three element control of boiler drum using particle ...

Three Element Control Of Boiler Drum Using Particle Swarm Optimization Technique

THREE ELEMENT CONTROL OF BOILER DRUM USING PARTICLE SWARM OPTIMIZATION TECHNIQUE 1

R.MANOJKUMAR, 2M.ARAVINTH, 3R. MANIKANDAN 1

Final year student, 2Associate Professor, Department of Electronics and Instrumentation Engineering, Panimalar Engineering College

Abstract: Boilers are the important instrument in any production plant which is used to store large amount of water (demineralized) for the purpose of production of steam. The generated steam is used to drive the prime mover (turbine) which rotates the shaft in the generator for the production of power. Water level control in boiler drum is very important. Low level of water in the drum may cause explosion of boiler whereas higher level in the boiler reduces steam production as well as plant efficiency. Three element control is the main criteria involving in the boiler water level control. Now a days many plants doing the three element control using PI controller with the help of trained persons. This project provides a new way for very fast auto tuning, shorter calculation time and stable convergence PI controller for three element control in boiler. Steam flow, feed water flow and water level in drum are the three elements that has to be controlled. In this project, feed water flow is taken as the primary variable and other two elements are cascaded. Boiler specification and data are taken from the Madras Fertilizers Limited.

I.

1995 by Kennedy and Eberhart for unconstrained continuous optimization problems. Its development was based on observations of the social behavior of animals such as bird flocking, fish schooling and swarm theory. The PSO is initialized with a population of random solutions. The PSO has some attractive characteristics where it has memory and therefore, knowledge of good solutions is retained by all particles. There exist constructive cooperation between particles where particles in the swarm share information between them. The theoretical framework of PSO is very simple and PSO is easy to be coded and implemented using computer. In fact, the PSO technique can generate a high quality solution within shorter calculation time and stable convergence characteristics than other stochastic methods. Thus, this technique has gained much attention and wide applications in various fields recently. The rest of the paper is organized as follows. In topic follows, a brief discussion about PI controller, boiler and three element control on boiler drum level are presented. Next, the PSO method and its implementation into the PSO-PI controller are viewed in detail. Further, the simulation results are presented in table form and discussed. Finally, the discussion of the results followed by conclusion of the research is provided.

INTRODUCTION

Even in a decade where advanced control algorithms mostly based on some kind of optimization procedure have achieved a high degree of maturity, Proportional Integral Derivative (PI) controllers are still widely used in industrial applications even though many new control techniques have been proposed. The reason is that it has a simple structure which is easy to be understood by the engineers, and under practical conditions, it has been performing more reliably compared to more advanced and complex controllers. The main propose of designing a PI controller is to determine the three gains and they are proportional gain (kp), integral gain (ki) of the controller. However, the three adjustable PI controller parameters should be tuned appropriately. Over the years, several heuristic methods have been developed for the tuning of PI controllers. The first method used the classical tuning rules proposed by Ziegler and Nichols [5]. Generally, it is always hard to determine optimal or almost optimal PI parameters with the Ziegler-Nichols method in many industrial plants [5]. Other than original works done by Ziegler and Nichols, a great number of methods have been proposed to obtain optimal gains of the PI such as by Cohen and Coon in 1953, Åström and Hägglund in 1984 or by Zhuang and Atherton in 1993. To obtain the optimal parameter tuning, it is highly desirable to increase the capabilities of PI controllers by adding new features. Most in common, artificial intelligence (AI) techniques have been employed to improve the controller performances for a wide range of plants while retaining the basic characteristics. AI techniques such as artificial neural network, fuzzy system and neural-fuzzy logic have been widely applied in order to get proper tuning of PI controller parameters. Recently, a new evolutionary technique, Particle Swarm Optimization (PSO) was first introduced in

1.1 Pi Controller The PI controller is used to improve the dynamic response as well as to reduce or eliminate the steadystate error. The integral controller adds a pole at the origin, thus increasing system type by one and reducing the steady state-error due to a step function to zero. The continuous form of a PI controller, with input e(t) and output UPI(t)⋅ , is generally given as : u PI (t )= u PI (t )= ( )+ ∫ ( ) 0

