Adaptive predictive control of a small capacity SMES unit for improved frequency control of a wind‐diesel power system
2017; Institution of Engineering and Technology; Volume: 11; Issue: 14 Linguagem: Inglês
10.1049/iet-rpg.2017.0074
ISSN1752-1424
AutoresMubashar Yaqoob Zargar, Mairaj-ud-Din Mufti, Shameem Ahmad Lone,
Tópico(s)Wind Turbine Control Systems
ResumoIET Renewable Power GenerationVolume 11, Issue 14 p. 1832-1840 Research ArticleFree Access Adaptive predictive control of a small capacity SMES unit for improved frequency control of a wind-diesel power system Mubashar Yaqoob Zargar, Corresponding Author Mubashar Yaqoob Zargar myzargar@gmail.com Department of Electrical Engineering, National Institute of Technology Srinagar, J&K, 190006 IndiaSearch for more papers by this authorMairaj Ud-Din Mufti, Mairaj Ud-Din Mufti Department of Electrical Engineering, National Institute of Technology Srinagar, J&K, 190006 IndiaSearch for more papers by this authorShameem Ahmad Lone, Shameem Ahmad Lone Department of Electrical Engineering, National Institute of Technology Srinagar, J&K, 190006 IndiaSearch for more papers by this author Mubashar Yaqoob Zargar, Corresponding Author Mubashar Yaqoob Zargar myzargar@gmail.com Department of Electrical Engineering, National Institute of Technology Srinagar, J&K, 190006 IndiaSearch for more papers by this authorMairaj Ud-Din Mufti, Mairaj Ud-Din Mufti Department of Electrical Engineering, National Institute of Technology Srinagar, J&K, 190006 IndiaSearch for more papers by this authorShameem Ahmad Lone, Shameem Ahmad Lone Department of Electrical Engineering, National Institute of Technology Srinagar, J&K, 190006 IndiaSearch for more papers by this author First published: 20 October 2017 https://doi.org/10.1049/iet-rpg.2017.0074Citations: 26AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Energy storage is becoming increasingly important for isolated power systems having overall low inertia. Among many energy storage devices, superconducting magnetic energy storage (SMES) is most suited for improved frequency control in isolated power systems, due to its outstanding advantages. However, a small rating SMES device has operational constraints, therefore a suitable control strategy is required for its profitable and constrained operation. An adaptive controller which encapsulates on-line identification with model predictive control is proposed in this paper. A recursive least-squares algorithm is used to identify a reduced-order model of wind-diesel power system on-line. Based on the identified model and a simple discrete time model of SMES unit, an adaptive generalized predictive control scheme (AGPC) considering constraints on SMES current level and converter rating is formulated. The scheme yields a control signal which on one hand keeps the system frequency deviations to minimum and on the other hand forces the SMES device to operate within and near its operational constraints, for profitable operation. Simulation studies are performed to illustrate the potency of the proposed strategy in achieving all the control objectives. 1 Introduction There are many remote public communities worldwide which are not connected to the grid and electric power to them is usually supplied from diesel generators. However, use of diesel incurs heavy fuel, transportation and storage costs. Fortunately, many such isolated areas have good wind potential, therefore wind power generation systems can be installed and linked to the existing diesel generator sets resulting in economic wind-diesel power systems. The control of power quality in such systems is, however, a challenging task. The changing nature of generated wind power and overall low system inertia would adversely affect such power systems [1, 2]. These systems, therefore, require suitable remedial measures in order to provide good quality service to the consumers. Fast acting energy storage devices are more appropriate candidates to achieve this end [3, 4]. A superconducting magnetic energy storage (SMES) device is considered to be an attractive energy storage device because of its outstanding advantages like quick response time, a significantly higher power density, very long life time and high cyclic efficiency [5]. Its dynamic performance is much superior to other energy storage devices. Its operation and life are not affected by the number of charge/discharge cycles, unlike battery storage systems. The application of the SMES device to power systems has been under study since the early 1970s. The possible applications of SMES include transient stability, dynamic