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Thursday, 16 May 2019

Artificial Neural Network based Dynamic Voltage Restorer for Improvement of Power Quality

Dynamic Voltage Restorer (DVR) is a custom power device used as an effective solution in protecting sensitive loads from voltage disturbances in power distribution systems. The efficiency of the control technique, that conducts the switching of the inverters, determines the DVR efficiency.Proportional-Integral-Derivative (PID) control is the general technique to do that. The power quality restoration capabilities of this controller are limited, and it produces significant amount of harmonics – all of which stems from this linear technique’s application for controlling non-linear DVR. As a solution, this paper proposes an Artificial Neural Network (ANN) based controller for enhancing restoration and harmonics suppression capabilities of DVR. A detailed comparison of Neural Network controller with PID driven  controller and Fuzzy logic driven controller is also illustrated, where the proposed controller demonstrated superior performance with a mere 13.5% Total Harmonic Distortion.
1.      Power quality
2.      Dynamic Voltage Restorer (DVR)
3.      PID
4.      Fuzzy logic
5.      Artificial Neural Network (ANN)



Fig. 1. Block diagram of the proposed DVR system to mitigate voltage


Fig. 2. Three phase sag mitigation based on ANN controlled DVR. (a) Instantaneous voltage at stable condition; (b) Instantantaneous voltage when sag occurs; (c) Voltage required to mitigate voltage sag; (d) Output voltage of the inverter circuit; (e) Generated PWM for inverter; (f) Instantaneous voltage after voltage restoration.

Fig. 3. Restored Voltage Using (a) PID controller; (b) Fuzzy controller; (c) ANN controller; (d)THD comparison: the least THD can be seen at ANN based DVR, the range of the harmonics is also truncated by a huge amount by this method.


DVRs are a popular choice for enhancing power quality in power systems, with an array of control system on offer to drive these devices. In this paper, application of ANN to operate DVR for providing better performance than existing systems to mitigate voltage sag, swell, and harmonics has been demonstrated. Problem statement and theoretical background, structure of the proposed method, training procedure of the ANN used have been described in detail. Simulation results showing the DVR performance during voltage sag have been presented. Comparison of the proposed method with the popular PID controller, and nonlinear Fuzzy controller has been carried out, where the proposed ANN controller appeared as the best option to restore system voltage while mitigating THD to the greatest extent.
[1] M. H. Bollen, R. Das, S. Djokic, P. Ciufo, J. Meyer, S. K. Rönnberg, et al., "Power quality concerns in implementing smart distribution-grid applications," IEEE Transactions on Smart Grid, vol. 8, pp. 391-399, 2017.
[2] V. Khadkikar, D. Xu, and C. Cecati, "Emerging Power Quality Problems and State-of-the-Art Solutions," IEEE Transactions on Industrial Electronics, vol. 64, pp. 761-763, 2017.
[3] X. Liang, "Emerging power quality challenges due to integration of renewable energy sources," IEEE Transactions on Industry Applications, vol. 53, pp. 855-866, 2017.
[4] T. Sutradhar, J. R. Pal, and C. Nandi, "Voltage Sag Mitigation by using SVC," International Journal of Computer Applications, vol. 71, 2013.
[5] F. M. Mahdianpoor, R. A. Hooshmand, and M. Ataei, "A new  approach to multifunctional dynamic voltage restorer implementation for emergency control in distribution systems," IEEE transactions on power delivery, vol. 26, pp. 882-890, 2011.

Saturday, 11 May 2019

A Fuzzy Logic Control Method for MPPT of PV Systems

Maximum power point trackers are so important in photovoltaic systems to increase their efficiency. Many methods have been proposed to achieve the maximum power that the PV modules are capable of producing under different weather conditions. This paper proposed an intelligent method for maximum power point tracking based on fuzzy logic controller.  The system consists of a photovoltaic solar module connected to a DC-DC Buck-boost converter. The system has been experienced under disturbance in the photovoltaic temperature and irradiation level. The simulation results show that the proposed maximum power tracker could track the maximum power accurately and successfully in all condition tested. Comparison of different performance parameters such as: tracking efficiency and response time of the system shows that the proposed method gives higher efficiency and better performance than the conventional perturbation and observation method.


Fig. 1: System used for simulation.


Fig. 2: case 1: changing the solar radiation

Fig. 3: Case 1: performance of FLC method

Fig. 4: Case I: performance of P&O method

Fig, 5: Case 2: changing the solar radiation

Fig, 6: Case 2: performance of FLC method

Fig, 7: Case 2: performance of P&O method

Fig, 8: Changing the temperature

Fig, 9: Performance of FLC method

Fig, 10: Performance of P&O method


Photovoltaic model using Matlab/STMULTNK and design of appropriate DC-DC buck-boost converter with a maximum power point tracking facility are presented in this paper. A new method for MPPT based fuzzy logic controller is presented and compared with the conventional P&O MPPT method. The models are tested under disturbance in both solar radiation and photovoltaic temperature. Simulation results show that the proposed method effectively tracks the maximum power point under different ambient conditions.The oscillation around MPP is decreased and the response is faster in compared with the conventional methods. Comparing the tracking efficiency of both methods indicates that the proposed method has a higher efficiency than the conventional P&O MPPT method.