Proceedings of 4th IRF International Conference, Chennai, 9th March-2014, ISBN: 978-93-82702-64-1 1

Three Element Control Of Boiler Drum Using Particle Swarm Optimization Technique

where kp is the proportional gain, ki = kp / Ti is the integral gain. In simple form, the PI controller transfer function is C (s )= kp + ki/s + kds 1.2 Boiler Drum Level Control The drum level must be controlled to the limits specified by the boiler manufacturer. If the drum level does not stay within these limits, there may be water carryover. If the level exceeds the limits, boiler water carryover into the super heater or the turbine may cause damage resulting in extensive maintenance costs or outages of either the turbine or the boiler. If the level is low, overheating of the water wall tubes may cause tube ruptures and serious accidents, resulting in expensive repairs, downtime, and injury or death to personnel. A rupture or crack most commonly occurs where the tubes connect to the drum. Damage may be a result of numerous or repeated low drum level conditions where the water level is below the tube entry into the drum. Some companies have had cracked or damaged water tubes as a result of time delayed trips or operators having a trip bypass button. When the drum level gets too low, the boiler must have a boiler trip interlock to prevent damage to the tubes and cracks in the tubes where they connect to the boiler drum. The water tubes may crack or break where they connect to the drum, or the tubes may rupture resulting in an explosion. The water tube damage may also result in water leakage and create problems with the drum level control. The water leakage will affect the drum level because not all the water going into the drum is producing steam. Poor level control also has an effect on drum pressure control. The feed water going into the drum is not as hot as the water in the drum. Adding feed water too fast will result in a cooling effect in the boiler drum reducing drum pressure and causing boiler level shrinkage. This can be demonstrated by pouring tap water into a pan of boiling water. The boiler drum is having the capacity of 54 tons. II.

Fig1: Existing System

2.1 Ziegler-Nichols Tuning In the 1940's, Ziegler and Nichols devised two empirical methods for obtaining controller parameters. Their methods were used for non-first order plus dead time situations, and involved intense manual calculations. With improved optimization software, most manual methods such as these are no longer used. However, even with computer aids, the following two methods are still employed today, and are considered among the most common. 2.1.1 Ziegler-Nichols Closed-Loop Tuning Method The Ziegler-Nichols closed-loop tuning method allows you to use the ultimate gain value, Ku, and the ultimate period of oscillation, Pu, to calculate Kc . It is a simple method of tuning PID controllers and can be refined to give better approximations of the controller. You can obtain the controller constants Kc , Ti , and Td in a system with feedback. The ZieglerNichols closed-loop tuning method is limited to tuning processes that cannot run in an open-loop environment. Determining the ultimate gain value, Ku, is accomplished by finding the value of the proportional-only gain that causes the control loop to oscillate indefinitely at steady state. This means that the gains from the I and D controller are set to zero so that the influence of P can be determined. It tests the robustness of the Kc value so that it is optimized for the controller. Another important value associated with this proportional-only control tuning method is the ultimate period (Pu). The ultimate period is the time required to complete one full oscillation while the system is at steady state. These two parameters, Ku and Pu, are used to find the loop-tuning constants of the controller (P, PI, or PID). To find the values of these parameters, and to calculate the tuning constants, use the following procedure:

EXISTING SYSTEM

The main criteria on the boiler control is to maintain the boiler drum level in the range of 295mm to 301 mm. The most safety value is 297mm. Level greater than 301 mm and lesser than 295 mm leads to a tedious problems. The current technique that has been adopted to control the boiler drum level technique is Ziegler-Nichols Tuning method which is a conventional one. The technique leads to a approximate results rather than accurate one. Still any problem occurs, the controller requires a manual intervention for changing the controller parameter with respect to the change in the boiler input, say feed water flow rate.

2.1.2 Closed Loop (Feedback Loop) 1. Remove integral and derivative action. Set integral time (Ti) to 999 or its largest value and set the derivative controller (Td) to zero.

