A Novel PSS Design for Single Machine Infinite Bus System Based on Artificial Bee Colony Prof. MS Jhamad*, Surbhi Shrivastava** *Department of EEE, Chhattisgarh Swami Vivekananda Technical University, Bhilai ** Department of EEE, Chhattisgarh Swami Vivekananda Technical University, Bhilai ABSTRACT With the growth of interconnected power systems and particularly the deregulation of the industry, problems related to low frequency oscillation have been widely reported, including major incidents. As the most costeffective damping controller, power system stabilizer (PSS) has been widely used to suppress the low frequency oscillation and enhance the system dynamic stability. Traditional methods for determining PSS placements are based on the analysis of the interconnected system. However, the design of the PSS is based on a simplified single machine infinite bus (SMIB) model. Traditional methods for determining PSS placements are based on the analysis of the interconnected system. This paper explains the design of the PSS is based on a simplified single machine infinite bus (SMIB) model using Artificial Bee Colony algorithm. The MATLAB / SIMULINK model is used to implement the SMIB-PSS model. Keywords PSS, MB-PSS, TCSC, STATCOM. I. INTRODUCTION A power system stabilizer (PSS) is an instrument installed in a generator to stabilize the power system. From a system-wide point of view, PSSs are used to stabilize the power system as the demand for power increases and more power is delivered over longer distances. From a design point of view, a PSS is a device used to increase the damping torque component of a machine. Although a PSS can be installed at every machine, only a few PSSs are needed to stabilize a power system The power system stabilizer can conquer the electromechanical oscillation and improve the power system stability with the help of its additional excitation mechanism. For more economical and consistent generation and transmission of electric energy, the electric power systems become larger and larger, which covers a vast area and include many synchronous generators, transmission lines, loads and variety of controllers. The stability of the power system is the ability to extend restoring forces equal to or greater than the disturbing forces to sustain the state of equilibrium. Power industries are updated to provide effective utilization to more consumers at lower prices and better power efficiency. The complexity of the power systems has been increasing as they become inter-connected. Load demand also increases linearly with the increase in users. Since stability phenomenon limits the transfer capability of the system, there is a need to confirm stability and reliability of the power system due to economic reasons. With these conditions, authorities and researchers were continually tasked to find simple, effective and economical strategy of achieving stabilization of the power system, which is considered of highest priority. Thus, because of the importance of the stability of the power systems, stabilizing control techniques have been used for the single-machine power system with the help of intelligent methods. The optimal sequential design for single-machine power systems is very essential. As a result, serious consideration is now being given on the concern of stabilization control. In recent times, the utilization of optimization techniques becomes possible to deal with control signals in power system. II. POWER SYSTEM STABILITY Power system stability may be broadly defined as that property of a power system that enables it to remain in a state of operating equilibrium under normal operating conditions and to regain an acceptable state of equilibrium after being subjected to a disturbance. Power system stability can be divided into four different phenomena s: wave, electromagnetic, electromechanical and thermodynamic. Here we consider only electromechanical phenomenon, which takes place in the windings of a synchronous machine. A disturbance in the electrical network will create power fluctuations between the generating units and the electrical network. In addition the electromechanical phenomenon will also disturb the stability of the rotating parts in the power system [12]. Security of the power system relies on its ability to survive any disturbances which may occur without any
