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Abstract

In this paper, a novel Power-Frequency Droop Control (PFDC) is introduced to perfectly bring back the system frequency and share the reactive power in isolated microgrid with virtual power plant (VPP). The frequency-based power delivery must be essentially implemented in VPP which can operate as a conventional synchronous generator. It has been attained by enhancing the power processing unit of each VPP to operate as an active generator. The inverter coupling impedance which has been assigned by the virtual impedance technique has reduced the affected power coupling resulting from line resistance. The reference has been subsequently adjusted to compensate the frequency deviation caused by load variation and retrieve the VPP frequency to its nominal value. In addition, the line voltage drop has compensated the voltage drop and load sharing error to obliterate the reactive power sharing imprecision resulting from the voltage deviation. The voltage feedback confirms the correct voltage after compensating the voltage drop. As an illustration, conventional PFDC after a load change cannot restore the system frequency which is deviated from 50 Hz and rested in 49.9 Hz while, proposed PFDC strategy fades away the frequency deviation via compensating the variation of the frequency reference. Likewise, the frequency restoration factor ( γ) has an effective role in retrieving the system frequency, i.e., the restoration rate of the system frequency is in proportion with γ. As a whole, the simulation results have pointed to the high performance of proposed strategy in an isolated microgrid.
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Bibliography

