Nghiên cứu xây dựng hệ điều khiển tốc độ tuabin thuỷ điện liên kết vùng trên cơ sở logic mờ và mạng nơron nhân tạo tt tiếng anh

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF TRANSPORT AND COMMUNICATIONS

NGUYỄN DUY TRUNG

RESEARCH ON CONSTRUCTING
THE HYDROELECTRIC TURBINE SPEED CONTROL
SYSTEM FOR INTERCONNECTED AREA
BASED ON FUZZY LOGIC
AND ARTIFICIAL NEURAL NETWORKS

Course: Control Engineering and Automation
Code: 9520216

SUMMARY OF
ENGINEERING DOCTORAL THESIS

HÀ NỘI – 2020


The Thesis was completed at:
UNIVERSITY OF TRANSPORT AND COMMUNICATIONS

Scientific Instructors::
1. Prof., Dr. Lê Hùng Lân
2. Associate Prof., Dr. Nguyễn Văn Tiềm
Reviewer 1:
Reviewer 2:
Reviewer 3:

The thesis will be defended to the University - Level Doctoral

Dissertation Reviewer Council at Room Conference room on 4th
floor, Building A8 In University of Transport and Communications
(No. 3 Cau Giay, Hanoi) at .......................... date........../............./2020.

The thesis can be found at:
- Vietnam National Library
- Library of University of Transport and Communications


1
PREAMBLE
1. The reason for choosing the topic
For the current Vietnamese electricity system, raising the capacity
and stabilizing the system to meet the electricity demand is very urgent
and necessary. In order to meet the electricity demand for power loads
and improve the quality of electricity, the Government, ministries,
branches and localities have introduced many preferential policies to
encourage corporations and private enterprises. and foreign enterprises
invest in building power generation plants to supply the Vietnamese
electricity system, with special priority given to renewable energy in
order to improve the quality of electricity and ensure national energy
safety and security. .
In our country today, the construction of smart grid system requires
integrating many diverse energy sources to ensure national energy
security, so the connection of power sources and hydropower plants is
important and necessary. This problem will be focused on solving by the
PhD student in the thesis with the topic: "Research on building regionallinked hydroelectric turbine speed control systems on the basis of fuzzy
logic and artificial neural networks".
2. The purpose of the topic
Researching and building models of regional-linked hydropower

turbine speed control system.
Research on building regional-linked hydroelectric turbine speed
control system on the basis of fuzzy logic and artificial neural networks
to improve control quality.
3. Research method
Study the actual technological process of the operating mode of the
hydroelectric automation system.
Research, construct and survey a simulation model of a hydraulic
generator turbine based on Matlab / Simulink simulation tool with actual
parameters of the unit, using new intelligent control algorithms.
4. Object and scope of the study
- Researching equipment and technology for turbines for
hydroelectric plants in single and two regions.
- Study the process of operating the plant and power system, study the
fault types of the unit and the influence of parameters such as: Unit
failure, generator capacity, frequency when load changes. , linking with
factories in the power supply area.
Designing PI type fuzzy logic controller is based on optimization
algorithms such as instrumentation optimization (PSO), genetic
algorithm (GA), differential evolution (DE).


2
Synthesis of the neuron controller combined with predictive control
algorithms (ANN - MPC), nonlinear regression (NARMA), adaptive
control with reference model (MRAC) applied in frequency control load
numbers of hydropower systems linking the two regions.
5. Scientific and practical significance
* Scientific significance:
Develop the intelligent control algorithms based on the application of

fuzzy control and neural networks for synthesis of hydroelectric turbine
speed controller of two – area interconnected system when the load
changes.
* Practical significance:
The results of this study is the basis for experimentation towards
manufacturing smart controllers to improve the control quality of
hydroelectric turbine controller for current hydroelectric plants in
Vietnam.
6. New results achieved
- Synthesize the optimal PI, PD fuzzy controller for two-area
connected hydropower turbine speed (frequency) controller with 03
algorithms using Particle swarm optimization (PSO), Genetic algorithm
(GA) and Differential evolution (DE) algorithm.
- Synthesize the optimal neural controller for two-area connected
hydropower turbine speed (frequency) controller with 03 algorithms
using Predictive control (MPC), Nonlinear regression control (NARMA
) and Model reference adaptive control (MRAC). The correction
parameters were determined through the PSO algorithm.
7. Research content
The thesis is structured with: Introduction and 5 chapters, conclusions
and recommendations, list and research works, annexes of drawings and
references.
CHAPTER 1
OVERVIEW OF INTERCONNECTED AREA
HYDROELECTRIC TUABIN SPEED CONTROL FOR GRID
FREQUENCY STABILIZATION
1.1. Introduction to Vietnam hydroelectricity
In our country, hydroelectricity accounts for a high proportion in the
structure of electricity production. Currently, although the power sector
has diversified its power sources, hydroelectricity still accounts for a

significant proportion. In 2014, hydroelectricity accounted for about
32% of total electricity production. According to forecasts of Power


