Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 MFO Ptimized Fractional Order Based Controller on Power System Stability Bidyadhar Rout * , Bibhuti Bhusan Pati Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India. Received 05 Sept ember 2017; received in revised form 06 Sept ember 2017; accept ed 17 November 2017 Abstract This paper presents a novel idea of designing the Fractional-Order PID (FOPID) type static synchronous series compensator (SSSC). A power system stabilize r( PSS) is installed to enhance the system transient stability by damping the oscillat ions . Also the superiority of the propos ed method is verified by comparing with conventional PI, PI-PD and PID controllers. The determination of the controlle r para meters has been considered as an optimizat ion problem using Moth Fly Optimization (MFO). It is shown that MFO is more effective as well as giving robust response than Differentia l Evo lution (DE) optimizat ion . The superiority of the controlle r is tested on Single-Machine Infinite-Bus (SMIB) power system at various operating conditions and fault locations . Keywor ds: moth fly optimization, fractional order controller, pid controller, power system, transient stability 1. Introduction Recently, the us ing of MFO optimization technique is based on the motion of a moth in a transverse orientation for navigation. It keeps a fixed angle with respect to the moon or the fla me. The superiority of the Moth Fly Optimization (M FO) algorith m over Fire fly A lgorith m (FA ) and other algorith ms have been reported in many researches [1]. Th is work has focussed on the application of the MFO algorithm to a ty pical SMIB power system. In a power system, the synchronous series compensator (SSSC), is very effective in damp ing the electromechanica l oscillations along with power flow control. It consists of voltage source self-co mmutated switching converters which synthesises the three phase voltages (in quadrature) with line current to establish the compensation of the power system voltage imbalance [2-6]. In dynamic state; SSSC ma inly controls da mping of oscillations by injecting th e series voltage to the line [3]. To imp rove the system dynamic performance, an e xte rnal control loop is added to SSSC which consists of a controller changing the series injected voltage during transient period [4-6]. Such a stabiliser is co mmonly called as lead lag type SSSC da mp ing controller. The synchronizing torque, damp ing torque an d transient stability limit fo r both small signal as well as for transient stability are successfully improved with rea l power in lead-lag (LL) based SSSC da mping controller and PSS design [6-8]. It is well known that the conventional controller design methods are not attractive enough for robust stability, due to computational burden, more t ime consuming, and slow convergence and moreover, the controller para meters are trapped to their local min ima and not optima l. Therefore, various optimizat ion methods such as particle swarm optimization (PSO), Diffe rential Evolution (DE) etc.[2, 9-11] are e xtensively used for tuning PSS and au xiliary controlle r based SSSC controller parameters[2, 11-13]. * Corresponding author. Email address: bdr23@rediffmail.com, bbpati_ee@vssut.ac.in mailto:bdr23@rediffmail.com Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 47 Because of its robustness and simple structure, the proportional integral derivative (PID) controlle rs have been largely imple mented. [5-7, 14-15]. Since, there are difficult ies in finding the mathematica l model as well as determination of parameters of PID controlle r in co mple x/nonlinear higher o rder power systems, artific ia l intelligence techniques have been used [14-17]. In this work MFO optimized controller parameters have been analyzed. In this investigation, the fractional PID type SSSC controller will be used and the results will be compared with other conventional controlle rs such as PI type, PI-PD type, and PID type SSSC controllers to show the robustness of the da mping at same and different operating conditions for the proposed SMIB powe r system. The para meters of fractional PID type SSSC and PSS controllers will be simultaneously optimized by the proposed MFO algorithm [1, 16-18]. 