A 13-levels Module (K-Type) with two DC sources for Multilevel Inverters

 Abstract

This paper presents a new reconfiguration module for asymmetrical multilevel inverters in which the capacitors are used as the DC links to creates the levels for staircase waveforms. This configuration of multilevel converter makes a reduction in DC sources. On the other hand, it is possible to generate 13 levels with lower DC sources. The proposed module of multilevel inverter generates 13 levels with two unequal DC sources (2VDC and 1VDC). It also involves two chargeable capacitors and 14 semiconductor switches. The capacitors are self-charging without any extra circuit. The lower number of components makes it desirable to use in wide range of applications. The module is schematized as two back-to-back T-type inverters and some other switches around it. Also, it can be connected as cascade modular which lead to a modular topology with more voltage levels at higher voltages. The proposed module makes the inherent creation of the negative voltage levels without any additional circuit (such as H-bridge circuit). Nearest level control switching modulation (NLC) scheme is applied to achieve high quality sinusoidal output voltage. Simulations are executed in MATLAB/Simulink and a prototype is implemented in the power electronics laboratory which the simulation and experimental results show a good performance.

Index Terms— asymmetric, capacitors, multilevel inverter, power electronics, self-charging, nearest level control switching

Block Diagram:

Fig.1 The general conceptual asymmetric MLIs with capacitors.

Expected simulation results:

Fig.2 The waveform of output Voltage (simulation) for the proposed module: (a) Waveform (b) Harmonics spectrums.

Fig.3 The waveform of output Voltage (simulation) for the first cascade topology (25 Levels): (a) Waveform (b) Harmonics spectrums.

Fig.4 The waveform of output Voltage (simulation) for the second cascade topology (169 Levels): (a) Waveform (b) Harmonics spectrums.

Conclusion

This paper introduced one module for asymmetrical multilevel inverter to produce 13 levels by two DC sources. The proposed multilevel is designed based on two back to back T-Type modules with some switches around them. The proposed module is named K-Type. The configuration of K-type provides two extra DC links by capacitors (as the virtual DC supply) to achieve more levels to create staircase waveform. The module needs lower components including two DC sources, two capacitors, 14 semiconductors. It can be used in power applications with unequal DC sources (with ratio 1:2). It can also be easily modularized in two strategies in cascade arrangements to form high voltage outputs with low stress on semiconductors and lowering the number of devices. This ability can be used in some special applications such as solar farm along with a lot of DC sources. DC sources can also have different voltage amplitudes. In the conventional methods, it should be considered one inverter for each DC resources and fix the output voltage the same amplitude. It increases complexity and losses from this aspect, but in asymmetrical multilevel converters, it is possible to combine some DC resources together and generate a unique AC output. It reduces the number of separated inverter, components, losses and etc. The other advantage of K-Type module is its capability to generate both positive and negative output voltage without any additional circuit. Module is tested and it shows a good performance. THD% for one module is obtained 3.87% and 4.07% in simulation and experimental results, respectively that satisfy harmonics standard (IEEE519). THD% for cascade connection (two module) is calculated 1.99% in simulation and 2.26% in experimental results.

References

[1]Essakiappan, S.; Krishnamoorthy, H.S.; Enjeti, P.; Balog, R.S.; Ahmed, S., "Multilevel Medium-Frequency Link Inverter for Utility Scale Photovoltaic Integration," in Power Electronics, IEEE Transactions on , vol.30, no.7, pp.3674-3684, July 2015

[2]A. Nami, J. Liang, F. Dijkhuizen and G. D. Demetriades, "Modular Multilevel Converters for HVDC Applications: Review on Converter Cells and Functionalities," in IEEE Transactions on Power Electronics, vol. 30, no. 1, pp. 18-36,                       Jan. 2015.

[3] I. A. Gowaid, G. P. Adam, A. M. Massoud, S. Ahmed and B. W. Williams, "Hybrid and Modular Multilevel Converter Designs for Isolated HVDC–DC Converters," in IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 6, no. 1, pp. 188-202, March 2018.

