Performance Recovery of Voltage Source Converterswith Application to Grid-connected Fuel Cell DGs


Most common types of distributed generation (DG) systems utilize power electronic interfaces and, in particular,  three-phase voltage source converters (VSCs) which are mainly  used to regulate active and reactive power delivered to the grid. The main drawbacks of VSCs originate from their nonlinearities, control strategies, and lack of robustness against uncertainties. In this paper, two time-scale separation redesign technique is proposed to improve the overall robustness of VSCs and address the issues of uncertainties. The proposed controller is applied to a grid-connected Solid Oxide Fuel Cell (SOFC) distributed generation system to recover the trajectories of the nominal system despite the presence of uncertainties. Abrupt changes in the input dc voltage, grid-side voltage, line impedance and PWM malfunctions are just a few uncertainties considered in our evaluations. Simulation results based on detailed model indicate that the redesigned system with lower filter gain (_) achieves more reliable performance in compare to the conventional current control scheme. The results also verified that the redesigned controller is quite successful in improving the startup and tracking responses along with enhancing the overall robustness of the system.


  1. Power converters
  2. Solid oxide fuel cell (SOFC)
  3. Distributed generation (DG)
  4. Time-scale separation redesign



 Fig. 1. Schematic diagram of a grid-connected SOFC power plant with redesigned controller.


 Fig. 2. Active (top) and reactive (bottom) output power in case 1 (input dc

voltage) uncertainty using PI and redesigned controller.

Fig. 3. Output voltage (top) and current (bottom) of each SOFC array in case

1 (input dc voltage) uncertainty using PI and redesigned controller.

Fig. 4. Active (top) and reactive (bottom) output power in case 2 (grid-side

voltage) uncertainty using PI and redesigned controller.

Fig. 5. d-axis (top) and q-axis (bottom) currents of the VSC in case 2 (gridside

voltage) uncertainty using PI and redesigned controller.

Fig. 6. Active (top) and reactive (bottom) output power in case 3.1 (line

resistance) uncertainty using PI and redesigned controller.

Fig. 7. Active (top) and reactive (bottom) output power in case 3.2 (line

inductance) uncertainty using PI and redesigned controller.

Fig. 8. Additive random Gaussian noises on duty ratio of phase a (top), b

(middle), and c (bottom) of the VSC.

Fig. 9. Active (top) and reactive (bottom) output power in case 4 (duty

ratio) uncertainty using PI and redesigned controller.


 This paper presents a new control technique based on two time-scale separation redesign for the VSC of a grid connected SOFC DG system. A three-phase VSC is used to regulate active and reactive power delivered to the grid. In addition, variations in the input dc voltage, line impedance, grid-side voltage and duty ratio are mathematically formulated as additive uncertainties based on the nonlinear model of the VSC. As a result, the proposed controller is able to address the issues of robustness and further enhance the system stability in the presence of uncertainties. The redesigned controller also presents a fast and accurate startup response and delivers superior decoupling performance as compared to the conventional PI controller. Moreover, the redesigned controller significantly reduces the maximum overshoot in the output power while the system with a conventional controller exhibits deterioration in the output response which leads to excessive current and voltage variations in the FC arrays.


[1] P. Kundur, Power System Stability and Control. New York, NY, USA:McGraw-Hill, 1994.

[2] R. Seyezhai and B. L. Mathur, “Modeling and control of a PEM fuel cell based hybrid multilevel inverter,” International Journal of Hydrogen Energy, vol. 36, pp. 15029-15043, 2011.

[3] T. Erfanmanesh and M. Dehghani, “Performance improvement in gridconnected fuel cell power plant: An LPV robust control approach,”

International Journal of Electrical Power & Energy Systems, vol. 67, pp. 306-314, 2015.

[4] S. A. Taher and S. Mansouri, “Optimal PI controller design for active power in grid-connected SOFC DG system,” International Journal of Electrical Power & Energy Systems, vol. 60, pp. 268-274, 2014.

[5] R. Teodorescu, M. Liserre, and P. Rodriguez, Grid Converters for Photovoltaic and Wind Power Systems. Hoboken, NJ, USA: John Wiley & Sons, 2011.

Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid



This paper presents a microgrid consisting of different distributed generation (DG) units that are connected to the distribution grid. An energy-management algorithm is implemented to coordinate the operations of the different DG units in the microgrid for grid-connected and islanded operations. The proposed microgrid consists of a photovoltaic (PV) array which functions as the primary generation unit of the microgrid and a proton-exchange membrane fuel cell to supplement the variability in the power generated by the PV array. A lithium-ion storage battery is incorporated into the microgrid to mitigate peak demands during grid-connected operation and to compensate for any shortage in the generated power during islanded operation. The control design for the DG inverters employs a new model predictive control algorithm which enables faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. The design concept is verified through various test scenarios to demonstrate the operational capability of the proposed microgrid, and the obtained results are discussed.


  1. Distributed generation (DG)
  2. Energy management
  3. Microgrid
  4. Model predictive control (MPC)



Fig. 1. Overall configuration of the proposed microgrid architecture.