Proceedings of 4th IRF International Conference, Chennai, 9th March-2014, ISBN: 978-93-82702-64-1 2

Three Element Control Of Boiler Drum Using Particle Swarm Optimization Technique

2. Create a small disturbance in the loop by changing the set point. Adjust the proportional, increasing and/or decreasing, the gain until the oscillations have constant amplitude. 3. Record the gain value (Ku) and period of oscillation (Pu). 4. Plug these values into the Ziegler-Nichols closed loop equations and determine the necessary settings for the controller.

Table 1: closed loop calculations of kc, ti and td.

2.2 Disadvantages Of The Existing System It always require the presence of trained persons. Whenever the change of modes (cascade or auto) by not trained persons may lead to boiler trip that leads to entire plant trip. Frequent check on feed water control valve is required. Calculation on gain is difficult and time consuming process. Gain value is set only for approximate results. Experiment can be time consuming Can venture into unstable regions while testing the P controller, which could cause the system to become out of control. III.

The parameter max g,v determined the resolution, or fitness, with which regions were searched between the present position and the target position. If max g v is too high, particles might fly past good solutions but if max g v is too low, particles may not explore sufficiently beyond local solutions. The constant C1 and C2 represent the weighting of the stochastic acceleration terms that pull each particle toward pbest and gbest. C1 and C2 were often set to be 2.0 according to past experience. This because low values allow particle to fly far from the target region before being tugged back while high values result in abrupt movement toward or past target regions. Generally, the inertia weight w is set according to equation (11) below. Suitable selection of w provides a balance between global and local explorations, thus requiring less iteration on average to find a sufficiently optimal solution.

PROPOSED SYSTEM

The particle swarm optimization (PSO) has been introduced by Kennedy and Eberhart in 1995. PSO is derived from the social-psychological theory, and has been found to be robust in complex systems. Each particle is treated as a valueless particle in gdimensional search space, and keeps track of its coordinates in the problem space associated with the best solution (evaluating value) and this value is called pbest. The overall best value and its location obtained so far by any particle in the group that was tracked by the global version of the particle swarm optimizer gbest. The PSO concept consists of changing the velocity of each particle toward its pbest and gbest locations at each time step. As example, the jth particle is represented as = ,1, ,2,… . in the g dimensional space. The best previous position of the jth particle is recorded and represented as ,= ( , 1, , 2,….. , ). The index of best particle among all particles in the group is represented by the gbestg. The rate of the position change (velocity) for particle j is represented as vj = (vj,1, vj,2, . . . , vj,g). The modified velocity and position of each particle can be calculated using the current velocity and distance from , to , as shown in the following formulas:

where itermax is the maximum number of iterations or generations and iter is the current number of iterations. 3.1 Implementation Of Pso-Pi Controller The PID controller using the PSO algorithm was developed to improve the step transient response of typical servo motion system. It was also called the PSO-PI controller. The PSO algorithm was mainly utilized to determine three optimal controller

Proceedings of 4th IRF International Conference, Chennai, 9th March-2014, ISBN: 978-93-82702-64-1 3

Three Element Control Of Boiler Drum Using Particle Swarm Optimization Technique

parameters kp, and ki, such that the controlled system could obtain a good step response output. STEP1: Check the flow rate value, if it is lesser than 52MT/hr then execute PSO algorithm. STEP2: Set the C1, C2, r1, r2 value. STEP3: Get the current PI controller parameters. STEP4: Set the Pbest and Gbest value. STEP 5: Find the accurate PI controller parameters (PB & Ti) using the following formula v(i)= v(i) + r1( Pbest-x(i))+ r2(Gbest-Pbest). STEP6: Calculate Y(i) (PI parameters). STEP7: If the value is not converged one, alter the r1,r2 and repeat from step 3.

Fig4: Graph Obtained From Ziegler-Nichols Tuning.

4.1.2 Performance Criteria Of The System With Pso-Pi Controller

TABLE II: Performance Criteria Of The System With ZieglerNichols PI Controller.

IV.

