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Control of electric power generation of thermal power plant in TamilNadu
Author’s Accepted Manuscript Control of Electric Power Generation of Thermal Power Plant in TamilNadu M.S. Murshitha Shajahan, V. Aparna, D. Najumnissa Jamal, M.K.A. Ahamed Khan www.elsevier.com/locate/csite
PII: DOI: Reference:
S2214-157X(18)30254-5 https://doi.org/10.1016/j.csite.2018.08.008 CSITE329
To appear in: Case Studies in Thermal Engineering Cite this article as: M.S. Murshitha Shajahan, V. Aparna, D. Najumnissa Jamal and M.K.A. Ahamed Khan, Control of Electric Power Generation of Thermal Power Plant in TamilNadu, Case Studies in Thermal Engineering, https://doi.org/10.1016/j.csite.2018.08.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Control of Electric Power Generation of Thermal Power Plant in TamilNadu M. S. Murshitha Shajahana, Aparna Va, D. Najumnissa Jamala, Dr.M.K.A.Ahamed Khanb a
Dept. of EIE, B. S. Abdur Rahman Crescent Institute of Science & Technology, Chennai, India b Faculty of Engg, UCSI University, & Vice Chairman - IEEE RAS, Malaysia
[email protected] [email protected] najumnissa.d@ crescent. education
Abstract— A control configuration for controlling the generated power in NLC TamilNadu Power Limited (NTPL) Thermal Power Station by manipulating the coal flow rate, air flow rate and feed water flow rate is proposed here. Real Time data from NTPL Thermal Power Station Unit-Tuticorin unit of 500 MW capacity is recorded over an interval of time. This real-time data is used to model the coal flow rate, air flow rate and feed water flow rate loops which will be controlled to eventually control the generated power. The controller for the three loops is tuned using evolution and optimization techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results of the controller tuned using both the techniques are provided and they are compared with each other. From the simulation results, it can be emphasized that the controller performs fairly well in servo and regulatory configurations. Keywords— Boiler-Turbine-Generator; Co-ordinated Control; Modeling; Thermal Power Plant; Evolutionary Technique.
I. INTRODUCTION Usually, a thermal power station fuelled by coal comprises of a boiler-turbine-generator setup in which the chemical energy generated by the combustion of coal in the furnace in the presence of air will heat the feed water in the boiler drum. This heat energy will generate steam from the boiler drum which in turn will be fed to a set of turbines to convert the kinetic energy to mechanical energy. This generated mechanical energy will be finally converted to electrical energy by the generator connected to the turbine. Now controlling the fuel-air flow rate in the power plant will lead to efficient combustion in the furnace which in turn will lead to efficient power generation. So the control of fuel and air flow rate in the furnace along with the feed water flow rate control in the boiler drum is necessary to control or maintain the generated power in a thermal power station. In general, three control configurations are described pertaining to a thermal power station, one is boiler following mode, where the generated power output is maintained or controlled by the firing rate of the boiler, second one is referred to as the turbine following mode, where the generated power output is controlled by the position of the throttle valve as the generated power output will depend on the steam at the inlet of the turbine. The third control configuration is called the coordinated mode, which utilizes both the turbine following mode and boiler following mode. Moreover, the thermal power generation unit may be used either as a boiler-turbine-generator configuration, where the steam generated from the boiler drum will be fed to a super heater and then to a turbine. Apart from this, several boiler drum and turbines can be utilized where a header will accommodate steam from the boiler drums which will be fed to the turbines. This arrangement is mostly used in large utility stations. In this paper, a boiler-turbine-generator thermal power station is controlled by the boiler following mode. Generally, a thermal power station comprising a boiler, turbine and generator is a nonlinear system. Here, there is multiloop (three) single-input single-output loops to control the output. As such, due to the nonlinearity, the system parameters will vary under various operating conditions and intelligent control strategies are necessary to produce a robust output which is not feasible using a conventional controller [1-3]. R. Dimeo and K.Y. Lee [4] proposed genetic algorithm to tune PI controller and state feedback controller to control a non-linear boiler-turbine station. Linear multivariable controllers cannot produce desired results in a practical power generation utility as they are designed around a set of equilibrium values only [5-6]. Fuzzy based control configuration can be used in controller tuning as it will reduce the overshoot and rapidity even though its disturbance rejection capability can be improved further [7]. In the recent R.D. Bell and K.J. Åström [8] described the response of a large coal-fired
boiler-turbine generator unit in detail which is referred for the simulation and control design. In [9], the behaviour of a boiler system of 160 MW capacity is described, where the manipulated variable is fuel flow rate to achieve the desired power generation. In the recent years, optimization algorithms have been implemented widely in intelligent control configuration for nonlinear systems even though it makes the controller design process complicated as it depends on the user's experience. But they are helpful in the accurate design of controllers and also because they produce better output than linear multivariable controllers and conventional controllers, they are preferred for intelligent control configurations [10], [18] and [19]. In this paper, section II describes the modelling of the NTPL Thermal Power Station Unit; III describes the various steps in a Genetic Algorithm; section IV describes the particle swarm optimization algorithm. Finally, section V describes the results of the coal flow rate, air flow rate and feed water flow rate controllers tuned using GA and PSO and compares the resulting error performance indices and step response characteristics. II. REAL-TIME MODEL Here, real-time data from a 500 MW NTPL boiler-turbine-generator station is modelled as a nonlinear multi-input singleoutput system. NTPL Thermal Power Station situated at Tuticorin, Tamil Nadu is a 1000 MW coal-fired power plant with two 500 MW power generation stations. Data such as coal flow rate, air flow rate, feed water flow rate, and steam flow rate in Tonnes/hr along with the corresponding load in MW is recorded for every minute from 8AM to 5PM (9hrs) for 3 trials have been recorded and this data is modeled as a nonlinear three-input one-output system.