interruption in the services. Figure 1 shows the functional block diagram of a typical excitation control system for a large synchronous generator [5]. Ref. Regulator Exciter Generator Power System Stabilizer Output Figure 1: Functional block diagram of a synchronous generator excitation control system [5] Power system stabilizers (PSS) are used on a synchronous generator to increase the damping of oscillations of the rotor / turbine shaft. The conventional PSS was first suggested in the 1960s and classical control theory, defined in transfer functions, was employed for its design. Later the revolutionary work of DeMello and Concordia [1] in 1969, control engineers, as well as power system engineers, have exhibited great interest and made significant assistances in PSS design and applications for both single and multi-machine power systems. Optimal control theory for stabilizing SMIB power systems was developed by Anderson [2] as well as by Yu [3]. These optimal controllers were linear. Adaptive control techniques have also been proposed for SMIB, most of which involve linearization or model approximation. Klein et al. [4, 5] presented the simulation studies into the effects of stabilizers on inter-area and local modes of oscillations in interconnected power systems. It was shown that the PSS location and the voltage characteristics of the system loads are significant factor in the ability of a PSS to increase the damping of inter-area oscillations. Nowadays, the conventional lead-lag power system stabilizer is widely used by the power system utility [6]. Other types of PSS such as proportional-integral power system stabilizer (PI -PSS) and proportional-integralderivative power system stabilizer (PID -PSS) have also been proposed [7-8]. Several approaches have been applied to PSS design problem. These include pole placement,, optimal control, adaptive control, variable structure control, and different optimization and artificial intelligence techniques [9]. III. SINGLE MACHINE INFINITE BUS SYSTEM (SMIB) The power system is a high order complex nonlinear system. In order to simplify the analysis and focus on one machine, the multi-machine power system is reduced to the single machine infinite bus (SMIB) system. In the SMIB system, the machine of interest is modelled in detail while the rest of the power system is equated with a transmission line connected to an infinite bus. As shown in figure 2, Single machine is connected to infinite bus system through a transmission line having resistance and inductance. Figure 2: Single machine infinite bus system The generator is modelled by transient model, according to the following equations. All system data can be found. Stator winding equations: Where is the stator winding resistance is the d-axis transient resistance is the q-axis transient resistance is the q-axis transient voltage is the d-axis transient voltage. Rotor winding equations: (1) (2) (3) (4) Where, is the d-axis open circuit transient time constant, is the q-axis open circuit transient time constant is the field voltage.
Torque equation: Rotor equation: Then (5) (6) (7) Where is the mechanical torque, which is constant in this model. is the electrical torque. is the damping torque and is the damping coefficient. IV. METHODOLOGY Additionally, ABC consists of three control parameters: a) Population size (SN) is the number of food sources (or solutions) in the population. SN is equal to the number of employed bees or onlooker bees. b) Maximum Cycle Number (MCN) refers to the maximum number of generations. c) The limit is used to diversify the search, to determine the number of allowable generations for which each non-improved food source is to be abandoned. 2. Initialization of the Food Source Memory (FSM) The Food Source Memory (FSM) is an augmented matrix of size comprised in each row, a vector representing a food source as in equation (8). Note that the vectors in the FSM are sorted in ascending order, according to proximity cost function values. Artificial Bee Colony (ABC) Algorithm The ABC algorithm was first proposed by Karaboga in 2005. Similar to other intelligent swarm algorithms, it simulates the foraging behaviour of honeybees. There are three groups of honeybees in the ABC algorithm; employed bees, onlooker bees, and scout bee. Employed bees take the responsibility of searching new food sources. After the process completed, they fly back to the hive and share the position and nectar amount information with onlooker bees in the dancing area. By observe the dance of employed bees, onlooker bees decide the food sources which they want. Scout bees carry out the random search while the food source is exhausted. The procedure of ABC could be described in the following seven steps: Generally, each vector is generated as follows: Note that between 0 and 1. (8) (9) generates a uniform random number 3. Assigning employed bees to the food sources In this step, each employee bee is assigned to its food source and in turn, a new one is generated from its neighbouring solution, using equation (10) as is shown algorithm (1): 1. Initialization of ABC and optimization problem parameters In general, optimization problem could be formulated as follows: Where is the objective function to be minimized; is the set of decision variables is the possible range for each decision variable, where and and and are the lower and upper bound values for the variable N represents the number of decision variables and, and are the inequality and equality constraints, correspondingly. Algorithm (1): Employed Bee Phase 1. 2. 3. 4. 5. 6. 7. (10)
