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


Dynamic Voltage Restorer (DVR) is a custom power gadget utilized as a successful arrangement in shielding touchy burdens from voltage aggravations in power dissemination frameworks. The productivity of the control system, that directs the exchanging of the inverters, decides the DVR effectiveness. Corresponding Integral-Derivative (PID) control is the general method to do that. The power quality rebuilding capacities of this controller are constrained, and it produces critical measure of music – all of which comes from this straight procedure’s application for controlling non-direct DVR. As an answer, this paper proposes an Artificial Neural Network (ANN) based controller for improving rebuilding and sounds concealment abilities of DVR. A point by point examination of Neural Network controller with PID driven controller and Fuzzy rationale driven controller is additionally represented, where the proposed controller exhibited unrivaled execution with a unimportant 13.5% Total Harmonic Distortion.



Fig. 1 Simulation model for sag mitigation with ANN controller.



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 famous decision for upgrading power quality in power frameworks, with a variety of control framework on offer to drive these gadgets. In this paper, utilization of ANN to work DVR for giving preferable execution over existing frameworks to relieve voltage list, swell, and music has been illustrated. Issue articulation and hypothetical foundation, structure of the proposed strategy, preparing system of the ANN utilized have been portrayed in detail. Recreation results demonstrating the DVR execution amid voltage droop have been exhibited. Examination of the proposed technique with the well known PID controller, and nonlinear Fuzzy controller has been completed, where the proposed ANN controller showed up as the best choice to reestablish framework voltage while alleviating THD to the best degree.

Multiconverter Unified Power Quality Conditioning System Using Artificial Neural Network Technique


This paper displays another bound together power-quality molding framework (MC-UPQC), fit for synchronous pay for voltage and current in multibus/multifeeder frameworks. In this arrangement, one shunt voltage-source converter (shunt VSC) and at least two arrangement VSCs exist. The framework can be connected to neighboring feeders to adjust for supply-voltage and load current defects on the principle feeder and full pay of supply voltage blemishes on alternate feeders. In the proposed setup, all converters are associated consecutive on the dc side and offer a typical dc-connect capacitor. In this way, power can be exchanged from one feeder to neighboring feeders to adjust for droop/swell and intrusion. The execution of the MC-UPQC just as the received control calculation is outlined by recreation. The present work contemplate the pay standard and diverse control systems utilized here depend on PI and ANN Controller of the MC-UPQC in detail. The outcomes got in MATLAB/PSCAD on a two-transport/two-feeder framework demonstrate the viability of the proposed arrangement.



Fig.1.Block diagram of MC_UPQC with STATCOM



Fig2. BUS1 voltage,series compensating voltage, and load voltage in feeder1

Fig3.BUS2 voltage,series compensating voltage, and load voltage in feeder2

Fig 4.nonlinear load current,compensating current,feeder1 current and capacitor voltage

Fig 5.Bus1 loadcurrent,Bus2 load current,Bus1 load voltage,Bus2 load voltage wave forms using ANN controller in Mc- UPQC

Fig 6. Three phase source voltage(Va,Vb,Vc) wave form

Fig.7. load current with ANN controller

Fig.8 Load Voltage with ANN Controller


The present topology shows the activity and control of Multi Converter Unified Power Quality Conditioner (MCUPQC). The framework is reached out by including an arrangement VSC in a neighboring feeder. A reasonable numerical have been portrayed which builds up the way that in both the cases the pay is done yet the reaction of ANN controller is quicker and the THD is least for the both the voltage and current in delicate/basic load. The gadget is associated between at least two feeders originating from various substations. A non-straight/touchy load L-1 is provided by Feeder-1 while a delicate/basic load L-2 is provided through Feeder-2. The execution of the MC-UPQC has been assessed under different unsettling influence conditions, for example, voltage list/swell in either feeder, blame and load change in one of the feeders. If there should arise an occurrence of voltage list, the stage point of the transport voltage in which the shunt VSC (VSC2) is associated assumes an essential job as it gives the proportion of the genuine power required by the heap. The MC-UPQC can moderate voltage list in Feeder-1 and in Feeder-2 for long length.