Fig. 1. Three Control Loops in 500 MW Unit of NTPL Thermal Power Station
The three inputs in this nonlinear model are coal flow rate, air flow rate and feed water flow rate. Three process loops tuned using evolution and optimization algorithm is designed for these inputs. The outputs from the process loops will control or maintain the output from the system, generated power. The recorded data is modelled in MATLAB using system identification and the three loops are modelled into the transfer functions as shown in the following equations, (1)
(2)
(3)
Here, equation (1) represents the coal flow rate loop, (2) and (3) represent the air flow rate and feed water flow rate loops respectively. III. GENETIC ALGORITHM (GA) In this algorithm, a set of solutions are evolved repeatedly by altering certain bits of the solution using some genetic operations with respect to an optimal fitness function such that the evolved solutions will produce the ideal result from the optimization problem, as explained by D.E. Goldberg [13]. Some important terminologies in a genetic algorithm are Chromosomes, Objective Function and Fitness values. Chromosomes comprise the entire set of solutions. It is a matrix of the order N x L, where, N is the total number of solutions in the set and L is the length of each solution in the set. All the solutions should have the same value of L. The result from the optimization problem is evaluated pertaining to the objective function. Further, the rank of the objective function values will give the fitness values. A. Various steps in GA The major steps in a genetic algorithm for tuning the gains of a nominal proportional-derivative controller is summarized as follows [11-14], Step 1 - Initialize a random set of solutions (Kp and Kd) (chromosomes). Step 2 - Calculate fitness function for each solution in the set by performing a simulation with each set of controller gain. Step 3 - The following steps 'i', 'ii', and 'iii' will be repeated until suitable number of solutions (offspring) is obtained from the initial random solutions, i) Based on higher/lower fitness value (ISE) of the random solutions in the set, create a suitable number of parent solutions. ii) Apply crossover operator to the parent solution at any number of random points to produce two or more offspring solutions. iii) Apply mutation operator to the offspring solutions obtained from the crossover operation at any number of random points to produce more offspring solutions. These steps 'i', 'ii', and 'iii' will produce new solutions (Kp and Kd) such that they replace all the initially generated random solutions in the solution set. Step 4 - Now replace the initially generated random solutions in the solution set with the offspring solutions. Step 5 - Go to Step 2 again. One iteration completes one generation of the genetic algorithm and optimization is carried out for several generations until suitable gains of the nominal controller which produces a low value of ISE is obtained [15]. IV. PARTICLE SWARM OPTIMIZATION ALGORITHM (PSO) Particle Swarm optimization is a swarm intelligence based technique developed by J. Kennedy, R.C. Eberhart [21] which uses the foraging behaviour of natural species. In this algorithm, all the particles in an environment travel towards that one particle that is closest to the ideal position in the environment. The technique of particle swarm optimization will ensure that all the particles in the environment reach the ideal position. The algorithm starts by initializing random position and velocity values to the particles. In each iteration, the particle will move closer to the global best position by updating its velocity and its corresponding position. The individual best position of the particle and its fitness value will also be stored. In general, the particle velocity will depend on the past velocity of the particle and its past position and best position along with the best position of the neighbouring particles. Similarly, the particle position in the environment will depend on its past position and velocity. The equation for updating particle velocity in a PSO algorithm is given by [21], (
)
( )
(4)
where, (
() (
( ))) and
(
() (
( )))
The equation for updating particle position is given by, (
)
( )
( )
(5)
where, Vx(n+1) is the updated velocity of particle 'x' Vx(n) is the velocity of a particle 'x' at time 'n' W1 is the weight constant for the individual best position of the particle rand( ) function will generate random value; 0 ≤ value ≤ 1 Pxbest is the best position of the particle 'x' Px(n) is the position of a particle 'x' at time 'n' W2 is the weight constant for the global best position in the search space PGbest is the global best position in the search space and Px(n+1) is the updated position of particle 'x' These equations (4) and (5) will be used to continuously update all the particle velocity and particle position in the surrounding environment. A. Various steps in PSO Pseudo code for a particle swarm optimization algorithm to obtain the optimal gain values of a controller is given below [20][23], Initialize Population Size and Global Best Position (gbestp) Compute fitness of gbestp For each particle in the population or until stop condition (particle reaches global best position) is reached Initialize random position and velocity values to the particles Set Local Best Position (lbestp) = random position Compute fitness of lbestp If fitness (lbestp ) ≥ fitness (gbestp ) then gbestp = lbestp Else Iteration : Update particle velocity and position (lbestp) using Equation (4) and (5) Compute fitness of lbestp If fitness (lbestp ) ≥ fitness (gbestp ) then gbestp = lbestp Else Goto Iteration End If End If Next End To find the gains (Kp and Kd) of the nominal controller, 50 swarms are considered with weight constants, W1 and W2 equal to 2. A maximum number of 500 generations is considered. The algorithm is implemented for 10 times and the best gain values which produce the lowest value of ISE is used in the simulation. V. RESULTS PD controller is used for controlling or maintaining the generated power by controlling coal flow rate, air flow rate and feed water flow rate. In this proposed method, the controller gains are tuned using optimization and evolution techniques like Particle Swarm Optimization Algorithm and Genetic Algorithm. ISE is considered as the optimization function for the algorithms. Controller parameters will be computed for the most minimal Integral Square Error (ISE) value. In a nonlinear thermal power plant, small deviations from setpoint are insignificant but large overshoots and undershoots should not be permitted. Hence, ISE is chosen as the optimization parameter. Moreover, the servo, regulatory and servo-regulatory response of the controller is analyzed in this simulation. Therefore, ISE, IAE and ITAE are chosen as the performance criteria among several other criteria as reduction of ITAE will attempt to improve the setting time of servo response and ISE and IAE are good measures to analyze the overshoots and undershoots in the process and to analyze the variation of the process in the regulatory control scheme.
