Hopfield neural network based selective harmonic elimination for h bridge
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Hopfield neural network based selective harmonic elimination for h bridge Hopfield neural network based selective harmonic elimination for h bridge Document Transcript

  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 32 HOPFIELD NEURAL NETWORK BASED SELECTIVE HARMONIC ELIMINATION FOR H-BRIDGE INVERTER N. Veeramuthulingam1 , S. Sivajanani @ Santhoshma 2 and Thebinaa Venugopal 3 1 Department of Electrical and Electronics Engineering, Surya Group of Institution/ School of Engineering & Technology, Villupuram, India 2 Department of Electrical and Electronics Engineering, Manakula Vinayagar Institute of Technology, Pondicherry, India 3 Department of Electrical and Electronics Engineering, Manakula Vinayagar Institute of Technology, Pondicherry, India ABSTRACT The major consequence of the paper is about the selective harmonic elimination (SHE) of harmonics for the H-Bridge inverter output voltage waveform using artificial neural network (ANN). PWM inverters are of wide and great impact in no end of engineering disciplines. Its plays for an implant role in the domain of power electronics and many. These papers clarify the use of artificial neural network in gate signals control in Pulse width modulation voltage source inverter. In the SHE stand on inverter, the fundamental voltage and the harmonics chosen for deletion are unmistakable, using a neural network. For the SHE technique, the results of achieve switching angle patterns, using the ANN, for step on H-bridge inverter, show nearly accurate resemblance, when compared to those obtained using conventional methods. Also this technique has many advantages like quick response in choosing and generating the PWM patterns, bush delay time etc., which are vital to shape up the inverter output voltage. Keywords: H-bridge inverter, SHE-PWM, Hopfield Neural Network I. INTRODUCTION Power electronic converters are widely used in industrial power conversion systems both for utility and drives applications. The Power Quality and Reliability has become a major concern for electrical engineers. These harmonics are to be kept below a safe limit to avoid their detrimental effects and for maintaining power factor of the system. One of these systems is the Voltage source INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), pp. 32-41 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2013): 5.5028 (Calculated by GISI) www.jifactor.com IJEET © I A E M E
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 33 inverter, is used in various fields of power converters such as SMPS, HVDC, UPS, drives and tractions [1]. Voltage Source Inverter it’s nearly now to convert DC to AC supply at needed number of phases and values of voltage and frequency. Various PWM methods were used to generate the gate signals for switching the power switching devices in the inverter. The ultimate efficient method is the PWM that has ability to shape up the magnitude and frequency of inverter output voltage, and also eliminating serious harmonic components of the output voltage. This nature of Voltage Source Inverter, based on Pulse Width Modulation method, can be performed different types of approach. The fundamental, types are; natural sampling, suboptimal, optimal and harmonic elimination strategies. Every one of this type depends on an algorithm that generates the Gate Pulse which are used to drive the inverter switching power devices [2]. The most effective type is SHE technique, which eliminates low order harmonics from the spectrum and also make less the total harmonic distortion (THD) [3]. In order to obtain the PWM pattern, first the non linear equations of ANN technique should be solved. These non linear equations are formed by the unknown switching angles which are in turn decided by the number of harmonic components to be eliminated. These can be reach a goal in off line and stored as look up tables. Due to the description of nonlinearity between the switching angles and the fundamental of the PWM, a large number of look up tables are vital. These look up tables are stored in a programmable memory and using for example, microcomputer or microcontroller board which has been programmed to obtain the value of modulation index and generate the conformable switching angles. The problems of the predominant methods are the off line look up tables calculations, the choosing values of PWM pattern , or using the analogy and digital hardware because the solution of the equations for solving switching angles is challenging in on-line [4,9]. Therefore a propose techniques are applied by using the artificial neural network. The neural network methods are effective for this case because they imitate nonlinear and complex models and apply this theory by simulating it with MATLAB/SIMULINK program. Section II deals with the problem formulation. Section III comprises of the theoretical analysis of gate pulse generation using conventional Newton Rapshson algorithm. Section IV depicts gate pulse generation and the steps involved in the proposed Hopfield Neural network algorithm. Section V deals with the simulation of the H-bridge inverter using both the conventional and proposed algorithm along with the comparison of the simulated results. Section VI deals with the conclusion of the paper based on the comparison. II. PROBLEM FORMULATION The SHE-PWM technique is forthwith used to arrange an output voltage waveform of a full- bridge inverter. In this study, a three-level Selective Harmonic Elimination pulse pattern generated by an H-bridge inverter is considered. A H-bridge voltage source inverter, which involves four switches and a dc source, is depicted in Fig. 1. And, Fig. 2 shows a discover three-level SHE-PWM waveform, which was synthesized using the inverter circuit. The output waveform is chopped N number of times per quarter cycle. Every switch is therefore switched 2N times per half cycle to generate such a voltage waveform. The H-bridge inverter operates on Positive, Negative and zero. Consider the discover three – level SHE – PWM waveform shown in Fig. 2.