3
Planning VII (PDP VII), by 2020 and 2030, the proportion of
hydropower is still quite high, corresponding to 23%.

Figure 1.1. Hydroelectric plant model
1.2. Automation systems in hydroelectric plants
In the hydroelectric plant, the automation system in the plant is very
important, because all operations and troubleshooting are done
automatically.
1.3. The problem of controlling frequency and active power in the
electrical system
1.4. The problem of frequency control of generation with regional
linkage
1.5. Review of studies
- Overseas research:
In the world, the research on integrated control systems for singlearea has been studied for a long time, now basically solved the small load
and independent power generation. Currently getting more attention in
applying intelligent control theory such as fuzzy system and artificial
neural network system.
The problem of automatic generator control (AGC) or LFC load
frequency control in electrical systems has a long history and is one of
the most important topics of interconnected electrical systems. In an
electrical system, the LFC controller as an auxiliary generator plays an
important and fundamental role in maintaining the electrical system
reliability at an adequate level. In LFC practice, components rapidly
change system signals that are virtually invisible due to the filters

involved in the process. That is why further reduction in LFC response
time is neither possible nor desirable. In practice, the quality of an LFC


4
system depends on the quality of the control signals. This compensation
generation is related to the short-term balance of power, the frequency of
the power system has a key role to enable power exchange and better
power supply to electrical loads [60], [61] , [66], [71], [73].
Additional controls have been applied to effectively adjust the ACE
to zero. Research work also contributes to LFC's designs based on
various control techniques.
Discrete modeling of the LFC in two-zone power systems is shown in [21].
LFC

One area
(1 area)

PS – For
HVDC

Classic control
method

GRC and GDB
nonlinear

Research gap

Two area

(2 regions)

PS for DG and
RERs

The optimal
control method

Objective
functions

Trend research
direction

Three area
(3 regions)

Smart gird

Adaptive
controls

Computer –
based control

Four area
(4 regions)

Small gird

Sustainable
controls

Compression

Figure 1.6. LFC system
Table 1.2. Comparison between recent studies on the topics
LFC / AGC in the document
Document
[101]
[96
[102]
[97]
[98]
[103]
[99]
[78]
[79]
[80]
[81]
[82]
[83]
[84]
[85]
[86]
[87]
[88]

Area
Source type

number
2
Thủy điện - Nhiệt điện
2
Thủy điện - Nhiệt điện, Ga
2 Hydropower- Thermal power, Gas
2
Hydropower - Wind - Diesel
2
Thermal power, Gas
2
Thermal power
2
Thermal power
3 Hydropower- Thermal power, Gas
2
Hydropower- Thermal power
2
Hydropower- Thermal power
3 Hydropower- Thermal power, Gas
2 Hydropower- Thermal power, Gas
2
Thermal power
2 Hydropower- Thermal power, Gas
2
Hydropower- Thermal power
2
Thermal power
2
Thermal power

2
Thermal power

Controller Type
FLC
I
OOPC
PIDD
I,PI,ID,PID
Fuzzy - PID
I
I
PID
DMPC
PID
FOPID
DMPC
FOFPID
ANFISC
PID
CHB _I
I

Skill
optimization
Fuzzy
IPSO
TLBO
DE
FA

IPSO
ICA
DMPC
QOHC
IPSO
DMPC
BFOA
ANFIN -PS
PSA
CSA


5
Document
[89]
[90]
[91]
[92]
[93]
[94]
[95]

Area
Source type
number
2
Thermal power
2 Hydropower- Thermal power, Gas
2 Hydropower- Thermal power, Gas
Hydropower- Thermal power 2

Diesels
2
Hydropower- Thermal power
2
Hydropower- Thermal power
2 Hydropower- Thermal power, Gas