stability, load levelling, power quality improvement and automatic generation control [6-8]. For improved frequency control of standalone power systems/micro-grids, a small rating SMES device can be employed as a buffer storage for transient compensation [9]. In frequency control application, several approaches [10-14] have been used for designing a controller for the SMES unit. In [10], an application of an adaptive control scheme based on the set-membership affine projection algorithm (SMAPA) was proposed for the SMES unit. It has been demonstrated that with SMAPA-based adaptive control SMES units, the wind farm's output can be smoothened easily avoiding the huge effort for fine tuning of the controller parameters. However, constraints on upper and lower values of the SMES current have not been considered. In [11], the use of non-linear neural adaptive predictive control for active power modulation of SMES was proposed. Though limits on the control signal and thus the converter rating have been considered in an ad-hoc manner, yet the scheme does not consider any constraints on the SMES current level. The step load changes of the magnitude of 1% are considered and there is no guarantee that the scheme will function properly with load changes of higher magnitude. In [12], a discrete time model of a power system with SMES was developed and the effect of a small capacity SMES was studied in relation to supplying a sudden active power requirement. However, the potency of the proposed scheme in tackling the various control issues like non-violation of operational constraints of the SMES unit and continuous control have not been touched and demonstrated. In [13], adaptive automatic generation control with SMES is presented. The scheme is effective in damping out power frequency and tie-power oscillations. Though there is provision for continuous control yet operational constraints of SMES are not considered in controller formulation. Moreover, the SMES unit considered uses thyristor-based technology which has a drawback of draining reactive power from the system while exchanging active power with it. In [14], a fuzzy logic controlled SMES device to damp the frequency oscillations of a two-area power system under load excursions was proposed. The simulation results indicate the superiority of the proposed scheme over the conventional proportional integral (PI) controller in damping the system oscillations. However, important control issues like the handling of operational constraints of SMES are not addressed in this work. Keeping in view the deficiencies with the control schemes existing in the literature (described above), in this study, a novel scheme is proposed for control of the SMES unit. The achievements of the proposed scheme are as follows: (i) The frequency deviations are kept to a minimum both under load and wind perturbations. (ii) SMES is forced to operate near its constraints for effective and profitable operation. (iii) Power handled by the SMES converter never exceeds its rated value. (iv) SMES current always remains within the limits dictated by operational constraints. (v) The scheme ensures continuous control in the presence of disturbances of various magnitudes. The details of the scheme are given below. The power command for the SMES control loop is generated by an adaptive generalised predictive control (AGPC) scheme so that various control objectives are met. Though the main objective of the SMES unit is to exchange real power with the wind-diesel system so that improved frequency control is established in the presence of load and wind disturbances, yet small rating SMES imposes certain constraints. The kW rating of the SMES unit is restricted by its power conversion system (PCS); moreover, for continuous control, SMES current after tackling a disturbance must return to its initial value and should remain within upper and lower permissible limits [15, 16]. The characteristics of the control signal issued by AGPC as a set point for the SMES control unit should, therefore, be such that all the above issues are handled and profitable operation of SMES is ensured. The control scheme proposed is based on AGPC and is able to handle all the control objectives related to the profitable and constrained operation of a small rating SMES device. The inclusion of constraints in the controller formulation in an explicit manner allows the AGPC to predict and prevent the violation of system constraints systematically. The implementation of AGPC demands a suitable prediction model. However, in practice, in most cases, it is difficult to obtain a