MATLAB-Simulink Model Based Shunt Active Power Filter Using Fuzzy Logic Controller to Minimize the Harmonics


The issue of value electrical vitality gave to the clients has emerged. This is because of the expanding nearness in system of nonlinear loads.They establish a consonant contamination wellspring of the system, which produce numerous aggravations, and exasperate the ideal task of electrical types of gear. This work, proposed an answer for take out the sounds presented by the nonlinear burdens. It displays the investigation and reenactment utilizing Matlab Simulink of a active power filter (APF) repaying the sounds and receptive power made by nonlinear loads in unfaltering and in drifters. The convenience of the reenactment way to deal with APF is shown , have a superior power quality knowledge utilizing Matlab Simulink so as to grow new fuzzy logic controller based dynamic power channel.



Figure 1 Block diagram of Basic Active Power Filter



 Fig. 2 Three phase voltage and current waveform with non linear load

 Fig.3 THD analysis of three phase voltage waveform with nonlinear load

 Fig.4 Three phase voltages and current waveform with shunt active power filter with connected fuzzy logic controller

 Fig.5 THD analysis of voltages with shunt active power filter using fuzzy logic controller



The paper exhibits the utilization of the fuzzy logic controller to control the repaying voltage. The Mamdani max-min approach is utilized for the fluffy induction and the defuzzification technique, separately. The structure of info and yield enrollment for the fluffy rationale controller is essential for the framework execution. The reproduction results demonstrate that the fuzzy logic controller gives a decent execution to control the remunerating voltage of shunt dynamic power channel. The %THD of the voltages at PCC point can be pursued the IEEE Std. 519-1992.


Simulation Analysis of DVR Performance for Voltage Sag Mitigation


Voltage sag is truly one of intensity quality issue and it end up extreme to mechanical clients. Voltage hang can cause miss task to a few touchy electronic types of gear. That issue can be moderating with voltage infusion strategy utilizing custom power gadget, Dynamic Voltage Restorer (DVR). This paper presents displaying and investigation of a DVR with heartbeat width tweak (PWM) based controller utilizing Matlab/Simulink. The execution of the DVR relies upon the effectiveness of the control strategy associated with exchanging the inverter. This paper proposed two control procedures which is PI Controller (PI) and Fuzzy Logic Controller (FL). Complete outcomes are introduced to evaluate the execution of every controller as the best power quality arrangement. Different components that likewise can influence the execution and ability of DVR are displayed also.



 Figure1. DVR Modelling using Matlab/Simulink


Figure 2. (a) Injection voltage from DVR controlled by PI ; (b) injection voltage controlled by FL

Figure 3. (a) Output voltage at load 1 after injection voltage from DVR controlled by PI; (b) Output voltage at load 1after injection voltage controlled by FL.

Figure 4. (a) Injection voltage from DVR controlled by PI; (b) injection voltage controlled by FL.

Figure 5. (a) Output voltage at load 1 after injection voltage from DVR controlled by PI; (b) Output voltage at load 1after injection voltage controlled by FL.

Figure 6. THD generated when PI controller is applied

Figure 7. THD generated when FL controller is applied.


In this examination, the displaying and reenactment of DVR controlled by PI and FL Controller has been produced utilizing Matlab/Simulink. For both controller, the reenactment result demonstrates that the DVR repays the hang rapidly (70μs) and gives great voltage direction. DVR handles numerous types, adjusted and unequal blame with no troubles and infuses the proper voltage segment to address any blame circumstance happened in the supply voltage to keep the heap voltage adjusted and steady at the ostensible esteem. The two controllers demonstrate an incredible execution and create low THD (<5%). Notwithstanding, it very well may be seen that FL Controller gives better execution with THD produced with just 0.64% while PI created 1.68% THD. In any case, other a few factors that can influence the execution of DVR should be tended to for improvement of the yield voltage. These variables are the vitality stockpiling limit and transformer rating. From the recreation, it unmistakably demonstrates the significance of these two factors and how they influence the execution of DVR. Hence, with regards to usage, it is urgent to think about these elements, so the execution of DVR is enhanced.