I+ FLC
I
FOFPID

Skill
optimization
BFO
OHS
ICA

I

CRPSO

I
I
PI

CRPSO
ICA
PSO -SCA

Controller Type

- Research in the country
In which [9] studied PID controller with fuzzy correction applied to
the problem of hydroelectric turbine operating load in independent mode.
In [8] "Application study of neural fuzzy network to build control
algorithm for hydroelectric turbine velocity control" applied fuzzy neural
network algorithm to adjust PID controller parameters. In [10], research
and application of modern measurement and control solutions to improve
the quality of frequency stability in small and medium hydropower
plants. The method of backstepping, optimal control and Kalman
filtration has been introduced to build adaptive controller to improve the
quality and stability of turbine rotation frequency in small and medium
hydro power plants.
1.6. Select a topic name and research direction
Through analysis, the author chooses the title of the topic: "Research
on building regional-linked hydroelectric turbine speed control system
on the basis of fuzzy logic and artificial neural networks"
1.7. Thesis objectives
- Researching and building models
of interconnected area hydropower
turbine speed control system.
Research
on
building
interconnected area hydroelectric
turbine speed control system on
the basis of fuzzy logic and
artificial neural network, using
optimization
algorithms

to
improve control quality.
- Compare the proposed control
strategies to find the most suitable Figure 1.7. Thesis implementation
control solution for the given
process
problem.


6
1.8. Conclusion chapter 1
- The thesis has analyzed the problem of hydropower turbine speed
control, an overview of domestic and foreign studies on design of
hydroelectric turbine speed control system.
On the basis of these analyzes, the thesis sets out the design of a
hydroelectric turbine speed control system linking two regions to
stabilize the load frequency based on the application of intelligent control
techniques of fuzzy logic and neural network, applying optimization
algorithm PSO, GA, DE ...
The published results [CT5] belong to the list of published scientific
works of the thesis.
CHAPTER 2
DYNAMIC MODEL OF THE INTERCONNECTED AREA
HYDROELECTRIC GENERATOR TURBINE SYSTEM
2.1. Structure diagram of single-area hydropower system

Figure 2.2. Model of single-area hydropower system
2.1.1. Pressure piping model
 ht ( s )
 TW ( s)

ut ( s)

(2.1)

where TW  Lur is constant water start time at rated load, (s),
ag hr

2.1.2. Model of electric - hydraulic servo system,
Wg ( s) 

 g e ( s)
1

 xe ( s) 1  s.Tg

2.1.3. Model of hydraulic turbine
1  Tw s
 P m ( s)
w t ( s) 

 g ( s) 1  0.5Tw s
2.1.4. Model generator

(2.3)

(2.4)


7
 ( s)

1

 P m ( s)   P e ( s) Ms  D
2.1.5. Investigation of system dynamics
2.2. Hydropower system model linking two regions
w p ( s) 

(2.5)

Figure10 (a)

Figure 10 (b)
Figure 2.10. Hydropower system links the two regions
2.3. Hydroelectric generator turbine speed control system model
linking two regions
B1

Pref1

1
R1

Xe1

g1(s)

P1HV(s)

1
Tg1.s  1

Remote
controll 1

Pm1

PL1

Tw1.s  1
0.5Tws
. 1

1
Tp1.s1

1
M 1s  D1

ACE1
Speed ​1

Wing direction 1

Tua bin 1

Generator 1

1
s

2T12

-1
-1

Xe2

Pref 2

1
`
Tg 2.s  1

Remote
controll 2

Speed ​2

B2

ACE2

P2HV(s)

g2(s)
1
Tp2.s1

Wing direction 2

Tw2.s  1
0.5Tws
. 1

Tua bin 2

Pm2
1
M 2 s  D2

PL 2

Generator 2

1
R2

Figure 2.15. Control system mathematical model diagram
Hydropower links the two regions


8
In the thesis, the simulation examples are performed with values
of system parameters as follows [11,16,18]:
Tg1  Tg 2  48.7(s) ; Tw1  Tw 2  1(s)

Tr1  Tr 2  0.513 (s); M1  M 2  0.6 (s);
D1  D2  1 (pu); R1  R2  2.4 (Hz/p.u)
T12  0.0707 (pu)

