Optimized Control Strategy for a Medium-Voltage DVR—Theoretical Investigations and Experimental Results

ABSTRACT:  

Most power quality problems in distribution systems are related to voltage sags. Therefore, different solutions have been examined to compensate these sags to avoid production losses at sensitive loads. Dynamic Voltage Restorers (DVRs) have been proposed to provide higher power quality. Currently, a system wide integration of DVRs is hampered because of their high cost, in particular, due to the expensive DC-link energy storage devices. The cost of these DC-link capacitors remains high because the DVR requires a minimum DC-link voltage to be able to operate and to compensate a sag. As a result, only a small fraction of the energy stored in the DC-link capacitor is used, which makes it impractical for DVRs to compensate relatively long voltage sags. Present control strategies are only able to minimize the distortions at the load or to allow a better utilization of the storage system by minimizing the needed voltage amplitude. To avoid this drawback, an optimized control strategy is presented in this paper, which is able to reduce the needed injection voltage of the DVR and concurrently to mitigate the transient distortions at the load side. In the following paper, a brief introduction of the basic DVR principle will be given.  Next, three standard control strategies will be compared and an optimized control strategy is developed in this paper. Finally, experimental results using a medium-voltage 10-kV DVR setup will be shown to verify and prove the functionality of the presented control strategy in both symmetrical and asymmetrical voltage sag conditions.

KEYWORDS:

1.      Asymmetrical voltage sag

2.      Dynamic voltage restorer (DVR)

3.      In-phase compensation

4.      Optimized compensation

5.      Pre-sag compensation

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Basic concept of a DVR.

 EXPECTED SIMULATION RESULTS:

                                       Fig. 2. Measured voltages during a long, balanced sag.

Fig. 3. Measured voltages during a long, unbalanced sag.

CONCLUSION:

Voltage sags are a major problem in power systems due to the increased integration of sensitive loads. DVR systems are able to compensate these short voltage sags. The control and the design  of these systems are critical. Present control strategies are able either to minimize load distortions or the needed voltage amplitude. Both requirements are of utmost importance, especially the needed voltage amplitude for compensating a voltage sag leads to a strict limitation of the range of operation without oversizing the converter significantly.

In this paper, the basic concept of an optimized solution is presented. Based on a combination of the pre-sag and in-phase compensation methods, the proposed optimized DVR control strategy can react to a short voltage sag avoiding disturbances to the protected load. While for a long voltage sag, the proposed method is still able to generate the appropriate voltage without over modulation (or oversized DC-link capacitor) and with minimized load voltage transient distortions. Furthermore, medium voltage level experimental results are presented to verify the feasibility of this control strategy in both balanced and unbalanced voltage sag situations. Although, the effect of the control strategy has only been shown for long but shallow sags, similar results occur for deep sags or large phase jumps.

In this study, it was found that the required voltage amplitude of the DVR with the proposed optimized control strategy was reduced by 25%, compared to the pre-sag controller. In other words, the maximum compensation time is increased by approximately the same amount. Taking into consideration that a phase jump of 12 is not extremely high and that the advantages increases with larger phase jumps, an even higher gain is  possible in practical systems. Summarizing all advantages up, it can be stated that the compensation time of existing DVR systems under pre-sag control can be significantly improved when applying the proposed optimized strategy. In newly designed DVRs, the DC-link capacitance can be decreased without reducing the range of operation.

REFERENCES:

[1] M. Bollen, Understanding Power Quality Problems, Voltage Sags and Interruptions. New York: IEEE press, 1999.

[2] A. Kara, D. Amhof, P. Dähler, and H. Grüning, “Power supply quality improvement with a dynamic voltage restorer (DVR),” in Proc. Appl.Power Electron. Conf., 1998, no. 2, pp. 986–993.

[3] P. Dähler, M. Eichler, O. Gaupp, and G. Linhofer, “Power quality devices improve manufacturing process stability,” ABB Rev., vol. 1, pp.  62–68, 2001.

