Voltage Sag/Swell Compensation Using Z-source Inverter DVR based on FUZZY Controller


The power quality necessity is one of the serious issues for power organizations and their clients. The examination of intensity unsettling influence attributes and discovering answer for the power quality issues have brought about an expanded enthusiasm for power quality. The most concerning aggravations influencing the nature of the power in the conveyance framework are voltage list/swell. The DVR is utilized to alleviate the voltage list/swell on touchy load. In this paper Z-source inverter (ZSI) based DVR is proposed to improve the voltage rebuilding property of the framework. The ZSI utilizes a LC impedance lattice to couple control source to inverter circuit and readies the likelihood of voltage buck and lift by shortcircuiting the inverter legs. Furthermore a fluffy rationale control plot for Z-source inverter based DVR is proposed to acquire wanted infusing voltage. Displaying and reproduction of the proposed DVR is actualized in MATLAB/SIMULINK stage.



Fig. 1 DVR general configuration



Fig.2Three phase voltage at load point during three phase fault without DVR

Fig 3. Three phase voltage by DVR

Fig.4Three phase compensated voltage with DVR

Fig.5comparison of performance of DVR using Z-source inverter during fault condition

Fig.6Three phase voltage at load point during three phase fault with DVR and PI controller

Fig.7Three Phase voltage at load point during three phase fault with DVR and PI and Fuzzy controller



DVR fills in as a successful custom power gadget for relieving voltage hang/swell in the dispersion framework. If there should arise an occurrence of outside aggravations the proposed DVR infuses suitable voltage part to powerfully address any deviation in supply voltage so as to keep up adjusted and steady load voltage at ostensible esteem. In this paper Z – source inverter based DVR alongside fluffy controller is displayed and the equivalent is introduced in the conveyance framework to give required load side pay. The reenactment of the DVR alongside the proposed controller is completed utilizing MATLAB/SIMULINK stage. The reenactment results demonstrates that the execution of Z – source inverter based DVR alongside fluffy controller is better contrasted with PI controller.

Performance Investigation of Dynamic Voltage Restorer using PI and Fuzzy Controller


This paper researches the execution of Dynamic Voltage Restorer for repaying distinctive voltage droop levels with different flaws and to lessen the Total Harmonic Distortion amid the alleviation procedure. The DVR is actualized with three stage voltage source inverter and is associated at the purpose of normal coupling so as to direct the heap side voltage. The pay depends on PI and Mamdani Fuzzy Controller. Broad reproduction examines under various size of hang for flaws on load side for adjusted and lopsided conditions are directed utilizing shortcoming generator. Reenactment result investigation uncovers that DVR performs consummately with PI and Fuzzy control approach. What’s more, ability and execution of DVR for different vitality stockpiling limits and infusion transformer rating are additionally broke down. The execution of these controllers is approved with recreation results utilizing Matlab/Simulink.



Fig. 1. Block Diagram of DVR model



Fig.2 Unbalanced three-phase to ground fault (PI CONTROL)

Figure 3. Unbalanced three-phase to ground fault (FLC)

Fig.4 Single-line-to-ground fault with 50% sag (PI Control)

Fig.5 Single-line-to-ground fault with 50% sag (FLC)

Fig.6 Balanced three-phase fault with 50% sag (PI CONTROL)

Fig.7 Balanced three-phase fault with 50% sag (FLC)

Fig.8 Three Phase fault with nearly 100% sag (PI)

Fig.9 Three Phase fault with nearly 100% sag (FLC)



The DVR handles both adjusted and uneven conditions viably and infuses the digressed voltage part under supply unsettling influences to keep the heap voltage adjusted and consistent at the ostensible esteem. In this manner the proposed DVR can alleviate different dimensions of voltage hang and distinctive sorts of shortcomings. Reenactment results in MATLAB/SIMULINK demonstrate that the control conspire gives a precise following of the voltage reference and a quick transient reaction. Both the controllers shows great execution and limit the THD level. It is discovered that FLC gives better execution with THD of 0.42% where as PI gives 0.46% THD. The expansion in KVA rating of infusion transformer and DC stockpiling esteem successfully repays the voltage droop and diminish the THD level. Be that as it may, higher estimation of DC stockpiling and transformer rating makes it increasingly costly. The adequacy of a DVR framework basically relies on the rating of DC stockpiling limit, infusion transformer rating and the heap. From the recreation, it obviously demonstrates the significance of these components and how it influences the execution of DVR is dissected.

Maximum Power Point Tracking Using Fuzzy Logic Controller under Partial Conditions

Scientific Research Publishing, Smart Grid and Renewable Energy, 2015.

