A HYBRID KRILL HERD-GENETIC ALGORITHM BASED TRANSIENT STABILITY ENHANCEMENT OF COORDINATED PSS AND SSSC CONTROLLER IN MULTI-MACHINE POWER SYSTEM

Transcription

1 Volume 118 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu A HYBRID KRILL HERD-GENETIC ALGORITHM BASED TRANSIENT STABILITY ENHANCEMENT OF COORDINATED PSS AND SSSC CONTROLLER IN MULTI-MACHINE POWER SYSTEM 1 A. Kathiravan, 2 Alamelu Nachiappan 1 Research Scholar Department of Electrical and Electronics Engineering Pondicherry Engineering College, Puducherry, India. 2 Department of EEE, Pondicherry Engineering College, India 1 banu.kathir@gmail.com, 2 nalam63@pec.edu Abstract: Power system oscillations are the major crises in a large interconnected network. It certainly affects the power system stability and remains a challenge for scheduling and planning of the power demand in a deregulated market. This paper represents the Hybrid Krill Herd-Genetic Algorithm (KH-GA) optimization method based on global optimization for analyzing the stability of multi-machine power system. The two algorithms are inspired by biological and environmental characterization. A typical three generator nine bus benchmark power system is considered for small signal stability analysis using MATLAB/Simulink. The angular frequency in a power system is affected by deviation in power generation, variation in load, and faults. The angular frequency deviation causes fluctuation in generator speed and rotor angle leading to electromechanical oscillations ina power system. So, the deviation in angular frequency is considered as a reliable parameter for the stability study of the system. Several optimization techniques such as Firefly, Cuckoo and Krill Herd (KH)algorithms are employed for the coordinated tuning of power system stabilizer (PSS) and Static Synchronous Series Compensator (SSSC) along with proposed Hybrid KH-GA algorithm to improve transient stability of the power system. The capability of the suggested Hybrid KH-GA algorithm is validated with the other optimization techniques taken into consideration. The simulated results show higher convergence rate and better low frequency oscillation (LFO) damping capability. Index Terms: Multi- machine, Krill Herd algorithm, Genetic Algorithm, Hybrid KH-GA, PSS, SSSC, low frequency Oscillation. 1. Introduction According to power systems, the electrical utilities often encounter a stability problem. Power system inter connection imposes a different kind of oscillations to the power system. To ensure better stability, the frequency and terminal voltage of the power system needs to be maintained at their rated values [1]. In order to minimize these oscillations and increase the system dynamic stability, the most convincing and reliable solution is to install a PSS in the generator excitation system. However, the application of PSSs has limitations for damping of inter-area oscillations and nonlinear systems [2-4]. A Flexible AC Transmission System (FACTS) has numerous advantages in comparison with conventional PSS. FACTS controllers use the application of high power solid state devices for Sub-synchronous Resonance (SSR) mitigation, voltage regulation, real power flow enhancement, and Low Frequency Oscillation (LFO) damping [5]. The wide application of the voltage source converter (VSC)based FACTS controllers are better choices for improving the power systems stability. SSSC finds an important part for its robustness and capability to vary the reactance characteristics between capacitive region to inductive region which gives the strength to improve the real power flow and damp the LFOs. Apart from its robustness and reliability, SSSC can also perform various tasks in stability studies, schedule power flow, minimize net power loss, and enhance overall stability [6]. Heuristic and meta-heuristic optimization algorithms are the two major classifications of stochastic optimization technique. Heuristic algorithms reach the solution in quick time but no guarantee for best solution is reached [7]. A meta-heuristic algorithm uses the randomization and local search makes it convenient for global search. The best examples of Meta- heuristic techniques are Genetic Algorithm (GA),Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Algorithm (CA) and Bat Algorithm (BA) [8]. Optimal settings of rule-based PSS using PSO search have been discussed in [9]. Multi-machine PSS with a Multiobjective design using PSO is considered in [10]. The 1043

