practical case simulation in MATLAB & Simulink are present using Simulink as a modeling environment and Neural Network Toolbox to create the neural ...
Power Op-Amp Based Active Filter Design with Self Adjustable Gain Control by Neural Networks for EMI Noise Problem Kayhan Gulez, Mehmet Uzunoglu, Omer Caglar Onar, and Bulent Vural, Yildiz Technical University, Faculty of Electrical and Electronics, Department of Electrical Engineering, 34349, Besiktas, Istanbul, Turkey {gulez, uzunoglu, conar, bvural}@yildiz.edu.tr
Abstract. An induction motor control system fed by an AC/DC rectifier and a DC/AC inverter group is a nonlinear and EMI generating load causes harmonic distortions and EMI noise effects in power control systems. In this paper, a simulation model is designed for the control circuit and the harmonic effects of the nonlinear load are investigated using FFT analyses. Also, the EMI noise generated by the switching-mode power electronic devices measured using high frequency spectrum scopes. An LISN based active filter is used to damp the harmonic distortions and EMI noises in the simulation environment. Neural network based control system is used to tune the power op-amp gain of series active filter to obtain the voltage value stability at the equipment side as well.
1 Introduction Recently, the broader use of power electronic based loads (rectifiers, inverters, motor control systems, etc.) has led to a growth of power pollution because of the nonlinear voltage-current characteristics of these loads. Current and voltage harmonics that are generated by the non-linear loads or by the switching devices in power electronics system may cause a serious damage to any electrical equipment [1]. Thus, load currents and voltages are nonsinusoidal and it is necessary to compensate voltage and current harmonics. Also, EMI noise caused by load characteristics and switching power electronic elements is an important problem for the network because of the conducted EMI emissions on the common mode line of the grid propagated by the switching elements. The compensation of these harmonics and EMI noise effects is recently being more and more important and causing widespread concern to the power system engineers have attracted special interests on active filtering. The AF is classified into two types as series and shunt active filter. The series type active filter is installed series to the nonlinear loads or harmonic generating loads and works as the harmonic compensation voltage source. The shunt type active filter is usually installed parallel to the loads. It works as the current source and compensates the harmonic current of the load [2]. D.S. Huang, X.-P. Zhang, G.-B. Huang (Eds.): ICIC 2005, Part I, LNCS 3644, pp. 243 – 252, 2005. © Springer-Verlag Berlin Heidelberg 2005
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In the engineering environment, EMI filters are called as “Black Magic” because there has not been a well defined design method, the input and the output impedances in the related circuit are not constant over the band of interest and the filter insertion lost test method specifications often confuse or influence the design methods [1, 3]. In this paper, a three-phase active filter topology based on a basic one introduced and developed in [1], [3], [4], [5], [6] is reviewed and used. In a series active filter application, usually the output voltage of the filter is lower than the input voltage of the filter because of the non-ideal cut-off characteristic of the filter. This non-ideal characteristic causes the filter to cut-off an amount of fundamental frequency component as leakage current. Also, the voltage drop on the filter and line impedances, occurs a lowered voltage at the load side. To provide the RMS voltage value stability at the equipment side, a self adjustable gain control mechanism is used in this study. In this paper, “Fast Back-propagation Algorithm” based artificial neural network architecture is taken as a control system to adjust the filter gain at fundamental frequency to maintain the voltage value stability as well. Artificial Neural Networks (ANN) is successfully used in a lot of areas such as control, DSP, state estimation and detection. Here, the results of a practical case simulation in MATLAB & Simulink are present using Simulink as a modeling environment and Neural Network Toolbox to create the neural network structure. 1.1 EMI Noise Effects EMI noises have bad effects on power system elements. EMI noises caused by the switching-mode semiconductor devices or load characteristics can be divided into two parts: Conducted EMI Emissions and Radiated EMI Emissions. In this study, conducted EMI emissions are investigated in the simulation environment. Generally, EMI noises causes [7], • Uncontrolled switching states on power electronic devices working near the EMI noise generating systems, • Instability or oscillation problems in control systems, • Parasitic effects on communication lines and data loss, • Encoder feedback failures in motor control systems, • Failures at programmable controller, • Problems in remote controlled I/O systems, • Wrong evaluations of sensing and measuring devices.
2 Simulation Network A converter – inverter based induction motor control system is taken as a harmonic and EMI noise source in this study. A general block diagram of the system is given below in Fig. 1.