[4] Xibo Yuan, "A Set of Multilevel Modular Medium-Voltage High Power Converters for 10-MW Wind Turbines," in Sustainable Energy, IEEE Transactions on , vol.5, no.2, pp.524-534, April 2014

[5] Ahmadi, D.; Jin Wang, "Online Selective Harmonic Compensation and Power Generation With Distributed Energy Resources," in Power Electronics, IEEE Transactions on , vol.29, no.7, pp.3738-3747, July 2014

Ziegler-Nichols based Controller Parameters Tuning for Load Frequency Control in a Microgrid

ABSTRACT

This paper deals with the load frequency control of a small scale microgrid consisting of wind, solar, diesel generator and fuel cell as power generating sources and battery, flywheel and aqua electrolyzer as energy storage elements. To improve the load frequency control, the controllers are properly tuned so as to reduce the mismatch between the real power generation and the load demand leading to minimum power and frequency deviations. A systematic approach to obtain frequency bias parameter followed by tuning the gains of Proportional, Integral and Derivative controller (PID) using Integral Square Time Error evaluation criterion (ITSE)and Ziegler Nichols method respectively is proposed. The simulation studies are carried out for different cases and it is found that the dynamic responses of the frequency and power of the microgrid is quite acceptable.

INDEX TERMS—Automatic generation control, frequency and power deviations ,proportional, integral and derivative Control (PID), integral square time error evaluation criterion (ITSE), simulation analysis, Ziegler-Nichols method.

BLOCK DIAGRAM:

Fig. 1. The block diagram of the microgrid with primary sources : solar, wind energy system and secondary sources: diesel generators, fuel cell, aqua electrolyzer, battery, flywheel and power system.

SIMULATION RESULTS:

Fig. 2. Simulation results of Case 1: (a) Supply power PS (b) Power supply form diesel generator Pdg (c) Fuel cell Pf c (d) Aqua electrolyzer Pae (e) Battery Pbat (f) Flywheel Pfw (g) Error in power supply ∆P (h) Frequency deviation of power systems ∆f

Fig. 3. Simulation results of Case 2: (a) Supply power PS (b) Power supply form diesel generator Pdg (c) Fuel cell Pf c (d) Aqua electrolyzer Pae (e) Battery Pbat (f) Flywheel Pfw (g) Error in power supply ∆P (h) Frequency deviation of power systems ∆f

Fig. 4. Simulation results of Case 3: (a) Supply power PS (b) Power supply form diesel generator Pdg (c) Fuel cell Pf c (d) Aqua electrolyzer Pae (e) Battery Pbat (f) Flywheel Pfw (g) Error in power supply ∆P (h) Frequency deviation of power systems ∆f.

CONCLUSION

In this paper a systematic approach for tuning of PID controllers in microgrid and calculation of optimal frequency bias are presented. The frequency bias calculation is an important aspect in the power system dynamics and plays a key role in controller gains. This factor directly effects the individual components and subsequently the overall performance of the microgrid. So the selection of frequency bias is very crucial and is addressed in this paper. The tuning of the PID controller through Zeigler Nichols approach is quite robust to tackle different types of disturbances. The simulation analysis of microgrid with PID controller shows acceptable dynamic performance with zero steady state error. It is also found that when the load is less than the power generated by the primary sources the excess power goes into the battery and flywheel. Similarly when load is more than the power generated by the primary sources, the excess power requirement is mitigated by diesel generator and fuel cell. Thus, the controllers work in coordination with the demand from load to obtain a proper energy management scenario.

REFERENCES

[1] T. Senjyo, T. Nakaji, K. Uezato, and T. Funabashi, “A hybrid power system using alternative energy facilities in isolated island,” IEEE Trans. Energy Convers., vol. 20, no. 2, pp. 406-414, Jun. 2005.