8. 9. 10. 4. Sending the onlooker bees The onlooker bee has the same number of food sources as the employed. It initially calculates the selection probability of each food source generated by the employed bee in the previous step. The fittest food source is selected by the onlooker, using Roulette Wheel selection mechanism. The process of selection at the onlooker phase works as follows: 1. assign for each employed bee a selection probability as follows: (11) 2. The food source of the employed bee with the highest fitness is selected by the onlooker bee, based on its selection probability and adjusted as shown in the algorithm (2). In the algorithm, is the accumulated probability of all the employed bees; where the of solution is unity Algorithm (2): Onlooker Bee Phase 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 5. Sending the Scout to search for possible new food sources The scout bee carries out a random search to replace the abandoned food sources, using equation ( 9). The abundant food source is one that cannot be improved upon after a certain number of cycles, as determined by the limit parameter. Algorithm ( 3) describes the process of the scout bee; In algorithm (3), Scout is a vector of size (SN), which contain information related to the improvement in any of the food sources during search. Algorithm (3): Scout Bee Phase 1. 2. 3. 4. 5. 6. Memorizing the best food source This involves memorizing the fitness and position of the best food source, found so far in FSM. 7. Stop condition Steps 3 to 6 are repeated until a stop criterion is met. This is originally determined by the MCN value. The PSS parameters are tuned by using ABC algorithm. The PSS configuration is as follows; it comprises two compensators with time constants, with an additional gain. Stabilizer output The optimum values of K and computed using ABC Algorithm. are accurately V. SIMULATION AND RESULTS Figure 3: Simulink model of PSS
Figure 4: Simulink model of SMIB Figure 7: Convergence plot of Artificial Bee Colony Algorithm Figure 5: Speed deviation in SMIB without PSS Figure 8: Speed deviation in SMIB with PSS Figure 6: Power Angle deviation in SMIB without PSS Figure 9: Power Angle deviation in SMIB with PSS
VI. CONCLUSION This paper ensures system stability, in order to provide faster responses over a wide range of power system operation, a power system stability (PSS) of single machine infinite bus system (SMIB) is developed and its parameters are tuned by a robust evolutionary algorithm. The Artificial Bee Colony (ABC) algorithm offers designers the flexibility to achieve a compromise between conflicting design objectives, the power angle and speed deviation in SMIB. REFERENCE [1] F. P. Demello and C. Concordia, Concepts of Synchronous Machine Stability as Affected by Excitation Control, IEEE Trans. on Power Apparatus and Systems, vol. PAS-88, no. 4, April 1969 [2] Angeline J. H, The control of a synchronous machine using optimal control theory, Proceedings of the IEEE, vol. 1, pp. 25 35, 1971 [3] Yu YN and Moussa HAM, Optimal stabilization of a multi-machine system, IEEE Transactions on Power Apparatus and Systems, vol. 91, no. 3, pp. 1174 1182, 1972 [8] Y.Y. Hsu and K.L. Liou, Design of Self- Tuning PID Power System Stabilizers for Synchronous Generators, IEEE Trans. on Energy Conversion, Vol. 2, No. 3, pp. 343-348, 1987 [9] V. Samarasinghe and N. Pahalawaththa, Damping of Multimodal Oscillations in Power Systems Using Variable Structure Control Techniques, IEEE Proc. Genet. Transm. Distrib. Vol. 144, No. 3, Jan. 1997, pp. 323-331 [10] Youssef A. Mobarak, A Simulink Multi- Band Power System Stabilizer, Electric Engineering Department, High Institute of Energy, South Valley University, Aswan, Egypt [11] Yong-Hua Song, Allan T. Johns, Flexible Alternating Current Transmission Systems (FACTS) IET, 1999 [12] J. Machowski, J. W. Bialek, and J. R. Bumby, Power system dynamics: stability and control. Chichester: Wiley, 2008 [4] Klein, M.; Rogers, G.J.; Kundur, P., A fundamental study of inter-area oscillations in power systems, IEEE Transactions on Power Systems, Volume: 6 Issue: 3, Aug. 1991, Page(s): 914-921 [5] Klein, M., Rogers G.J., Moorty S., and Kundur, P., Analytical investigation of factors influencing power system stabilizers performance, IEEE Transactions on Energy Conversion, Volume: 7 Issue: 3, Sept. 1992, Page(s): 382-390 [6] G. T. Tse and S. K. Tso, "Refinement of Conventional PSS Design in Multimachine System by Modal Analysis," IEEE Trans. PWRS, Vol. 8, No. 2, 1993, pp. 598-605 [7] Y.Y. Hsu and C.Y. Hsu, Design of a Proportional-Integral Power System Stabilizer, IEEE Trans. PWRS, Vol. 1, No. 2, pp. 46-53, 1986