2 integrated design of FACTS and PSS is investigated for stability enhancement [11]. In [12], the authors uses real-coded GA for optimize the system damping using Eigen value based objective function. However, the authors claimed that PSO outperformed GA and it reaches the best solution in less iteration than GA. The authors have also tested various stabilizers like PSS, SVC, TCSC, STATCOM and TCPS individually to enhance system damping. In brief literature, many researchers come out with combined PSS and FACTS based stabilizers have enhanced dynamic stability in a power system. In [13], a parameter-constrained nonlinear algorithm is developed using global tuning procedure. The power system stability is enhanced by considering the Eigen values as an objective function for an integrated PSSS and FACTS based stabilizer using GA is investigated [14]. The SSSC is a VSC based series compensator which injects a manageable voltage quadrature to the line current[15]. The SSSC has the ability to provide compensation between capacitive reactance to inductive reactance independent of line current. Moreover, SSSC effectively damp the LFOs with help of external damping controller [16]. The FA is a biologically inspired meta-heuristic optimization algorithm characterized by the flashing habit of fireflies [17]. In fact, the FA has many resemblances of other swarm intelligence based algorithms like PSO, BA, Bacterial Foraging algorithms (BFA), Cuckoo Optimization Algorithm (COA)and Artificial Bee Colony Optimization (ABC). The COA searches the best solution globally and converge it quickly [18]. The Krill Herd Algorithm (KHA) is a recent optimization technique connected with swarm nature of krill herds. The krill swarm searches the area of maximum food density based on distinct environmental and biological behaviors [21, 22]. In this study, a new Hybrid KH-GA technique is purposed for single objective function. The aim of Hybrid KH-GA is to produce the faster convergence rate. The proposed Hybrid KH-GA algorithm is verified on three generator nine bus standard test system for best location of integrated SSSC and PSS stabilizer. Hence, the integrated PSS and FACTS stabilizers with optimal placement obtained by hybrid KH-GA can fetch best damping effect and strengthen the overall stability. The time-domain simulations with different loading situations are carried out for analyzing the potency of proposed controllers individually and also simultaneously. The rest of the paper is coordinatedd as follows: Section II describes the complete dynamical modelling of a synchronous machine, Excitation system, PSS, SSSC and AVR are discussed. Section III presents a complete methodology of a proposed Hybrid KH-GA algorithm. Section IV briefly explains the objective function and implementation. Section V validates the simulation results and discusses the efficiency of the proposed algorithm. Finally, Section VI deals with the summary and conclusion. 2. Power System Modelling A. Synchronous Generator Modelling A benchmark three-machine nine-bus test system is shown in Fig. 1. Data for generators, lines, buses and loads are found in [4]. The generators and PSSs are modeled by specified third order differential equations as in [3].The generator dynamic equations are given in equations (1)-(3), which consist of q-axis generator voltage equation and electromechanical swing equation. (1) (2) Where, is the variation in internal rotor angle or load angle in degrees, is change in rotor speed or generator speed deviation in rad/sec, is the generator synchronous speed in rad/sec, is the internal rotor speed in rad/sec, is the generatorr damping coefficient, is the generator inertia constant, is mechanical power input to the rotor in p.u, is the electromagnetic power in p.u.!" % (3) Where, is the variation in effective internal voltage in volts, # $ is the transient open circuit time constant, is thep.u field voltage, is the direct axis synchronous reactance, is the direct axis transient reactance, " is the direct axis stator current in p.u, and is the generator internal voltage in p.u. Figure 1. Three-machine nine-bus power system B. Exciter Modelling The excitation system is designed to produce the control the excitation voltage with the aid of automatic voltage regulator (AVR) and damping voltage can be obtained from PSS is displayed in Fig. 2.PSS comprises of gain, 1044