2. System Investigated The robustness of damp ing performances has been assessed for the co-ord inately designed controllers and is tested in a SMIB system as shown in Fig. 1 [4-9, 19]. This setup contains a generator connected to an infin ite-bus through a double circuit (DC) t ransmission line. The generator is provided with the SSSC, and e xc itation system a long with PSS. The SSSC is connected in series in between Bus-1 and Bus -2 through coupling transforme r. A line transformer T lin ks to the generator and Bus-1 where as Bus-2 and Bus-3 are connected through DC trans mission lines. VT and VB are the voltages at the generator terminal and inf inite-bus respectively. V1 and V2 are the bus voltages at Bus-1 and Bus-2 respectively as shown. Current I is the line current, PL1 is the rea l powe r flowing in one of the DC transmission line, where the three phase fault is to be created and PL is the t ie line active powe r flo wing in a transmission line. Fig. 1 shows the model power system is developed by using Matlab/Simu lin k. A ll the re levant parameters of this system are can be found in [13]. The use of PSS can also be described in a similar way[5-7]. Fig. 1 The Single Machine Infinite Bus (SMIB) Power System with a SSSC[2-3] 2.1. System modelling The SMIB nonlinear dynamic mode l deals with the transient analysis [1-5]. The speed deviation is the input to the controller. It is the remote signal fro m the fault location. The following mechanica l dynamics are taken for the proposed system analysis . 1 ( ) r e r m d P B P dt J     (1) r dt d   (2) where 𝜔𝑟 and 𝜃represent the angular speed and the rotor angle as state variables of the generator, Pe and Pm are the electrical output and mechanical input power respectively, J and B represent inertia and the coefficient of viscous friction of the rotor respectively. Detail mathematical expression interlinking the system state variables are described in [9-14]. SSSC Infinit e-bus Double tr. line VS C Bus1 Bus2 Bus3 B V3V2V T V 1L P L P dc V q V T I 1 V ac V C B     C B C B C B LO AD 2L P cnv V Generat or Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 48 2.2. Structure of SSSC and control system As discussed, SSSC is composed of a three-phase voltage source converter (VSC), a series coupling transformer, a dc capacitor Vdc , and A C and dc voltage regulators[2-7]. The objective o f using VSC is to convert a dc voltage into three phase AC voltage with fundamental frequency which is to be fed into line and in phase quadrature (independent fro m line current) with the line current I. The injected A C voltage Vq changes in its magnitude due to variable fictit ious capacitive or inductive reactance during transient conditions. This voltage controls the active power flow efficiently and da mps out the power swings. In capacitive mode, Vcnv is greater than Vac and it supplies active and reactive power to power system and in inductive mode, Vcnv is lower than Vac. The control device ma intains the voltage profile of transmission line unchanged by controlling the converter voltage[2, 5, 19]. 2.3. Fractional order PID (FOPID) controller Widespread interest in FOPID controlle r has attracted many researchers in power system to provide insight into the transient stability study. It is represented by PI D   which provides added degree of freedom for design ing controller gains (KP, KI, KD) where the orders of integral and derivative are real nu mbers not necessarily only integers . The FOPID controller has the merit of provid ing an e xtra degree of freedom and is less sensitive to para meter variation co mpared to a c lassical PID controller [17]. The transfer function of such FOPID controller: ( ) ( )I c P D K G s K K s s      (3) 3. The Structures of Various Controllers The structures of various types of controllers e.g. PI type, PI-PD type, PID type and the FOPID controller have been described in the following section for both the SSSC as well as PSS installation in the example system. 3.1 PI type SSSC controller structure Fig. 2 shows the block diagra m o f PI type SSSC controlle r structure. Here Kp is the proportional ga in and KI is the integral ga in. The PI block is connected to a washout block(for high pass filter action) followed by a two stage Lead Lag compensation block. The wash out block with time constant TW ma ke it sure that there is no steady state error of the voltage reference due to the speed deviation ∆𝑤. Time constant TW is not critica l and may be in the range of 1 to 20 sec [12]. He re, TW = 10s is taken into consideration. As the input signal (i.e. ∆𝑤) to the controller is remote signal, the sensor time with transmission time delay TTD = 65ms is provided at the input to the controller. 