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:: Prof., Dr Lê Hùng Lân 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 Cau Giay, Hanoi) at date / /2020 The thesis can be found at: - Vietnam National Library - Library of University of Transport and Communications PREAMBLE 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" 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 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 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) 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 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 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 Research content The thesis is structured with: Introduction and chapters, conclusions and recommendations, list and research works, annexes of drawings and references CHAPTER 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 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 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 Thủy điện - Nhiệt điện Thủy điện - Nhiệt điện, Ga Hydropower- Thermal power, Gas Hydropower - Wind - Diesel Thermal power, Gas Thermal power Thermal power Hydropower- Thermal power, Gas Hydropower- Thermal power Hydropower- Thermal power Hydropower- Thermal power, Gas Hydropower- Thermal power, Gas Thermal power Hydropower- Thermal power, Gas Hydropower- Thermal power Thermal power Thermal power 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 Document [89] [90] [91] [92] [93] [94] [95] Area Source type number Thermal power Hydropower- Thermal power, Gas Hydropower- Thermal power, Gas Hydropower- Thermal power Diesels Hydropower- Thermal power Hydropower- Thermal power 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 1.8 Conclusion chapter - 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 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)   xe ( s)  s.Tg 2.1.3 Model of hydraulic turbine  Tw s  P m ( s) w t ( s)    g ( s)  0.5Tw s 2.1.4 Model generator (2.3) (2.4)  ( s)   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 R1 Xe1 g1(s) P1HV(s) Tg1.s  Remote controll Pm1 PL1 Tw1.s  0.5Tws 1 Tp1.s1 M 1s  D1 ACE1 Speed ​1 Wing direction Tua bin Generator 1 s 2T12 -1 -1 Xe2 Pref ` Tg 2.s  Remote controll Speed ​2 B2 ACE2 P2HV(s) g2(s) Tp2.s1 Wing direction Tw2.s  0.5Tws 1 Tua bin Pm2 M s  D2 PL Generator R2 Figure 2.15 Control system mathematical model diagram Hydropower links the two regions In the thesis, the simulation examples are performed with values of system parameters as follows [11,16,18]: Tg1  Tg  48.7(s) ; Tw1  Tw  1(s) Tr1  Tr  0.513 (s); M1  M  0.6 (s); D1  D2  (pu); R1  R2  2.4 (Hz/p.u) T12  0.0707 (pu) 2.4 Conclusion chapter 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 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 zones to stabilize the frequency when the load changes FLC CONTROL-AREA ∆PL1 ACE 1(t) Governor Turbine Generator ∆f1 Compute ∆Ptie12 ACE 2(t) Governor FLC Turbine Generator ∆PL2 ∆f2 CONTROL-AREA Figure 3.7 Hydropower network diagram linking two regions 11 T T 0 J   | e(t ) | dt   | r (t )  y (t ) | dt  (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 (t ) |  | Ptie,12 (t ) | dt  (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 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 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 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 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 zones to stabilize load frequency Figure 5.4 Model of application controllers for linking zones Figure 5.5 Load changes for each region Figure 5.6 Speed deviation response for zone using different turbine speed controllers Figure 5.7 Speed deviation response for zone using different turbine speed controllers Figure 5.8 Valve position control signal in zone Figure 5.9 Zone valve position control signal 21 Figure 5.10 Mechanical power deviation for zone Figure 5.11 Mechanical power deviation for zone Figure 5.12 Speed deviation for zone (a) Speed deviation (b) Speed deviation Figure 5.13 Speed deviation for zone (a) Power deviation (b) Power deviation Figure 5.14 Power deviation on line by area 1.2 - In area (zone 1) 22 (a) (b) Figure 5.15 Area control area deviation (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 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 (zone 2) Figure 5.18 Zone control