3 wash-out filter, and two stage lead lag compensator. Wash-out filter attenuates low frequencies and allows the high frequencies, whereas the lead-lag compensator provides a necessary phase lead to compensate the phase lag between excitation and the synchronous machine electrical torque. The dynamic equation is given [3] by: ' ( ) * ) +, -.. % % (4) & Where, V ref is the reference generator terminal voltage,) + is the generator terminal voltage, T A and K A is the AVR time constant and gain respectively, is the excitation voltage in volts, is the deviation in excitation voltage in p.u, and U PSS is the output of the PSS. DC link current, m is the modulation ratio defined by PWM pulses, k refers fixed ratio between AC voltage and DC voltage of VSI[16]. A. KHA Figure 3. Power system model with SSSC 3. Hybrid KH-GA Technique Figure 2. AVR and PSS based generic excitation system C. SSSC Modelling The SSSC is a series based FACTS device providing series compensation to a transmission line is displayed in Fig. 3. The important functional elements of SSSC are three-phase controlled voltage source inverter (V INV ), a boosting series coupling transformer with a leakage reactance of X SCT and a DC link capacitor (C DC ). Two input signals m and ψ are controlling the performance of the SSSC [16].The signal m refers amplitude modulation ratios of Pulse Width Modulated (PWM)VSI pulses that manipulate the inserted voltage, and the signal ψrefers phase angle between injected voltage and line current ignoring the inverter losses[18, 25]. The stability study of SSSC by considering the dynamic model is as follows [25]: "///" " +. " 1 (5) Where, "/// +. is series injected current, " +. is direct axis series current, 0" +. is the quadrature axis series current, refers phase angle between direct and quadrature axis series currents. )//////78) 456 9:;<=>0=?@>A78) 9 > (6) Where: > ±90 (7) 6 EF + 4 EF 9 EF G H EF :" +. ;<=>" +. =?@>A (8) Where,) 9 indicates DC link voltage," 9 indicates The KHA is a biologically inspired meta-heuristic optimization algorithm inspired by herding nature of the krill swarm. Krill swarm has a tendency to find a place where highest food density is available with minimum distance. In a two-dimensional surface, the time stamped movements of an individual krill is identified by three important activity [21]; (1) Movement induced by other krill individuals, (2) Foraging activity and (3) Random diffusion. The KHA uses Lagrangian model to expand the search area to an n-dimensional decision space as: I J + K (9) Where, N i indicates the movement of ith krill induced by neighbors, F i represents the foraging behavior of ith krill individual and D i denotes the physical diffusion of ith krill individual[22]. Description of basic KHA technique as discussed below: (1) Motion induced by other krill individuals According to krill nature of living, individual krill attracted by other krills mutually to form a large krill swarm. The direction of motion induced (L ), is estimated from local swarm density (local effect), target swarm density (target effect), and repulsive swarm density (repulsive effect). The motion of the krill individual can be estimated as: J MN J OP $Q L M J (10) Where: L L Q$HOQ +O*R+ L (11) 1045