precise model of the system a priori. Moreover, the system dynamics frequently changes with time. For such systems introduction of adaptive feature into the predictive control scheme is an appropriate solution. The purpose of this scheme which is known as AGPC is to achieve in an uncertain, complex, multi-input multi-output non-linear, higher-order system like the wind-diesel power system, the result that would otherwise be achieved by the generalised predictive control (GPC) scheme if a good plant model is available, a priori. The AGPC scheme will allow the wind-diesel power system to be represented by an equivalent low-order linear model whose parameters will be estimated at each sampling period and the estimated model will be used by GPC to compute the control signal. For profitable multi-objective and constrained operation of the SMES unit, an appropriately regulated variable is formulated for AGPC. In addition, a simple discrete time model is proposed for SMES energy-level prediction which is appended with the identified on-line model of the system to take care of SMES coil current constraints. For assessing the performance of the proposed control strategy, relevant S-functions blocks are developed in MATLAB2010a. The scheme is tested on a multi-machine wind-diesel power system and its effectiveness is demonstrated through simulation studies. 2 System architecture Fig. 1 shows the configuration of the studied wind-diesel system along with the SMES unit. The system includes two synchronous machines coupled to diesel engines and eight induction generators driven by wind turbines. Synchronous and induction machines are connected through a transmission network and supply a common distribution system. Fig. 1Open in figure viewerPowerPoint Block diagram of the complete wind-diesel hybrid system The predictively controlled SMES unit is connected to load bus through a PCS. The details of the PCS unit and SMES control loop are given in the following sub-sections. 2.1 SMES unit PCS A SMES unit exchanges power with the power system through PCS [11, 12, 17]. For exchange of energy between the SMES and the hybrid power system, the SMES coil is exposed to a dc voltage of an appropriate magnitude and polarity by proper adjustment of the duty cycle of a dc/dc chopper in a way explained in next sub-section. The dynamics of the SMES unit including its converter is very fast unlike the governor system which includes electromechanical/mechanical parts and moreover, a delay is involved between the actuator oil injection and production of mechanical torque. When there is a deficiency in active power, the SMES unit instantaneously participates in energy balancing by releasing its stored energy through its PCS to the wind-diesel system. As the diesel governor mechanism starts working to set the new equilibrium, the SMES coil charges back to its nominal current. Similarly, when there is surplus power in the system, the SMES unit immediately absorbs some portion of excess energy and charges to its full value and as the system returns to a new steady state the excess energy is released by the SMES unit [18, 19]. 2.2 SMES control loop The SMES unit is receiving the power command from the predictive controller and in turn, the duty cycle (D) for the dc/dc chopper is computed using the control model for SMES which is shown in Fig. 2. In this figure Vdc is the dc link voltage, LSM is the inductance of SMES coil, ISM is the actual SMES current and PSMES is the actual power supplied to SMES. The three regions of operation are possible depending on the value of the duty cycle [11, 20]. The timing diagrams for the three regions of operation are shown in Fig. 3. Fig. 2Open in figure viewerPowerPoint Control model for the SMES system Fig. 3Open in figure viewerPowerPoint ON positions of chopper switches for different values of duty cycles Though an algorithm for modelling the converter operation with switching actions as shown in Fig. 3 has been proposed in [11], yet the results obtained are similar to those obtained using an average value model of Fig. 2. Thus, in the present study, the average value model is used to save computation time. 3 Generation of power command for SMES unit using AGPC In this study, AGPC is proposed to generate set points for SMES control. AGPC commands the SMES unit to react to wind/load disturbances by immediately exchanging power with the wind-diesel system. To ensure the continuous operation and make full use of the SMES unit capacity, the SMES current must be maintained within an appropriate range and should acquire the nominal value after handling