31 2 4 111 ( )( )( )( )( ) 0.065 1 1 1 1 W II I SSSCPI p W I I sT sTK sT U K y s s sT sT sT        (4) Fig. 2 Structure of PI type SSSC damping controller 3.2 PI type PSS controller structure 311 11 1 21 41 111 ( )( )( )( )( ) 0.015 1 1 1 1 W II I PSSPI p W I I sT sTK sT U K y s s sT sT sT        (5)   Washout 2-St age Lead-Lag W W sT sT 1 1 2 1 1 I I sT sT   3 4 1 1 I I sT sT   q max V q min VSensor wit h T ransmission time delay Out put I K p K    qref V q V qcnv V  P roportional Gain 1 1 TD sT Integral Gain  Input Signal Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 49 Fig. 3 Structure of PI type PSS damping controller The controller gains and time constant parameters of PI type SSSC and PSS controlle r must be tuned for achieving best performances. A first order sensor with sensor time de lay Ttd = 15ms is chosen by this controller for sensing the low frequency speed deviation ∆𝑤 during disturbances [8]. 3.3. PI-PD type SSSC controller structure Improved system performance is e xpected with the cascade control system. Fig. 4 shows a cascade PI-PD controller fo r SSSC controller [7-10]. The performances are improved by putting a first order filter with tuning pole and filter constant N = 100 Similar PI-PD type PSS controller structure can be used for analysing the behaviour of PSS damping controller. 32 1 2 3 3 2 4 111 ( )( )( )( )( )( ) 0.065 1 1 1 1 W IDI ID SSSCPI PD p d p W ID ID sT sTK sTsN U K K K y s s s N sT sT sT           (6) Fig. 4 Structure of PI-PD type SSSC damping controller[19] 3.4 PID type SSSC controller structure Conventional PID type SSSC controlle r structure is taken for co mparing the responses with the previous controller structures. For the conventional PID type SSSC damping controller, the output is : 1 3 2 4 1 11 ( )( )( )( )( ) 0.065 1 1 1 1 Ic W c c SSSCPID dc pc W c c K sT sT sTsN U K K y s s s N sT sT sT           (7) Fig. 5 Structure of conventional PID type SSSC controller Similar PID type PSS controller structure can be used for analysing the behavious of PSS damping controller s. Here, Kpc, KIc Kdc, Kp1c, KI1c, Kd1c, T1c, T2c, T3c, T4c, T11c, T21c, T31c, T41c are gains and time constant parameters of PID type SSSC and its PID type PSS controller that are to be tuned for achieving best performances [20].    Input Signal Washout 2-St age Lead-Lag W W sT sT 1 1 2 1 1 c c sT sT   3 4 1 1 c c sT sT   q max V q min VSensor wit h t ransmission t ime delay Out put Ic K pc K    qref V q V qcnv V dc K  Derivat ive Gain P roportional Gain 1 1 TD sT  N Int egral Gain s 1     Input Signal Washout 2-St age Lead-Lag W W sT sT 1 1 2 1 1 ID ID sT sT   3 4 1 1 ID ID sT sT   q max V q min V Sensor wit h T ransmission t ime delay Out put    qref V q V qcnv V  P roportional Gain 1 1 TD sT  2I K  1 s 2p K    3d K 3p K N 1 s  P roportional Gain Derivat ive Gain P roportional Gain   Input Signal Washout 2-St age Lead-Lag 1 W W sT sT 11 21 1 1 I I sT sT   31 41 1 1 I I sT sT   f max V f min VSensort imedelay Out put 1I K 1p K    qref V q V f V  P roportional Gain 1 1 td sT Int egral Gain  Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 50 3.5 FOPID type SSSC controller structure For fractional type SSSC controller, the output is: 31 2 4 111 ( )( )( )( )( ) 0.065 1 1 1 1 WI SSSCFPID d p W sT sTK sT U K s K y s s sT sT sT           (8) The proposed fractional PID type SSSC is a supplementary da mp ing controlle r whose structure is shown in Fig. 6, is to modulate the SSSC injected voltage .qV Similar FOPID type PSS controlle r structure has been applied to analyse the damping behaviour. A first order sensor with sensor time de lay 𝑇𝑡𝑑 =15ms is taken with this controller for sensing the low frequency speed deviation w during disturbances. The controller gain 𝐾𝑝 ,𝐾𝐼 𝐾𝑑 ,𝐾𝑝1 ,𝐾𝐼1 , 𝐾𝑑1 and the time constant values like𝑇1 , 𝑇2, 𝑇3 , 𝑇4, 𝑇11 , 𝑇21 , 𝑇31, 𝑇41 , , , 1 and 𝜇 1are to be tuned optimally [21]. Fig. 6 Structure of FOPID type SSSC damping controller 3.6. Control Objectives Power system after passing through a large disturbance exhib it oscillation which can be minimized or quenched by application of SSSC and this improves the stability. These oscillations are observed in terms of powe r angle, rotor speed and line power. The objective is to reduce any one or all of these deviations . In the present study, an integral time absolute error (ITA E) of the speed signals corresponding to the remote modes of oscillat ions is taken as the objective function. The objective function is expressed as dttwJ simtt t .. 