2.4. Conclusion chapter 2
In this chapter, the thesis has built the mathematical model of the
basic functional blocks of the single-zone hydropower turbine control
system and the regional connection.
Surveying the working characteristics of functional blocks of 2-zone
linked hydropower system, giving a schematic diagram of the speed
control system of hydropower turbines linking two regions.
The results are published [CT4] in the list of published scientific
works of the thesis.
CHAPTER 3
DESIGNING A INTERCONNECTED AREA HYDROELECTRIC
TUABINE SPEED CONTROLLER ON THE BASIS OF FUZZY LOGIC
TO STABILIZE THE LOAD FREQUENCY
3.1. PID law fuzzy controller
* Fuzzy controller according to PID law
* Fuzzy controller according to PD law
* Fuzzy controller according to PI law
3.2 Controller parameter optimization algorithms
3.2.1 The algorithm of PSO swapping
3.2.2 GA genetic algorithm
3.2.3 DE differential evolution algorithm
3.3. Hydropower turbine speed controller design linking 2 zones to stabilize
the frequency when the load changes
FLC 1

CONTROL-AREA 1

∆PL1

ACE 1(t)

Governor

Turbine

Generator

∆f1

Compute
∆Ptie12

ACE 2(t)

Governor
FLC 2

Turbine

Generator
∆PL2

∆f2

CONTROL-AREA 2

Figure 3.7. Hydropower network diagram linking two regions


9
3.2.1. Design FLC1 and FLC 2 type PI controller

PI-type Fuzzy
logic controller

Knowledge
base
Defuzzification
interface

Fuzzification
E(t) interface

e(t )

Ge
d
dt

Decisionmaking
logic

Gce
ce(t) CE(t)

r(t)
_

e(t)

Setpoint

Fuzzy logic
controller

un(t)



Gu

u(t)

U(t)

u(t)

Control
plant

y(t)

ym(t)
Sensor &
transmitter

Figure 3.8. Typical PI type fuzzy logic controller architecture for the controller

Table 3.1. Suggested fuzzy rule table for PI / PD type fuzzy controller
E(t)
NB
NM

NS
ZE
PS
PM
PB

NB
PB
PB
PB
PM
PM
PS
ZE

NM
PB
PM
PM
PM
PS
ZE
NS

NS
PB
PM
PS
PS
ZE

NS
NM

DE(t)
ZE
PM
PM
PS
ZE
NS
NM
NM

PS
PM
PS
ZE
NS
NS
NM
NB

PM
PS
ZE
NS
NM
NM
NM
NB

PB
ZE
NS
NM
NM
NB
NB
NB

3.2.2. Design FLC1 and FLC 2 type PD controller

Figure 3.9. PD-type fuzzy logic controller structure incorporates
optimal PSO algorithm


10
3.2.3. Optimization of fuzzy controller parameters

Figure 3.10. PI-type fuzzy logic controller structure incorporates
optimal PSO algorithm
3.2.4. Simulation of hydroelectric turbine speed control system linking
2 regions

Figure 3.11. PID controller parameters
3.2.4.1. Simulate a PI-type FLC controller with PSO combination
3.2.4.2. Some simulation results
Single area hydroelectric plant
The objective function is to minimize the criteria of the integral error
[1], [11].

11
T

T

0

0

J   | e(t ) | dt   | r (t )  y (t ) | dt  min

(3.14)

Figure 3.17. Simulation results for a single-area hydropower plant
(a) Load change; (b) Frequency deviation (speed) response

Figure 3.18. Compare the three FLC controllers for the case
single area hydroelectric plant
Hydropower system links the two regionsThe target function used in
optimization is given by the formula (3.15) below:
T

J   | f1 (t ) |  | f 2 (t ) |  | Ptie,12 (t ) | dt  min
0

(3.15)

12

Figure 3.20. Convergence of PSO algorithm

Figure 3.21. Update correction coefficients using PSO algorithm

Figure 3.22. Compare three fuzzy controllers applying different biooptimization algorithms (PSO, GA and DE)


13

Figure 3.23. Target functions in two-zone linked hydroelectric systems
applying fuzzy logic controllers applying three optimization algorithms
3.4. Conclusion chapter 3
In this chapter, the thesis proposes design options for FLC fuzzy controller
to control hydroelectric turbine speed connecting two regions. FLC fuzzy
controllers have a PI or PD structure with three parameters that need to be
adjusted. These parameters can be optimized by applying biological
optimization algorithms such as PSO herd optimization, GA evolution algorithm
and DE differential evolution algorithm. The numerical simulation results
deployed in MATLAB / Simulink software contributed to confirm the efficiency
of the proposed FLC controllers. The proposed fuzzy controllers offer better
control quality when compared to conventional PID controllers and exhibit an
effective control strategy for handling a class of complex engineering objects.
The results were published at the International Conference (ICACR 2019) in
the work No. [CT1]. Under the SCOPUS category, in the published list of the
thesis's scientific works.
CHAPTER 4
DESIGNING A INTERCONNECTED AREA HYDROELECTRIC
TUABINE SPEED CONTROLLER ON THE BASIS OF ARTIFICIAL