 Fig. 2. Per-phase currents drawn by loads 1, 2, and 3.

Fig. 3. Waveform of the SB current during charging.

Fig. 4. SOC of the SB during charging.

Fig. 5. Waveforms of three-phase load current (top), three-phase DG current (middle), and three-phase grid current (bottom).

Fig. 6. Waveforms of grid voltage and grid current for phase a.

Fig. 7. Real (top) and reactive (bottom) power consumed by loads.

Fig. 8. Real (top) and reactive (bottom) power delivered by the main DG unit.

Fig. 9. Real (top) and reactive (bottom) power delivered by the grid.

Fig. 10. Real (top) and reactive (bottom) power delivered by the grid.

Fig. 11. Real power delivered by SB.

Fig. 12. Real (top) and reactive (bottom) power delivered by the grid.

Fig. 13. Real power delivered by SB.

Fig. 14. Real (top) and reactive (bottom) power consumed by loads.


In this paper, a control system that coordinates the operation of multiple DG inverters in a microgrid for grid-connected and islanded operations has been presented. The proposed controller for the DG inverters is based on a newly developed MPC algorithm which decomposes the control problem into steady-state and transient subproblems in order to reduce the overall computation time. The controller also integrates Kalman filters into the control design to extract the harmonic spectra of the load currents and to generate the necessary references for the controller. The DG inverters can compensate for load harmonic currents in a similar way as conventional compensators, such as active and passive filters, and, hence, no additional equipment is required for power-quality improvement. To realize the smart grid concept, various energy-management functions, such as peak shaving and load shedding, have also been demonstrated in the simulation studies. The results have validated that the microgrid is able to handle different operating conditions effectively during grid-connected and islanded operations, thus increasing the overall reliability and stability of the microgrid.


[1] S. Braithwait, “Behaviormanagement,” IEEE Power and EnergyMag., vol. 8, no. 3, pp. 36–45, May/Jun. 2010.

[2] N. Jenkins, J. Ekanayake, and G. Strbac, Distributed Generation. London, U.K.: IET, 2009.

[3] M. Y. Zhai, “Transmission characteristics of low-voltage distribution networks in China under the smart grids environment,” IEEE Trans. Power Del., vol. 26, no. 1, pp. 173–180, Jan. 2011.

[4] G. C. Heffner, C. A. Goldman, and M. M. Moezzi, “Innovative approaches to verifying demand response of water heater load control,” IEEE Trans. Power Del., vol. 21, no. 1, pp. 1538–1551, Jan. 2006.

[5] R. Lasseter, J. Eto, B. Schenkman, J. Stevens, H. Vollkommer, D. Klapp, E. Linton, H. Hurtado, and J. Roy, “Certs microgrid laboratory test bed, and smart loads,” IEEE Trans. Power Del., vol. 26, no. 1, pp. 325–332, Jan. 2011.

Dynamic Modeling of Microgrid for Grid Connected and Intentional Islanding Operation



 Microgrid is defined as the cluster of multiple distributed generators (DGs) such as renewable energy sources that supply electrical energy. The connection of microgrid is in parallel with the main grid. When microgrid is isolated from remainder of the utility system, it is said to be in intentional islanding mode. In this mode, DG inverter system operates in voltage control mode to provide constant voltage to the local load. During grid connected mode, the Microgrid operates  in constant current control mode to supply preset power to the main grid. The main contribution of this paper is summarized as

  • Design of a network based control scheme for inverter based sources, which provides proper current control during grid connected mode and voltage control during islanding
  • Development of an algorithm for intentional islanding detection and synchronization controller required during grid
  • Dynamic modeling and simulation are conducted to show system behavior under proposed method using

From the simulation results using Simulink dynamic models, it can be shown that these controllers provide the microgrid with a deterministic and reliable connection to the grid.


  1. Distributed generation (DG)
  2. grid connected operation
  3. intentional islanding operation and islanding detection
  4. Microgrid



Fig.1. Dynamic model of microgrid with controller.


Fig. 2. Line Current without current controller

Fig.3. Line Voltage without Voltage controller

Fig. 4. Line Voltage with voltage controller

Fig. 5. Phase voltage waveform (a) without re-closure controller (b) with re-closure controller

Fig. 6. Synchronization for grid reconnection (a) without re-closure algorithm (b) with re-closure algorithm


Current and voltage Control techniques have been developed for grid connected and intentional islanding modes of operation using PI controllers. An intentional islanding detection algorithm responsible for switching between current control and voltage control is developed using logical operations and proved to be effective. The reconnection algorithm coupled with the synchronization controller enabled the DG to synchronize itself with the grid during grid reconnection. The performance of the microgrid with the proposed controllers and algorithms  has been analyzed by conducting simulation on dynamic model using SIMULINK. The simulation results presented here confirms the effectiveness of the control scheme.


[1] L. Shi, M.Y. Lin Chew. “A review on sustainable design of renewable energy systems,” science direct journal present in Renewable and Sustainable Energy Reviews, Vol. 16, Issue 1, 2012, pp. 192–207.