[2] A. Keyhani and Jin-Woo Jung,“Distributed energy systems,” Journal of Iranian Association of Electrical and Electronics Engineers, vol. 1, no. 2, pp. 33-40,Summer and Fall 2004.

 [3] D. J. Hall and R. G. Colclaser,“Transient modeling and simulation of a tubular solid oxide fuel cell,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 749-753, Sep. 1999.

[4] M. D. Lukas, K. Y. Lee, and H. Ghezel-Ayagh, “Development of a stack simulation model for control study on direct reforming molten carbonate fuel cell power plant,” IEEE Trans. Energy Convers., vol. 14, no. 4, pp.1651-1657, Dec. 1999.

[5] P. S. Dokopoulos, A. C. Saramourtsis, and A. G. Bakirtzis, “Prediction and evaluation of the performance of wind-diesel energy systems,” IEEETrans. Energy Convers., vol. 11, no. 2, pp. 385-393, Jun. 1996.

Tuning of Microgrid Controllers using Cuckoo Search Algorithm

ABSTRACT

Microgrid at islanding mode is operated with renewable energy sources like Solar, Wind and non-renewable energy sources like Diesel generator and Battery which supply load to the system efficiently. With the change in load there is frequency deviation and controllers are required. There is a requirement to tune controllers to have optimal utilization of electrical energy and to maintain frequency at desired level. Cuckoo Search Algorithm (CSA) has been implemented to tune the controllers of microgrid. CSA gives optimal solutions in MATLAB using Integral Time Square Error principle (ITSE). The proposed results using CSA in comparison to the trial and error method is improving the steady state response of the considered microgrid, maintaining the system frequency constant. We have proposed a method for tuning the controller to have the frequency of the system at desired level.

KEYWORDS: Microgrid, Tuning, Diesel generator, Battery, Cuckoo Search Algorithm.

BLOCK DIAGRAM:

Fig. 1. The block diagram of microgrid with renewable energy sources and controllers.

SIMULATION RESULTS:

Fig. 2. Comparison of frequency deviation using PID, PI, and P controller for conventional method.

Fig. 3. The change in frequency (∆f) response of microgrid using PID controller by conventional and CSA method

Fig. 4. Response of the microgrid with battery using CSA method.

Fig. 5. The change in frequency (∆f) response of Microgrid with and without battery using PID controller.

CONCLUSION

In this paper the optimal values of the controller gains in the microgrid were calculated using the conventional methods and CSA. In this work renewable and non renewable energy sources are used to meet the load. The principle behind the new stratagem adopted is to somehow or other steady the system at the output end and independent of variations in the system due to load fluctuations on all counts. Thus the Cuckoo Search Algorithm has been implemented in the tuning of controller parameters MATLAB atmosphere. By trial and error method, the results achieved were comprehensively compared with Cuckoo Search Algorithm. The resultant conclusion proved that the latter is more efficient and better choice among other optimization techniques.

REFERENCES

[1] S. C. S. Chowdhury and P. Crossley, “Microgrids and active distribution networks,” Institution of Engineering and Technology, 2009.

[2] T. Senjyu, T. Nakaji, K. Uezato, and T. Funabashi, “A hybrid power system using alternative energy facilities in isolated island,” IEEE Transactions on Energy Conversion, vol. 20, no. 2, pp. 406–414, June 2005.

[3] N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “Microgrids,” IEEE Power and Energy Magazine, vol. 5, no. 4, pp. 78–94, July 2007.

[4] P. Basak, S. Chowdhury, S. P. Chowdhury, and S. H. nee Dey, “Simulation of microgrid in the perspective of integration of distributed energy resources,” in 2011 International Conference on Energy, Automation and Signal, Dec 2011, pp. 1–6.

[5] M. Bhoye, S. N. Purohit, I. N. Trivedi, M. H. Pandya, P. Jangir, and N. Jangir, “Energy management of renewable energy sources in a microgrid using cuckoo search algorithm,” in 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Mar 2016, pp. 1–6.