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July Figure 1. A Full ‘ Figure 2. Three By applying Fourier series to the above out 1 V ( ) sin( )n n t a n tω ω ∞ = = ∑ 1 1 4 ( 1) cos( ), N k n k k E a n for odd n n α+ = = − Π ∑ N is the number of the switching angles per quarter. the following condition: 1 2 3.... 2 kα α α α ∏ < < < < E is the amplitude of the dc source method is applied to solve the SHE PWM switching angles III. ANALYSIS OF GATE PULSE GENERATION USING NEWTON RAPHSON ALGORITHM 1) The switching angle matrix αj = [ α1 j , α2 j , α3 j , α4 j , α International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 34 Figure 1. A Full – Bridge Voltage Source Inverter Figure 2. Three – Level SHE PWM Waveform By applying Fourier series to the above model waveform, the output voltage is given by, a n for odd n N is the number of the switching angles per quarter. αk is the switching angles, which must satisfy and N is the harmonic order. In Section III, Newton Raphson method is applied to solve the SHE PWM switching angles. II. ANALYSIS OF GATE PULSE GENERATION USING NEWTON RAPHSON The switching angle matrix , α5 j ] T (1) International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – August (2013), © IAEME model waveform, the output voltage is given by, is the switching angles, which must satisfy and N is the harmonic order. In Section III, Newton Raphson II. ANALYSIS OF GATE PULSE GENERATION USING NEWTON RAPHSON (1) View slide
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 35 2) The nonlinear matrix Cos(α1 j )-Cos(α2 j )+Cos(α3 j )-Cos(α4 j )+Cos(α5 j ) Cos(3α1 j )-Cos(3α2 j )+Cos(3α3 j )-Cos(3α4 j ) +Cos(3α5 j ) F = Cos(5α1 j )-Cos(5α2 j )+Cos(5α3 j )-Cos(5α4 j )+Cos(5α5 j ) (2) Cos(7α1 j )-Cos(7α2 j )+Cos(7α3 j )-Cos(7α4 j )+Cos(7α5 j ) Cos(9α1 j )-Cos(9α2 j )+Cos(9α3 j )-Cos(9α4 j )+Cos(9α5 j ) Sin(α1 j )-Sin(α2 j )+Sin(α3 j )-Sin(α4 j )+Sin(α5 j ) Sin(3α1 j )-Sin(3α2 j )+Sin(3α3 j )-Sin(3α4 j )+Sin(3α5 j ) [ డ௙ డఈ ] = Sin (5α1 j )-Sin(5α2 j ) +Sin(5α3 j )-Sin(5α4 j )+Sin(5α5 j ) (3) Sin(7α1 j )-Sin(7α2 j )+Sin(7α3 j )-Sin (7α4 j )+Sin(7α5 j ) Sin (9α1 j )-Sin(9α2 j )+Sin (9α3 j )-Sin(9α4 j )+Sin(9α5 j ) 3) The harmonic amplitude matrix T= [ ሺ଴.଼ହሻగ ସ 0 0 0 0] T (4) Thus, equation (1) to (4) can be rewritten in the following matrix format: F (α) =T (5) By using matrices (1) to (4) and the Newton-Raphson method, the statement of algorithm is shown as follows: 1) Guess a set of initial values for αJ with j=o; Assume αj = [ α1 0 , α2 0 , α3 0 , α4 0 , α5 0 ] T (i) 2) Calculate the value of F (α0 ) = F0 (ii) 3) Linearize equation (4) about α0 F0 - [ డ௙ డఈ ] T dα0 = T (iii) dα0 = [ dα1 0 , dα2 0 , dα3 0 , dα4 0 , dα5 0 ] (iv) 4) Solve dα0 from equation (3), i.e. dα0 =INV [ డ௙ డఈ ] 0 (T-F0 ) (v) Where INV [ డ௙ డఈ ] 0 is the inverse matrix of [ డ௙ డఈ ] 0 5) As updated the initial data αj+1 = αj + dαj (vi) 6) Repeat the above process for equations (2) to (6) until dαj is satisfied the condition: α1< α2< α3< α4< α5< ௽ ଶ (vii) VI. ANALYSIS OF GATE PULSE GENERATION USING HOPFIELD NEURAL NETWORK ALGORITHM The implementation of the Hopfield neural network based SHE in single phase voltage source inverter is entertaining and it makes the neural network controls the magnitude of fundamentals harmonic (H1) easier in cases like even its value changes, this technique would make it returns to the desired value. A continuous Hopfield Neural Network is designed for the View slide
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July optimization of a set of non-linear transcendental equations given by (1) and (2). Fig. 5 shows the Hopfield network corresponding to five pulse converted into an optimization equation. Figure 3. Typical Processing unit of Artificial Neuron Min (Fk) = Where, Fk = Output to be optimized the network constraint Bpk(t+1) = For 0 Bk Where Bpk(t) represents an array of pattern Bpk(t) = activation of the j Bk = self-bias Wxy = connection Wxy= Wyx= for continuous Hopfield network for Optimal function; -0.7 ≤ Wxy N = learning Sgn = 1/(1-exp( International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 36 linear transcendental equations