0     (9) where tsim is the simulat ion time period. The percentage overshoot and the settling time can be imp roved by minimizing this objective function. The constraints here are the parameters of SSSC controlle r. The optimisation problem can be as the following optimization problem: Minimizing J, and subject to For PI type SSSC damping controller min max min max , p p p I I I K K K K K K    (10) min max min max min max min max 1 1 1 2 2 2 3 3 3 4 4 4 , , , I I I I I I I I I I I I T T T T T T T T T T T T        (11) For PI type PSS controller min max min max 1 1 1 1 1 1 , p p p I I I K K K K K K    (12) min max min max min max min max 11 11 11 21 21 21 31 31 31 41 41 41 , , , I I I I I I I I I I I I T T T T T T T T T T T T        (13) For PI-PD type SSSC damping controller  1 s     Input Signal Washout 2-St age Lead-Lag W W sT sT 1 1 2 1 1 sT sT   3 4 1 1 sT sT   q max V q min VSensor wit h t ransmission t ime delay Out put 1p K    qref V q V q V1dK s   Int egral Gain Derivat ive Gain P roportional Gain 1 1 TD sT 1I K Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 51 min max min max min max min max 2 2 2 2 2 2 3 3 3 3 3 3 , , , p p p I I I p p p d d d K K K K K K K K K K K K        (14) min max min max min max min max 1 1 1 2 2 2 3 3 3 4 4 4 , , , ID ID ID ID ID ID ID ID ID ID ID ID T T T T T T T T T T T T        (15) For PI-PD type PSS controller min max min max min max min max 12 12 12 12 12 12 13 13 13 13 13 13 , , , p p p I I I p p p d d d K K K K K K K K K K K K        (16) min max min max min max min max 11 11 11 21 21 21 3 31 31 41 41 41 , , , ID ID ID ID ID ID ID ID ID ID ID ID T T T T T T T T T T T T        (17) For PID type SSSC damping controller min max min max min max , , pc pc pc Ic Ic Ic dc dc dc K K K K K K K K K      (18) min max min max min max min max 1 1 1 2 2 2 3 3 3 4 4 4 , , , c c c c c c c c c c c c T T T T T T T T T T T T        (19) For PID type PSS controller min max min max min max 1 1 1 1 1 1 1 1 1 , , p c p c p c I c I c I c d c d c d c K K K K K K K K K      (20) min max min max min max min max 11 11 11 21 21 21 31 31 31 41 41 41 , , , c c c c c c c c c c c c T T T T T T T T T T T T        (21) For FOPID type SSSC damping controller min max min max min max , , p p p I I I d d d K K K K K K K K K      (22) max 44 min 4 max 33 min 3 max 22 min 2 max 11 min 1 ,,, TTTTTTTTTTTT  (23) maxmin   (24) For FOPID type PSS controller min max min max min max 1 1 1 1 1 1 1 1 1 , , p p p I I I d d d K K K K K K K K K      (25) min max min max min max min max 11 11 11 21 21 21 31 31 31 41 41 41 , , ,T T T T T T T T T T T T        (26) min max 1 1 1     (27) In this investigation DE and MFO techniques are applied to search for optima l set of SSSC-based damping controlle r parameters. A brief introduction of Moth-Flame Optimization(MFO) technique is described in the next section. 4. Overview of MFO This has been wide ly used in many such important applications and giving very pro mising results. It is brie fly d iscussed in this section to make the paper self-content. The behavior of the fly is mathe matica lly modeled by an algorithm ca lled MFO algorith m, assuming the candidate solutions are moths and the proble m’s variab les are the position of moths in the space. This algorith m is a population -based algorithm, where the set of moths is represented in a matrix M consisting of the array as their corresponding fitness values. The fla me matrix is represented by where the array of F also stores the fitness values. The init ial point, final po int of the fla me and range of fluctuation of spira l in the search space are required for the spira l movement of the moth which is expressed as ( , ) i i j M S M F (28) Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 52 where Mi indicates the i - th moth position, Fi indicates the j - th fla me position. Knowing the init ial