Maiden Application of Ziegler-Nichols Method to AGC of Distributed Generation System

 Abstract

 This paper deals with the load frequency control of Distributed Generation Systems (DGS) consisting of Wind, Solar and Diesel Generator. The Diesel Generator is controlled either by P or PI or PID controller to inject regulated amount of real power to the power system based on its rating. As a result it regulates the mismatch between the real power generation and the load which will lead to a minimum power and frequency deviations. A systematic way of deciding frequency bias parameter along with tuning the gains of the Proportional, Integral and Derivative controller (PID) based on Ziegler-Nichols method and ITSE performance criterion is proposed. The simulation studies are carried out for different types of controllers, and disturbances and it is found that it regulates the frequency with less number of oscillations, minimum peak over shoot, and settling time in the case of PID controller.

Index Terms—Distributed Generation Systems (DGS), Proportional, Integral and Derivative Control (PID), ZieglerNichols method, Optimization methods, Tuning, Frequency Control, Diesel Generators, Wind and Solar, Simulation Analysis.

BLOCK DIAGRAM

Fig. 1. The Block diagram of the Distribution Generation System with Diesel Generator, Wind, Solar power supply and Power System.

SIMULATION RESULTS

Fig. 2. Simulation results of case 1 when wind (0.6 pu), solar (0.3 pu) constant and change in load (0.9 to 0.95 pu) at 100sec : (a) Power Demand and Power Supply in pu (b) Power generated by diesel generator in pu (c) Frequency deviation in Hz.

 

Fig. 3. Simulation results of case 2 when Load (0.9 pu), solar (0.3 pu) constant and change in wind power (0.6 to 0.4 pu) at 100sec : (a) Power Demand and Power Supply in pu (b) Power generated by diesel generator in pu (c) Frequency deviation in Hz.

Fig. 4. Simulation results of case 3 when Load (0.9 pu), wind (0.6 pu) constant and change in solar power (0.3 to 0.2 pu) at 250sec : (a) Power Demand and Power Supply in pu (b) Power generated by diesel generator in pu (c) Frequency deviation in Hz.

CONCLUSION

In this paper a systematic approach for tuning of PID controllers in DGS and calculation of optimal frequency bias are presented. The robustness of the proposed controller is checked with different case studies. The simulation studies of DGS with PID controller shows a better performance in terms of time domain specifications: rise time, peak over shoot, peak time, settling time, and steady state error, than P and PI controllers. When the load or power generation changes occur in the DGS, the PID controller acts such that the Diesel Generator will compensate for the required power. This resulted in the minimum oscillations in the frequency and power. Finally the PID controllers stabilize the system quickly with zero steady state error in less settling time. The frequency bias calculation is very important in the power system dynamics and played a key role in controller gains. This factor directly effects the individual components like Diesel Generators and finally overall performance of the DGS. So the selection of frequency bias is very crucial and is addressed in this paper.

REFERENCES

[1] D.Lee and Li Wang, ”Small Signal Stability analysis of an Autonomous Hybrid Renewable Energy Power Generation/Energy Storage system time domain simulations”, IEEE Trans. Energy Convers., vol.23,no.1, March.2008.

[2] A. Keyhani and Jin-Woo Jung, “Distributed energy systems,” Journal of Iranian Association of Electrical and Electronics Engineers, vol. 1, no. 2, pp. 33-40,Summer and Fall 2004

[3] 3] D. J. Hall and R. G. Colclaser, “Transient modeling and simulation of a tubular solid oxide fuel cell,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 749–753, Sep. 1999.

[4] M. D. Lukas, K. Y. Lee, and H. Ghezel-Ayagh, “Development of a stack simulation model for control study on direct reforming molten carbonate fuel cell power plant,” IEEE Trans. Energy Convers., vol. 14, no. 4, pp. 1651–1657, Dec. 1999.