4 s Where, J MN is the new induced speed of ith krill, N max is the maximum induced speed, ω n refers inertia $Q weight of motion induced (between 0 to 1 second), J Q$HOQ is the last motion induced, L refers local effect contributed by neighbors, and L +O*R+ indicates target direction effect contributed by best individual krill. The sensing distance S. between krill individuals and neighbors is found by their behavior. S., 5 W U5 XY X W (12) Where,S., denotes the sensing distance for the ith krill and N refers number of krill and X i indicates the related positions of ith krill. If the distance of (Xi-X j ) <S., then X j is the neighbor of X i. (2) Foraging motion The krill neighbors are induced and attracted towards best food location and also possess previous knowledge about the food location. This activity of krills is referred as Foraging Motion. The foraging activity of the ith krill is expressed as: $Q K ) Z K (13) Where: Z Z $$ [.+ Z (14) Where,V f denotes foraging speed, ω f denotes inertia weight of the motion induced (between 0 to 1),F old i is the last foraging motion value,z $$ is the attractive food, and Z [.+ denotes the optimal fitness of an ith krill so far. The core of the food density is found by the fitness distribution for every iteration is expressed as: $$ ^ \ _\ I ] \ ^ _\ ] (15) Where, $$ denotes the core region of food density, K i refers fitness value of ith krill individual. (3) Physical diffusion Every krill individuals physically diffuse themselves randomly. The ith krill motion depends on maximum diffusion speed and a random directional vector. The expression of D i is as follows: OP`1 4 4 bcd e (16) Where, D max is the maximum diffusion speed, is the random directional vector, I refers the actual iteration number and I max indicates the maximum iteration number. (4) Motion process of KHA The movements of the individual krills are always approaches towards best fitness value. This algorithm utilizes local and global conditions makes it most efficient one. The positional vector during the interval t to t+ g can be denoted as: :g ga :ga g I (17) + Where, :g ga denotes the updated krill individual position, and :ga represents the current position of the krills. The gis an important operator in tuning the optimization problem. The g constant is considered as a scaling parameter for the speed vector. It is expressed as: 56 gh + XY,i j ki X! (18) Where, NV refers total count of variables, ki X and,i j are the lower bound and upper bound of jth variables (j = 1, 2.NV). A constant number C t is carefully chosen between the values 0 and 2. The low values of C t direct the krills to search the space more carefully. The KHA has been analyzed for so many non-linear multi-objective systems in end result it cannot always produce optimal solutions in global search space [21-23]. Since the KHA applies random search and it cannot rapidly finds the optimal solution. B.GA The GA is an efficient heuristic technique which finds best solution for the complex non-linear problems [24]. It effectively searches the various regions of a solution space and finds a numerous set of solutions even for complex problems [21-24]. The important aspect of this algorithm is the genetic operators. They are (i) Selection, (ii) Crossover, and (iii) Mutation. These operators are intending to produce the best child by random selection of best mates (studs) [23]. The reproduction steps are as follows, (i) Rearrange two stud particles (Random selection) (ii) Check the divergence according to Hamming interval between the rearranged stud and current mate: 1. If the diversity is greater than the set threshold, perform crossover to generate one offspring. 2. If the diversity is less than the set threshold, mutate the current mate to generate the new child. The conventional GA is customized by choosing the best mate (stud) to produce the best child is framed as Stud Genetic Algorithm (SGA). The selected stud is combining genetically with other stud individuals through the genetic operators like crossover and mutation to produce new population [23]. (1) Function of Genetic operators The crossover operation is based on binomial crossover method to update the mth components of ith krill by the following expression: *,mn@s, <h *, l s (19), qr=q Where: h * 0.2'v,[.+ (20) Where,h * is the crossover probability, which is randomly selected between 0 and 1. The Mutation operation is depends on the principle of adaptive mutation scheme. It is expressed as:, w R[.+, x -,,!mn@s, <y, qr=q (21) 1046

5 Where: z (22) 'v,[.+ Where, z isa mutation probability and µ is a constant number chosen between 0 and 1. C. Proposed Hybrid KH GA Algorithm The purposed Hybrid KH-GA optimization algorithm is a novel hybrid technique combining the KHA and GA. Since KHA cannot find optimal solution always in global search area but the existence of genetic operators certainly helps to refine the search locally in a global search space and quickly converges the ideal solution. Hence, the hybrid KH-GA optimization technique definitely accelerates convergence speed. Flowchart of proposed Hybrid KH-GA algorithm is displayed in Fig. 4. Figure 4. Flowchart of Hybrid KH-GA algorithm 1047