[4] W. E. Brumsickle, R. S. Schneider, G.A. Luckjiff, D. M. Divan, and M. F. McGranaghan, “Dynamic sag correctors: Cost effective industrial power line conditioning,” IEEE Trans. Ind. Appl., vol. 37, no. 1, pp. 212–217, Jan.–Feb. 2001.

[5] C. Meyer and R. De Doncker, “Solid-state circuit breaker based on active thyristor topologies,” IEEE Trans. Power Electron., vol. 21, no.2, pp. 450–458, Nov. 2006.

Dynamic Modular Modeling of Smart Loads Associated with Electric Springs and Control

ABSTRACT:  

Smart loads associated with electric springs (ES) have been used for fast demand-side management for smart grid. While simplified dynamic ES models have been used for power system simulation, these models do not include the dynamics of the power electronic circuits and control of the ES. This paper presents a dynamic and modular ES model that can incorporate controller design and the dynamics of the power electronic circuits. Based on experimental measurements, the order of this dynamic model has been reduced so that the model suits both circuit and system simulations. The model is demonstrated with the radial chordal decomposition controller for both voltage and frequency regulation. The modular approach allows the circuit and controller of the ES model and the load module to be combined in the d-q frame. Experimental results based on single and multiple smart loads setup are provided to verify the results obtained from the model simulation. Then the ES model is incorporated into power system simulations including an IEEE 13 node power system and a three-phase balanced microgrid system.

KEYWORDS:

1.      Electric spring

2.      Parameter estimation

3.      Radial-chordal decomposition

4.      Smart loads

5.      Microgrids

SOFTWARE: MATLAB/SIMULINK

SCHEMATIC DIAGRAM:

Fig. 1 System setup in Phase III.

 EXPECTED SIMULATION RESULTS:

(a)Full results of experiment and the theoretical model.

(b)Zoom in results of experiment and the theoretical model.

(c) Full results of experiment and the estimated model.

(d) Zoom in results of experiment and the estimated model.

Fig. 2 Experimental and simulation (theoretical and estimated models) results of ES output voltage.

(a) PCC Voltage (V_g).

(b) Voltage output of ES (V_es).

(c) Current of the Smart load (I_sl).

(d) P-Q power of the smart load.

Fig. 3 Experimental and simulation results on Phase II setup with a ZIP load.

(a) PCC Voltage (V_g).

(b) Voltage output of ES (V_es).

411

(c) Current of the Smart load (I_sl).

(d) P-Q power of the smart load.

Fig. 4 Simulation results on Phase II setup with a thermostatic load.

(a) PCC voltage (V_g1/2/3).

 

(b)Voltage output of ES 1 (V_es1).

(c) Voltage output of ES 2 (V_es2).

(d) Voltage output of ES 3 (V_es3).

(e) P-Q power of smart load 1.

(f) P-Q power of smart load 2.

(g) P-Q power of smart load 3.

Fig. 5 Experimental and simulation results on Phase III setup.

(a) Power delivered by the renewable energy source.

(b)Phase A voltage of node 634 (V_s).

(c) Power absorbed in phase A of node 634.

(d)Sum power absorbed by smart load 1,2 and 3.

(e) Power absorbed by smart load 4.

(f) Power absorbed by smart load 5.

Fig. 6 Simulation results on Phase IV setup.

(a) Utility frequency

(b) PCC voltage (V_g)

Fig. 7 Simulation results on Phase V setup.

CONCLUSION:

In this paper, the dynamic model of an ES is firstly analyzed as a theoretical model in state space. An order-reduced model is derived by estimation based on experimental measurements. A theoretical model of the order of 6 with 4 inputs has been simplified into a 2nd-order model with 2 inputs. The RCD control is adopted as the outer-controller module in the smart load. Two models of noncritical loads, namely ZIP and thermostatic load models, are analyzed to cooperate with the ES. The estimated ES model (the inner model), outer controller and the load model can be modelled separated as modules and then combined to form the smart load model. The modular approach offers the flexibility of the proposed model in outer-controller design and the noncritical load selection. The results obtained from the proposed model are compared with experimental measurements in different setups for model verification. The proposed model has been tested for voltage and frequency regulation. This simplified modular modeling method could pave the way for future work on modeling widely-distributed ESs in distribution networks so that various control strategies can be studied.