Photovoltaic Based Dynamic Voltage Restorer with Energy Conservation Capability using Fuzzy Logic Controller


In this paper, a Photovoltaic based Dynamic Voltage Restorer (PV-DVR) is proposed to deal with profound voltage droops, swells and blackouts on a low voltage single stage private dispersion framework. It can recoup hangs up to 10%, swells up to 190% of its ostensible esteem. Else, it will work as a Uninterruptable Power Supply (UPS) when the utility network neglects to supply. It is likewise intended to diminish the use of utility power, which is produced from atomic and warm power stations. An arrangement infusion transformer is associated in arrangement with the heap while reestablishing voltage droop and swell and it is reconfigured into parallel association utilizing semiconductor switches when it is working in UPS and power saver mode. The utilization of high advance up dc-dc converter with high-voltage gain lessens the size and required power rating of the arrangement infusion transformer. It likewise enhances the dependability of the framework. The Fuzzy Logic (FL)  controller with two data sources keeps up the heap voltage by distinguishing  the voltage varieties through d-q change strategy. Reproduction results have demonstrated the capacity of the proposed DVR  in moderating the voltage list, swell and blackout in a low voltage single stage private appropriation framework.




Fig. 1. Structural block diagram of the proposed system.



  • (a) Supply Voltage
  • (b) Injected Voltage
  • (c) Load Voltage
  • (d) Load Current

(e) Load voltage THD

Fig. 2. Supply voltage, Injected voltage, Load voltage, Load Current and

Fig. 3. Load Voltage with PI controller

  • (a) PV array output voltage without low power boost converter

(b) PV array output voltage with low power boost converter

Fig. 4. PV array output voltage without and with boost converter

Fig. 5. Output voltage of the high step up DC-DC converter


This paper proposed another PV based DVR to lessen the vitality utilization from the utility network. The plan of a Dynamic Voltage Restorer (DVR) which consolidates a PV exhibit module with low and high power support converters as a DC voltage source to relieve voltage hangs, swells and blackouts in low voltage single stage conveyance frameworks utilizing FL controller has been introduced. The displaying and reenactment of the proposed PV based DVR utilizing MATLAB simulink has been exhibited. The FL controller uses the blunder motion from the comparator to trigger the switches of an inverter utilizing a sinusoidal PWM conspire. The proposed DVR uses the vitality drawn from the PV cluster and the utility source to charge the battries amid typical task. The put away energies in battery are changed over to a customizable single stage air conditioning voltage for alleviation of voltage list, swell and blackout. The recreation result demonstrates that the PV based DVR with FL controller gives better unique execution in alleviating the voltage varieties. The proposed DVR is worked in:

Reserve Mode: when the PV exhibit voltage is zero and the inverter isn’t dynamic in the circuit to hold the voltage to its ostensible esteem.

Dynamic Mode: when the DVR faculties the list, swell and blackout. DVR responds quick to infuse the required single stage pay voltages.

Sidestep Mode: when DVR is separated and skirted if there should arise an occurrence of support and fix.

Power Saver mode: when the PV cluster with low advance up dc-dc converter yield control is sufficient to deal with the heap.

Further work will incorporate a correlation with research facility investigates a low voltage DVR so as to think about recreation and trial results. The various elements of DVR require further examination.

Performance Improvement of DVR by Control of Reduced-Rating with A Battery Energy Storage


Performance improvement of Voltage infusion strategies for DVRs (Dynamic Voltage Restorers) and working modes are settled in this paper. Utilizing fuzzy logic control DVR with dc link& with Battery Energy Storage System frameworks are worked. Power quality issues for the most part consonant contortion, voltage swell and droop are diminished with DVR utilizing Synchronous Reference Theory (SRF hypothesis) with the assistance of fuzzificaton waveforms are watched.



 Fig.1.Block Diagram of DVR


Fig.2 Voltage waveforms at common coupling point (PCC) and load during harmonic distortion

Fig.3. the dc voltage injection from Battery energy Storage System connected DVR system at voltage swelling period

 Fig.4. DVR waveforms during voltage sag at time of voltage in phase injection

 Fig.5 Amplitude of load voltages and PCC voltages w.r.t time

 Fig 6.DVR waveforms during harmonic distortion at the time of voltage in phase injection


By applying distinctive voltage infusion conspires the job of DVR has been appeared with a most recent control strategy. The introduction of DVR has been offset with different plans with a decreased rating VSC. For gaining the power of DVR, the reference stack voltages have been resolved with the assistance of unit vectors, for which the blunder of voltage addition is diminished. By utilizing SRF hypothesis the reference DVR voltages have been resolved. At last, the outcome inferred are that the in stage voltage addition with PCC voltage diminishes the DVR rating and yet at its DC transport the vitality source is squandered. battery energy storage system. Performance Improvement of DVR by Control of Reduced-Rating with A Battery Energy Storage.