6 1048

7 D. Implementation of objective Function An objective function (Fitness function) is a mathematical expression which is taken as an input parameter for an optimization algorithm. The objective function for a standard three generator nine bus system with an integrated PSS and SSSC stabilizer is designed. In this research, Objective function is formulated on the basis of two performance indices namely Integral of a Time-multiplied Absolute value of Error (ITAE) and Integral Square Error (ISE).The ISE index minimizes the overshoot and ITAE reduces the steady state error. The performance indices are intended to achieve the quick settling time and to minimize the LFOs. However, the overall stability of a test system can be improved by minimizing the speed deviations of generators. Hence the integration of speed variations of the generators is considered as an objective function. The objective function is given by, + }~ g Sg (23) Where, J indicates objective function, t is the total simulation time, ω1, ω2, and ω3 are speeds of generators G1, G2, and G3 respectively. In this research, an objective function is provided in Eq. (23) is taken as an input parameter for the purposed Hybrid KH-GA Algorithm for optimal location of SSSC and to boost the damping effect tand also system stability. 4. Results and Discussion The three generator nine bus system is designed for the stability analysis. The generators G2 and G3 are installed with PSS and generator G1 connected with a simple exciter. The efficiency of the test system is analyzed under different load settings given in Table 1.1. The non-linear time zone simulation is executed using Matlab/Simulink programming for a three phase fault. Let us consider, If a three phase to ground fault occurs at bus 7 at t = 0.5 sec. The element Y77 is increased 1000 times to represent very high admittance to ground in order to simulate this fault. The fault is cleared at t = 0.6 sec. The fault clearance is simulated by restoring the value of Y77 to its pre-fault value at t = 0.6 sec Table 1.1 Test system with different load settings Nominal Loading Heavy Loading Lightly Loading P Q P Q P Q Generator G G G Load L L L L L L L L L A. Nominal Loading Condition The efficiency of proposed optimization technique for the integrated controller is validated with the test system under nominal loading. The Fig. 5.1(a), (b) & (c) represents the rotor angle deviations of Generator 1, 2 and 3 with respect to synchronous angular speed. Due to the large disturbance, the sustained oscillations start grows and cause stability issue if the system may not install a controller. Whereas, the test system with optimally located SSSC show better damping effect to the LFO s when correlated with other optimization algorithms. The time taken to attain steady state condition of these oscillations is also very good for the system optimized by Hybrid KH-GA. Power flow results after installing SSSC is listed in Table 1.2. Injected SSSC Series Voltage with proposed Hybrid KH-GA technique at line 8:Vse (pu) = @

8 Table 1.2 Power flow after SSSC installed - Normal load Bus V Angle Injection Generation Load No pu Deg MW MVar MW Mvar MW MVar Total Figure 5.1 (c) Generator 3 frequency Figure 5.1 (a) Generator 1 frequency Figure 5.1 (b) Generator 2 frequency B.Heavy Loading Condition The proposed optimization technique is utilized for the test system with coordinated controller under heavy loading condition to check its effectiveness. The Fig. 5.1(a), (b) & (c) represents the rotor angle deviations of Generator 1, 2 and 3 with respect to synchronous angular speed. The system without controller will cause power oscillations due to the large disturbance since it may not adequately damped. Whereas, the test system with optimally located SSSC show better damping effect to the LFO s when correlated with other optimization algorithms. The time taken to attain steady state condition of these oscillations is also very good for the system optimized by Hybrid KH-GA. Power flow results after installing SSSC is listed in Table 1.3. Injected SSSC Series Voltage with proposed Hybrid KH-GA technique at line 9:Vse (pu) =

9 Table 1.3 Power flow after SSSC installed - Heavy load Bus V Angle Injection Generation Load No pu Deg MW MVar MW Mvar MW MVar Total Figure 5.2 (a)generator 1 frequency Figure 5.2 (c) Generator 3 frequency C. Light Loading Condition Figure 5.2 (b) Generator 2 frequency The proposed optimization technique is utilized for the test system with coordinated controller under light loading condition to check its effectiveness. The Fig. 5.1(a), (b) & (c) represents the rotor angle deviations of Generator 1, 2 and 3 with respect to synchronous angular speed. Due to the large disturbance, the sustained oscillations start grows and cause stability issue if the system may not install a controller. Whereas, the test system with optimally located SSSC show better damping effect to the LFO s when related with other optimization algorithms. The time taken to attain steady state condition of these oscillations is also very good for the system optimized by Hybrid KH-GA. Power flow results after installing SSSC is listed in Table 1.4. Injected SSSC Series Voltage with proposed Hybrid KH-GA technique at line 2: Vse (pu) =