[5] P. S. Dokopoulos, A. C. Saramourtsis, and A. G. Bakirtzis, “Prediction and evaluation of the performance of wind-diesel energy systems,” IEEETrans. Energy Convers., vol. 11, no. 2, pp. 385–393, Jun. 1996.

Automatic Generation Control of Microgrid using Artificial Intelligence Techniques

ABSTRACT

 Microgrid is a small scale independent power system consisting of renewable energy sources: solar and wind power generation and backup by controllable sources: diesel generator, fuel cell, aqua electrolyzer and battery. In the microgrid, the ramp rate limit in power change in controllable sources has been implemented by means of generation rate constraint (GRC) and power frequency (P-f) droop characteristics (R) is also included for the parallel operation of generating sources participating in automatic generation control (AGC). These GRC and P-f droop make the system non linear and we have used artificial intelligence techniques (AI) like bacterial foraging optimization (BFO), particle swarm optimization (PSO), genetic algorithm (GA) to tune the important parameters simultaneously in AGC of microgrid. Simulation results show the superiority of BFO for optimal calculation of multiple parameters in microgrid over PSO, GA and classical methods.

INDEX TERMS

Automatic generation control (AGC), bacterial foraging optimization (BFO), generation rate constraint (GRC), genetic algorithm (GA), microgrid, power frequency (P-f) droop, particle swarm optimization (PSO), simulation analysis, tuning of parameters.

BLOCK DIAGRAM:

Fig. 1. The block diagram of the microgrid with diesel generator, fuel cell, aqua electrolyzer, battery wind, solar power supply and power system.

SIMULATION RESULTS:

Fig. 2. Response of the microgrid with and without GRC for a increase in load by 10%

Fig. 3. Simulation results of Case 1: PG : power generation , PL : load demand, Pw: wind power, Ps: Solar power, H2s: hydrogen stored, Pb : battery bower, Qb : battery state of the charge and Δf : frequency deviation of microgrid.

Fig. 4. Frequency deviation Δf in microgrid for 5 % increase in wind power for different methods.

Fig. 5. Simulation results of Case 2: PG : power generation , PL : load demand, Pw: wind power, Ps: Solar power, Pdg : diesel generator power, Pfc : fuel cell power, H2s: hydrogen stored, Pb : battery bower, Qb : battery state of the charge and Δf : frequency deviation of microgrid.

Fig. 6. Frequency deviation Δf in microgrid for increase in 10 % load demand for different methods.

Fig. 7. Simulation results of Case 3: PG : power generation , PL : load demand, Pw: wind power, Ps: Solar power, H2s: hydrogen stored, Pb : battery bower, Qb : battery state of the charge and Δf : frequency deviation of microgrid.

CONCLUSIONS

For the first time a systematic approach for tuning of controller gains, frequency bias, droop in the presence of GRC in microgrid is presented in the paper. The difficulty in tuning large number of parameters in complex systems can be achieved through the artificial intelligence techniques. From the simulation results it is observed that for optimal tuning of multiple parameters in non linear microgrid BFO is better than PSO, GA and classical methods. It is also observed that when the load is less than the power generated by the renewable sources the excess power goes into the energy storage devices. Similarly when load is more than the power generated by the renewable sources, the excess power requirement is mitigated by diesel generator, fuel cell and battery. Thus, the controllers work in coordination with the demand from load to obtain a proper energy management.

REFERENCES

[1] A. Keyhani and Jin-Woo Jung, “Distributed energy systems,” Journal of Iranian Association of Electrical and Electronics Engineers, vol. 1, no. 2, pp. 33-40, 2004.

 [2] The renewable energy in India, Available: http: //mnes. gov.in.

 [3] S. P. Chowdhury, P. Crossley, S. Chowdhury, and E. Clarke, Microgrids and Active Distribution Networks. London: Institution of Engineering and Technology, 2009.