Maximum Power Point  ABSTRACT: This study proposes a fuzzy system for tracking the maximum power point of a PV system for solar panel. The solar panel and maximum power point tracker have been modeled using MATLAB/Simulink. A simulation model consists of PV panel, boost converter, and maximum power point tack MPPT algorithm is developed. Three different conditions are simulated: 1) Uniform irradiation; 2) Sudden changing; 3) Partial shading. Results showed that fuzzy controller successfully find MPP for all different weather conditions studied. FLC has excellent ability to track MPP in less than 0.01 second when PV is subjected to sudden changes and partial shading in irradiation.


  • Fuzzy Logic Controller
  • Maximum Power Point
  • Photovoltaic System
  • Partial Shading




Figure 1. Schematic diagram of PV system with MPPT.



Figure 2. P-V characteristics at different irradiations.

Figure 3. P-V characteristics when partial shading from 1000 to 600 Watt/m2.

Figure 4. Output of fuzzy at1000 Watt/m2.

Figure 5. Output of fuzzy controller. (a) Full shading from 600 to 300 Watt/m2; (b) Full shading from 700 to 400 Watt/m2; (c) Full shading from 900 to 400 Watt/m2; (d) Increasing shading from 300 to 800 Watt/m2.

Figure 6. Comparison between fuzzy and P & O partial shading (partial shading 1000 to 800 Watt/m2).


 In this study, FLC has been developed to track the maximum power point of PV system. PV panel, boost converter with FLC connected to a resistive load has been simulated using Matlab/Simulink. Simulation results have been compared to nominal power values. The proposed system showed its ability to reach MMP under uniform irradiation, sudden changes of irradiation, and partial shading. Simulation results have shown that using FLC has great advantages over conventional methods. It is found that Fuzzy controller always finds the global MPP. It is found that fuzzy logic systems are easily implemented with minimal oscillations with fast convergence around the desired MP


 [1] Devabhaktuni, V., Alam, M., Reddy Depuru, S.S.S., Green II, R.C., Nims, D. and Near, C. (2013) Solar Energy: Trends and Enabling Technologies. Renewable and Sustainable Energy Reviews, 19, 555-556. http://dx.doi.org/10.1016/j.rser.2012.11.024

[2] Bataineh, K.M. and Dalalah, D. (2012) Optimal Configuration for Design of Stand-Alone PV System. Smart Grid and Renewable Energy, 3, 139-147. http://dx.doi.org/10.4236/sgre.2012.32020

[3] Bataineh, K. and Dalalah, D. (2013) Assessment of Wind Energy Potential for Selected Areas in Jordan. Journal of Renewable Energy, 59, 75-81.

[4] Bataineh, K.M. and Hamzeh, A. (2014) Efficient Maximum Power Point Tracking Algorithm for PV Application under Rapid Changing Weather Condition. ISRN Renewable Energy, 2014, Article ID: 673840. http://dx.doi.org/10.1155/2014/673840

[5] International Energy Agency (2010) Trends in Photovoltaic Applications. Survey Report of Selected IEA Countries between 1992 and 2009. http://www.ieapvps.org/products/download/Trends-in Photovoltaic_2010.pdf

Maximum Power Point

A Comparative Study on the Speed Response of BLDC Motor Using Conventional PI Controller, Anti-windup PI Controller and Fuzzy Controller

A Comparative Study on the Speed Response of BLDC Motor Using Conventional PI Controller, Anti-windup PI Controller and Fuzzy Controller


Brushless dc motors (BLDC) are widely used for various applications because of high torque, high speed and smaller size. This type of motors are non linear in nature and are affected highly by the non-linearities like load disturbance. Speed control of this motor is traditionally handled by conventional PI and PID controllers. This paper presents the speed control of BLDC motor using anti wind up PI controller. Problems like rollover can arise in conventional PI controller due to saturation effect. In order to avoid such problems anti wind up schemes are introduced. As the fuzzy controller has the ability to control and as it is simple to calculate, a fuzzy controller is also designed for speed control of BLDC motor. The control and simulation of BLDC motor have been done using software MATLAB/SIMULINK. The simulation results using anti wind up PI controller and fuzzy controller are compared with PI controller.


  1. BLDC
  2. Speed response
  3. PI controller
  4. Fuzzy
  5. Anti windup






Fig.1. Simulation block diagram




Fig.2. Speed response under no load

Fig.3. Speed response for step increase in speed

Fig.4. Speed response for step increase in speed

Fig.5. Speed response under loaded condition

Fig.6.Speed response under load condition



 This paper presents the speed control of BLDC motor using anti wind up PI controller and fuzzy controller for three phase BLDC motor. The simulation results are compared with PI controller results. The conventional PI controller results are slower compared to fuzzy and anti wind up controllers. From the simulation results, it is clear that for the load variation anti wind up PI controller gave better response than conventional PI and fuzzy controller. Hence anti wind up PI controller is found to be more suitable for BLDC motor drive during load variation. It can also be observed from the simulation results that performance of fuzzy controller is better during the case of speed variation.