a disturbance. This is achieved by properly formulating the AGPC algorithm which combines GPC with on-line plant identification. The GPC scheme is based on the receding horizon principle shown in Fig. 4 and comprises the following steps: (i) At each sampling instant, the forecast of the controlled and constrained variables is made and the optimisation problem is solved. (ii) The first element u(k) of the control sequence computed is actually applied to the process. (iii) At the next sampling instant, the procedure is repeated. Fig. 4Open in figure viewerPowerPoint Moving horizon concept of model predictive control (MPC) A typical AGPC scheme is shown in Fig. 5. It combines the advantage of GPC with the adaptive plant identification and allows the representation of the hybrid wind-diesel power system by a low order input–output model [21]. It is sufficient to represent the wind-diesel power system by a third-order model [11]. The parameters and states of the low-order model do not have any physical denotations. Fig. 5Open in figure viewerPowerPoint AGPC scheme The cause–effect relationship between control variable 'u' and controlled variable 'y' can thus be described by an autoregressive with exogenous input model given below (1) The above equation means that the present output can be obtained by using n sets of past input and output measurements. In this equation ԑ(k) is the white noise. A recursive least squares (RLS) identification algorithm is used to estimate the model parameters vector [21] A state variable model-based GPC formulation is used and for this purpose system of (1) can be cast into the following observable form: (2) (3) (4) where X(k) is the state vector and . The output prediction over the prediction horizon N and control horizon Nu can now be written as (4) and in compact form expressed as (5) Now the optimal control problem for GPC can be stated as (6) subjected to constraints being satisfied. ѡ is the desired value of the system output, Q and R are weight matrices. Substituting (5) in (6), the optimisation problem is reduced to a general quadratic programming problem, i.e. (7) where The inequality constraints imposed by the small rating SMES are formulated in the following steps. 3.1 Power rating of the SMES unit Power command which is a control variable for the SMES cannot exceed its converter rating during charging and discharging. Thus constraints on control vector U, need to be incorporated using (8) In this study, we are considering the disturbances of the magnitude of 10% that is 0.065 p.u. and the converter rating for the SMES unit considered is just 0.04 p.u. This is to show that with the proposed control scheme, even smaller converter rating can effectively deal with large disturbances without violating the operational constraints. The setting up of Pmax = +0.04 and Pmin = −0.04 p.u. will therefore take care of the converter constraints. 3.2 Current/energy level prediction For every energy storage device, the cost of investment will increase with size, so there has to be a limit to its capacity [22]. Similarly for avoiding the discontinuous control, the researchers [16] have been proposing a lower limit to the stored energy level. Thus for profitable use and continuous control of the SMES unit, the SMES energy/current should remain within maximum/minimum values. In this study, data pertaining to SMES unit has been taken from [23]; moreover, the upper and lower limits of the SMES coil current are chosen as 460 and 210 A, respectively. This corresponds to a minimum stored energy of 5.82 kJ which is 20% of the maximum. SMES coil current constraints are translated into energy level constraints and energy level predictions are obtained using the discrete-time model as shown in Fig. 6, where ZOH is the zero order hold, T is the time constant of the SMES unit, and ESMES are the nominal and actual energy levels of the SMES unit, respectively. Fig. 6Open in figure viewerPowerPoint SMES energy level prediction The sampling time (Ts) chosen has a value much greater than the time constant T [24], so the z-transfer function of the system shown above can be approximated by (9) The discrete time representation of the above equation is (10) Based on the above difference equation, the predictions for the constrained variable ESMES(k) over the prediction horizon N and control horizon Nu can be obtained as (11). The constraints on the energy stored in the SMES unit can thus be written as (12) (11) (12) Combining (11) and (12), the inequality constraints pertaining to the SMES current limits are expressed as (13) The inequality constraints used in (7) are obtained by combining inequality constraints of (8) and (13). 