10 1052

11 Table 1.4 Power flow after SSSC installed - Light load Bus V Angle Injection Generation Load No pu Deg MW MVar MW Mvar MW MVar Total Figure 5.3 (a) Generator 1 frequency Figure 5.3 (c) Generator 3 frequency The objective function (J) is effectively minimized by adopting proposed optimization technique. The convergence rate of the different optimization techniques are displayed in Fig.5.3 (d). It is clearly evident that proposed Hybrid KH-GA algorithm converges in fewer iterations and the objective function (Fitness function) is also minimized effectively. Hence the purposed algorithm finds the best location of SSSC, and also reduces the electro mechanical oscillations effectively. Figure 5.3 (b) Generator 2 frequency 1053

12 1054

13 Figure 5.3 (d) The Convergence for Objective function minimization using FA, COA, KH and Hybrid KH-GA. 5. Conclusions In this research, a coordinated control of PSS and SSSC is discussed. An objective function is effectively minimized using Hybrid KH-GA technique for finding best location of SSSC so as to minimize the power oscillation. The non-linear system with a coordinated controller is simulated through MATLAB/Simulink to assess the influence of the purposed algorithm. The soundness of the purposed coordinated controller is investigated by testing its performance under normal, heavy and light load settings. The simulated results reveal that the dynamic performance and LFO damping capability are significantly enhanced by optimal placement of SSSC. Therefore, the purposed algorithm provides a better power oscillation damping effect to the integrated PSS and SSSC controller and improves the overall stability. 6. Acknowledgment The authors wish to thank the authorities of Electrical and Electronics Engineering department, Pondicherry Engineering College for the facilities provided to the research work. References [1] M. Farahani and S. Ganjefar (2017), Intelligent power system stabilizer design using adaptive fuzzy sliding mode controller, Neurocomputing, vol. 226, pp [2] BehrouzMohammadzadeh, Reza Gholizadeh- Roshanagh, and SajadNajaravadanegh (2014), Optimal designing of SSSC Based Supplementary Controller for LFO Damping of Power System Using COA, ECTI Transactions on Electrical Eng., Electronics, and Communications, vol. 12, pp [3] P. Kundur, Power System Stability and Control. New York: McGraw-Hill, [4] P. M. Anderson and A. A. Fouad, Power system control and stability. Piscataway, N.J.: Wiley- Interscience, [5] G. Hingorani and L. Gyugyi, Understanding FACTS-Concepts and Technology of Flexible AC Transmission Systems New York: IEEE Press, [6] Gyugyi L., Schauder C. D. &Sen K. K (1997), Static synchronous series compensator: A solid-state approach to the series compensation of transmission lines Power Delivery, IEEE Transactions on, vol. 12(1), pp [7] X. S. Yang, Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley & Sons, New Jersey, [8] SankalapArora, Satvir Singh (2013), The Firefly Optimization Algorithm: Convergence analysis and parameter selection Int. J. of Comp. Appl. ( ), vol. 69, no.3, pp [9] M. A. Abido (2002), Optimal Design of Power-System Stabilizers Using Particle Swarm Optimization IEEE Trans. Energy Conv., vol. 17, no. 3, pp [10] H. Shayeghi, H.A. Shayanfar, A. Safari, and R. Aghmasheh (2010), A robust PSSs design using PSO in 1055

