Sri Ramakrishna Institute of Technology, Coimbatore 2 Deparment of Electrical and Electronics Engineering,

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1 Effective Woodward Governor Parameters for Biomass Based Gas Turbine Power Plant Sidharth A 1, Vinod Kumar R L 1, M Mohamed Iqbal 1, Sarumathi S 2 1 Deparment of Electrical and Electronics Engineering, Sri Ramakrishna Institute of Technology, Coimbatore 2 Deparment of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore Abstract Distributed generation becomes the alternative solution for energy crisis all over the world. Effective control at the point of generation plays the significant role in predicting overall system performance and efficiency. Frequent load disturbances and set point variations may cause severe instability problems. Biomass gas turbine plants are preferred because of its fast response to the load variations. Speed governors are used for controlling the fuel flow rate to maintain turbine speed and hence the system frequency. Woodward governors followed the rapid advancement and are also well preferable for simple cycle and combined cycle power plants. In order to maintain the system frequency, the governor parameters need to be tuned properly and identify suitable model parameters. In this paper, the Woodward governor parameters viz. proportional integral and derivative gains are properly tuned by various tuning rules of Ziegler-Nicholas method. The simulation results are compared based on time domain specifications and performance indices and identified an effective governor parameters suitable for the simple cycle operation. The governor parameters identified in this paper are suitable for further power system studies. Keywords Woodward Governor; PID Controller; Biomass plant: Simple cycle operation I. INTRODUCTION Deployment and integration of renewable energy is a global phenomenon, where numerous countries putting effort to support the trend. Renewable energy plays a vital role in the move towards a sustainable future [1]. The energy sources are being combined to perform better efficient mode of working as hybrid energy systems in order to serve the load demand while meeting the economic criteria [2]. Biomass is attractive as a potential energy resource and connected to small networks or even in isolated operation such as, oil fields in desert area, offshore installations and bio-gas plants. Biomass based gas turbine plants are basically dynamic devices, and have the tendency to become unstable after a severe disturbance which leads in inevitable plant shut down. Therefore an efficient control system required for parallel operation and to maintain stability. Based on the surveys and conclusions made already, India is very much blessed with biomass. Availability of 500 metric tons of biomass per year in India makes it an inevitable option of energy production [3]-[5]. Design aspects such as turbine and governor models having vital role in the improvement of overall system efficiency in simple cycle power plants. A simple cycle power plant is a power plant in which the heat energy produced after combustion is directly fed to the turbine which is coupled with an electrical system [6]. It consists of compressor, gas turbine, combustor, generator and inlet-exhaust valves. The intake air is compressed to hightemperature high-pressure which is then fed to a turbine coupled with compressor as well as generator. The flue gases will move to atmosphere through exhaust valve. Detailed dynamic simulation models for two types of governor controllers (Woodward and Speedtronic) are presented in [7]-[10]. Brief introduction about the Woodward Governor and its maintenance details are explained in[11]- [13]. The model and basic operation principle is explained well which will help in getting a clear idea about how the Governor is useful for heavy duty gas turbine plants. Lot of research works have been carried out on Speedtronic type governor already. There are more complexities when the grid interconnection takes place and efficient controllers were developed using soft computing techniques such as Fuzzy logic, genetic algorithm etc [14], [18]. As there is not much work attempted on Woodward governor based gas turbine plant, an attempt has been made in this paper to identify an effective governor parameters suitable for the simple cycle operation. A simple cycle gas turbine power plant consists of three main blocks; a compressor, a combustor and a gas turbine as shown in Figure 1.