[1] Jancarle L. Dos Santos, Fernando L. M. Antunes and Anis Chehab, "A Maximum Power Point Tracker for PV Systems Using a High Performance Boost Converter", Solar Energy, Issue 7, Vol. 80, pp. 772- 778,2005.
[2] Ting-Chung Yu and Tang-Shiuan Chien, "Analysis and Simulation of Characteristics and Maximum Power Point Tracking for Photovoltaic Systems", Conference,P prpo.c 1e3ed3i9n g- s1 3o4f4 ,PT aoiwpeeri, 2E0l0e9c.t ronics and Drive Systems
[3] Roberto Faranda, Sonia Leva, "Energy Comparison of MPPT techniques for PV Systems", Wseas Transctions on Power System, Issue 6, Vol. 3, pp. 446-455, June 2008.
[4] D. P. Hohm and M. E. Ropp, "Comparative Study of Maximum Power Point Tracking Algorithms using an experimental, programmable, maximum power point tracking test bed",P roceedings of Photovoltaic Specialists Conference ,pp. 1699 - 1702, USA,2000.
[5] Trishan Esram and Patrick 1. Chapman, "Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques", Energy ConverSion, Issue 2, Vol. 22, pp. 439 - 449, May 2007.

Thursday, 9 May 2019

Performance Recovery of Voltage Source Converters with 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.

Sunday, 5 May 2019

Modeling, Analysis and Testing of Autonomous Operation of an Inverter-Based Microgrid

The analysis of the small-signal stability of conventional power systems is well established, but for inverter based microgrids there is a need to establish how circuit and control features give rise to particular oscillatory modes and which of these have poor damping. This paper develops the modeling and analysis of autonomous operation of inverter-based microgrids. Each sub-module is modeled in state-space form and all are combined together on a common reference frame. The model captures the detail of the control loops of the inverter but not the switching action. Some inverter modes are found at relatively high frequency and so a full dynamic model of the network (rather than an algebraic impedance model) is used. The complete model is linearized around an operating point and the resulting system matrix is used to derive the eigenvalues. The eigenvalues (termed “modes”) indicate the frequency and damping of oscillatory components in the transient response. A sensitivity analysis is also presented which helps identifying the origin of each of the modes and identify possible feedback signals for design of controllers to improve the system stability. With experience it is possible to simplify the model (reduce the order) if particular modes are not of interest as is the case with synchronous machine models. Experimental results from a microgrid of three 10-kW inverters are used to verify the results obtained from the model.
1.      Inverter
2.      Inverter model
3.      Microgrid
4.      Power control
5.      Small-signal stability

Fig. 1. Typical structure of inverter-based microgrid.


Fig. 2. Active power (filtered) response of micro-sources with 3.8 kW of step
change in load power at bus 1.

Fig. 3. Reactive power exchange between the micro sources with 3.8 kW of
step change in load power at bus 1 (Initial values: Q1 =0, Q2 = 􀀀200, Q3 =
+200; Final values: Q1 = +600, Q2 = 􀀀300, Q3 = 􀀀200).

Fig. 4. Active power (filtered) response of micro-sources with 16.8 kW and
12 kVAR RL load step change at bus 1.

Fig. 5. Reactive power (filtered) response of micro-sources with 16.8 kW and
12 kVAR RL load step change at bus 1.

Fig. 6. Output voltage (d-axis) response with 27 kW of step change in load
power at bus 1.

Fig. 7. Inductor current (d-axis) response with 27 kW of step change in load
power at bus 1.


In this paper, a small-signal state-space model of a microgrid is presented. The model includes inverter low frequency dynamics dynamics, high frequency dynamics, network dynamics, and load dynamics. All the sub-modules are individually modeled and are then combined on a common reference frame to obtain the complete model of the microgrid.
The model was analyzed in terms of the system eigenvalues and their sensitivity to different states. With the help of this analysis the relation between different modes and system parameters was established. It was observed that the dominant low-frequency modes are highly sensitive to the network configuration and the parameters of the power sharing controller of the micro sources. The high frequency modes are largely sensitive to the inverter inner loop controllers, network dynamics, and load dynamics.
Results obtained from the model were verified experimentally on a prototype microgrid. It was observed that the model successfully predicts the complete microgrid dynamics both in the low and high frequency range.
Small signal modeling has had a long history of use in conventional power systems. The inverter models (and the inclusion of network dynamics) illustrated in this paper allow microgrids to be designed to achieve the stability margin required of reliable power systems.

[1] R. H. Lasseter, “Microgrids,” in Proc. Power Eng. Soc.Winter Meeting, Jan. 2002, vol. 1, pp. 305–308.
[2] A. Arulapalam, M. Barnes, A. Engler, A. Goodwin, and N. Jenkins, “Control of power electronic interfaces in distributed generation microgrids,” Int. J. Electron., vol. 91, no. 9, pp. 503–523, Sep. 2004.
[3] R. Lassetter, “Integration of Distributed Energy Resources: The CERTS Microgrid Concept,” CERT Rep., Apr. 2002.
[4] M. S. Illindala, P. Piagi, H. Zhang, G. Venkataramanan, and R. H. Lasseter, “Hardware Development of a Laboratory-Scale Microgrid Phase 2: Operation and Control of a Two-Inverter Microgrid,” Nat. Renewable Energy Rep., Mar. 2004.
[5] Y. Li, D. M. Vilathgamuwa, and P. C. Loh, “Design, analysis and realtime testing of a controller for multibus microgrid system,” IEEE Trans. Power Electron., vol. 19, no. 5, pp. 1195–1204, Sep. 2004.