  1.  G.U. Atmo, C.F. Duffield, and D. Wilson, “Structuring procurement to improve sustainability outcomes of power plant projects”, Energy Technol. Policy 2(1), 47‒57 (2015).
  2.  P. Kumar, P.S. Sikder, and N. Pal, “Biomass fuel cell based distributed generation system for Sagar Island”, Bull. Pol. Ac.: Tech. 66(5), 665‒674 (2018).
  3.  M. Wieczorek, M. Lewandowski, and W. Jefimowski, “Cost comparison of different configurations of a hybrid energy storage system with battery-only and supercapacitor-only storage in an electric city bus”, Bull. Pol. Ac.: Tech. 44(6), 1095‒1106 (2019).
  4.  W. Marańda and M. Piotrowicz, “Efficiency of maximum power point tracking in photovoltaic system under variable solar irradiance”, Bull. Pol. Ac.: Tech. 62(4), 713‒721 (2014).
  5.  U. Akram, M. Khalid, and S. Shafiq, “An innovative hybrid wind-solar and battery-supercapacitor microgrid system-development and optimization”, IEEE Access 5(10), 25897‒25912 (2017).
  6.  M.A. Hannan, M.G.M. Abdolrasol, M. Faisal, P.J. Ker, R.A. Begum, and A. Hussain, “Binary particle swarm optimization for scheduling MG integrated virtual power plant toward energy saving”, IEEE Access 7(6), 107937‒07951 (2019).
  7.  T. Wu, Z. Liu, and J. Liu, “A unified virtual power decoupling method for droop-controlled parallel inverters in microgrids”, IEEE Trans. Power Electron. 31(8), 5587‒5603 (2016).
  8.  F. Shahnia and A. Ghosh, “Coupling of neighbouring low voltage residential distribution feeders for voltage profile improvement using power electronics converters”, IET Renew. Power Gener. 10(2), 535‒547 (2016).
  9.  X. Tang, X. Hu, and N. Li, “A novel frequency and voltage control method for islanded based on multienergy storages”, IEEE Trans. Smart Grid 7(1), 410‒419 (2016).
  10.  H. Zhang, S. Kim, Q. Sun, and J. Zhou, “Distributed adaptive virtual impedance control for accurate reactive power sharing based on consensus control in microgrids”, IEEE Trans. Smart Grid 8(4), 1749‒1761 (2017).
  11.  M. Eskandari and L. Li, “Microgrid Operation Improvement by Adaptive Virtual Impedance”, IET Renew. Power Gener. 13(2), 296‒307 (2018).
  12.  Z.A. Obaid, L.M. Cipcigan, L. Abrahim, and M.T. Muhsin, “Frequency control of future power systems: reviewing and evaluating challenges and new control methods”, J. Mod. Power Syst. Clean Energy 7(1), 9‒25 (2019).
  13.  R.M. Imran, S. Wang, and F.M.F. Flaih, “DQ-Voltage droop control and robust secondary restoration with eligibility to operate during communication failure in autonomous microgrid”, IEEE Access 7(12), 6353‒6361 (2019).
  14.  N.N. AbuBakar, M.Y. Hassan, M.F. Sulaima, M. Na’im, M. Nasir and A. Khamisd, “Microgrid and load shedding scheme during islanded mode: A review”, Renewable Sustainable Energy Rev., 71(6), 161‒169 (2017).
  15.  T.A. Jumani, M.W. Mustafa, M.M. Rasid, N.H. Mirjat, Z.H. Leghari, and M.S. Saeed, “Optimal Voltage and Frequency Control of an Islanded Microgrid Using Grasshopper Optimization Algorithm”, Energies 11(11), 1‒20 (2018).
  16.  Y. Han, P. Shen, and X. Zhao, “An enhanced power sharing scheme for voltage unbalance and harmonics compensation in an islanded AC microgrid”, IEEE Trans. Energy Convers. 31(3), 1037‒1050 (2016).
  17.  M. Kosari and S.H. Hosseinian, “Decentralized reactive power sharing and frequency restoration in islanded microgrid”, IEEE Trans. Power Syst. 32(4), 2901‒2912 (2017).
  18.  Y.A. Mohamed and E.F. El-Saadany, “Adaptive decentralized droop controller to preserve power sharing stability of paralleled inverters in distributed generation microgrids”, IEEE Trans. Power Electron. 23(6), 2806‒2816 (2008).
  19.  X. Hou, Y. Sun, H. Han, Z. Liu, W. Yuan, and M. Su, “A fully decentralized control of grid-connected cascaded inverters”, IEEE Trans. Power Deliv. 10(1), 315‒317 (2019).
  20.  L. Li, Y. Sun, Z. Liu, X. Hou, G. Shi, and M. Su, “A decentralized control with unique equilibrium point for cascaded-type microgrid”, IEEE Trans. Sustain. Energy 10(1), 324‒326 (2019).
  21.  F. Guo, C. Wen, and J. Mao, “Distributed secondary voltage and frequency restoration control of droop-con-trolled inverter-based microgrids”, IEEE Trans. Ind. Electron. 62(7), 4355‒4364 (2015).
  22.  S. Zuo, A. Davoudi, and Y. Song, “Distributed finite-time voltage and frequency restoration in islanded AC microgrids”, IEEE Trans. Ind. Electron. 63(10), 5988‒5997 (2016).
  23.  C. Dou, Z. Zhang, and D. Yu, “MAS-based hierarchical distributed coordinate control strategy of virtual power source voltage in low- voltage microgrid”, IEEE Access 5(1), 11381‒11390 (2017).
  24.  N.M. Dehkordi, N. Sadati, and M. Hamzeh, “Distributed robust finite-time secondary voltage and frequency control of islanded microgrids”, IEEE Trans. Power Syst., 32(5), 3648‒3659 (2017).
  25.  N.M. Dehkordi, N. Sadati, and M. Hamzeh, “Fully distributed cooperative secondary frequency and voltage control of islanded microgrids”, IEEE Trans. Energy Convers. 32(2), 675‒685 (2017).
  26.  D.O. Amoateng, M.A. Hosani, and M.S. Elmoursi, “Adaptive voltage and frequency control of islanded multi-microgrids”, IEEE Trans. Power Syst. 33(4), 4454‒4465 (2018).
  27.  Q. Shafiee, J.M. Guerrero, and J.C. Vasquez, “Distributed secondary control for islanded microgrids-a novel approach”, IEEE Trans. Power Electron. 29(2), 1018‒1031 (2014).
  28.  U. Sowmmiya and U. Govindarajan, “Control and power transfer operation of WRIG-based WECS in a hybrid AC/DC microgrid”, IET Renewable Power Gener. 12(3), 359‒373 (2018).
  29.  Z. Zhang, C. Dou, and D. Yu, “An event-triggered secondary control strategy with network delay in islanded microgrids”, IEEE Syst. J. 13(2), 1851‒1860 (2019).
  30.  J. He and Y. Li, “An enhanced microgrid load demand sharing strategy”, IEEE Trans. Power Electron. 27(9), 3984‒3995 (2012).
  31.  Y. Fan, G. Hu, and M. Egerstedt, “Distributed reactive power sharing control for microgrids with event-triggered communication”, IEEE Trans. Control Syst. Technol. 25(1), 118‒128 (2017).
  32.  X. Lu. J. Lai, and X. Yu, “Distributed coordination of islanded microgrid clusters using a two-layer intermittent communication network”, IEEE Trans. Ind. Inf. 14(9), 3956‒3969 (2018).
  33.  X. Wu, C. Shen, and R. Iravani, “A distributed, cooperative frequency and voltage control for microgrids”, IEEE Trans. Smart Grid, 9(4), 2764‒2776 (2018).
  34.  G. Lou, W. Gu, and L. Wang, “Decentralized secondary voltage and frequency control scheme for islanded microgrid based on adaptive state estimator”, IET Gener. Transm. Distrib., 11(15), 3683‒3693 (2017).
  35.  B. Wang, S. Liu, and Y. Zhang, “Reactive power sharing control based on voltage compensation strategy in microgrid”, 36th Chinese Control Conference (2017).
  36.  H.E.Z. Farag, S. Saxena, and A. Asif, “A robust dynamic state estimation for droop controlled islanded microgrids”, Electr. Power Syst. Res. 140(11), 445‒455 (2016).
  37.  K. Sabzevari, S. Karimi, F. Khosravi, and H. Abdi, “Modified droop control for improving adaptive virtual impedance strategy for parallel distributed generation units in islanded microgrids, Int. Trans. Electr. Energy Syst., 29(1), e2689 (2019).
  38.  C. Dou, Z. Zhang, D. Yue, and M. Song, “Improved droop control based on virtual impedance and virtual power source in low-voltage microgrid”, IET Gener. Transm. Distrib. 11(4), 1046‒1054 (2017).
  39.  P.K. Ray, N. Kishor, and S.R. Mohanty, “Islanding and power quality disturbance detection in grid-connected hybrid power system using wavelet and S-transform”, IEEE Trans. Smart Grid, 3(3), 1082‒1094 (2012).
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Authors and Affiliations