SIMULATION RESULTS

The results shown here are for the optimization of feed water flow rate control. Like the same, we can apply this PSO algorithm for level and steam flow rate too. 4.1 Drum Level Control System The block diagram of the Typical Drum Level Control System with result of this step response of the PI controller has shown in Fig. 3. Fig5: Graph Obtained From Pso Tuning.

4.2 Drum Level Control System With Disturbance: The block diagram of the Typical Drum Level Control System with disturbance and result of this step response of the PI controller has shown in Fig. 6. FIG3: Typical Drum Level Control System

4.1.1Performance Criteria Of The System With Ziegler-Nichols Pi Controller

TABLE I: Performance Criteria of the System with ZieglerNichols PI controller.

FIG6: Typical Drum Level Control System with Disturbance.

Proceedings of 4th IRF International Conference, Chennai, 9th March-2014, ISBN: 978-93-82702-64-1 4

Three Element Control Of Boiler Drum Using Particle Swarm Optimization Technique

This PSO-PI controller has been compared ZieglerNichols PI method to verify it being more superior .The comparison from rise time show that the PSO-PI achieves less PB that is 102.6 compared to ZieglerNichols PI that is 274.656. The system using ZieglerNichols method takes more oscillation making it very difficult to incorporate into any high performance motion control application. In contrast, the PSO method has less oscillation. The PSO method instantaneously reduces the maximum overshoot of the system to 1.78 compared to the Ziegler-Nichols method that is 1.82. Therefore, it is clear from the results that the proposed PSO method has more robust stability and efficiency and can solve the searching and tuning problems of PI controller parameters more easily and quickly than the ZieglerNichols method.

TABLE II: Performance Criteria with Disturbance

REFERENCES Fig7: Graph Obtained From Ziegler-Nichols Tuning. [1]

Pedret, C., Vilanova, R., Moreno, R. and Serra, I. (2002). A refinement procedure for PID controller tuning. Computer and Chemical Engineering, Vol.26, 903-908. [2] Chang, W.-D., Hwang, R.-C. and Hsieh, J. –G. (2003). A multivariable on-line adaptive PID controller using autotuning neurons. Engineering Applications of Artificial Intelligence, Vol.16, 57-63. [3] Tan, K.K., Huang, S. and Ferdous, R. (2002). Robust selftuning PID controller for nonlinear systems. Journal of Process Control, Vol.12, 753-761. [4] Valério, D. and Costa, J. S. (2006). Tuning of fractional PID controllers with Ziegler-Nichols-type rules. Signal Processing, Vol. 86, 2771-2784. [5] Gaing, Z.L. (2004). A Particle Swarm Optimization approach for optimum design of PID controller in AVR [6] system. IEEE Transactions on Energy Conversion, Vol.19(2), 384-391. [7] Tuning of Optimum PID Controller Parameter Using Particle Swarm Optimization Algorithm Approach by Wan Azhar Wan Yusoff Nafrizuan Mat Yahya, Azlyna Senawi [8] Engineering Optimization Theory and Practice Fourth Edition, Singiresu S. Rao [9] http://www.isa.org/InTechTemplate.cfm?Section=features3 &template=/TaggedPage/DetailDisplay.cfm&ContentID=83 045 [10] PSO-Based PID Controller Design for a Class of Stable and Unstable Systems By K. Latha,1 V. Rajinikanth,2 and P. M. Surekha2. [11] PSO based tuning of PID controller for a Load frequency control in two area power system by Ranuva Nageswara Rao, P.Rama Krishna Reddy

Fig8: Graph Obtained From Pso Tuning.

CONCLUSION This paper presents a novel design method for determining the PI controller parameters using the PSO method. The proposed method integrates the PSO algorithm with the new time-domain performance criterion into a PSO-PI controller. Through the simulation of a typical drum level control system, the results show that the proposed controller can perform an efficient search to obtain optimal PID controller parameter that achieve better performance criterion that are rise time, peak overshoot, peak time, proportional band, integral time and decay ratio.

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Proceedings of 4th IRF International Conference, Chennai, 9th March-2014, ISBN: 978-93-82702-64-1 5