point, final point of the flame and range of fluctuation of spiral in the search space, the spiral movement of the moth is expressed as j bt iji FtCoseDFMS  )2(..),(  (29) where t is a random number [-1, 1], and 𝐷𝑖 is the distance of the i th moth for the j th flame. And is calculated as: iji MFD  (30) where Mi indicates the i th moth, Fj indicates the j th fla me, and Di indicates the distance of the i th moth for the j th flame wh ich is to be minimized. The next position of a moth is defined with respect to a fla me following Eq. (29). The t-para meter in the equation which closeness to flame equals to -1 and 1 imp lies farther to the fla me. Due to change of order of fla mes and revision of moth’s position at each iteration, the e xp loitation of the pro misingly best solutions is obtained through an adaptive mechanism proposed by using the number of flames. ) 1 *(_ T N lNroundnoflame   (31) where N and T represents ma ximu m no. of fla mes , and iterat ions respectively, l is the current nu mber of iteration . The algorithm flow chart is shown in Fig. 7. Fig. 7 Flow chart of the proposed MFO algorithm[1] Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 53 5. Simulation Results And Analysis The system described in Fig. 1 is simulated in MATLA B under three phase disturbance at different locations . The objective function is min imised by tuning the fractional PID type SSSC and PSS controller para meters with M FO and compared with DE algorith m as shown in Tab le 4. The result is compared with PI type SSSC and PSS (Table 1), PI-PD type SSSC and PSS (Table 2), PID type SSSC and PSS da mp ing controllers (Tab le 3). The robustness and effectiveness of the proposed FPID type SSSC and PSS is verified at va rious generator loadings. After hundred iterations, the optimized parameters a long with the performance indices of ITAE are noted down fro m both DE and MFO a lgorithms. The simu lation results for all controllers are shown in Figs. 8-11. 5.1. Case-1: PI type SSSC-PSS Controller The nomina l loading 0 0(0.8 . , 48.48 )p u   , is the Self-clea red between Bus -2 and Bus -3. A three phase line fault is created at one of the double section line between Bus -2 and Bus -3 at t = 1s in SMIB power system for nominal loading and self-clea red for5 cycles. The post fault oscillat ion are da mped out through MFO and DE optimised PI type SSSC and PSS controller and the effective results are found through MFO optimized para meters. The para meters comparison is shown in table-1 and the simulat ion responses are shown in Fig s. 8-11with the legend “PI type SSSC (MFO)” with a solid green line and “PI type SSSC (DE)” with dotted pink line. Table 1 DE and MFO tuned optimal parameter of PI type SSSC and PSS controller at nominal loading 0 0 (0.8 . , 48.48 )p u   Optimisation Controller Controller Parameters ITAE *10e-3 DE PI type SSSC Kp KI T1I T2I T3I T4I 3.8 195.403 12.9818 0.5881 0.6896 1.4308 1.9145 PI type PSS Kp1 KI1 T11I T21I T31I T41I 21.0723 11.0937 1.2773 1.6716 0.0146 1.9139 MFO PI type SSSC Kp KI T1I T2I T3I T4I 3.7 158.3171 6.3069 1.7456 1.9974 0.4081 0.8803 PI type PSS Kp1 KI1 T11I T21I T31I T41I 1.7652 11.4863 1.1977 0.0010 1.7735 1.6014 Fig. 8 Speed deviation w in p.u Fig. 9 Rotor angle in in degree Fig. 10 The line active power L P in MW Fig. 11 SSSC-injected voltage q V in p.u Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 54 5.2. Case-2:PI-PD type SSSC and PSS controller The nominal loading 0 0(0.8 . , 48.48 )p u   , is the self-clea red at between Bus -2 and Bus -3. Table 2 shows the optimised parameters with both DE and MFO for PI-PD type SSSC and PSS controller during three phase Bus -2 and Bus -3 and self- cleared condition fo r 5 cyc les . The effective responses are analysed in Fig.9. It is observed in Fig. 12-15 for speed deviation, tie line power and termina l voltage. The improve ment of settling time and decrement of overshot of transient responses are observed in this MFO optimised PI-PD type SSSC and PSS controller more effectively than controller. Table 2 DE and MFO tuned optimal parameter of PI-PD type SSSC and PSS controller with nominal loading 0 0 (0.8 . , 48.48 )p u   line outage Op timisation Controller Controller Parameters ITAE *10e-3 DE PI-PD ty p e SSSC Kp2 KI2 Kp3 Kd3 T1ID T2ID T3ID T4ID 4.2 8.9062 7.7024 91.2532 32.1083 0.9168 1.7648 