Fig. 2 shows the block diagram of the multiloop controller tuned using PSO and GA in the three-input one-output boilerturbine-generator system [16][17] and Table I shows the PD controller gain parameters computed using PSO and GA [22].
Fig. 2. Block diagram of the intelligent PD controller tuned using Particle Swarm Optimization Algorithm and Genetic Algorithm for the nonlinear system TABLE I. CONTROLLER GAIN PARAMETERS
Algorithm
Gain Parameters for Coal Flow Rate Control
Gain Parameters for Air Flow Rate Control
Gain Parameters for Feedwater Flow Rate Control
Controller tuned using GA
Kp= 12.786 Kd = 9.791
Kp= 0.294 Kd = 6.756
Kp= 9.498 Kd = 0.135
Controller tuned using PSO Algorithm
Kp= 12.34 Kd = 9.5
Kp= 6.29 Kd = 6.77
Kp= 9.4 Kd = 1.35
A. Servo Response The setpoint tracking capability of the controller is analyzed by providing variable setpoint. The variable setpoint are provided at an interval of ±13 for coal flow rate, +250/300 for air flow rate and +150 for feed water flow rate. It is verified that the output from each of the process loops tracks the variable setpoint. Table II presents the error performance indices of the servo control response from the three process loops tuned using both GA and PSO algorithms. TABLE II. ERROR PERFORMANCE INDICES FOR SERVO CONTROL RESPONSE Output Coal Flow Rate Air Flow Rate Feedwater Flow Rate
ISE 497.68 70 3.4390 e7 1.4800 e6
GA IAE 200.13 74 5.3059 e4 1.1100 e4
ITAE 1.1552 e4 3.3305 e6 6.7676 e5
ISE 529.44 55 3.5342 e7 1.4928 e6
PSO IAE 206.81 16 5.3551 e4 1.1154 e4
ITAE 1.1939 e4 3.3861 e6 6.8048 e5
B. Regulatory Response Disturbance rejection capability of the controller is analyzed and the response of the system when it is subjected to an additional disturbance of 0.25% to 1 in addition to the already existing disturbance and nonlinearities is verified. From the analysis, it is emphasized that the designed controller attains the desired output even during a disturbance. Table III displays the error performance indices of the regulatory control response from the three process loops tuned using GA & PSO algorithm. TABLE III. ERROR PERFORMANCE INDICES FOR REGULATORY CONTROL RESPONSE
Output
GA
PSO
ISE
IAE
ITAE
ISE
IAE
ITAE
Coal Flow Rate Air Flow Rate
1.3291 e3 6.4171 e7
279.71 44 8.0105 e4
1.2629 e4 4.0086 e6
1.3769 e3 6.6348 e7
288.04 32 8.1412 e4
1.3054 e4 4.1320 e6
Feedwater Flow Rate
2.7745 e6
1.6232 e4
7.7421 e5
2.7898 e6
1.6319 e4
7.7908 e5
C. Servo-Regulatory Response Both the setpoint tracking and disturbance rejection capability of the controller is analyzed. The variable setpoint are provided at an interval of ±13 for coal flow rate, +250/300 for air flow rate and +150 for feed water flow rate along with a disturbance of 0.25% to 1% in addition to the already existing disturbance and nonlinearities. The designed controller provides efficient control even though both variable setpoint and disturbance are applied simultaneously. Figures 3 to 5 represent the servo-regulatory response from the process loops obtained from PSO Algorithm. Table IV presents the error performance indices of the servoregulatory control response from the three process loops tuned using PSO & GA.
Fig. 3. Coal Flow Rate Servo-Regulatory Control Response from the process loop obtained using PSO. The response within the box with dashes depicts the
regulatory action
Fig.4. Air Flow Servo-Regulatory Control Response from the process loop obtained using PSO. The response within the box with dashes depicts the regulatory
action Fig.5. Feed water Flow Rate Servo-Regulatory Control Response from the process loop obtained using PSO. The response within the box with dashes depicts the regulatory action TABLE IV. ERROR PERFORMANCE INDICES FOR SERVO-REGULATORY CONTROL RESPONSE
Output Coal Flow Rate Air Flow Rate Feedwater Flow Rate
ISE 636.87 76 3.4375 e7 1.5246 e6
GA IAE 203.76 59 5.3044 e4 1.1193 e4
ITAE 1.1596 e4 3.3295 e6 6.7967 e5
ISE 665.95 33 3.5023 e7 1.5349 e6
PSO IAE 209.91 51 5.3318 e4 1.1248 e4
ITAE 1.1973 e4 3.3661 e6 6.8342 e5
The offset in the coal flow rate and feed water flow rate control could not be reduced by tweaking the parameters in GA and PSO or even by increasing the number of generations of the algorithm. So further research will be carried out in this system by tuning the controller using other robust or meta-heuristic approaches. D. Discussions The output from the three process loops controls or maintains the power output from the system. The output response from the system with the gains of the controllers computed using Genetic Algorithm and Particle Swarm Optimization Algorithm, as shown in Fig. 6, is compared. Table V contains the error performance indices like Integral Square Error, Integral Absolute Error and Integral Time Weighted Absolute Error from the output response from PSO-PD Controllers and GA-PD Controllers. Similarly, Table VI displays the time domain characteristics pertaining to power output response from the PSO-PD Controllers and GA-PD Controllers. From Table V and VI, it can be seen that the controller tuned using PSO provides effective control action, with lower values of error indices, only in the coal flow rate control loop compared to the control action of GA based controller in all the three control loops. Therefore, GA based controller seems to be providing electric power generation control with quicker settling times and lesser overshoot whereas PSO-PD controller produces output with significantly less offset than GA-PD controller but with large overshoot and settling time.