NEURAL NETWORK TO STABILIZE THE LOAD FREQUENCY
4.1. Question
4.2. Applying artificial neural network to synthesize zone-linked
hydroelectric turbine speed controller
4.2.1. Basic concepts of neural networks
4.2.2. Methods of training artificial neural networks

4.2.2.1. Supervised Learning
4.2.2.2. Reinforcement learning
4.2.2.3. Unsupervised Learning (Unsupervised Learning)
Table 4.1 Comparing three learning methods of neural networks
Human brain
Learn with the guidance of a teacher
Learning with teacher evaluation
Self-study

Artificial neural network
Supervised Learning
reinforcement learning
Unattended study


14
4.2.2.4. Single layer transmission network
4.3. Strategies for controlling turbine speed in the problem of
hydropower system frequency control using artificial neural networks
4.3.1. The frequency-load control strategy uses a NARMA-L2 controller

Figure 4.9. Model of 2-zone linked hydroelectricity using
NARMA - L2 controller based on ANN

4.3.2. The LFC controller is based on MRAC
4.3.3. MPC ANN application for LFC
4.3.3.1. Structure of MPC is based on ANN

Figure 4.11. ANN model of MPC applied for the ith control area
4.3.3.2. LFC strategy Apply MPC ANN application
(i) Scenario 1: The ANN application MPC is applied to the singlearea electricity system as shown in Figure 4.20 (a).
(ii) Scenario 2: ANN based MPC type LFC controller applied for
hydropower system linking two regions as shown in Figure 4.20 (b).
T


J     fi (t )   Ptie,ij dt. (4.16)
i, j

0 i


15

(b)
Figure 4.12. Control structure of hydroelectric systems Adopt LFC controller

(a) Single-area hydropower plant (b) Hydropower system linking two regions

4.4. The simulation results
4.4.1. Single-zone hydroelectric controller using a neuron controller

Figure 4.13. Single-region hydroelectricity model
4.4.2 Hydropower control connects two regions using a neuron

controller
4.4.2.1. Simulation results for NARMA and MRAC controller
(i) In the first scenario, the variable load occurs in each area at
different times and intensity (see Figure 4.16-4.18).

Figure 4.16. Simulation results for the first simulation scenario
(a) Load changes; (b) Dynamic response of frequency deviation in the first
region; (c) Dynamic response of frequency deviation in the second region


16

Figure 4.17. Deviation of exchanged power on the line
in the first simulation case

Figure 4.18. The target function for the first simulation scenario
Table 4.3. Comparison results are based on several control criteria
in the first simulation case
Standard
IAE
ISE
ITAE
ITSE*10-3

PID
ACE1
ACE2
8.6040
9.0902
0.4119

0.5678
1233.0
1317.0
6243.2
9055.6

NARMA
ACE1
ACE2
2.4292
2.9809
0.0792
0.1844
215.6
325.8
850.4
2575.7

MRAC
ACE1
ACE2
3.0097
3.4023
0.0942
0.2010
293.0
374.8
1059.8
2836.0

Figure 4.19. Simulation results for the second scenario
(a) Change the load in the first sector
(b) Variation of frequency deviation in the first region


17

Figure 4.20. Objective functions for the second simulation
Table 4.4. Quality comparison of controllers based on two control
standards IAE and ISE for the second simulation case
Standard
IAE
ISE

PID
39.1473
6.7665

NARMA
24.9040
2.8997

MRAC
24.6673
2.8608

The simulation results show superior efficiency of NARMA and MRAC
neuron controllers compared to PID controllers.
4.4.2.2. Simulation results for MPC controller

Figure 4.21. Training of two LFC controllers
based on ANN - typical results


18

Figure 4.22. Hydroelectric plant simulation results linking two regions
(a) Load changes; (b) the frequency deviation of the first control zone;
(c) Frequency deviation of the second control area

Figure 4.23. Power exchanged on the line and the target function
(a) Associated current; (b) The objective function