Power Conditioning in Distribution Systems Using ANN Controlled Shunt Hybrid Active Power Filter

2014 IEEE

ABSTRACT: This paper focuses on an Artificial Neural Network (ANN) controller based Shunt Hybrid Active Power Filter (SHAPF) for compensating the harmonics of the distribution system. To enhance the performance of the conventional controller (Hysteresis controller) and to take advantage of intelligent controllers, a back propagation algorithm based feed forward-type ANN technique is implemented in shunt active power filter for producing the controlled pulses required for IGBT inverter. The proposed approach mainly work on the principle of energy stored by capacitor to maintain the DC link voltage of a shunt connected filter and thus reduces the transient response time when there is abrupt variation in the load. The complete power system set model of the proposed filter technique has been developed in MATLAB. The control algorithm developed is very simple. Simulations are carried out for the proposed scheme by using MATLAB, it is noticed that the %THD is reduced to 2.27% from 29.71% by ANN controlled filter. The simulated experimental results also show that the novel control method is not only easy to be computed and implemented, but also very successful in reducing harmonics.



  1. Shunt Hybrid Active Power Filter
  2. Total Harmonic Distortion (THD)
  3. Neural Network Controller
  4. Back propagation algorithm
  5. Distribution System.




shunt active power filter

Fig 1. Configuration of Shunt Active Power Filter



Fig.2. Wave forms of load current and source current of uncompensated system

Fig.3. FFT analysis of source current


Fig.4. Simulation results of Shunt Hybrid Active Filter with ANN Controller’


Fig.5. FFT analysis of source current with ANN controller



In this paper, a detailed analysis of Shunt Hybrid Active Power Filter with ANN controller has been proposed to mitigate harmonics of the three phase distribution system. The obtained results show the simplicity and the effectiveness of the proposed intelligent controller under nonlinear load conditions. From the results, it can be observed that the current total harmonic distortion reduces better with ANN controlled active filter. The simulation and experimental results also show that the new control method is not only easy to be calculated and implemented, but also very effective in reducing harmonics.



  • K.Jain, Agrawal and H.O.Gupta, “Fuzzy logic controlled shunt activepower filter for power quality improvement,” IEE proceedings in Electrical Power Applications, vol.149, no.5, Sept.2002.
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Artificial Neural Network based Three Phase Shunt Active Power Filter

2016 IEEE

ABSTRACT: This work describes artificial neural network (ANN) based control algorithm for three phase three wire shunt active power filter (SAPF) to compensate harmonics and improve power quality. System consists of three phase insulated gate bipolar transistors IGBT based current controlled voltage source inverter (CC-VSI), series coupling inductor and self supported DC bus. Increasing application of non-linear loads causes power quality problem. SAPF is one of the possible configurations to improve power quality. Traditional SAPF have PLL based unit template generator for extraction of fundamental signal. Traditional PLL needs to be tuned to obtain optimal performance for frequency estimation. It requires initial assumptions for fundamental frequency and minimum frequency. With varying frequency, it can’t be dynamically tuned for optimal performance. A new ANN based fundamental extraction based on Lavenberg Marquardt back propagation algorithm is proposed. Proposed SAPF is modeled in Simulink environment. Simulated results show the capability of proposed system.



  1. Shunt Active Power Filter
  2. Artificial Neural Networks
  3. Indirect Current Control Technique
  4. Power Quality




configuration block with SAPF

Fig.1. Proposed system configuration block with SAPF



Fig.2. Source voltages


Fig.3. Unbalanced load voltages


Fig.4. Unbalanced load currents


Fig.5. Simulation result for proposed system under non linear with

unbalance load condition


Fig.6. DC link voltage

Fig.7. Active power

Fig.8. Reactive power

Fig.9. Power factor

Fig.10. Harmonic spectrum of load current before compensation for three phase SAPF with non linear load

Fig.11. Harmonic spectrum of source currents (phase a, phase b phase c respectively) after compensation for ANN based three phase APF with non linear load

Fig.12. Harmonic spectrum of source currents (phase a) after compensation for ANN based three phase APF with non linear load with unbalance



ANN based phase-locking scheme has been proposed in this paper to control three phase-three wire shunt APFs. Widrow-Hoff weights updating algorithm has been incorporated to reduce calculation time in estimation of harmonic components. To validate effectiveness of proposed approach for real-time applications, indirect current control theory based controller has been developed. Design parameters of power circuit and control circuit have been calculated and robustness of proposed system has been established with Matlab/Simulink. Simulation result and spectral response show that, obtained source current THDs is below 5% as prescribed by IEEE-519 standard. Dynamic performance of proposed approach has been found satisfactory under sudden change in load and frequency.