80
60 Power (MW)
GA PSO
40
20
0 0
100
200 300 Time (sec)
400
500
Fig. 6. Electric Power Output (Load) Response from the system obtained using GA and PSO
TABLE V. ERROR PERFORMANCE INDICES FOR OUTPUT RESPONSE
Output Electrical Power (Coal Flow Rate) Electrical Power (Air Flow Rate) Electrical Power (Feedwater Flow Rate)
ISE
GA IAE
ITAE
ISE
PSO IAE
ITAE
8.6789 e4
2.9376 e3
1.4727 e5
8.4034 e4
2.8894 e3
1.4106e 5
5.3675 e7
7.3263 e4
3.6627 e6
5.3476 e7
7.3311 e4
3.6689e 6
1.0217 e7
3.1963 e4
1.5977 e6
1.0248 e7
3.2011 e4
1.6039e 6
TABLE VI. STEP RESPONSE CHARACTERISTICS FOR OUTPUT RESPONSE Characteristics Rise Time Settling Time Overshoot Offset
GA 1.3043 5.3237 4.8472 1.758
PSO 0.5691 23.6000 16.5656 0.3046
VI. CONCLUSION An intelligent control configuration for controlling the power generation in NTPL Thermal Power Station by manipulating the coal flow rate, air flow rate and feed water flow rate is implemented here. The controller is verified for Servo, Regulatory and Servo-Regulatory configurations and, from the results, it is inferred that although there is considerable offset in the individual results from the process loops, the control response from the process loops produce the desired electric power output response with very less offset and quick settling times considering it is a real-time boiler-turbine-generator model. Both the intelligent control schemes achieve desired response, but GA-PD controllers produce desired output response with lesser overshoot and quicker settling time than PSO-PD controllers which produce output response with lesser offset but at the expense of overshoot and settling time. VII. ACKNOWLEDGEMENT We are very much grateful to NLC TamilNadu Power Limited (NTPL) for their technical assistance. We would also like to thank all staff members involved in this research work.
REFERENCES [1] T.R. Rangaswamy, J. Shanmugam, Fuzzy based coordinated controller for thermal power plant, in: Proc. India Int. Conf. Power Electron., Chennai, 2006: pp. 377–382. doi:10.1109/IICPE.2006.4685401. [2] W. Tan, H.J. Marquez, T.W. Chen, J.Z. Liu, Analysis and control of a nonlinear boiler-turbine unit, J. Process Control. 15 (2005) 883–891. doi:https://doi.org/10.1016/j.jprocont.2005.03.007. [3] C. Maffezzoni, Boiler-turbine dynamics in power-plant control, Control Eng. Pract. 5 (n.d.) 301–312. doi:https://doi.org/10.1016/S0967-0661(97)00007-5. [4] R. Dimeo, K.Y. Lee, Boiler-turbine control system design using a genetic algorithm, IEEE Trans. Energy Convers. 10 (1995) 752–759. [5] R. Cori, C. Maffezzoni, Practical-optimal control of a drum boiler power plant, Automatica. 20 (n.d.) 163–173. doi:https://doi.org/10.1016/00051098(84)90022-0. [6] Y.N. Yu, K. Vongsuriya, L.N. Wedman, Application of an Optimal Control Theory to a Power System, IEEE Trans. Power Appar. Syst. PAS-89 (n.d.) 55–62. doi:10.1109/TPAS.1970.292668. [7] K. Jawahar, N. Pappa, Selftuning nonlinear controller, in: Proc. 6th WSEAS Int. Conf. Fuzzy Syst., Lisbon, Portugal, 2005: pp. 118–126. [8] R.D. Bell, K.J. Åström, Dynamic models for boiler-turbine-alternator units: Data logs and parameter estimation for a 160 MW Unit, Sweden, 1987. [9] K.J. Åström, K. Eklund, A simplified non-linear model of a drum boiler turbine unit, Int. J. Control. 16 (1972) 145–169. [10] A. Isidori, Nonlinear Control Systems: An Introduction, 2nd ed., Springer-Verlag Berlin Heidelberg, 1989. doi:10.1007/978-3-662-02581-9. [11] E.E. Vladu, T.L. Dragomir, Controller Tuning Using Genetic Algorithms, in: 1st Rom. - Hungarian Jt. Symp. Appl. Comput. Intell., Timisoara, Romania, 2004. [12] S. Panda, N. P. Padhy, R.N. Patel, Application of Genetic Algorithm for FACTS based Controller Design, Int. J. Electr. Comput. Eng. 1 (2007). scholar.waset.org/1307-6892/1255. [13] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989. [14] C. Houck, J. Joines, M. Kay, A genetic algorithm for function optimization: A MTLAM implementation, 1995. [15] A. Soltoggio, Evolutionary Algorithms in the Design and Tuning of a Control System, Norwegian University of Science and Technology, 2004. [16] M. Seyedkazemi, Designing Optimal PID controller with Genetic Algorithm In view of controller location in the plant, in: Proc. 7th WSEAS Int. Conf. SIGNAL Process. Robot. Autom. (ISPRA ’08), University of Cambridge, UK, n.d. [17] M. Ejaz, Application of Genetic Algorithm for Tuning of Reduced Ordered Robust PID Controller and Computer Simulations, Int. J. Comput. Appl. 42 (n.d.). [18] J.G. Ziegler, N.B. Nichols, Optimum settings for automatic controllers, Trans. ASME. 64 (1942) 759–768. [19] G.P. Liu, J.B. Yang, J.F. Whidborne, Multiobjective Optimization and Control, Prentice Hall, New Delhi, India, 2008. [20] K. Latha, V. Rajinikanth, P.M. Surekha, PSO-Based PID Controller Design for a Class of Stable and Unstable Systems, Int. Sch. Res. Not. Artif. Intell. 2013 (2013). doi:http://dx.doi.org/10.1155/2013/543607. [21] J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proc. IEEE Int. Conf. Neural Networks, 1995: pp. 1942–1948. [22] A. O’Dwyer, Handbook of PI and PID Controller Tuning Rules, 3rd ed., Imperial College Press, London, UK, 2009. [23] A. Alfi, H. Modares, System identification and control using adaptive particle swarm optimization, Appl. Math. Model. 35 (2011) 1210–1221.