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Fig. 1. Structure of the induction motor control system
The simulation network is modeled using MATLAB, Simulink and SimPowerSystems which provides an effective platform for dynamic system simulations [8, 9, and 10]. The Simulink model of the system is given in Fig. 2.
Fig. 2. Simulink model of the motor control system
3 EMI Measurement A brief review of conducted EMI measurement is important before the filter design stages. The Line Impedance Stability Network (LISN) is based on military standards and is used by many electromagnetic interference and electromagnetic compatibility test institutions. The LISN, required in the measurement, contains 56µH inductors, 22.5µF capacitors and 50Ω resistors. The single phase schematic of LISN circuit is given in Fig. 3. of which topology is used by widespread EMI and EMC applications [3, 4, 5 and 6].
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Fig. 3. Single phase schematic of LISN network
At EMI noise frequency, the inductors are essentially open circuit, the capacitors are essentially short circuit and the noise sees 50Ω measurement point. The noise voltage, measured from the 50Ω resistors contains both common-mode (CM) noise and differential-mode (DM) noise. Each mode of noise is dealt with by the respective section of an active filter.
4 Active Filter Topology As mentioned before, the active filter topology used in the paper are based on and developed in [1], [3], [4], [5], [6]. The active filter topology is depicted as a basic schematic in Fig. 4. The op-amp circuit is modeled in Simulink environment. In modeling procedure, the voltage controlled current source based equivalent circuit of an op-amp circuit taken as a sample. The Simulink model of the op-amp circuit is given below in Fig. 5.
Fig. 4. Active filter topology
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Fig. 5. The Simulink model of the equivalent op-amp circuit
The input and output impedance values and the internal gain of the op-amp are taken from a catalogue of a real power op-amp circuit especially used in industrial filtering applications. In the active filter topology, the capacitor Cr absorbs most of the current source of power supply generating a ripple voltage which must be dropped across the active circuit with a minimal ripple current conducted through the utility. This function is realized and achieved by the amplifier with gain A, input and output impedances Zin and Zout, respectively. The other problem with the circuit is that the full line voltage is across the output of the active circuit. It requires that Zout include a series blocking capacitor, CO. Since this capacitor CO appears in series with the output of the amplifier, its impedance is reduced by the magnitude of the amplifier’s gain. The other important condition of active EMI filter is the compensation of feedback loop. The point is that the loop transmission is simply the product of the amplifier gain and the negative feed back [1, 2].
5 Artificial Neural Networks for Gain Control A neural network can be trained to perform a particular function by adjusting the values of the connections (weights) between elements. Commonly neural networks are adjusted, or trained, so that a particular input leads to a specific target output. Such a situation is shown below. There, the network is adjusted, based on a comparison of the output and the target, until the network output matches the target. Typically many such input/target pairs are used, in this supervised learning, to train a network. The general diagram of such a training algorithm is given in Fig. 6 [11, 12].
Fig. 6. General diagram of neural network training structure
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In this study, a fast back-propagation NN algorithm with 2 hidden layers, which has 4 and 2 neurons in order, is used. Layers are tansig and output layer is purelin. Maximum epoch is 250 and wanted goal is 1e-3. The architecture of ANN controller is depicted in Fig. 7.
Fig. 7. Neural network structure control diagram
The neural network architecture including the layers is given in Fig. 8.
Fig. 8. Neural network layer and connection architecture
The Simulink diagram of the neural network structure is given in following figures. A general input/output structure of the neural network and weights connections are depicted in Fig. 9. and 10., respectively.
Fig. 9. I/O control structure of the neural network
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Fig. 10. Weights diagram
The gain value of the active filter is trained using the voltage values at the equipment side. To obtain the voltage value stability at the equipment side, the gain of the active filter is determined using the neural network algorithm given above. Some values of phase-ground RMS voltage of the load side used in the training process is given in Table 1. Table 1. Some values of training set of ANN
Voltage (V) 220 219 218 217 216 215
Gain 1 1.0046 1.0092 1.0138 1.0185 1.02223
The training performance is depicted in Fig. 11. including the goal and training performance of the neural network.
Fig. 11. Neural network training performance
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6 Simulation Results The simulation results are investigated in terms of two other criteria; the EMI noise suppress performance of the active filter and the neural networks based adaptive gain control performance of the filter, respectively. 6.1 Active Filter EMI Suppress Performance Analysis In the induction motor control system, the common mode EMI noises are distributed to the other power system and control components by the common mode line of the inverter output phases. The common mode current waveform including high frequency EMI noises is given in Fig. 12 which obtained as a simulation result.