area deviation 23 Figure 5.20 ITAE indicator for zone control bias signal 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 Area 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 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 24 - For regional linkage: Depending on the location, there are appropriate optimal solutions, with the control areas being NARMA-L2 neuron controllers, and the link line capacity is the controller MPC neuron For example, if the hydropower system is single-area, does not require fast action time, you can choose an MPC controller However, if fast action time is required, the NARMA-L2 controller selection solution can be considered The fuzzy controller has the advantage when the subject is uncertain CONCLUSIONS AND RECOMMENDATIONS A Results achieved - Construction of hydraulic turbine control system for interconnected area - Design of PI and PD-type optimized fuzzy logic controllers based on applying PSO flocking optimization algorithms, GA evolution algorithm and DE differential evolution for interconnected area hydraulic turbine control system - Synthesize 03 artificial neural network controllers based on the principles of predictive control MPC, adaptive control with reference model MRAC, linear regression NARMA - L2 for interconnected area hydraulic turbine control system Applying the bio-optimization algorithm of PSO herds to find the optimal correction coefficients for the proposed controllers - Building a computer simulation program to compare and evaluate control solutions set out in the thesis B The new results of the thesis: - 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 C Recommendation: The research results achieved in the dissertation can be used for the purpose of training, testing research, and manufacturing to replace the current PID controllers used for hydroelectric plants in Vietnam Continue to research, perfect and develop smart controllers and use optimal algorithms to manufacture controller applications for hydroelectric plants in Vietnam PUBLICATION [CT1] DuyTrung-Nguyen,NgocKhoat-Nguyen,HungLan-Le, VanTiem-Nguyen (2019), Publication: ICACR 2019: Proceedings of the 2019 3rd International Conference on Automation, Control and Robots, October 2019 Pages 61-66 Designing PSO-Based PI-type Fuzzy Logic Controllers: A Typical Application to Load-Frequency Control Strategy of an Interconnected Hydropower System https://doi.org/10.1145/ 3365265.3365278 (Ei Compendex and Scopus) [CT2] DuyTrung-Nguyen,NgocKhoat-Nguyen,HungLan-Le, VanTiem-Nguyen (2020), Intellgent ann - Based load frequency control strategies for an interconnected hydropower system Journal of military science and technology research Page (43-54).No 65 [CT3] DuyTrung-Nguyen,NgocKhoat-Nguyen,HungLan-Le, VanTiem-Nguyen (2020); Study on application of ann - based MPC controller for load - frequency control of an interconnested hydropower Plant, Journal of military science and technology research Page (86 -97).No 65 [CT4].DuyTrung-Nguyen,NgocKhoat-Nguyen,HungLanLe,VanTiem-Nguyen, Thi Thu Huong- Hoang, Thi Mai Phuong- Dao (2020), Research to build mathematical models for regional-linked hydropower systems in the problem of stable turbine speed Journal of Science and Technology: Volume 56, No jun /2020 Page (29-34) P -ISSN;1859; E-ISSN 2615 - 9619 [CT5] DuyTrung-Nguyen,NgocKhoat-Nguyen,HungLanLe, VanTiem-Nguyen,ThiThu Huong- Hoang, Thi Mai PhuongDao (2020), Overview of turbine speed control in regional linked hydropower systems for grid frequency stabilization, Journal of Science and Technology: Volume 56, No Apr 2020 Page (38-43) P -ISSN;1859; E-ISSN 2615 - 9619 [CT6] Nguyen Duy Trung Hoang Thi Thu Huong, Le Hung Lan, Nguyen Van Tiem, Comparison analysis of intelligent controllers for hydropower turbine speed control, Journal of Science and Technology: Vol 56, No ( October 2020) P -ISSN; 1859; E-ISSN 2615 - 9619 (accept post) ... [79] [80] [81] [82] [83] [84] [85] [86] [87] [88] Area Source type number Thủy điện - Nhiệt điện Thủy điện - Nhiệt điện, Ga Hydropower- Thermal power, Gas Hydropower - Wind - Diesel Thermal power,... 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... turbine speed control system on the basis of fuzzy logic and artificial neural networks to improve control quality Research method Study the actual technological process of the operating mode of the