PII: DOI: Reference:
S2214-157X(18)30254-5 https://doi.org/10.1016/j.csite.2018.08.008 CSITE329
To appear in: Case Studies in Thermal Engineering Cite this article as: M.S. Murshitha Shajahan, V. Aparna, D. Najumnissa Jamal and M.K.A. Ahamed Khan, Control of Electric Power Generation of Thermal Power Plant in TamilNadu, Case Studies in Thermal Engineering, https://doi.org/10.1016/j.csite.2018.08.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Control of Electric Power Generation of Thermal Power Plant in TamilNadu M. S. Murshitha Shajahana, Aparna Va, D. Najumnissa Jamala, Dr.M.K.A.Ahamed Khanb a
Dept. of EIE, B. S. Abdur Rahman Crescent Institute of Science & Technology, Chennai, India b Faculty of Engg, UCSI University, & Vice Chairman - IEEE RAS, Malaysia
[email protected] [email protected] najumnissa.d@ crescent. education
Abstract— A control configuration for controlling the generated power in NLC TamilNadu Power Limited (NTPL) Thermal Power Station by manipulating the coal flow rate, air flow rate and feed water flow rate is proposed here. Real Time data from NTPL Thermal Power Station Unit-Tuticorin unit of 500 MW capacity is recorded over an interval of time. This real-time data is used to model the coal flow rate, air flow rate and feed water flow rate loops which will be controlled to eventually control the generated power. The controller for the three loops is tuned using evolution and optimization techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results of the controller tuned using both the techniques are provided and they are compared with each other. From the simulation results, it can be emphasized that the controller performs fairly well in servo and regulatory configurations. Keywords— Boiler-Turbine-Generator; Co-ordinated Control; Modeling; Thermal Power Plant; Evolutionary Technique.
I. INTRODUCTION Usually, a thermal power station fuelled by coal comprises of a boiler-turbine-generator setup in which the chemical energy generated by the combustion of coal in the furnace in the presence of air will heat the feed water in the boiler drum. This heat energy will generate steam from the boiler drum which in turn will be fed to a set of turbines to convert the kinetic energy to mechanical energy. This generated mechanical energy will be finally converted to electrical energy by the generator connected to the turbine. Now controlling the fuel-air flow rate in the power plant will lead to efficient combustion in the furnace which in turn will lead to efficient power generation. So the control of fuel and air flow rate in the furnace along with the feed water flow rate control in the boiler drum is necessary to control or maintain the generated power in a thermal power station. In general, three control configurations are described pertaining to a thermal power station, one is boiler following mode, where the generated power output is maintained or controlled by the firing rate of the boiler, second one is referred to as the turbine following mode, where the generated power output is controlled by the position of the throttle valve as the generated power output will depend on the steam at the inlet of the turbine. The third control configuration is called the coordinated mode, which utilizes both the turbine following mode and boiler following mode. Moreover, the thermal power generation unit may be used either as a boiler-turbine-generator configuration, where the steam generated from the boiler drum will be fed to a super heater and then to a turbine. Apart from this, several boiler drum and turbines can be utilized where a header will accommodate steam from the boiler drums which will be fed to the turbines. This arrangement is mostly used in large utility stations. In this paper, a boiler-turbine-generator thermal power station is controlled by the boiler following mode. Generally, a thermal power station comprising a boiler, turbine and generator is a nonlinear system. Here, there is multiloop (three) single-input single-output loops to control the output. As such, due to the nonlinearity, the system parameters will vary under various operating conditions and intelligent control strategies are necessary to produce a robust output which is not feasible using a conventional controller [1-3]. R. Dimeo and K.Y. Lee [4] proposed genetic algorithm to tune PI controller and state feedback controller to control a non-linear boiler-turbine station. Linear multivariable controllers cannot produce desired results in a practical power generation utility as they are designed around a set of equilibrium values only [5-6]. Fuzzy based control configuration can be used in controller tuning as it will reduce the overshoot and rapidity even though its disturbance rejection capability can be improved further [7]. In the recent R.D. Bell and K.J. Åström [8] described the response of a large coal-fired
boiler-turbine generator unit in detail which is referred for the simulation and control design. In [9], the behaviour of a boiler system of 160 MW capacity is described, where the manipulated variable is fuel flow rate to achieve the desired power generation. In the recent years, optimization algorithms have been implemented widely in intelligent control configuration for nonlinear systems even though it makes the controller design process complicated as it depends on the user's experience. But they are helpful in the accurate design of controllers and also because they produce better output than linear multivariable controllers and conventional controllers, they are preferred for intelligent control configurations [10], [18] and [19]. In this paper, section II describes the modelling of the NTPL Thermal Power Station Unit; III describes the various steps in a Genetic Algorithm; section IV describes the particle swarm optimization algorithm. Finally, section V describes the results of the coal flow rate, air flow rate and feed water flow rate controllers tuned using GA and PSO and compares the resulting error performance indices and step response characteristics. II. REAL-TIME MODEL Here, real-time data from a 500 MW NTPL boiler-turbine-generator station is modelled as a nonlinear multi-input singleoutput system. NTPL Thermal Power Station situated at Tuticorin, Tamil Nadu is a 1000 MW coal-fired power plant with two 500 MW power generation stations. Data such as coal flow rate, air flow rate, feed water flow rate, and steam flow rate in Tonnes/hr along with the corresponding load in MW is recorded for every minute from 8AM to 5PM (9hrs) for 3 trials have been recorded and this data is modeled as a nonlinear three-input one-output system.