DVR with Fuzzy Logic Controller and Photovoltaic for Improving the Operation of wind farm


Wind control is a standout amongst the most imperative sort of sustainable power sources. Wind cultivate as a gadget which gets this vitality needs some exceptional conditions to work appropriately. The most widely recognized kind of wind turbine is the variable-speed straightforwardly associated with the matrix. Blames in the power framework can begin the detachment of wind ranches. Dynamic voltage reestablish (DVR) is a custom power gadget utilized for disposing of voltage sages and swells which is the aftereffect of the issues. This paper exhibits a reproduction model of a 12-beat DVR utilizing photovoltaic (PV) as a mean of giving an elective vitality source to the DVR. In this examination, the plan of a fluffy rationale (FL) controlled DVR are exhibited and reached out to perform quick blame identification. Another control technique for DVR is proposed by consolidating FL with a bearer adjusted PWM inverter. Recreations were completed utilizing the MATLAB SIMULINK. The recreation results demonstrated the ability of PV-based DVR in wiping out voltage droop and swell disseminated framework. Enhancing the task of wind cultivate as a vitality generator and balancing out its voltage is the principle consequence of this work.



Fig 1 General system



Fig. 2 supply voltage in swell condition

Fig.3 DVR injection voltage in swell condition

Fig.4 wind farm voltage in swell condition after compensation

Fig.5. supply voltage in sag condition

Fig.6 DVR injection voltage in sag condition

Fig.7 wind farm voltage after compensation

Fig.8. Wind farm current after compensation (in both sag and swell condition)



In this paper, A 12-beat DVR is planned and through utilizing new control technique all voltage hangs and swells in the circuit is commonly redressed. For this situation the terminal voltage which is associated with the breeze turbine remain consistent and in spite of the voltage flimsiness in system wind generators will have the capacity to stay associated with the system and work in stable condition through utilizing DVR. In this article we could remunerate an appropriation frameworks when droop and swell voltages happen in an exact and controlled way. This controlling strategy depends on fluffy control which is mimicked by Matlab/Simulink programming. Additionally in this paper to give a wellspring of DC DVR we have utilized PV which is a sort of normal vitality source. The reproduction results affirm all.

A Novel Dynamic Voltage Restorer with Outage Handling Capability Using Fuzzy Logic Controller


This paper exhibits a novel dynamic voltage restorer (DVR) equipped for taking care of profound hangs including blackout on a low voltage circulation framework. The DVR recoups droops up to 10% of ostensible voltage; else, it will work as a uninterruptible power supply (UPS). A transformer is associated in arrangement with the heap while reestablishing lists, and is reconfigured into parallel association utilizing switches when dealing with blackouts. The controller utilizes a Fuzzy Logic with 3 contributions to keep up the heap voltage through d-q change. Primer outcomes from research facility tests are additionally displayed in this paper.



Fig. 1. Block diagram of the proposed DVR



(a) Grid Voltage

(b) Load Voltage

(c) Inverter Voltage

Figure 2. The grid, load and inverter voltages under 3-phase fault

Figure 3. Grid and load voltages during 1-phase to ground fault



A novel dynamic voltage restorer utilizing Fuzzy Logic controller was shown to cure lists and blackout voltages. Stage bounces and lopsided conditions because of single stage to-ground blame can likewise be remunerated by the DVR. In case of blackout, where the voltage dips under 10%, the DVR reestablish the voltage into the typical. In this way, the heap voltage is unaffected by the unsettling influences including lists and blackouts.

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.

[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.

[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.

[5] International Energy Agency (2010) Trends in Photovoltaic Applications. Survey Report of Selected IEA Countries between 1992 and 2009. Photovoltaic_2010.pdf

Maximum Power Point

Mitigation of Voltage Sag and Swell in Transmission Line using DPFC with PI and Fuzzy Logic Control