given by (1) and (2). Fig. 5 shows the Hopfield network corresponding to five pulse-positions. The set of equation mentioned above are converted into an optimization equation. Figure 3. Typical Processing unit of Artificial Neuron K Where, Fk = Output to be optimized the network equation is represented by (2) kjBpj(t)+Bk ) , represents an array of pattern = activation of the jth neuron at t = connection to the weight between neuron X and neuron = for continuous Hopfield network for Optimal function; xy ≤ 0.7 rate of the network exp(-B)), sigmoidal function International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – August (2013), © IAEME linear transcendental equations given by (1) and (2). Fig. 5 shows the positions. The set of equation mentioned above are equation is represented by (2) subject to given and neuron Y = for continuous Hopfield network for Optimal function;
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 37 Figure 4. Hopfield Neural Network Hopfield neural network algorithm Initialize the population P (0); while condition is not satisfied, Do Determine potential parents from current population P(t); Apply evolutionary operators, yielding offspring O(t); Obtain P(t +1) from P(t) U O(t); end-while; Return best candidate solution from current population. Figure 5. Block Diagram of Inverter The neural network is used to obtain pulse-positions such that the value of function Fk will be minimized. The node values are refreshing based on its immediate net weighted input. Fig. 6 shows the algorithm for Evolution Program. An Evolution Program is used to obtain appropriate connection weights between various nodes. The connection weights are optimized using the Evolution Program. V. SIMULATION RESULTS The MATLAB simulation results of the H-Bridge inverter employed with the gate pulse generated by the conventional Newton Raphson method and the proposed Hopfield Neural method are enlisted in this section. The parameters employed in the simulation are listed in Table 1. Pulse- positions given by the conventional Newton-Raphson methods are listed in Table 2. Table 3 lists a
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 38 few pulse-positions given by Hopfield neural network. Trigger pulses produced at the pulse- positions given by both the techniques resulted in variation of harmonic components and Total Harmonic Distortion. It is inferred that the ANN pulse –position give better harmonic reduction as compared to the NR pulse positions. The output obtained from the ANN controlled Inverter is fed to a single phase induction motor (IM). The parameters of IM are also enlisted in the Table 1. TABLE 1. SIMULATION PARAMETERS Parameters Values Input Voltage of inverter Output frequency of inverter Power rating of the IM Motor input frequency Motor input voltage 230V 50Hz 200W 50Hz 230V Table 2. Pulse Positions using ANN method Table 3. Pulse Positions using N-R method The gate pulse waveform obtained from the simulation of ANN controlled inverter is depicted in Fig. 6. The Fig. 7. Shows the output voltage waveform of the inverter which is followed by the FFT analysis of the Voltage waveform in bar and listed mode in Fig. 8. Figure 6. Trigger pulses produced by ANN Harmonics 1st 3nd 5th 7th 9th Switching Angle 22.58 33.60 46.64 68.49 75.09 Eliminate % 100 0.07 0.12 0.15 0.08 Harmonics 1st 3nd 5th 7th 9th Switching Angle 22.38 33.68 46.74 68.99 75.28 Eliminate % 100 0.7 0.5 0.9 0.8
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 39 Figure 7. Output Voltage of ANN controlled Inverter Figure 8. FFT analysis of the voltage waveform in Bar & listed mode