Amir Khanjanzadeh
1
Soodabeh Soleymani
1
Babak Mozafari
1

  1. Electrical and Computer Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Abstract

The continuing efforts for reduction of the torque and flux ripples using Finite Set Model Predictive Direct Torque Control methods (FS-MPDTC) have been currently drowning a great attention from the academic communities and industrial applications in the field of electrical drives. The major problem of high torque and flux ripples refers to the consideration of just one active voltage vector at the whole control period. Implementation of two or more voltage vectors at each sampling time has recently been adopted as one of the practical techniques to reduce both the torque and flux ripples. Apart from the calculating challenge of the effort control, the parameter dependency and complexity of the duty ratio relationships lead to reduction of the system robustness. those are two outstanding drawbacks of these methods. In this paper, a finite set of the voltage vectors with a finite set of duty cycles are employed to implement the FS-MPDTC of induction motor. Based on so-called Discrete Duty Cycle- based FS-MPDTC (DDC-FS-MPDTC), a base duty ratio is firstly determined based on the equivalent reference voltage. This duty ratio is certainly calculated using the command values of the control system, while the motor parameters are not used in this algorithm. Then, two sets of duty ratios with limit members are constructed for two adjacent active voltage vectors supposed to apply at each control period. Finally, the prediction and the cost function evaluation are performed for all of the preselected voltage vectors and duty ratios. However, the prediction and the optimization operations are performed for only 12 states of inverter. Meanwhile, time consuming calculations related to SVM has been eliminated. So, the robustness and complexity of the control system have been respectively decreased and increased, and both the flux and torque ripples are reduced in all speed ranges. The simulation results have verified the damping performance of the proposed method to reduce the ripples of both the torque and flux, and accordingly the experimental results have strongly validated the aforementioned statement.
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Bibliography