 [4] P. Kundur, Power System Stability and Control, Tata Mc Graw Hill, Inc., New York,1994. [5] Ngo, M.-L.D., King, R.L., and Luck, R."Implications of frequency bias settings on AGC," IEEE Proceedings of the Twenty-Seventh Southeastern Symposium on system theory, Mar 1995, pp.83 – 86

Fuzzy Logic Controller-based Synchronverter in Grid-connected Solar Power System with Adaptive Damping Factor*

 Abstract

In the last few years, the development of solar power systems has been rapid due to their technological maturity and cost-effectiveness. However, integrating solar power into the grid can negatively impact frequency stability, as the lack of rotating masses and inertial response can destabilize the power grid. To address this issue, a synchronverter, an inverter that mimics the operation of a synchronous generator, is crucial. It stabilizes the power grid by emulating a virtual inertia. However, a conventional proportional-integral (PI)-based synchronverter lacks an adaptive damping factor (Dp) or a digitalized smart controller to manage fast-responding solar inputs. Therefore, a novel fuzzy logic controller (FLC) framework is proposed to operate the synchronverter in a grid-connected solar power system. The FLC controls Dp in real-time, achieving a balance between speed and stability for frequency error correction based on frequency difference. The results of four case studies performed in Matlab/Simulink demonstrate that the proposed FLC-based synchronverter can stabilize the grid frequency, reducing the frequency deviation by at least 0.2 Hz (0.4%) compared to the conventional PI-based synchronverter.

Keywords

Fuzzy logic controller (FLC), synchronverter, renewable energy system (RES), grid stability, solar power system

BLOCK DIAGRAM:

                                                           Fig. 1 Power section of synchronverter

EXPECTED SIMULATION RESULTS:

                                             Fig. 2 Active power for varying resistive loads (RL)

                                        Fig. 3 Outputs of synchronverter for first case study

                                                Fig. 4 Testing environment for second case study

                                      Fig. 5 Outputs of synchronverter for second case study

                                            Fig. 6 Testing environment for third case study

                                       Fig. 7 Outputs of synchronverter for third case study

 Fig. 8 Testing environment for fourth case study

Fig. 9 Outputs of synchronverter for fourth case study

CONCLUSION:

Herein, a novel FLC-based framework was proposed to control a synchronverter in a grid- connected solar power system under dynamic weather conditions. Four case studies were simulated in Matlab/Simulink, and the results validated the ability of the proposed controller in stabilizing fg by reducing the frequency deviation by at least 0.2 Hz (0.4%), as compared with the conventional PI-based synchronverter. The performance of the FLC-based synchronverter was optimal even under sudden load changes or varying irradiances and temperatures. P was injected or absorbed whenever the frequency decreased or increased, respectively. The Dp controlled by the FLC was able to balance between transient speed and stability, whereby a larger Dp afforded a more prominent dampening effect, and vice versa.

REFERENCES:

[1] H Zsiborács, N H Baranyai, A Vincze, et al. Intermittent renewable energy sources: The role of energy storage in the European Power System of 2040. MDPI Electronics, 2019, 8(7): 729.

[2] M Z Saleheen, A A Salema, S M M Islam, et al. A target-oriented performance assessment and model development of a grid-connected solar PV (GCPV) system for a commercial building in Malaysia. Renewable Energy, 2021, 171: 371-382.

[3] Y Wang, V Silva, A Winckels. Impact of high penetration of wind and PV generation on frequency dynamics in the continental Europe interconnected system. IET Renewable Power Generation, 2014, 10(1): 10-16.

[4] F Li, C Li, K Sun, et al. Capacity configuration of hybrid CSP/PV plant for economical application of solar energy. Chinese Journal of Electrical Engineering, 2020, 6(2): 19-29.

[5] G Perveen, M Rizwan, N Goel. Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system. IET Energy Systems Integration, 2019, 1(1): 34-51.