4 Functional block diagram of the proposed scheme The implementation of the proposed control strategy on a standalone wind-diesel-SMES hybrid power system is depicted in Fig. 7. The description of various blocks and variables involved is given below. Fig. 7Open in figure viewerPowerPoint Block diagram representation of the proposed scheme The block labelled as the SMES unit is actually the representation of the control model for the SMES unit which is described in sub-section 2.2. The block labelled as the wind-diesel power system represents the studied multi-machine system shown in Fig. 1. Block labelled as generalised model predictive control is explained in detail in Section 3. The on-line identification block represents a well-known RLS identification algorithm [20], which is used to estimate the parameters of the hybrid wind-diesel-SMES system. Conversion to the state space model block is representative of the mathematical operation used to extract the state space model in the observable form from the identified parameters. Details of this operation are given in Section 3. Regulated variable y is formed using the following expression (14) where Δf is the frequency deviation and is calculated using the concept of centre of inertia [25]. is the nominal current of the SMES unit. For the system considered which consists of two synchronous generators, centre of inertia is calculated as (15) where and are the rotor speeds of two synchronous generators in electrical radians per second and H1 and H2 are the inertia constants of two synchronous generators. Frequency deviation Δf is thus calculated as (16) where fo is the nominal frequency of the system which is 50 Hz. The second term on the right-hand side of (14) is introduced to force the SMES coil current to return to its nominal value after dealing with a disturbance. The value of constant kc is properly chosen. The large value of kc will make the SMES unit ineffective in reducing the frequency deviations. On the other hand, a very small value of kc will not allow the SMES coil current to return to its nominal value. w is the reference value for the regulated variable (y) is set to zero because the regulated variable is a combination of deviation variables Δf and ΔISM. Both these deviations, Δf and ΔISM, have to be zero in the steady state. 5 Simulation studies For simulation studies, the wind-diesel-SMES system is represented by a SIMULINK model and the other blocks like on-line system identification, conversion to state space and MPC are developed using the S-function code in MATLAB/SIMULINK and the graphs are plotted using origin software. System performance with the proposed scheme is assessed by subjecting the system to two types of disturbances viz load disturbance and wind power disturbance. The disturbances along with the corresponding simulation results are shown in Figs. 8-11. In the first case, the wind speed is assumed constant and the system is subjected to step wise load disturbances as given by (17) and depicted in Fig. 8a. The percentage change in PL is with respect to 260 kW. (17) Fig. 8Open in figure viewerPowerPoint Disturbances in the proposed system in p.u. (a) Load disturbance, (b) Wind disturbance Fig. 9Open in figure viewerPowerPoint Estimated values of parameters (a) Under load disturbance, (b) Under wind disturbance Fig. 10Open in figure viewerPowerPoint Dynamic responses of machines under disturbance (a) Frequency deviation of the synchronous generator under load disturbance, (b) Frequency deviation of the synchronous generator under wind disturbance, (c) Induction generator slip under load disturbance, (d) Induction generator slip under wind disturbance Fig. 11Open in figure viewerPowerPoint Dynamic responses of the SMES unit (a) SMES current under load disturbance, (b) SMES current under wind perturbation, (c) Power absorbed by the SMES unit under load disturbance, (d) Power supplied by the SMES unit under wind perturbation In the second case study, the system is subjected to wind power perturbation of the form shown in Fig. 8b, keeping the load constant, where a negative sign indicates the generating mode of induction machine operation. The simulation results are summarised as follows. (i) Figs. 9a and b show the trajectory of identified model parameters of the system. The self-tuning phenomenon, i.e. the tuning of controller parameters in the presence of disturbances is well observed from these figures. It can be seen that some of the parameters change their sign during the course. (ii) As