0.2907 0.7114 PI-PD ty p e PSS Kp12 KI12 Kp13 Kd13 T11ID T21ID T31ID T41ID 55.8208 17.3684 15.7298 12.5812 0.8535 1.4744 0.2200 1.4390 M FO PI-PD ty p e SSSC Kp2 KI2 Kp3 Kd3 T1ID T2ID T3ID T4ID 2.9 0.9990 8.2677 102.8586 92.6227 1.0339 0.6070 0.9069 1.6722 PI-PD ty p e PSS Kp12 KI12 Kp13 Kd13 T11ID T21ID T31ID T41ID 0.9990 0.0000 99.87 9.3844 0.0001 1.9974 0.0001 1.8503 Fig. 12 Speed deviation Fig. 13 Rotor angle in degree Fig. 14 Tie line active power Fig. 15 Terminal voltage 5.3. Case-3: PID type SSSC and PSS controller The nominal loading at ( 0 0 0.8, 48.4 e P   ), is the line outage Bus -2 and Bus -3. PID type SSSC and PSS controlle r has also been tested with this SMIB system at line outage disturbance condition at same fault location between Bus -2 and Bus -3.The three phase line outage disturbance is done at t = 1s for 5 cycles and the e ffective responses are analysed in Figs. 16-18 following optimized controlle r para meters in table -3. It is found that, the responses for PID type SSSC and PSS controller are better as co mpared to PI and PI-PD type SSSC and PSS controlle r. The M FO optimized controller para meters of PID type SSSC and PSS g ives better responses than the DE tuned PID type SSSC and PSS controlle r. The settling time in PID type SSSC is significantly improved than PI and PI-PD type SSSC controllers. Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 55 Fig. 16 Speed deviation Fig. 17 The line active power Fig. 18 SSSC-injected voltage Table 3 DE and MFO tuned optimal parameter of PID type SSSC and PSS controller with nominal loading (0.8p 0 0 (0.8 . , 48.48 )p u   , line outage Optimisation Controller Controller Parameters ITAE *10e-3 DE PID type SSSC Kpc KIc Kdc T1c T2c T3c T4c 3.7 134.1158 13.9487 12.3751 1.7451 1.5531 0.2802 0.5056 PID type PSS Kp1c KI1c Kd1c T11c T21c T31c T41c 111.8589 10.1969 48.8007 1.559 0.4859 0.1774 1.6069 MFO PID type SSSC Kpc KIc Kdc T1c T2c T3c T4c 3.2 469.7830 3.2563 18.4041 1.4773 1.6225 1.1597 1.7668 PID type PSS Kp1c KI1c Kd1c T11c T21c T31c T41c 0.9990 40.4269 2.4261 0.7406 1.8584 1.8608 1.8763 5.4. Case-4: Fractional PID type SSSC and PSS controller The nominal loading at ( 0 0 0.8, 48.4 e P   ) line outage disturbance is at Bus-1. The proposed fractional PID type SSSC and PSS controllers have been imple mented in this work to show their better quality over all the controllers. The transient analysis is done for the line outage disturbances of SMIB test system at no minal loading. The d isturbance is create d at 1 sec at one of the double circuit transmission line for 5 cycles and simulat ion results are studied from the Fig. 19-22 for speed deviation, tie line active power, terminal voltage and SSSC injected voltage , respectively. The settling time, overshoot and undershoot of the transient responses are improved effect ively with this proposed controller as shown in Table 4. The time de lay of 50 m sec is introduced between the FOPID type SSSC and PSS. Optimized with M FO algorith m, the proposed controller significantly improves the transient response as compared to DE optimized FOPID type SSSC and PSS controller and all other controllers. Table 4 Case-4: DE and MFO tuned optimal parameter of FOPID type SSSC and PSS controller with nominal loading 0 0 (0.8 . , 48.48 )p u   , line outage Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 56 Fig. 19 Speed deviation Fig. 20 Terminal voltage Fig. 21 Tie line active power Fig. 22 SSSC-injected voltage controller 5.5 Case-5: Fractional PID type SSSC and PSS controlle r The nomina l loading at 0 0(0.8 . , 48.48 )p u   line outage disturbance is between Bus -2 and Bus -3. The MFO optimized speed responses of all the controllers are taken together in Fig. 23 and revealed that, the fractional PID with fractional number of integral ga in and derivative gain e ffective ly improves the dynamic responses as compared to rest of the controllers. The zoo m portion of speed deviation shown in Fig. 24 c learly shows the speed response has no second overshoot, undershoot is less and it takes 2 sec to settle for damping out the complete oscillation s. Op timisation Controller Controller Parameters ITAE *10e-3 DE FPID ty p e SSSC kp kI kd T1 T2 T3 T4  μ 3.4 294.7739 54.5911 16.8004 0.5892 1.1404 1.6048 0.7965 0.6887 0.9087 FPID ty p e PSS kp1 KI1 kd1 T11 T21 T31 T41 1  μ1 57.7629 