Fig. 1. Three Control Loops in 500 MW Unit of NTPL Thermal Power Station
The three inputs in this nonlinear model are coal flow rate, air flow rate and feed water flow rate. Three process loops tuned using evolution and optimization algorithm is designed for these inputs. The outputs from the process loops will control or maintain the output from the system, generated power. The recorded data is modelled in MATLAB using system identification and the three loops are modelled into the transfer functions as shown in the following equations, (1)
(2)
(3)
Here, equation (1) represents the coal flow rate loop, (2) and (3) represent the air flow rate and feed water flow rate loops respectively. III. GENETIC ALGORITHM (GA) In this algorithm, a set of solutions are evolved repeatedly by altering certain bits of the solution using some genetic operations with respect to an optimal fitness function such that the evolved solutions will produce the ideal result from the optimization problem, as explained by D.E. Goldberg [13]. Some important terminologies in a genetic algorithm are Chromosomes, Objective Function and Fitness values. Chromosomes comprise the entire set of solutions. It is a matrix of the order N x L, where, N is the total number of solutions in the set and L is the length of each solution in the set. All the solutions should have the same value of L. The result from the optimization problem is evaluated pertaining to the objective function. Further, the rank of the objective function values will give the fitness values. A. Various steps in GA The major steps in a genetic algorithm for tuning the gains of a nominal proportional-derivative controller is summarized as follows [11-14], Step 1 - Initialize a random set of solutions (Kp and Kd) (chromosomes). Step 2 - Calculate fitness function for each solution in the set by performing a simulation with each set of controller gain. Step 3 - The following steps 'i', 'ii', and 'iii' will be repeated until suitable number of solutions (offspring) is obtained from the initial random solutions, i) Based on higher/lower fitness value (ISE) of the random solutions in the set, create a suitable number of parent solutions. ii) Apply crossover operator to the parent solution at any number of random points to produce two or more offspring solutions. iii) Apply mutation operator to the offspring solutions obtained from the crossover operation at any number of random points to produce more offspring solutions. These steps 'i', 'ii', and 'iii' will produce new solutions (Kp and Kd) such that they replace all the initially generated random solutions in the solution set. Step 4 - Now replace the initially generated random solutions in the solution set with the offspring solutions. Step 5 - Go to Step 2 again. One iteration completes one generation of the genetic algorithm and optimization is carried out for several generations until suitable gains of the nominal controller which produces a low value of ISE is obtained [15]. IV. PARTICLE SWARM OPTIMIZATION ALGORITHM (PSO) Particle Swarm optimization is a swarm intelligence based technique developed by J. Kennedy, R.C. Eberhart [21] which uses the foraging behaviour of natural species. In this algorithm, all the particles in an environment travel towards that one particle that is closest to the ideal position in the environment. The technique of particle swarm optimization will ensure that all the particles in the environment reach the ideal position. The algorithm starts by initializing random position and velocity values to the particles. In each iteration, the particle will move closer to the global best position by updating its velocity and its corresponding position. The individual best position of the particle and its fitness value will also be stored. In general, the particle velocity will depend on the past velocity of the particle and its past position and best position along with the best position of the neighbouring particles. Similarly, the particle position in the environment will depend on its past position and velocity. The equation for updating particle velocity in a PSO algorithm is given by [21], (
)
( )
(4)
where, (
() (
( ))) and
(
() (
( )))
The equation for updating particle position is given by, (
)
( )
( )
(5)
where, Vx(n+1) is the updated velocity of particle 'x' Vx(n) is the velocity of a particle 'x' at time 'n' W1 is the weight constant for the individual best position of the particle rand( ) function will generate random value; 0 ≤ value ≤ 1 Pxbest is the best position of the particle 'x' Px(n) is the position of a particle 'x' at time 'n' W2 is the weight constant for the global best position in the search space PGbest is the global best position in the search space and Px(n+1) is the updated position of particle 'x' These equations (4) and (5) will be used to continuously update all the particle velocity and particle position in the surrounding environment. A. Various steps in PSO Pseudo code for a particle swarm optimization algorithm to obtain the optimal gain values of a controller is given below [20][23], Initialize Population Size and Global Best Position (gbestp) Compute fitness of gbestp For each particle in the population or until stop condition (particle reaches global best position) is reached Initialize random position and velocity values to the particles Set Local Best Position (lbestp) = random position Compute fitness of lbestp If fitness (lbestp ) ≥ fitness (gbestp ) then gbestp = lbestp Else Iteration : Update particle velocity and position (lbestp) using Equation (4) and (5) Compute fitness of lbestp If fitness (lbestp ) ≥ fitness (gbestp ) then gbestp = lbestp Else Goto Iteration End If End If Next End To find the gains (Kp and Kd) of the nominal controller, 50 swarms are considered with weight constants, W1 and W2 equal to 2. A maximum number of 500 generations is considered. The algorithm is implemented for 10 times and the best gain values which produce the lowest value of ISE is used in the simulation. V. RESULTS PD controller is used for controlling or maintaining the generated power by controlling coal flow rate, air flow rate and feed water flow rate. In this proposed method, the controller gains are tuned using optimization and evolution techniques like Particle Swarm Optimization Algorithm and Genetic Algorithm. ISE is considered as the optimization function for the algorithms. Controller parameters will be computed for the most minimal Integral Square Error (ISE) value. In a nonlinear thermal power plant, small deviations from setpoint are insignificant but large overshoots and undershoots should not be permitted. Hence, ISE is chosen as the optimization parameter. Moreover, the servo, regulatory and servo-regulatory response of the controller is analyzed in this simulation. Therefore, ISE, IAE and ITAE are chosen as the performance criteria among several other criteria as reduction of ITAE will attempt to improve the setting time of servo response and ISE and IAE are good measures to analyze the overshoots and undershoots in the process and to analyze the variation of the process in the regulatory control scheme.
Fig. 2 shows the block diagram of the multiloop controller tuned using PSO and GA in the three-input one-output boilerturbine-generator system [16][17] and Table I shows the PD controller gain parameters computed using PSO and GA [22].