  1.  J.P. Wach, “Maximum Torque Control of 3-phase induction motor drives,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 2, pp. 433–445, 2018.
  2.  A. Sikorski, K. Kulikowski, and M. Korzeniewski, “Modern Direct Torque and Flux Control methods of an induction machine supplied by three-level inverter,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 61, no. 4, pp. 771–778, 2013.
  3.  D. Stando and M.P. Kazmierkowski, “Constant switching frequency predictive control scheme for three-level inverter-fed sensorless induction motor drive,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 5, pp. 1057–1068, 2020.
  4.  V. Talavat, S. Galvani, and M. Hajibeigy, “Direct predictive control of asynchronous machine torque using matrix converter,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 4, pp. 773–788, 2018.
  5.  I. Takahashi and T. Noguchi, “A new quick response and high efficiency control strategy of an induction motor,” IEEE Trans. Power App., vol. IA-22, no. 5, pp. 820–827, Sept. 1986, doi: 10.1109/TIA.1986.4504799.
  6.  M. Depenbrock, “Direct self-control (DSC) of a inverter fed induction machine,” IEEE Trans. Power Electron., vol. 3, no. 4, pp. 420–429, Oct. 1988.
  7.  Y.-S. Lai and J.-H. Chen, “A new approach to direct torque control of induction motor drives for constant inverter switching frequency and torque ripple reduction,” IEEE Trans. Energy. Convers., vol., 16, no. 3, pp. 220–227, Sep. 2001.
  8.  C. Lascu, I. Boldea, and F. Blaabjerb, “A modified direct torque control for induction motor sensorless drive,” IEEE Trans. Ind. Appl., vol. 36, no. 1, pp. 122–130, Jan/Feb. 2000.
  9.  L. Tang, L. Zhong, M. Rahman, and Y. Hu, “A novel direct torque controlled interior permanent magnet synchronous machine drive with low ripple in flux and torque and fixed switching frequency,” IEEE Trans. Ind. Appl., vol. 19, no. 2, pp. 346–354, Mar. 2004.
  10.  R. Narayan and D.B. Subudhi, “Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 20, no.3, pp. 363–376, 2010.
  11.  I. Bakhti, S. Chaouch, and A. Maakouf, “High performance backstepping control of induction motor with adaptive sliding mode observer,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 21, no.3, pp. 331–344, 2011.
  12.  B. Kenny and R. Lorenz, “Stator- and rotor-flux-based deadbeat direct torque control of induction machines,” IEEE Trans. Ind. Appl., vol. 39, no. 4, pp. 1093–1101, Jul/Aug. 2003.
  13.  J. Rodriguez, M.P. Kazmierkowski, J. Espinoza, P. Zanchetta, H. Abu-Rub, H. Young, and C.A. Rojas, “State of the art of finite control set model predictive control in power electronics,” IEEE Trans. Ind. Inform., vol. 9, no. 2, pp. 1003–1016, May. 2013.
  14.  Y. Zhang, Y. Bai, H. Yang and B. Zhang “Low switching frequency model predictive control of three-level inverter-fed im drives with speed-sensorless and field weakening operations,” IEEE Trans. Ind. Electron., vol. 66, no. 6, pp. 4262–4272, 2019, doi: 10.1109/ TIE.2018.2868014.
  15.  S.A. Davari, D.A. Khaburi, and R. Kennel, “An improved FCS-MPC algorithm for an induction motor with an imposed optimized weighting factor,” IEEE Trans. Power Electron., vol. 27, no. 3, pp. 1540–1551, 2012.
  16.  L. Yan, M. Dou, H. Zhang, and Z. Hua, “Speed sensorless dual reference frame predictive torque control for induction machines,” IEEE Trans. Power. Electron., vol. 34, no. 12, pp. 12285–12295, 2019, doi: 10.1109/TPEL.2019.2904542.
  17.  C.S. Vazquez, J. Rodriguez, M. Rivera, L.G. Franquelo, and M. Norambuena, “Model predictive control for power converters and drives: advanced and trends,” IEEE Trans. Ind. Electron., vol. 64, no. 2, pp. 935–947, 2017.
  18.  W. Xie et al., “Finite control set-model predictive torque control with a deadbeat solution for pmsm drives,” IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5402–5410, Sept. 2015, doi: 10.1109/TIE.2015.2410767.
  19.  Y. Zhang, B. Yang, H. Yang, and M. Nurambuena, “Generalized sequential model predictive control of im drives with field-weakening ability,” IEEE Trans. Power Elecron., vol. 34, no. 9, pp. 8944–8955, 2019, doi: 10.1109/TPEL.2018.2886206.