11.4368 31.4137 0.2451 1.3925 0.2039 0.9830 0.4103 0.9008 M FO FPID ty p e SSSC KP KI Kd T1 T2 T3 T4  μ 2.9 100.2096 37.6333 8.6803 1.8305 1.9974 1.5018 1.7566 1.0185 0.9987 FPID ty p e PSS KP1 KI1 Kd1 T11 T21 T31 T41 1  μ1 161.2293 94.0954 27.9560 0.0920 0.0001 0.5633 1.4125 1.1644 1.1644 Fig. 23 Speed deviation for different controllers compared with proposed FOPID type SSSC and PSS controller Fig. 24 Speed deviation in zoom version Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 57 5.6. Case-6: FOPID type SSSC and PSS controller nominal loading, permanent line tripping Under severe disturbance condition, the proposed controller is verified at nominal loading. A three phase fault for 5 cycles is created at the mid-point of the overhead line of Bus -2 and Bus -3 and the fault is clea red by indefin ite tripping of the faulted section. The responses of the system are shown in the Figs. 25-27. The speed deviation is shown in Fig. 25 in p.u, the rotor angle in deg in Fig. 26, and the healthy line power 2LP in MW in Fig. 27, respectively. The system responses are unstable without the controller and becomes stable by FOPID type SSSC type PSS da mping controller. This controlle r brings the initia l operating point of the speed deviation at 3 sec and the rotor angle in Fig. 26 is shifted to another operating point at 60° from its initial angle of 48.48°after 2.6 sec. Fig. 25 Speed deviation in p.u. Fig. 26 Rotor angle in degree Fig. 27 Power at healthy line 5.7. Case-7: FPID type SSSC and PSS controller With light loading 0(0.4 . , 22.85 )p u and self-clearing, light load perfo rmances are a lso performed by changing the generator loading and the stability of the SMIB system is checked with the proposed FPID type SSSC and PSS da mping controller. The responses are shown in the Fig. 28-30 with three phase fault for 5 cyc les at middle of the double transmission line. The rotor angle in Fig. 29 reduces 0 22.85 (without controller) to 0 22.5 (with controller) and settling time is achieved at 3 sec. The e ffect iveness of this proposed controller is also verified in Fig s . 28, 30 and 31 by showing the da mping of the oscillations and reduced overshoot and undershoot after the first swing. Fig. 28 Speed deviation Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 58 Fig. 29 Rotor angle Fig. 30 Tie line Powerin MW 6. Conclusions The main purpose here is to damp the powe r system oscillat ions and to enhance the system performance under disturbances. The coordinated PI, PI-PD, PID and FPID type SSSC and PSS da mping controllers are designed and imple mented in SMIB power system. The perfo rmance of Fractional PID type SSSC and PSS has been compared with other controllers by its ITAE based on speed deviation of the test system and found minimu m ITA E as objective function . With optimizing the controller para meters by MFO, the robustness and effectiveness are verified under various contingencies. The system damping has also been compared by using proposed controller with DE, and has concluded that the MFO technique yields better ITA E value and better dynamic response. The simu lation work has been done in MATLA B environ ment by running several times with adjusting controlling variables F= 0.5-0.8, CR= 0.5-0.8, strategy=1-3, iteration, populations and the range of other controller para meters within its limit in DE technique. Results obtained are good. However, in the proposed controller, 50 iterations, 10 population, F=0.8, CR=0.8, strategy=3 are co nsidered and much better result are obtained. Similarly, 5 agents and 50 iterat ions are taken for MFO technique for still better results as co mpared to DE in thi s proposed test systems. The results indicate the e ffectiveness of the proposed designs in prov iding good damp ing characterist ics to power system oscillat ions. This also helps in enhancement of dynamic performance. This approach can be imp le mented with mult i - machine grid connected power systems. References [1] S. Mirja lili, “Moth-fla me optimization algorithm: A novel nature -inspired heuristic paradigm,” Knowledge-Based System, vol. 89, pp. 228-249, November 2015. [2] S. M. H. Hosseini, H. samad zadeh, J. Ola mae i, and M. Fa rsadi, “SSR mit igation with SSSC thanks to fuzzy control,” Turkish Journal of Electrical Engineering & Computer Science s, vol. 