REFERENCES:

[1] J.M. Guerrero, J.C. Vasquez, J. Matas, M. Castilla and L. Garcia de Vicuna, “Control strategy for flexible microgrid based on parallel line-interactive UPS systems”, IEEE Transaction on Industrial Electronics, vol. 56, no.3, pp. 726-735, Mar. 2009.

[2] P. Khayyer and U. Ozguner, “Decentralized control of large-scale storage-based renewable energy systems”, IEEE Transactions on Smart Grid, vol. 5, no.3, pp. 1300-1307, May 2014.

[3] Yang, Y., H. Wang, F. Blaabjerg, and T. Kerekes. “A Hybrid Power Control Concept for PV Inverters With Reduced Thermal Loading.” IEEE Transaction on Power Electronics, vol 29, no. 12, pp. 6271– 6275, Dec. 2014.

[4] A. H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia, “Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid,” IEEE Transaction Smart Grid, vol. 1, no. 3, pp. 320– 331, Dec. 2010.

[5] A. J. Conejo, J. M. Morales and L. Baringo, “Real-time demand response model,” IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 236–242, Dec. 2010.

Enhancement of Voltage Stability and Power Oscillation Damping Using Static Synchronous Series Compensator with SMES

 ABSTRACT:  

 The power system network is becoming more complex nowadays and it is very difficult to maintain the stability of the power system. The main purpose of this paper proposes a 12-pulse based Static Synchronous Series Compensator (SSSC) with and without Superconducting Magnetic Energy Storage (SMES) for enhancing the voltage stability and power oscillation damping in multi area system. Control scheme for the chopper circuit of SMES coil is designed. A three area system is taken as test system and the operation of SSSC with and without SMES is analysed for various transient disturbances in MATLAB / SIMULINK environment.

KEYWORDS:

1.      Static Synchronous Series Compensator (SSSC)

2.      Superconducting Magnetic Energy Storage (SMES)

3.      Multi area system

4.      Transient disturbances

SOFTWARE: MATLAB/SIMULINK

SINGLE LINE DIAGRAM:

Fig. 1 Single line diagram of the test system with SSSC with SMES

 EXPECTED SIMULATION RESULTS:

Fig. 2.Simulation result of test system

Fig. 3 Power output for Case (a) and (b)

(a) With fault

(b) Case (a)

(c) Case (b)                     Time (sec)

Fig. 4 Simulation result of Voltage with fault

Fig. 5 Simulation result for current with fault

Fig, 6 Simulation result for P & Q with fault

CONCLUSION:

The dynamic performance of the SSSC with and without SMES for the test system are analysed with Matlab/simulink. In this paper SMES with two quadrant chopper control plays an important role in real power exchange. SSSC with and without has been developed to improve transient stability performance of the power system. It is inferred from the results that the SSSC with SMES is very efficient in transient stability enhancement and effective in damping power oscillations and to maintain power flow through transmission lines after the disturbances.

REFERENCES:

[1] S. S. Choi, F. Jiang and G. Shrestha, “Suppression of transmission system oscillations by thyristor controlled series compensation”, IEE Proc., Vol.GTD-143, No.1, 1996, pp 7-12.

[2] M.W. Tsang and D. Sutanto, “Power System Stabiliser using Energy Storage”, 0-7803-5935-6/00 2000, IEEE

[3] Hingorani, N.G., “Role of FACTS in a Deregulated Market,” Proc. IEEE Power Engineering Society Winter Meeting, Seattle, WA, USA, 2006, pp. 1-6.

[4] Molina, M.G. and P. E. Mercado, “Modeling of a Static Synchronous Compensator with Superconducting Magnetic Energy Storage for Applications on Frequency Control”, Proc. VIII SEPOPE, Brasilia, Brazil, 2002, pp. 17-22.

[5] Molina, M.G. and P. E. Mercado, “New Energy Storage Devices for Applications on Frequency Control of the Power System using FACTS Controllers,” Proc. X ERLAC, Iguazú, Argentina, 14.6, 2003, 1-6.