2 Figure 1. Schematic diagram of Simple Cycle Power Plant Figure 2. Block diagram of Gas Turbine controls. Gas turbine plants have considerable merits in power generation with certain limitations over steam turbine power plants. Among the main advantages, cheap plant installation, lesser land space needs, higher turbine inlet temperature at relatively low pressure and faster power generation start up are noticeable. On the other hand, the plant fuel sensitivity and need of high quality fuels as well as its efficiency restrictions due to its high exhaust temperature are the considerable limitations that the plant reveals. The compressor and the gas turbine can be mounted on the same shaft. The compressor unit is either centrifugal or of axial flow type. A gas turbine cycle operates on the principle of the Brayton cycle where compressed air is mixed with fuel, and burned under constant pressure conditions. There resulting hot gas is allowed to expand through a turbine perform work. Many modern mathematical models of reallife processes pose when used in numerical simulations due to complexity and large size (dimension). The dynamic simulation modeling of gas turbine plant is done using MATLAB Simulink model. The modeling is done based on Woodward governor. The response of the governorturbine systems to disturbances can be an important variable affecting the dynamic performance of electrical power system. Woodward governor control consists of a PID controller for the speed/load error input signal. Electrical power is measured by a watt transducer, scaled and added to the error signal to provide droop. II. MODELING OF GAS TURBINE Governor models and the response of governor turbine systems to disturbances are affecting the dynamic performance of electrical power system. A common source of power generation for small system is the combustion turbine which is becoming increasingly popular in cogeneration facilities and combined cycle installation. In some cases because of the economics of fuel supply, the majority of the generation for a power system may consist of combustion turbines. The block diagram of speed control loop, acceleration control loop and temperature control loop of Woodward governor based gas turbine plant are shown in Figure 2. PID controller structure of Woodward governor is presented Figure 3. Figure 3. PID controller structure for gas turbine model. The Low value select (LVS) combines all the 3 parameters such as speed, acceleration and temperature control signals together and is passed to the valve positioner after selecting the minimum value signal which is useful for the normal operation. The valve positioner is an instrument working on force balance principle to position the Control Valve stem (Pv) in accordance to a fuel demand signal (Wd) received from a controller. The transfer function of the valve positioner is shown in Equation (1). Vp s = a W d s bs +c The fuel system is composed of the pump, filter, fuel tank and injectors and is responsible for delivering fuel supply signal (Wf) to the combustor based on valve position. Corresponding transfer function described in Equation (2). W f s = 1 Vp s Ts +1 The transfer function model of gas turbine dynamics for obtaining the turbine torque (T T ) from the fuel supply signal (Wf) is given in Equation (3). TT W f (1) (2) = 1/(TDSs 1) (3) In the speed feedback loop, the function f 2 whose inputs are fuel flow and speed to produce a value of turbine torque is given in Equation (4). The transfer function of rotor dynamic is given in Equation (5). f 2 = a f2 + b f2 (w 12 ) c f2 (speed) (4) Tt s = 1 N s T1s (5) From the fuel system block, the fuel supply (Wf) contributes for both the temperature loop as well as speed control loops. The derivative of speed obtained and the

3 acceleration error is controlled by the acceleration controller. The integral controller acts as a acceleration controller. Initially radiation shield block is placed. Shielding reduces the intensity of radiation depending on the thickness of the shield. The transfer function of the radiation shield is shown in Equation (6). Rs s W f s = K4 + K5 T3s+1 (6) Figure 4. MATLAB/Simulink model of the Woodward governor controlled model III. TUNING OF WOODWARD GOVERNOR PARAMETERS Woodward governor is a typical type of turbine followed the rapid advancement of diesel engine applications. They are built for turbine control with deterministic performance and flexible input output options. The input and output of the radiation shield is W f and R s respectively. K 4 and K 5 are gains of the temperature control loop. From the radiation shield, the signal moves to the thermocouple. A thermocouple is an electrical device consisting of two different conductors forms electrical junctions at various temperatures.. Thermocouples are a widely used type of temperature sensor all over the world. Its transfer function is given below in Equation (4). Tc s = 1 Rs s T4s+1 The output of the thermocouple, ie. the actual temperature of the gas turbine is compared with a reference temperature. The temperature controller whose transfer function is given in Equation (8) takes the control action. f 1 = T R a f1 (1-W f1 ) b f1 (speed) (8) The resulting signal is taken as the temperature control signal, C T. The response signal is send to the LVS. The function f 1 whose inputs are fuel flow and turbine speed to produce a value of exhaust temperature. C N C T and C A are the control signals originated from the speed, temperature and acceleration controllers and are given to the Low value select (LVS). Based on the modeling of the physical components, the MATLAB/Simulink model of Woodward governor based Biomass Gas-Turbine power plant is obtained as shown in Figure 4. (7) In this article, the PID gains of Woodward governor has been implemented with proportional (K p ), integral (K i ), and derivative (K d ) gains as the function of speed error signal, e(t). The control signal output, u(t) from the PID controller has been obtained as shown in Equation (8) and the PID controller model is shown in Figure 4. PID controller gains of all the Biomass gas turbine models have been tuned by the well known ZN method proposed by John G Ziegler and Nathaniel B Nicholas. In the ZN method, PID gains have been tuned by different rules, namely classical, Pessen integral, some overshoot and no overshoot. The PID gains have been obtained by various tuning rules by using the respective ultimate time period, Tu, corresponding to sustained oscillations as given in Table 1. Table 1. PID tuning rules of ZN method Tuning Rules K p K i K d Classical rule 0.6*Ku 2Kp/Tu Kp*Tu/8 (ZN-C) Pessen integral rule (ZN-PI) 0.7*Ku 2.5Kp/Tu 0.15*Kp* Tu Some overshoot 0.33*Ku 2Kp/Tu Kp*Tu/3 (ZN-SO) No overshoot (ZN-NO) 0.2*Ku 2Kp/Tu Kp*Tu/3 The tuned vales of the Woodward governor parameters are implemented in MATLAB/Simulink model and the response are compared to identify the effective governor parameters. IV. SIMULATION RESULTS AND DISCUSSION The PID gains of the Woodward governor are tuned by ZN tuning rules and shown in Table 2 Table 2. Calculated gain values Tuning Rules K p K i K d ZN-C ZN-PI ZN-SO ZN-NO