Fig. 2. Block diagram of the intelligent PD controller tuned using Particle Swarm Optimization Algorithm and Genetic Algorithm for the nonlinear system TABLE I. CONTROLLER GAIN PARAMETERS
Algorithm
Gain Parameters for Coal Flow Rate Control
Gain Parameters for Air Flow Rate Control
Gain Parameters for Feedwater Flow Rate Control
Controller tuned using GA
Kp= 12.786 Kd = 9.791
Kp= 0.294 Kd = 6.756
Kp= 9.498 Kd = 0.135
Controller tuned using PSO Algorithm
Kp= 12.34 Kd = 9.5
Kp= 6.29 Kd = 6.77
Kp= 9.4 Kd = 1.35
A. Servo Response The setpoint tracking capability of the controller is analyzed by providing variable setpoint. The variable setpoint are provided at an interval of ±13 for coal flow rate, +250/300 for air flow rate and +150 for feed water flow rate. It is verified that the output from each of the process loops tracks the variable setpoint. Table II presents the error performance indices of the servo control response from the three process loops tuned using both GA and PSO algorithms. TABLE II. ERROR PERFORMANCE INDICES FOR SERVO CONTROL RESPONSE Output Coal Flow Rate Air Flow Rate Feedwater Flow Rate
ISE 497.68 70 3.4390 e7 1.4800 e6
GA IAE 200.13 74 5.3059 e4 1.1100 e4
ITAE 1.1552 e4 3.3305 e6 6.7676 e5
ISE 529.44 55 3.5342 e7 1.4928 e6
PSO IAE 206.81 16 5.3551 e4 1.1154 e4
ITAE 1.1939 e4 3.3861 e6 6.8048 e5
B. Regulatory Response Disturbance rejection capability of the controller is analyzed and the response of the system when it is subjected to an additional disturbance of 0.25% to 1 in addition to the already existing disturbance and nonlinearities is verified. From the analysis, it is emphasized that the designed controller attains the desired output even during a disturbance. Table III displays the error performance indices of the regulatory control response from the three process loops tuned using GA & PSO algorithm. TABLE III. ERROR PERFORMANCE INDICES FOR REGULATORY CONTROL RESPONSE
Output
GA
PSO
ISE
IAE
ITAE
ISE
IAE
ITAE
Coal Flow Rate Air Flow Rate
1.3291 e3 6.4171 e7
279.71 44 8.0105 e4
1.2629 e4 4.0086 e6
1.3769 e3 6.6348 e7
288.04 32 8.1412 e4
1.3054 e4 4.1320 e6
Feedwater Flow Rate
2.7745 e6
1.6232 e4
7.7421 e5
2.7898 e6
1.6319 e4
7.7908 e5
C. Servo-Regulatory Response Both the setpoint tracking and disturbance rejection capability of the controller is analyzed. The variable setpoint are provided at an interval of ±13 for coal flow rate, +250/300 for air flow rate and +150 for feed water flow rate along with a disturbance of 0.25% to 1% in addition to the already existing disturbance and nonlinearities. The designed controller provides efficient control even though both variable setpoint and disturbance are applied simultaneously. Figures 3 to 5 represent the servo-regulatory response from the process loops obtained from PSO Algorithm. Table IV presents the error performance indices of the servoregulatory control response from the three process loops tuned using PSO & GA.
Fig. 3. Coal Flow Rate Servo-Regulatory Control Response from the process loop obtained using PSO. The response within the box with dashes depicts the
regulatory action
Fig.4. Air Flow Servo-Regulatory Control Response from the process loop obtained using PSO. The response within the box with dashes depicts the regulatory
action Fig.5. Feed water Flow Rate Servo-Regulatory Control Response from the process loop obtained using PSO. The response within the box with dashes depicts the regulatory action TABLE IV. ERROR PERFORMANCE INDICES FOR SERVO-REGULATORY CONTROL RESPONSE
Output Coal Flow Rate Air Flow Rate Feedwater Flow Rate
ISE 636.87 76 3.4375 e7 1.5246 e6
GA IAE 203.76 59 5.3044 e4 1.1193 e4
ITAE 1.1596 e4 3.3295 e6 6.7967 e5
ISE 665.95 33 3.5023 e7 1.5349 e6
PSO IAE 209.91 51 5.3318 e4 1.1248 e4
ITAE 1.1973 e4 3.3661 e6 6.8342 e5
The offset in the coal flow rate and feed water flow rate control could not be reduced by tweaking the parameters in GA and PSO or even by increasing the number of generations of the algorithm. So further research will be carried out in this system by tuning the controller using other robust or meta-heuristic approaches. D. Discussions The output from the three process loops controls or maintains the power output from the system. The output response from the system with the gains of the controllers computed using Genetic Algorithm and Particle Swarm Optimization Algorithm, as shown in Fig. 6, is compared. Table V contains the error performance indices like Integral Square Error, Integral Absolute Error and Integral Time Weighted Absolute Error from the output response from PSO-PD Controllers and GA-PD Controllers. Similarly, Table VI displays the time domain characteristics pertaining to power output response from the PSO-PD Controllers and GA-PD Controllers. From Table V and VI, it can be seen that the controller tuned using PSO provides effective control action, with lower values of error indices, only in the coal flow rate control loop compared to the control action of GA based controller in all the three control loops. Therefore, GA based controller seems to be providing electric power generation control with quicker settling times and lesser overshoot whereas PSO-PD controller produces output with significantly less offset than GA-PD controller but with large overshoot and settling time.