shown in Figs. 10a and b, respectively, the proposed scheme results in the improved frequency control of the hybrid system under load/wind perturbations. Figs. 10c and d show the induction machine behaviour with and without SMES under load as well as wind perturbations. Since the frequency has been calculated from the speeds of synchronous machines using the concept of inertia, these plots (Figs. 10a–d) are indicative of dynamic interactions between the SMES unit, synchronous machines and induction generators. (iii) The SMES unit is forced to operate near its constraints as shown in Fig. 11. (iv) The SMES current with the proposed scheme remains within the prescribed limits as shown in Figs. 11a and b. These results also show that the SMES coil current attains its nominal value after tackling a disturbance and the SMES unit remains ready to deal with a new disturbance. In these figures also are shown the plots of the SMES current with an existing control scheme [26] for comparison. It is clear that the scheme of [26] is not able to maintain the SMES coil current within the prescribed limits unlike the scheme proposed in this study. (v) The power handled by the SMES unit never exceeds the converter rating which is considered to be 0.04 p.u., even when the system is subjected to the disturbances of higher magnitudes. This is clear from Figs. 11c and d. (vi) Peak deviations in both frequency and slip are reduced as shown in Figs. 10a and d. Frequency deviation is reduced by 32.96%. Moreover, these figures reveal that with the proposed scheme, oscillations are damped out quickly. (vii) Above all, the results shown in Figs. 11a and b prove that the simple discrete time model proposed in (9) for predicting the SMES energy/current level is justified as the SMES current always remains within the prescribed limits. Thus, all the control objectives are achieved with the proposed AGPC scheme. 6 System data Data pertaining to the wind-diesel system is given in Tables 1–3 Table 1. Network data Parameter Rating base kVA 400 line resistance, p.u. 0.145 Ω/km line reactance, p.u. 0.252 Ω/km capacitor bank power 10 kVAR capacitor bank voltage 400 V active load 260 kW reactive load 182 kVAR Table 2. Synchronous generator data Parameter Sny Gen 1 Syn Gen 2 inertia constant, p.u. 1.35 1.35 armature resistance, p.u. 0.176 0.176 direct axis reactance, p.u. 3.47 3.47 quadrature axis reactance, p.u. 2.08 2.08 direct axis transient reactance, p.u. 0.293 0.293 direct axis sub-transient reactance, p.u. 0.2 0.2 quadrature axis sub-transient reactance, p.u. 0.3 0.3 direct axis sub-transient time constant, s 0.015 0.015 direct axis open circuit time constant, s 1.18 1.18 quadrature axis sub-transient time constant, s 1.182 1.182 Table 3. Induction machine data Parameter Rating rotor time constant, s 0.2584 stator resistance, p.u. 0.0916 reactance of induction generator, p.u. 7.441 inertia constant, p.u. 0.06216 transient reactance, p.u. 1.43 7 Conclusion An effective control scheme based on an AGPC scheme has been proposed for control of a small SMES unit which is integrated with a hybrid wind-diesel power system. The AGPC scheme generates on-line a suitable power command for the SMES unit so that it exchanges real power with the hybrid wind-diesel system for improved frequency control in the presence of load and wind perturbations. The predictive control algorithm has been formulated so that various control objectives which include frequency control and constrained profitable operation of the SMES unit are achieved. Special S-function blocks are developed for implementation of the proposed predictive control based scheme, which is then integrated in the MATLAB/SIMULINK model of the hybrid wind-diesel-SMES system to carry out simulation studies. The simulation results demonstrate the potency of the scheme in handling all the control related issues for profitable and constrained operation of the SMES unit. 9 Appendix 1 Parameters of the predictive controller Parameters of the SMES unit 8 References 1Kaiee, M., Cruden, A., Infield, D., et al: 'Utilisation of alkaline electrolysers to improve power system frequency stability with a high penetration of wind power', IET Renew. Power Gener., 2014, 8, (5), pp. 529– 536 2Yao, J., Yu, M., Gao, W., et al: 'Frequency regulation control strategy for PMSG wind-power generation system with flywheel energy storage unit', IET Renew. Power Gener., 2017, 11, (8), pp. 1082– 1093 3Wang, S., Tang, Y., Shi, J., et al: 'Design and advanced control strategies of a hybrid energy storage system for the grid integration of wind power generations', IET