Fig. 12. Common mode line current of induction motor control system
The spectrum analyzer result showing noise condition before and after the active filter is given in Fig. 13.a and Fig. 13.b, respectively.
Fig. 13.a and Fig. 13.b Spectrum analyzer results and EMI suppress performance of the filter
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6.2 Active Filter Gain Control Performance In the neural network based active filter gain control performance, another induction motor control system with the same motor and mechanic load parameters is connected to equipment side of the power system. Because of the extra power demand and noise effects of the second induction motor control system, the filter loss and the voltage drop on the line and filter impedances are increased and the equipment side RMS voltage value is decreased. To connect the other motor and the control system, a three-phase switch is used closing at simulation time t=0.5 second. The simulation system used to analyze the gain control performance is given below in Fig. 14.
Fig. 14. Simulation model of the neural network based gain control
As given in Fig. 12., the line voltmeter and a RMS value calculator is used to measure the line-end voltage. The RMS value of the voltage is used as an input to the neural network based control system and the neural networks determine the gain of the active filter to maintain the voltage stability. The RMS value of the line end voltage of the power system is given with and without the NN based gain controller in Fig. 15.a and Fig. 15.b respectively.
Fig. 15.a and Fig. 15.b Line end voltage with and without neural network controller
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7 Conclusions Current and voltage harmonics and EMI noise generated by the non-linear loads or by the switching devices in power electronics system may cause a serious damage to any electrical equipment. Also, EMI noise caused by switching power electronic elements is an important problem for the network. In this study, an active filter with self adjustable gain control by neural networks is designed for EMI noise problem in power control systems. The active filter gain is controlled by neural network algorithms to regulate the line end voltage which decreases under varying load conditions and filter loss. In the simulation results, not only the EMI noise effects suppressed but also the equipment side voltage is regulated. Acknowledgment. The research carried out by the support of Scientific Research Coordinatorship of Yildiz Technical University for the research projects numbered 24-04-02-01 and 24-04-02-03. The authors are thankful for the support of Scientific Research Coordinatorship of Yildiz Technical University.
References 1. Gulez, K., Mutoh, N., Ogata, M., Harashima, F., Ohnishi, K.: Source Current Type Active Filter Application With Double LISN For EMI Noise Problem In Induction Motor Control Systems, SICE Annual Conference (2002) International Conference on Instrumentation, Control and Information Technology, Osaka, JAPAN 2. Peng, F., Z., Kohata, M., Akagi, H.: Compensation Characteristics of Shunt Active and Series Active Filter, T.IEE Japan, Vol. 110-D, No.1 (1993) 3. Ozenbough, R., L.: EMI Filter Design Second Edition Revised and Expanded, Mercel Dekker, Inc., New York, USA (2001) 4. Gulez, K., Hiroshi, Watanabe, Harashima, F.: Design of ANN (Artificial Neural Networks) Fast Backpropagation Algorithm Gain Scheduling Controller of Active Filtering, Vol. 1 TENCON (2000) 307-312 5. Farkas, T.: A Scientific Approach to EMI Reduction, M.S. Thesis, Massachusetts Institute of Technology, Cambridge, MA, August (1991) 6. Farkas, T.: Viability of Active EMI Filters for Utility Applications, IEEE Trans. On Power Electronics, Vol. 9 (1994) 328-336 7. MTE Corporation: EMI/RFI Filters, EMI Sources, Standards and Solutions Online Manual (2000) 8. MATLAB The Language of Technical Computing: Getting Started with MATLAB Version 7, The MathWorks Inc (MATLAB, Simulink, SimPowerSystems and Neural Network Toolbox are registred trademarks of The MathWorks Inc.) 9. SimPowerSystems For Use with Simulink: User’s Guide Version 4, Hydro-Québec TransÉnergie Technologies, The MathWorks Inc. 10. Uzunoglu, M., Kızıl, A., Onar, O. C.: Her Yonu ile MATLAB 2e (Prificiency in MATLAB, 2e) (in Turkish), Turkmen Publication, Istanbul (2003) 11. Neural Network Toolbox: User’s Guide Version 4.0, The MathWorks Inc. 12. Karayiannis, N. B., Venetsanopoulas, A. N.: Artificial Neural Networks –Learning Algorithms, Performance Evaluation and Applications, Kluwer Academic Publishers (1994) 161-195