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 40 VI. CONCLUSION It is observed that the trigger pulse-positions given by SHE controller produces better quality of voltage at the Inverter output obtained from MATLAB simulation when compared to that of the conventional numerical technique of Newton-Raphson method. The Intelligent SHE controller can handle the complex non-linear transcendental equation set in a better manner producing the optimum trigger pulse-positions. It is further observed that the Induction motor load is also delivering better performance when driven by Intelligent SHE controlled Inverter. Thus this paper provides a detailed analysis of SHE by using ANN technique. REFERENCE [1] N.Mohan, T.M. Undelanad and W.P Robbins, “Power Electronics: Converter, Applications and Design, 2nd Edition, New York; Wiley, 1995. [2] Zainal Salam, “DC to AC conversion (inverters)”, Power Electronics and Drives (Version 2) 2002. [3] Mohammed H. Rashid, “Power Electronics”, Prentice –Hall of India Pvt. Ltd, 2nd Edition, 1994. [4] Bimal. K. Bose, Bernard Goodwin, Michelle Vincenti, “Modern power electronics and AC Drives”, United States of America, 2008. [5] Akagi H, “New Trends in active filters for power Conditioning” IEEE Transaction on India Applications, Vol. 2, No. 6, Nov-Dec.’96. [6] L. Gyugy, E. C. Strycula, “Active power Filters", IEEE / IAS Annual Conference Proceedings, 1976, pp 529-535. [7] H. Fujita, H. Akagi, “A Practical Approach to Harmonic Compensation in Power Systems - Series Compensation of Passive and Active Filters”, IEEE/IAS Conference. Proceedings, 1990, pp 1107-1112. [8] B.E. Kushare, A.M. Jain, “ Overview of Harmonics Injected in Power Systems Network, their Effects & Elimination Techniques by Using Filters”, 4th International Seminar on Power Electronics & Automation, Jan. 1999, Mumbai. [9] Do-Hyun Jang, Gyu-Ha Choe, M. Ehsani, “Asymmetrical PWM Technique with Harmonic Elimination and Power Factor Control in AC Choppers", IEEE Transactions on Power Electronics, Volume. 10, No. 2, March 1995, pp 175-184. [10] A.Oliva, H. Chiacchiarini, A. Aymonino and P. Mandolesi, “Reduction of Total Harmonic Distortion in Power Inverter”, Latin American Applied Research, Vol.35, No.2, June 2005. [11] Albert N.S. and M.M. Beno, “An Intelligent Controller for a non-linear Power Electronic Boost Converter”, IEE Proc. On Int. Conference on Energy, Information Tech., Power Sector, Kolkata, pp 232-235. [12] S. S. Ambekar, M.A. Chaudhari, “Artificial Neural Network Controller for Performance Optimization of Single Phase Inverter”, Int. Journal of Artificial Intelligence and Applications, Vol. 3, No.1, Jan.2012, pp 53-64. [13] Vinesh Kapadia and Dr. Hina Chandwani, “Comparison of Modulation Techniques for Cascaded H-Bridge Type Multilevel Current Source Inverter”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 2, 2013, pp. 181 - 190, ISSN Print: 0976-6480, ISSN Online: 0976-6499. [14] Anuradha Tomar and Dr. Yog Raj Sood, “All About Harmonics in Non-Linear PWM AC Drives”, International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 1, 2012, pp. 138 - 144, ISSN Print : 0976-6545, ISSN Online: 0976-6553.
  • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 41 BIOGRAPHIES N.Veeramuthulingam was born on 16th Feb 1988, in, Pondicherry India. He received his B.Tech degree in Electrical and Electronics Engineering from Pondicherry University in 2009 and M.Tech Degree in Electric Drives and Control from Pondicherry Engineering College in 2011. He presently works as Assistant Professor in the Department of Electrical Engineering at, Surya Group of Institutions/ School of Engineering & Technology, Tamilnadu, India. His research area includes Harmonics Analysis in power converter, efficient pulse width modulation strategies. S.Sivajanani @ Santhoshma received her B.Tech in Electrical and Electronics Engineering from Pondicherry University in 2010 and also she completed her M.Tech in Electrical Drives & Control in Pondicherry Engineering College in 2012. Presently, she is working as Assistant Professor in the Department of Electrical and Electronics Engineering, Manakula Vinayagar Institute of Technology, Pondicherry, India. Her area of interest includes power electronics, harmonic analysis. V. Thebinaa obtained her Bachelor degree in Electrical and Electronics and Master Degree in Power Systems Engineering from Annamalai University, Chidambaram. She is currently working as Assistant Professor in the Department of Electrical and Electronics Engineering, Manakula Vinayagar Institute of Technology, Pondicherry. Her area of interests is power system, power quality.