21, pp. 2294-2306, January 2013. [3] M. Klein, G. J. Rogers, and P. Kundur, “A fundamental study of inter-area oscillation in powe r systems,” IEEE Press, August 1991, pp. 914-921. [4] C. Liu, G. Ca i, and D. Yang, “Design nonlinear robust damping controlle r for static synchronous series compensator based on objective holographic feedback-H,” Journal of advances in Mechanical Engineering (Sage Journal), vol. 8, no. 6, pp. 1-11, June 2016. [5] M. Bongiorno, J. Svensson, and L. Angquist, “On control of static synchronous series compensator for SSR mitigation,” IEEE Transactions on Power Electronics, IEEE Press, June 2007, pp. 735-743. [6] L. Bangjun and F. Shu min, “A brand new nonlinear robust control design of SSSC for t ransient stability and damp ing improve ment of mu lti-machine power systems via pseudo -generalized Ha miltonian theory,” Control Engineering Practice, vol. 29, pp. 147-157, August 2014. [7] U. Q. Sun, Y. K. Sun, and X. X. Liu, “ H2/HN cost -guaranteed control for the static synchronous compensator,” IET Control Theory, vol. 69, pp. 641-646, 2009. http://journals.sagepub.com/author/Liu%2C+Cheng http://journals.sagepub.com/author/Cai%2C+Guowei http://journals.sagepub.com/author/Yang%2C+Deyou Proceedings of Engineering and Technology Innovation, vol. 8, 2018, pp. 46-59 Copyright © TAETI 59 [8] M. S. Castro, H. M. Ayres, V. F. da Costa, and L. C. P. da Silva, “Impacts of the SSSC control modes on small-signal and transient stability of a power system,” Electrical Power System Research, vol. 77, no. 1, pp. 1-9, January 2007. [9] M. A. Abido, “ Pole p lace ment technique for PSS and T CSC based stabilizer design using simulated annealing,” International Journal of Electrical Power Systems Research, vol. 22, no. 8, pp. 543-554, November 2000. [10] M. E. About-Ela, A. A. Sa lla m, J. D. Mc Ca lley, and A. A. Fouad, “Damp ing controller design for power system oscillations using global signals,” IEEE Transactions on Power Systems, IEEE Press, May 1996, pp. 767-773. [11] M. A. Abido, “ Optima l design of power system stabilizers using particle swa rm optimization,” IEEE Trans Energy Convers, IEEE Press, November 2002, pp. 406-413. [12] Z. L. Ga ing, “A partic le swarm optimization approach for optimu m design of PID controller in A VR system,” IEEE Trans Energy Convers 19, IEEE Press, May 2004, pp. 384-391. [13] S. Panda, “Robust coordinated design of mult iple and mu lti-type damping controller using differentia l evolution algorithm,” International Journal of Electrical Power & Energy Systems, vol. 33, no. 4, pp. 1018-1030, May 2011. [14] H. E. Mostafa, M. A. El-Sharkawy, A. A. Emary, and K. Yassin, “Design and a lloca tion of powe r system stabilize rs using the particle swarm optimization technique for an interconnected power system,” Int ernational Journal Electrical Power Energy Systems ., vol. 34, no. 1, pp. 57-65, January 2012. [15] T. T. Nguyen and R. Gianto, “Application of optimization method for control co -ordination of PSSs and facts devices to enhance small-disturbance stability,” Proc. IEEE PES Transmission & Distribution Conf. May Da llas, IEEE Press, May 2006, pp. 21-24. [16] Y. Li and K. H. Ang, “PID control system analy sis and design,” IEEE Control Systems . Magazines , IEEE Press, June 2005, pp. 559-576. [17] C. H. Lee and F. K. Chang, “Fractional -order PID controlle r optimization v ia improved electro magnetis m-like algorithm,” Expert Systems With Application, vol. 37, no. 12, pp. 8871-8878, December 2010. [18] F. Padula and A. Visioli, “Tuning rules for optima l PID and fract ional-order PID controllers,” Journal of Process Control, vol. 21, no. 1, pp. 69-81, January 2011. [19] R. K. Khadanga and J. K. Satapathy, “Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO-GSA algorith m,” International Journal of Electrica l Powe r and Energy Systems, vol. 71, pp. 262 -273, October 2015. [20] S. S. Moha med, A. E. Mansour, and M. A. Abdel Ghany, “ Design of fractional order PID controller for SMIB power system with UPFC tuned by mu lti-object ives genetic algorith m,” Proc. 16 th Int. Conf. On Aerospace sciences & Aviation technology, pp. 26-28, May 2015. [21] M. R. Faieghi and A. Ne mati, “ On fract ional-order PID design, applications of MATLAB in science and engineering,” Book Edited by Prof. Tadeusz Michalowski, pp. 237-292, September 2011.