  • Kumar and D.K. Palwalia, “Decentralized autonomous hybrid renewable power generation”, Journal of Renew. Energy, pp. 1-18, 2015.
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A Review on PFC Cuk Converter Fed BLDC Motor Drive Using Artificial Neural Network


In this paper a Power Factor Correction Cuk converter fed Brushless DC Motor Drive using a Artificial Neural Network is used. The Speed of the Brushless dc motor is controlled by varying the output of the DC capacitor. A Diode Bridge Rectifier followed by a Cuk converter is fed into a Brushless DC Motor to attain the maximum Power Factor. Here we are evaluating the three modes of operation in discontinuous mode and choosing the best method to achieve maximum Power Factor and to minimize the Total Harmonic Distortion. We are comparing the conventional PWM scheme to the proposed Artificial neural network. Here simulation results reveal that the ANN controllers are very effective and efficient compared to the PI and Fuzzy controllers, because the steady state error in case of ANN control is less and the stabilization if the system is better in it. Also in the ANN methodology the time taken for computation is less since there is no mathematical model. The performance of the proposed system is simulated in a MATLAB/Simulink environment and a hardware prototype of the proposed drive is developed to validate its performance.


  1. Brushless dc motor,
  2. Discontinuous input inductor mode
  3. Discontinuous output inductor mode
  4. Discontinuous intermediate capacitor mode
  5. Cuk converter
  6. Power Factor Correction
  7. Total Harmonic Distortion
  8. Artificial Neural Network
  9. Pulse width modulation



Proposed Scheme using Artificial Neural Network

Proposed Scheme using Artificial Neural Network

Fig 1.Proposed Scheme using Artificial Neural Network


Simulation Waveforms a) Input voltage (Vin) b) Input current (Iin) c) Output voltage(Vcd)

Fig 2.Simulation Waveforms a) Input voltage (Vin) b) Input current (Iin) c) Output voltage(Vcd)

)Speed(rpm) b)Electromagnetic torque(Nm) c)Power factor

Fig.3 a)Speed(rpm) b)Electromagnetic torque(Nm) c)Power factor

 Stator back emfs (Ea,Eb,Ec)

Fig 4 Stator back emfs (Ea,Eb,Ec)


A Power Factor Corrected Cuk converter fed BLDC motor using Artificial neural network is simulated in the environment of MATLAB. A Diode Bridge Rectifier followed by a Cuk converter is fed into a Brushless DC Motor to attain the maximum Power Factor. Here we are evaluating the three modes of operation in discontinuous mode and choosing the best method to achieve maximum Power Factor and to minimize the Total Harmonic Distortion.The three modes Discontinuos DICM(Li),DICM(Lo),DCVM(Vco) is simulated at the given switching frequency 20Khz.The diode bridge followed by a Cuk converter is used here for maximum Power Factor Correction.The power factor obtaine in ANN is 0.9818 which is near to unity. The main advantage of using Artificial neural network is that in conventional PI only one value that is feed back is selected and comparing and producing the gating pulse but in our proposed scheme a set of values is compared and we are choosing the best out of them.


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  3. Chen, C. Chiu, Y. Jhang, Z. Tang, and R. Liang, “A driver for the singlephase brushlessDCfan motor with hybrid winding structure,” IEEE Trans.Ind. Electron., vol. 60, no. 10, pp. 4369–4375, Oct. 2013.
  4. Nikam, V. Rallabandi, and B. Fernandes, “A high torque density permanent magnet free motor for in-wheel electric vehicle application,” IEEE Trans. Ind. Appl., vol. 48, no. 6, pp. 2287–2295, Nov./Dec. 2012.
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