The Power Quality problems during the last two decades has been the major concern of the power companies. The operation of power systems has become complex due to growing consumption and increased number of non-linear loads because of which compensation of multiple power quality issues has become an compulsion. A new component within the flexible AC-transmission system (FACTS) family, called Distributed Power-flow controller (DPFC) is presented in this paper. DPFC is derived from the unified power-flow controller (UPFC). DPFC can be considered as a UPFC with an eliminated common dc link. The active power exchange between the shunt and series converters, which is through the common dc link in the UPFC, is now through the transmission lines at the third-harmonic frequency. The DPFC employs the distributed FACTS (D-FACTS) concept, which is to use multiple small-size single-phase converters instead of the one large-size three-phase series converter in the UPFC. Power quality issues are studied and DPFC is used to mitigate the voltage deviation and improve power quality. In this paper, the capability of DPFC is observed for the transmission line based on PI and fuzzy logic controllers (FLC). On comparing the two controllers performance, we can say that Fuzzy Logic Controller based DPFC gives better compensation than PI Controller based DPFC. Simulink models are developed with and without the controllers. The three phase fault is created near the load. Simulation results show the effectiveness between the two controllers.


  1. Power Quality
  2. D-FACTS
  3. DPFC
  4. Voltage Sag
  5. Swell
  6. PI Controller
  7. Fuzzy Logic Controller



Fig 1: The DPFC Structure



Fig 2: Voltage Sag without DPFC

Fig 3: Current Swell without DPFC

Fig 4: THD without DPFC

Fig 5: Voltage sag Compensation with DPFC using PI Controller

Fig 6: Current Swell Compensation with DPFC using PI Controller

Fig 7: THD with DPFC using PI Controller

Fig 8: Voltage Sag Compensation with DPFC using Fuzzy Logic Controller

Fig 9: Current Swell Compensation with DPFC using Fuzzy Logic Controller

Fig 10: THD with DPFC using Fuzzy Logic Controller


In this study mitigation of power quality issues like voltage sag and swell are simulated in Matlab/Simulink environment employing a new FACTS device called Distributed Power Flow Controller(DPFC). The DPFC is emerged from the UPFC and inherits the control capability of the UPFC, which is the simultaneous adjustment of the line impedance, the transmission angle, and the bus voltage magnitude. The common dc link between the shunt and series converters, which is used for exchanging active power in the UPFC, is eliminated. This power is now transmitted through the transmission line at the third harmonic frequency. The series converter of the DPFC employs the D FACTS concept, which uses multiple small single phase converters instead of one large size converter. The reliability of the DPFC is greatly increased because of the redundancy of the series converters. The total cost of the DPFC is also much lower than the UPFC, because no high voltage isolation is required at the series converter part and the rating of the components of is low. It is proved that the shunt and series converters in the DPFC can exchange active power at the third harmonic frequency, and the series converters are able to inject controllable active and reactive power at the fundamental frequency .Also the performance of DPFC is simulated using two mechanisms i.e., with PI and Fuzzy Logic controllers.The results prove that the DPFC with Fuzzy controller gives better voltage compensation than DPFC with PI controller.


[1] Zhihui Yuan, Sjoerd W.H de Haan, Braham Frreira and Dalibor Cevoric “A FACTS Device: Distributed Power Flow Controller (DPFC)” IEEE Transaction on Power Electronics, vol.25, no.10,October 2010.

[2] Krishna Mohan Tatikonda,N.Swathi,K.Vijay Kumar”A Fuzzy Control scheme for damping of oscillations in multi machine system using UPFC” International trends for emerging trends in engineering and development on September 2012

[3] Y. H. Song and A. Johns. Flexible ac transmission systems (FACTS). Institution of Electrical Engineers, 1999.

[4] ” Power quality improvement and Mitigation case study using Distributed Power Flow Controller “Ahmad Jamshidi ,S.Masoud Barakati and Mohammad Moradi Ghahderijani,IEEE Transactions on,2012

[5] N.G.Hingorani and L.Gyugyi, Understanding FACTS, Concepts and Technology of Flexible AC Transmission Systems. Piscataway, NJ: IEEE Press 2000

Speed Controller of Switched Reluctance Motor


Fuzzy logic control has become an important methodology in control engineering. The paper proposes a Fuzzy Logic Controller (FLC) for controlling a speed of SRM drive. The objective of this work is to compare the operation of P& PI based conventional controller and Artificial Intelligence (AI) based fuzzy logic controller to highlight the performances of the effective controller. The present work concentrates on the design of a fuzzy logic controller for SRM speed control. The result of applying fuzzy logic controller to a SRM drive gives the best performance and high robustness than a conventional P & PI controller. Simulation is carried out using Matlab/Simulink.