  20.  M. Norambuena, J. Rodrigez, Z. Zhang, F. Wang, C. Garcia, R. Kenel, and G.-D. Andreescu, “A very simple strategy for high-quality performance of AC machines using model predictive control,” IEEE Trans. Power Electron., vol. 34, no. 1, pp. 794–800, Jan. 2019.
  21.  J. Rodriguez, R.M. Kennel, J.R. Espinoza, M. Trincado, C.A. Silva, and C.A. Rojas, “High performance control strategies for electrical drives: An experimental assessment,” IEEE Trans. Ind. Electron., vol.29, no. 2, pp. 812– 820, Jan/Feb. 2012.
  22.  T. Geyer, “Tuning guidelines for model predictive torque and flux control,” IEEE Trans. Ind. Appl., vol. 54, no. 5, pp. 4464–4475, Oct. 2018.
  23.  F. Wang, G. Lin, and Y. He, “Passivity-based model predictive control of three-level inverter-fed induction motor,” IEEE Trans. Power. Electron., vol. 36, no. 2, pp. 1984–1993, Feb. 2021, doi: 10.1109/TPEL.2020.3008915.
  24.  M. Pacas and J. Weber, “Predictive direct torque control for the PM synchronous machine,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1350–1356, Oct. 2005.
  25.  F. Niu, F. Niu, K. Li, and Y. Wang, “Direct torque control for permanent-magnet synchronous machines based on duty ratio modulation,” IEEE Trans. Ind Electron., vol. 62, no. 10, pp. 6160–6170, Oct. 2015.
  26.  Y. Zhang and J. Zhu, “Direct torque control of permanent magnet synchronous motor with a reduced torque ripple and commutation frequency,” IEEE Trans. Power Electron., vol. 26, no. 1, pp. 235–248, Jan. 2011.
  27.  J.-K. Kang and S.-K. Sul, “New direct torque control of induction motor for minimum torque ripple and constant switching frequency,” IEEE Trans. Ind. Appl., vol. 35, no. 5, pp. 1076–1082, Sep/Oct. 1999.
  28.  K.K. Shyu, J.K. Lin, V.T. Pham, M.J. Yang, and T.W. Wang, “Global minimum torque ripple design for direct torque control of induction motor drives,” IEEE Trans. Ind Electron., vol. 57, no. 9, pp. 3148–3156, Sep. 2010.
  29.  Y. Ren, Z.Q. Zhu, and J. Liu, “Direct torque control of permanent-magnet synchronous machine drives with a simple duty ratio regulator,” IEEE Trans. Ind. Electron., vol. 61, no. 10, pp. 5249–5259, Oct. 2014.
  30.  Q. Liu and K. Hameyer, “Torque ripple minimization for direct torque control of pmsm with modified FSMPC,” IEEE Trans. Ind. Electron., vol. 52, no. 6, pp. 4855–4864, Aug. 2016.
  31.  Y. Zhang and H. Yang, “Torque ripple reduction of model predictive torque control of induction motor drives,” in Proc. Energy Convers. Congr. Expo., 2013, pp. 1176–1183.
  32.  Y. Zhang, H. Yang, and B. Xia, “Model predictive torque control of induction motor drives with reduced torque ripple,” IET Electr. Power Appl., vol. 9, no. 9, pp. 595–604, 2015.
  33.  Y. Zhang and H. Yang, “Model predictive torque control of induction motor drives with optimal duty cycle control,” IEEE Trans. Power Elecron., vol. 29, no. 12, pp. 6593–6603, Dec. 2014.
  34.  Y. Zhang and H. Yang, “Generalized two-vector-based Model-predictive torque control of induction motor drives,” IEEE Trans. Power Elecron., vol. 30, no. 7, pp. 6593–6603, Jul. 2015.
  35.  Y. Zhang, J. Zhu, and B. Xia, “A novel duty cycle control strategy to reduce both the torque and stator flux ripples for DTC of permanent- magnet synchronous motor drives with switching frequency reduction,” IEEE Trans. Power Electron., vol. 31, no. 5, pp. 3738–3753, May 2016.
  36.  C. Lascu and G.-D. Andreescu, “Sliding mode observer and improved integrator with dc-offset compensation for flux estimation in sensorless controlled induction motors,” IEEE Trans. Ind. Electron., vol. 53, no. 3, pp. 785–794, Jun. 2006.
  37.  P.H. Cortes, S. Kouro, B. La Rocca, R. Vargas, J. Rodrigues, J. Leon, S. Vazquez, and L. Franquelo, “Guidelines for weighting factors design in model predictive control of power converters and drives,” in Proc. IEEE ICIT, 2009, pp. 1–7.
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Authors and Affiliations

Babak Kiani
1
Babak Mozafari
1
Soodabeh Soleymani
1
Hosein Mohammadnezhad Shourkaei
1

  1. Department of Electrical Engineering, Science and research Branch, Islamic Azad University, Tehran, IRAN

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