Renew. Power Gener., 2015, 9, (2), pp. 89– 98 4Sebastian, R.: 'Battery energy storage for increasing stability and reliability of an isolated wind diesel power system', IET Renew. Power Gener., 2017, 11, (2), pp. 296– 303 5Molina, M.G., Mercado, P.E.: 'Power flow stabilization and control of microgrid with wind generation by superconducting magnetic energy storage', IEEE Trans. Power Electron., 2011, 26, (3), pp. 910– 922 6Ali, M.H., Wu, B., Dougal, R.A.: 'An overview of SMES applications in power and energy systems', IEEE Trans. Sustain. Energy, 2010, 1, (1), pp. 38– 47 7Chen, S.S., Wang, L., Lee, W.J., et al: 'Power flow control and damping enhancement of a large wind farm using a superconducting magnetic energy storage unit', IET Renew. Power Gener., 2009, 3, (1), pp. 23– 38 8Wu, C.J., Lee, Y.S.: 'Application of superconducting magnetic energy storage unit to improve the damping of synchronous generator', IEEE Trans. Energy Convers., 1991, 6, (4), pp. 573– 578 9Tripathy, S.C., Kalantar, M., Balasubramanian, R.: 'Dynamics and stability of wind and diesel turbine generators with superconducting magnetic energy storage on an isolated power system', IEEE Trans. Energy Convers., 1991, 6, (4), pp. 579– 585 10Hasanien, H.M.: 'A set-membership affine projection algorithm-based adaptive-controlled SMES units for wind farms output power smoothing', IEEE Trans. Sustain. Energy, 2014, 5, (4), pp. 1226– 1233 11Iqbal, S.J., Mufti, M.D., Lone, S.A., et al: 'Intelligently controlled superconducting magnetic energy storage for improved load frequency control', Int. J. Power Energy Syst., 2009, 29, (4), pp. 241– 254 12Tripathy, S.C., Juengst, K.P.: 'Sampled data automatic generation control with superconducting magnetic energy storage', IEEE Trans. Energy Convers., 1997, 12, (2), pp. 187– 192 13Tripathy, S.C., Balasubramanian, R., Chandramohan Nair, P.S.: 'Adaptive automatic generation control with superconducting magnetic energy storage in power system', IEEE Trans. Energy Convers., 1992, 7, (3), pp. 434– 441 14Hemeida, A.M.: 'A fuzzy logic controlled superconducting magnetic energy storage, SMES frequency stabilizer', Electr. Power Syst. Res., 2010, 80, pp. 651– 656 15Gong, K., Shi, J., Liu, Y., et al: 'Application of SMES in the microgrid based on fuzzy control', IEEE Trans. Appl. Supercond., 2016, 26, p. 3800205 16Ngamroo, I., Vachirasricirikul, S.: 'Design of optimal SMES controller considering SOC and robustness for microgrid stabilization', IEEE Trans. Appl. Supercond., 2016, 26, (7), p. 5403005 17Ali, M.H., Murata, T., Tamura, J.: 'Fuzzy logic controlled SMES for damping shaft torsional oscillation of synchronous generator', IEEJ Trans. Electr. Electron. Eng., 2006, 1, (1), pp. 116– 120 18Tripathy, S.C., Balasubramanian, R., Chandramohan Nair, P.S.: 'Effects of superconducting magnetic energy storage on automatic generation control considering governor deadband and boiler dynamics', IEEE Trans. Power Syst., 1992, 7, (3), pp. 1266– 1273 19Ngamroo, I., Mitani, Y., Tsuji, K.: 'Application of SMES coordinated with solid-state phase shifter to load frequency control', IEEE Trans. Appl. Supercond., 1999, 9, (2), pp. 322– 325 20Skiles, J.J., Kustom, R.L., Ko, K.P.: 'Performance of a power conversion system for superconducting magnetic energy storage (SMES)', IEEE Trans. Power Syst., 1996, 11, (4), pp. 1718– 1723 21Mufti, M.D., Iqbal, S.J., Lone, S.A., et al: 'Supervisory adaptive predictive control scheme for supercapacitor energy storage system', IEEE Syst. J., 2015, 9, (3), pp. 1020– 1030 22Zhang, K., Mao, C., Lu, J., et al: 'Optimal control of state-of-charge of superconducting magnetic energy storage for wind power system', IET Renew. Power Gener., 2014, 8, (1), pp. 58– 66 23Ise, T., Murakami, Y., Tsuji, K.: 'Charging and discharging characteristics of SMES with active filter in transmission system', IEEE Trans. Magn., 1987, MAG-23, (2), pp. 545– 548 24Dechanupaprittha, S., Hongesombut, K., Watanabe, M., et al: 'Stabilization of tie-line power flow by robust SMES controller for interconnected power system with wind farms', IEEE Trans. Appl. Supercond., 2007, 17, (2), pp. 2365– 2368 25Milano, F., Ortega, A.: 'Frequency divider', IEEE Trans. Power Syst., 2017, 32, (2), pp. 1493– 1501 26Mufti, M.D., Zargar, M.Y., Lone, S.A.: ' Predictive controlled SMES for frequency control of hybrid wind- diesel standalone system'. Presented in Int. Conf. Computing, Communication and Automation – ICCCA, 2017, pp. 1– 6 Citing Literature Volume11, Issue14December 2017Pages 1832-1840 FiguresReferencesRelatedInformation
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