KEYWORDS: P Controller, PI Controller, Fuzzy Logic Controller, Switched Reluctance Motor





Block diagram of SRM speed control

Figure 1. Block diagram of SRM speed control



Simulation model using P controller

Figure 2. Simulation model using P controller

Simulation model using PI controller.

Figure 3. Simulation model using PI controller.

Simulink model using FLC.

Figure 4. Simulink model using FLC.



Output flux.

Figure 5. Output flux.

Output current

Figure 6. Output current

Output torque

Figure 7. Output torque.


Figure 8. Speed.



Thus the SRM dynamic performance is forecasted and by using MATLAB/simulink the model is simulated. SRM has been designed and implemented for its speed control by using P, PI controller and AI based fuzzy logic controller. We can conclude from the simulation results that when compared with P & PI controller, the fuzzy Logic Controller meet the required output. This paper presents a fuzzy logic controller to ensure excellent reference tracking of switched reluctance motor drives. The fuzzy logic controller gives a perfect speed tracking without overshoot and enchances the speed regulation. The SRM response when controlled by FLC is more advantaged than the conventional P& PI controller.



  1. Susitra D, Jebaseeli EAE, Paramasivam S. Switched reluctance generator – modeling, design, simulation, analysis and control -a comprehensive review. Int J Comput Appl. 2010; 1(210):975–8887.
  2. Susitra D., Paramasivam S. Non-linear flux linkage modeling of switched reluctance machine using MVNLR and ANFIS. Journal of Intelligent and Fuzzy Systems. 2014; 26(2):759–768.
  3. Susitra D, Paramasivam S. Rotor position estimation for a switched reluctance machine from phase flux linkage. IOSR–JEEE. 2012 Nov–Dec; 3(2):7.
  4. Susitra D, Paramasivam S. Non-linear inductance modeling of switched reluctance machine using multivariate non- linear regression technique and adaptive neuro fuzzy inference system. CiiT International Journal of Artificial Intelligent Systems and Machine Learning. 2011 Jun; 3(6).
  5. Ramya A, Dhivya G, Bharathi PD, Dhyaneshwaran R, Ramakrishnan P. Comparative study of speed control of 8/6 switched reluctance motor using pi and fuzzy logic controller. IJRTE; 2012




Design and Control of SR Drive System using ANFIS


This paper presents the modeling and simulation of an adaptive neuro-fuzzy inference strategy (ANFIS) to control the speed of the switched Reluctance motor .The SRM control is thus a difficult to be in use in the nonlinear applications, particularly in the control of speed in automobiles. The Neuro-fuzzy system incorporates the advantages of both neural-network and fuzzy system. This controller is great additional effectual than Fuzzy logic and neural network based controller, while it has the ability of self-learning the gain values and acclimatizes accordingly to situations, thus accumulating more flexibility to the controller. A complete simulation, well-designed to the nonlinear model of Switched Reluctance Drive was premeditated using MATLAB/SIMULINK.



  1. SR Drive
  2. ANFIS
  3. ANN
  4. FLC




Block Diagram of ANFIS Controller for SRM Plant

Fig.1.Block Diagram of ANFIS Controller for SRM Plant




Fig.2: Response of the Speed and Torque Control of SRM using ANFIS with Speed Command 3000 Rpm under no load conditions.

Response of The Speed and Torque Control of SRM using Fuzzy, ANN and ANFIS with Speed command 4000 rpm.

Fig.3: Response of The Speed and Torque Control of SRM using Fuzzy, ANN and ANFIS with Speed command 4000 rpm.


Fig.4: Response of the Speed and Torque Control of SRM using ANFIS with Speed Command 4000 rpm.


Fig.5: Response of the speed control of SRM using FUZZY, ANN and ANFIS with speed Command 3000 RPM under load Conditions


Fig.6: Response of the speed and torque control of SRM using ANFIS with speed Command 3000 RPM under load conditions


In this paper, ANFIS-based controller was presented for SR drives. The speed and torque control method existing in this paper and comparing with the previous control schemes(fuzzy &ANN), while it can be used in both no load and load operating speeds and conditions including speed and torque transients, zero-speed standstill, and startup, and does not suppose the linear characteristics of the SR motor. Moreover, the proposed technique does not need of complex calculations to be carried out during the real-time operation, and no complex mathematical model of the SR motor is required. A main thought in the research was the robustness and reliability of the speed controlling method.



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