80
60 Power (MW)
GA PSO
40
20
0 0
100
200 300 Time (sec)
400
500
Fig. 6. Electric Power Output (Load) Response from the system obtained using GA and PSO
TABLE V. ERROR PERFORMANCE INDICES FOR OUTPUT RESPONSE
Output Electrical Power (Coal Flow Rate) Electrical Power (Air Flow Rate) Electrical Power (Feedwater Flow Rate)
ISE
GA IAE
ITAE
ISE
PSO IAE
ITAE
8.6789 e4
2.9376 e3
1.4727 e5
8.4034 e4
2.8894 e3
1.4106e 5
5.3675 e7
7.3263 e4
3.6627 e6
5.3476 e7
7.3311 e4
3.6689e 6
1.0217 e7
3.1963 e4
1.5977 e6
1.0248 e7
3.2011 e4
1.6039e 6
TABLE VI. STEP RESPONSE CHARACTERISTICS FOR OUTPUT RESPONSE Characteristics Rise Time Settling Time Overshoot Offset
GA 1.3043 5.3237 4.8472 1.758
PSO 0.5691 23.6000 16.5656 0.3046
VI. CONCLUSION An intelligent control configuration for controlling the power generation in NTPL Thermal Power Station by manipulating the coal flow rate, air flow rate and feed water flow rate is implemented here. The controller is verified for Servo, Regulatory and Servo-Regulatory configurations and, from the results, it is inferred that although there is considerable offset in the individual results from the process loops, the control response from the process loops produce the desired electric power output response with very less offset and quick settling times considering it is a real-time boiler-turbine-generator model. Both the intelligent control schemes achieve desired response, but GA-PD controllers produce desired output response with lesser overshoot and quicker settling time than PSO-PD controllers which produce output response with lesser offset but at the expense of overshoot and settling time. VII. ACKNOWLEDGEMENT We are very much grateful to NLC TamilNadu Power Limited (NTPL) for their technical assistance. We would also like to thank all staff members involved in this research work.
REFERENCES [1] T.R. Rangaswamy, J. Shanmugam, Fuzzy based coordinated controller for thermal power plant, in: Proc. India Int. Conf. Power Electron., Chennai, 2006: pp. 377–382. doi:10.1109/IICPE.2006.4685401. [2] W. Tan, H.J. Marquez, T.W. Chen, J.Z. Liu, Analysis and control of a nonlinear boiler-turbine unit, J. Process Control. 15 (2005) 883–891. doi:https://doi.org/10.1016/j.jprocont.2005.03.007. [3] C. Maffezzoni, Boiler-turbine dynamics in power-plant control, Control Eng. Pract. 5 (n.d.) 301–312. doi:https://doi.org/10.1016/S0967-0661(97)00007-5. [4] R. Dimeo, K.Y. Lee, Boiler-turbine control system design using a genetic algorithm, IEEE Trans. Energy Convers. 10 (1995) 752–759. [5] R. Cori, C. Maffezzoni, Practical-optimal control of a drum boiler power plant, Automatica. 20 (n.d.) 163–173. doi:https://doi.org/10.1016/00051098(84)90022-0. [6] Y.N. Yu, K. Vongsuriya, L.N. Wedman, Application of an Optimal Control Theory to a Power System, IEEE Trans. Power Appar. Syst. PAS-89 (n.d.) 55–62. doi:10.1109/TPAS.1970.292668. [7] K. Jawahar, N. Pappa, Selftuning nonlinear controller, in: Proc. 6th WSEAS Int. Conf. Fuzzy Syst., Lisbon, Portugal, 2005: pp. 118–126. [8] R.D. Bell, K.J. Åström, Dynamic models for boiler-turbine-alternator units: Data logs and parameter estimation for a 160 MW Unit, Sweden, 1987. [9] K.J. Åström, K. Eklund, A simplified non-linear model of a drum boiler turbine unit, Int. J. Control. 16 (1972) 145–169. [10] A. Isidori, Nonlinear Control Systems: An Introduction, 2nd ed., Springer-Verlag Berlin Heidelberg, 1989. doi:10.1007/978-3-662-02581-9. [11] E.E. Vladu, T.L. Dragomir, Controller Tuning Using Genetic Algorithms, in: 1st Rom. - Hungarian Jt. Symp. Appl. Comput. Intell., Timisoara, Romania, 2004. [12] S. Panda, N. P. Padhy, R.N. Patel, Application of Genetic Algorithm for FACTS based Controller Design, Int. J. Electr. Comput. Eng. 1 (2007). scholar.waset.org/1307-6892/1255. [13] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989. [14] C. Houck, J. Joines, M. Kay, A genetic algorithm for function optimization: A MTLAM implementation, 1995. [15] A. Soltoggio, Evolutionary Algorithms in the Design and Tuning of a Control System, Norwegian University of Science and Technology, 2004. [16] M. Seyedkazemi, Designing Optimal PID controller with Genetic Algorithm In view of controller location in the plant, in: Proc. 7th WSEAS Int. Conf. SIGNAL Process. Robot. Autom. (ISPRA ’08), University of Cambridge, UK, n.d. [17] M. Ejaz, Application of Genetic Algorithm for Tuning of Reduced Ordered Robust PID Controller and Computer Simulations, Int. J. Comput. Appl. 42 (n.d.). [18] J.G. Ziegler, N.B. Nichols, Optimum settings for automatic controllers, Trans. ASME. 64 (1942) 759–768. [19] G.P. Liu, J.B. Yang, J.F. Whidborne, Multiobjective Optimization and Control, Prentice Hall, New Delhi, India, 2008. [20] K. Latha, V. Rajinikanth, P.M. Surekha, PSO-Based PID Controller Design for a Class of Stable and Unstable Systems, Int. Sch. Res. Not. Artif. Intell. 2013 (2013). doi:http://dx.doi.org/10.1155/2013/543607. [21] J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proc. IEEE Int. Conf. Neural Networks, 1995: pp. 1942–1948. [22] A. O’Dwyer, Handbook of PI and PID Controller Tuning Rules, 3rd ed., Imperial College Press, London, UK, 2009. [23] A. Alfi, H. Modares, System identification and control using adaptive particle swarm optimization, Appl. Math. Model. 35 (2011) 1210–1221.