Table 4.5: Comparison results based on some control criteria
Standard
IAE (*10-3)
ISE (*10-3)
ITAE (*10-3)
ITSE (*10-3)

PID
0.0378
0.0047
7.4247
0.8990

MPC
0.0266
0.0034
5.1596

0.6432

4.5. Conclusion chapter 4
The simulation results using MATLAB / Simulink software have
demonstrated the superiority of the above three controllers over the
classic controller like PID.
When evaluating and comparing each LFC controller using artificial
neural network, some comments can be made as follows:


19
- NARMA-L2 controller has faster network training time because
there is no need to identify the control object, when the control object is
linearized.
- The MRAC controller requires two processes: controller object
identification and neural network training for the controller.
- The MPC controller only needs the process of identifying the control
object, but because MPC is the predictive controller, it takes a lot of time
to run the simulation.
The results are published in the work number [CT2], [CT3] in the
published list of scientific works of the thesis.
CHAPTER 5
ANALYZING AND EVALUATING THE EFFICIENCY OF
SOLUTIONS FOR INTELLIGENT CONTROL OF TURBINE SPEED
IN HYDROELECTRIC PLANTS

5.1. Question
5.2. Synthesize and analyze control solutions for single-area and
interconnected area hydropower plants
5.2.1. Schematic diagram of a single-area hydroelectric plant using

fuzzy controller and neural network

Figure 5.1. Model of application controllers for single zone

Figure 5.3. Comparing the response of the generator speed difference
when using controllers applying fuzzy logic and neural networks
and PID - single area


20
5.2.2. Hydroelectric turbine speed control system model linking 2 zones
to stabilize load frequency

Figure 5.4. Model of application controllers for linking 2 zones

Figure 5.5. Load changes for each region

Figure 5.6. Speed deviation
response for zone 1 using different
turbine speed controllers

Figure 5.7. Speed deviation
response for zone 2 using different
turbine speed controllers

Figure 5.8. Valve position control
signal in zone 1

Figure 5.9. Zone valve position
control signal 2

21

Figure 5.10. Mechanical power
deviation for zone 1

Figure 5.11. Mechanical power
deviation for zone 2

Figure 5.12. Speed deviation for zone 1

(a) Speed deviation

(b) Speed deviation

Figure 5.13. Speed deviation for zone 2

(a) Power deviation

(b) Power deviation

Figure 5.14. Power deviation on line by area 1.2
- In area 1 (zone 1).


22

(a)

(b)

Figure 5.15. Area control area deviation 1

(a) ACE region signal deviatio (b) ACE region signal deviation

Figure 5.16. ACE region signal deviation - absolute value

Figure 5.17. ITAE indicator for zone control error 1 signal
In Figure 5.17 we plot to compare the quality of the considered units, we
see good quality MPC controller then NARMA controller and MRAC
controller, next is FLC controller and finally PID controller.
In area 2 (zone 2)

Figure 5.18. Zone control area deviation 2


23

Figure 5.20. ITAE indicator for zone control bias signal 2
In Figure 5.20, we plot to compare the quality of the considered units,
we see the best quality MPC controller then MRAC controller,
NARMA next is FLC controller and finally PID controller.
Table 5.1. Comparison of the controllers based on the ITAE quality
index for generator speed error response
Comparison
criteria
Area 1
Area 2

MRAC
0.40091
0.40094

NARMA
0.26399
0.26401

Remote control
PID
FLC
MPC
0.43384 0.41620 0.342361
0.51285 0.41622 0.34238

Table 5.2. Comparison of the controllers based on ITAE quality
criteria for line interchange power deviation between two zones
Remote controll
Comparison
criteria P12_ MRAC P12_ NARMA P12_ PID P12_ FLC P12 _ MPC
ITAE
0.314738
0.32221
4.154547 0.32321 0.270638

5.3. Conclusion chapter 5
In this chapter, the thesis has synthesized, simulated, and compared
different solutions applying fuzzy logic controller and neural network
designed in the previous chapters for single-zone hydropower and 2-zone
linkage.

- Intelligent control solutions using fuzzy controller and neuron are
all higher quality than using PID controller.
- In principle, the system uses a higher speed neuron controller, but
needs time to train the network.
- With single area: Neural network controller using MPC gave the
best results in adjustment quality, static deviation, transient process
compared with PI and MRAC, NARMA-L2 fuzzy controllers.