[2] Q. Lei, Fang Zheng Peng, Shuitao Yang. “Multi loop control method for high performance microgrid inverter through load voltage and current decoupling with only output voltage feedback,” IEEE Trans. power. Electron, vol. 26, no. 3, 2011, pp. 953–960.

[3] J. Selvaraj and N. A. Rahim, “Multilevel inverter for grid-connected PV system employing digital PI controller,” IEEE Trans. Ind. Electron., vol. 56, no. 1, 2009, pp. 149–158.

[4] I. J. Balaguer, Fang Zheng Peng, Shuitao Yang, Uthane Supatti Qin Lei. “Control for grid connected and intentional islanding modes of operations of distributed power generation,” IEEE Trans. Ind. Electron., vol. 56, no. 3, 2009, pp. 726–736.

[5] R. J. Azevedo, G.I. Candela, R. Teodorescu, P.Rodriguez , I.E-Otadui “Microgrid connection management based on an intelligent connection agent,” 36th annual conference on IEEE industrial electronics society, 2010, pp. 3028–3033.

A Control Technique for Integration of DG Units to the Electrical Networks


This paper deals with a multi objective control technique for integration of distributed generation (DG) resources to the electrical power network. The proposed strategy provides compensation for active, reactive, and harmonic load current components during connection of DG link to the grid. The dynamic model of the proposed system is first elaborated in the stationary reference frame and then transformed into the synchronous orthogonal reference frame. The transformed variables are used in control of the voltage source converter as the heart of the interfacing system between DG resources and utility grid. By setting an appropriate compensation current references from the sensed load currents in control circuit loop of DG, the active, reactive, and harmonic load current components will be compensated with fast dynamic response, thereby achieving sinusoidal grid currents in phase with load voltages, while required power of the load is more than the maximum injected power of the DG to the grid. In addition, the proposed control method of this paper does not need a phase-locked loop in control circuit and has fast dynamic response in providing active and reactive power components of the grid-connected loads. The effectiveness of the proposed control technique in DG application is demonstrated with injection of maximum available power from the DG to the grid, increased power factor of the utility grid, and reduced total harmonic distortion of grid current through simulation and experimental results under steady-state and dynamic operating conditions.


  1. Digital signal processor
  2. Distributed generation (DG)
  3. Renewable energy sources
  4. Total harmonic distortion (THD)
  5. voltage source converter (VSC)




Fig. 1. General schematic diagram of the proposed control strategy for DG system.



Fig. 2. Load voltage, load, grid, and DG currents before and after connection of DG and before and after connection and disconnection of additional load into the grid.


Fig. 3. Grid, load, DG currents, and load voltage (a) before and after connection of additional load and (b) before and after disconnection of additional load.


Fig. 4. Phase-to-neutral voltage and grid current for phase (a).


Fig. 5. Reference currents track the load current (a) after interconnection of DG resources and (b) after additional load increment.


Fig. 6. Load voltage, load, grid, and DG currents during connection of DG link to the unbalanced grid voltage.


A multi objective control algorithm for the grid-connected converter-based DG interface has been proposed and presented in this paper. Flexibility of the proposed DG in both steady-state and transient operations has been verified through simulation and experimental results.

Due to sensitivity of phase-locked loop to noises and distortion, its elimination can bring benefits for robust control against distortions in DG applications. Also, the problems due to synchronization between DG and grid do not exist, and DG link can be connected to the power grid without any current overshoot. One other advantage of proposed control method is its fast dynamic response in tracking reactive power variations; the control loops of active and reactive power are considered independent. By the use of the proposed control method, DG system is introduced as a new alternative for distributed static compensator in distribution network. The results illustrate that, in all conditions, the load voltage and source current are in phase and so, by improvement of power factor at PCC, DG systems can act as power factor corrector devices. The results indicate that proposed DG system can provide required harmonic load currents in all situations. Thus, by reducing THD of source current, it can act as an active filter. The proposed control technique can be used for different types of DG resources as power quality improvement devices in a customer power distribution network.


[1] T. Zhou and B. François, “Energy management and power control of a hybrid active wind generator for distributed power generation and grid integration,” IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 95–104, Jan. 2011.

[2] M. Singh, V. Khadkikar, A. Chandra, and R. K. Varma, “Grid interconnection of renewable energy sources at the distribution level with power quality improvement features,” IEEE Trans. Power Del., vol. 26, no. 1, pp. 307–315, Jan. 2011.

[3] M. F. Akorede, H. Hizam, and E. Pouresmaeil, “Distributed energy resources and benefits to the environment,” Renewable Sustainable Energy Rev., vol. 14, no. 2, pp. 724–734, Feb. 2010.

[4] C. Mozina, “Impact of green power distributed generation,” IEEE Ind. Appl. Mag., vol. 16, no. 4, pp. 55–62, Jun. 2010.

[5] B. Ramachandran, S. K. Srivastava, C. S. Edrington, and D. A. Cartes, “An intelligent auction scheme for smart grid market using a hybrid immune algorithm,” IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4603–4611, Oct. 2011.