14 a multi-machine environment Energy Conversion &Management, vol. 51, pp [11] M. A. Abido (2004), Analysis of Power System Stability Enhancement via Excitation and Facts-Based Stabilizers Electrical Power Components and Systems, vol. 32, no. 1, pp [12] SidharthaPanda, and Narayana Prasad Padhy (2008), Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design Applied Soft Computing, vol.8, pp [13] Xianzhang Lei, Edwin N. Lerch, and DusanPovh (2001), Optimization and Coordination of Damping Controls for Improving System Dynamic Performance IEEE Trans. Power Syst., vol. 16, no. 3, pp [14] Y.L. Abdel-Magid, M.A. Abido (2004), Robust coordinated design of excitation and TCSC-based stabilizers using genetic algorithms Electric Power Syst. Res., vol. 69, pp [15] E.S. Ali, S.M. Abd-Elazim (2012), Coordinated design of PSSs and TCSC via bacterial swarm optimization algorithm in a multimachine power system Int. J. Elect. Power and Energy Syst., vol. 36, pp [16] PeymanFarhang, KazemMazlumi, and JavadGholinezhad (2012), Design of Output feedback SSSC and PSS Controllers to Enhance the Stability of Power System Using PSO ELECO th Int. conf. on Elect. & Elec. Eng. pp [17] Luke Jebaraj, Charles ChristoberAsirRajan, and Kumar Sriram (2014), Application of Firefly Algorithm in Voltage Stability Environment Incorporating Circuit Element Model of SSSC with Variable Susceptance model of SVC Advances in elect. Eng., vol., pp [18] H.F. Wang (1999), Design of SSSC Damping Controller to Improve Power System Oscillation Stability, IEEE conference, pp [19] X. S. Yang (2010), Firefly algorithm, stochastic test functions and design optimisation, Int. J. of Bio-Inspired Computation, vol. 2, no. 2, pp [20] BehrouzMohammadzadeh, Reza Gholizadeh- Roshanagh, and SajadNajaravadanegh and Seyed Reza SeyedNouri (2016), COA Based SSSC and STATCOM Optimal Controllers designing with The Aim Better Damping of Oscillations: A Comparative Study Int.Journal of Elect. Eng. & Informatics, vol.8, no.3, pp [21] HosseinShayeghi, AbdollahYounesi, and YasharHashemi (2015), Simultaneous Tuning of Multi-Band Power System Stabilizer and Various IPFC-based Damping Controllers: An Effective method for Mitigation of Low frequency Oscillations, Int. Journal of Mechatronics, Elect. And Comp. Tech., vol. 5(17), pp [22] Qin Li, and Bo Liu2017, Clustering using an Improved Krill Herd algorithm Algorithms 2017, 10,56, pp [23] Gai-Gewang, Amir H. Gandomi, and Amir H. Alavi, Stud Krill Herd Algorithm Neurocomputing, 128(2014), pp [24] YahyaNaderi, TohidRahimi, BabakYousefi, and SeyedHossainHosseini, Assessment Power Frequency Oscillation Damping using POD Controller and Proposed FOD Controller, Int. J. of Elect., comp., Energetic, Electronic and Comm., Eng., vol.8, no.11, pp , [25] S. Khani, M. Sadeghi, S.H. Hosseini, Optimal Design of SSSC Damping Controller to Improve Power System Dynamic Stability Using Modified Intelligent Algorithms, IEEE conference on power electronics and drive systems and technologies, pp , [26] S Nazeer Hussain, K Hari Kishore "Computational Optimization of Placement and Routing using Genetic Algorithm Indian Journal of Science and Technology, ISSN No: , Vol No.9, Issue No.47, page: 1-4, December [27] T. Padmapriya and V. Saminadan, Priority based fair resource allocation and Admission Control Technique for Multi-user Multi-class downlink Traffic in LTE-Advanced Networks, International Journal of Advanced Research, vol.5, no.1, pp , January [28] Rajesh, M., and J. M. Gnanasekar. " GCCover Heterogeneous Wireless Ad hoc Networks.& quot; Journal of Chemical and Pharmaceutical Sciences (2015): [29] S.V.Manikanthan and K.Baskaran Low Cost VLSI Design Implementation of Sorting Network for ACSFD in Wireless Sensor Network, CiiT International Journal of Programmable Device Circuits and Systems, Print: ISSN X & Online: ISSN , Issue : November 2011, PDCS

15 1057

16 1058