Turbine
Torque Dynamics
Speed(pu)
Reference
Speed
Ref, Temperature
+
-
Rotor
Dynamics
Fig. 1. Block diagram of microturbine.
A. Speed Contrller
Speed control is running of the microturbine under part load
condition. It operates on the error formed between the
reference speed and operating speed. It is operated mostly lead
lag transfer function or by PID controller, in this type of
functioning fuzzy logic based speed governor is implemented.
B. Acceleration Controller
The primary use of the acceleration controller is to limit the
rate of rotor acceleration before it reaches its operating speed
during start up. If the operating speed is close to the rated
speed then acceleration controller is eliminated. The output of
the governor goes to min value selector to decide the value of
fuel demand signal.
C. Temperature Control
The other signals received by the low value selector from the
temperature controller and thermocouple output, forces the
output of temperature control to stay at maximum limit,
permitting uninhibited governor/speed control. When the
thermocouple output exceeds the reference temperature, the
difference is negative. The fuel flow is reduced for burning in
the combustor resulting in turbine torque and the exhaust gas
temperature is measured by the thermocouple. The
thermocouple output is compared with reference value,
normally the reference value is greater then the temperature
control output. To limit the rated output, the governor output
formed value will pass through the low value selected [6] [7].
III. PI CONTROLLER
To achieve the dynamic performance and to provide accuracy,
PI controller or lead lag transfer function is used. The main
theme behind the controller is to actuate the rotor speed and
reference speed until the deviation becomes zero. Integral
controller provides zero steady state speed deviation and
proportional controller reduces overshoot. The speed
controller is based on tie line bias control where each area
tends to reduce the Area Control Error (AEC) to zero.
The controller signals are
U1 =−Kp *ACE1−Ki∫ACE1dt (1)
U2 =−Kp *ACE2 −Ki∫ACE2dt (2)
Where KP and Ki are proportional and integral gains,
respectively for a PI controller the gain Kp and Ki has been
optimized using integral square error criterion [8].