4 speed in p.u. Step load disturbance of 1.0 pu is given at the step time of 1 second and the PID controller gains by simulating the MATLAB/Simulink model of gas turbine up to 50 seconds. Figure 5 shows the system speed responses for different tuning rules ZN - C ZN - PI ZN - SO ZN - NO Based on the analysis of the time domain specifications, and performance indices, the ZN-SO rule based Woodward governor parameter yield better response than ZN-C, ZN-PI and ZN-NO based gains. The performance indices are also witnessed to be minimum for the ZN-SO based gains. Hence, the ZN-SO rule based Woodward governor parameters is identified as an effective governor parameter for the simple cycle operation of the gas turbine. V. CONCLUSION Time in second Figure 5. PID Controller Different ZN Method Response Comparison The main objective of the work is to find out which tuning method provides better response. For that, the Maximum peak overshoot (Mp), rise time (Tr), peak time (Ts), steady state error (Ess) and performance evaluation index are identified based on the gain values calculated from the tuning equations. From the step response, integral of speed (ISE) and integral of time multiplied with squared error (ITSE) as expressed in equation (9) and (10) have been used as the performance indices for analyzing the gas turbine responses for various governor parameters. The values are listed in Table 3. J ISE = e 2 dt (9) J ITSE = (e 2 ) t dt (10) Table 3. Performance indices Tuning Rule J ITSE J ISE ZN-C ZN-PI ZN-SO ZN-NO Time domain specifications namely Maximum Peak overshoot (Mp), rise time (Tr), peak time (Ts) and steady state error (Ess) are obtained as shown in Table 4. Table 4. Time domain specifications with various governor parameters Tuning Rule Mp Tr Ts Ess ZN-C ZN-PI ZN-SO ZN-NO In this article, PID controller tuning methods have been analyzed for Biomass gas turbine power plant Woodward governor using MATLAB/Simulink. The PID controller gain parameters have been tuned by ZN method and the step responses are obtained. The step responses are compared based on time domain specifications and performance indices. Among the ZN tuning rules, ZN-SO method found to be the better tuning rule as it provides the minimum time domain parameters. Also among the performance indices, ISE values are minimum based on the tuning. The optimal range of PID gains for all Biomass gas turbine models have been identified and furnished in this article. REFERENCES [1]. P. Pinson, L. Mitridati, C. Ordoudis, J. Ostergaard, Towards Fully Renewable Systems: Experience Trends in Denmark, CSEE Journal, Power and Energy systems, Vol.3, No. 1, March 2017 [2]. S. Chew, A. Majumdar, Opportunities and Challenges for a Sustainable Energy Future, Nature, No. 294, Vol. 488, 16 August 2012 [3]. L. Wang, C. Singh, Compromise Between Cost and Reliability in Optimum Design of an Autonomous Hybrid Power System Using Mixed-Integer PSO Algorithm, IEEE, pp , July 2007 [4]. Mohamed Iqbal M., Joseph Xavier R., Arun Kumar D., Raj Kumar G., Selva Kumar P., Tamilarasan C., A Sample Survey on Biomass Gasifier Power plants, International Conference on Emerging Technologies in Renewable Energy (ICETRE-2010), Ref. No. P-87, Anna University, Chennai, August [5]. Mohamed Iqbal M., Joseph Xavier R., Factors influencing the operation of the Biomass gasifier power plants, Proceedings of Second World Renewable Energy Technology Congress and Expo-2011, Hotel Le Meridien, New Delhi,April 2011 [6]. H. Wang, J. Luquan, G. Haiying, Project Management on the Establishment of Raw Material Procurement System for Biomass Power Plant, IEEE, Vol. 978, July 2014 [7]. RL. N. Hannet, Afsal Khan, Combustine Turbine Dynamic Model Validation from Tests. IEEE Transactions on Power Systems, Vol.8,No.1, February [8]. V. Tsourapas, J. Sun, Incremental Step Reference Governor for Load Conditioning of Hybrid Fuel Cell and Gas Turbine Power

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