[1] R. Arulmozhiyal, R. Kandibanv, “Design of Fuzzy PID Controller for Brushless DC Motor”, in Proc. IEEE International Conference on Computer Communication and Informatics, Coimbatore, 2012.

[2] Anirban Ghoshal and Vinod John, “Anti-windup Schemes for Proportional Integral and Proportional Resonant Controller”, in Proc. National Power electronic conference, Roorkee, 2010.

[3] M. F. Z. Abidin, D. Ishak and A. Hasni Abu Hassan, “A Comparative Study of PI, Fuzzy and Hybrid PI Fuzzy Controller for Speed Control of Brushless DC Motor Drive”, in Proc. IEEE International conference on Computer applications and and Industrial electronics, Malysia, 2011.

[4] J. Choi, C. W Park, S. Rhyu and H. Sung, “Development and Control of BLDC Motor using Fuzzy Models”,in Proc. IEEE international Conference on Robotics, Automation and Mechatronics, Chengdu, 2004.

[5] C. Bohn and D.P. Atherton, “An analysis package comparing PID anti-windup strategies,” IEEE Trans. controls system, Vol.15, No. 2, pp.34-40, 1995.


Simulation and Control of Solar Wind Hybrid Renewable Power System


The sun and wind based generation are well thoroughly considered to be alternate source of green power generation which can mitigate the power demand issues. This paper introduces a standalone hybrid power generation system consisting of solar and permanent magnet synchronous generator (PMSG) wind power sources and a AC load. A supervisory control unit, designed to execute maximum power point tracking (MPPT), is introduced to maximize the simultaneous energy harvesting from overall power generation under different climatic conditions. Two contingencies are considered and categorized according to the power generation from each energy source, and the load requirement. In PV system Perturb & Observe (P&O) algorithm is used as control logic for the Maximum Power Point Tracking (MPPT) controller and Hill Climb Search (HCS) algorithm is used as MPPT control logic for the Wind power system in order to maximizing the power generated. The Fuzzy logic control scheme of the inverter is intended to keep the load voltage and frequency of the AC supply at constant level regardless of progress in natural conditions and burden. A Simulink model of the proposed Hybrid system with the MPPT controlled Boost converters and Voltage regulated Inverter for stand-alone application is developed in MATLAB.


  1. Renewable energy
  2. Solar
  3. PMSG Wind
  4. Fuzzy controller
  5. P&O



Figure 1. Block diagram of PV-Wind hybrid system


Figure 2. PV changing irradiation level

Figure 3. Output voltage for PV changing irradiation level

  Figure 4. Wind speed changing level

Figure 5. Output current wind

Figure 6. Output Voltage wind

Case 1 : PI voltage regulated inverter

Figure 7. Output voltage for inverter

Figure 8. Power generation of the hybrid system under varying wind speed and irradiation

Case 2 : fuzzy logic voltage regulated inverter

Figure 9. Output voltage for inverter

Figure 10. Power generation of the hybrid system under varying wind speed and irradiation


Nature has provided ample opportunities to mankind to make best use of its resources and still maintain its beauty. In this context, the proposed hybrid PV-wind system provides an elegant integration of the wind turbine and solar PV to extract optimum energy from the two sources. It yields a compact converter system, while incurring reduced cost.

The proposed scheme of wind–solar hybrid system considerably improves the performance of the WECS in terms of enhanced generation capability. The solar PV augmentation of appropriate capacity with minimum battery storage facility provides solution for power generation issues during low wind speed situations.

FLC voltage regulated inverter is more power efficiency and reliable compared to the PI voltage regulated inverter, in this context FLC improve the effect of the MPPT algorithm in the power generation system of which sources solar and wind power generation systems.


[1] Natsheh, E.M.; Albarbar, A.; Yazdani, J., “Modeling and control for smart grid integration of solar/wind energy conversion system,” 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe),pp.1-8, 5-7 Dec. 2011.

[2] Bagen; Billinton, R., “Evaluation of Different Operating Strategies in Small Stand-Alone Power Systems,” IEEE Transactions on Energy Conversion, vol.20, no.3, pp. 654-660, Sept. 2005

[3] S. M. Shaahid and M. A. Elhadidy, “Opportunities for utilization of stand-alone hybrid (photovoltaic + diesel + battery) power systems in hot climates,” Renewable Energy, vol. 28, no. 11, pp. 1741–1753, 2003.

[4] Goel, P.K.; Singh, B.; Murthy, S.S.; Kishore, N., “Autonomous hybrid system using PMSGs for hydro and wind power generation,” 35th Annual Conference of IEEE Industrial Electronics, 2009. IECON ’09, pp.255,260, 3-5 Nov. 2009.

[5] Foster, R., M. Ghassemi, and A. Cota, Solar energy: renewable energy and the environment. 2010, Boca Raton: CRC Press.