Application of Artificial Neural Networks for Shunt Active Power Filter Control

ABSTRACT:

Artificial neural network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward multilayer neural network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the nonlinear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN-based control algorithms for shunt APF. A step-by-step procedure to implement these ANN-based techniques in MATLAB/Simulink environment is provided. Furthermore, a detailed analysis on the performance, limitation, and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.

KEYWORDS:

1.      Adaptive Linear Neuron (ADALINE)

2.       Artificial neural network (ANN)

3.       Feed-forward multilayer neural network (MNN)

4.       Shunt active power filter (APF)

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

Fig. 1. Shunt APF system configuration.

CONTROL SYSTEM:

Fig. 2. ADALINE used to extract the fundamental active load current amplitude.

Fig. 3. Shunt APF control template using either MNN or ADALINE structures

SIMULATION RESULTS:

Fig. 4. Dynamic performance of the feed-forward MNN shunt APF for a trained

load scenario.

Fig. 5. Dynamic performance of the feed-forwardMNNshunt APF for untrained load scenario.

Fig. 6. Dynamic performance of the ADALINE shunt APF.

CONCLUSION:

In this paper, two widely used ANN-based shunt APF control strategies are investigated: 1) the ADALINE; and 2) the feed forward MNN. A simple step-by-step procedure is provided to implement each method in MATLAB/Simulink environment. The ADALINE is trained online by the LMS algorithm, while the MNN is trained offline using the SCG back propagation algorithm to extract the fundamental load active current magnitude. The performance of these ANN-based shunt APF controllers is evaluated through detailed simulation and experimental studies. Based on the study conducted in this paper, it is observed that the ADALINE-based control technique performs better than the feed-forward MNN. For untrained load scenario, the feed forward MNN fails to extract the fundamental component, resulting in overcompensation from the dc-link PI regulator. On contrary, the online adaptiveness of ADALINE makes it applicable to any load condition.

REFERENCES

[1] P. Kanjiya, V. Khadkikar, and H. H. Zeineldin, “A noniterative optimized algorithm for shunt active power filter under distorted and unbalanced supply voltages,” IEEE Trans. Ind. Electron., vol. 60, no. 12, pp.5376–5390, Dec. 2013.

[2] B. Singh, K. Al-Haddad, and A. Chandra, “A review of active filters for power quality improvement,” IEEE Trans. Ind. Electron., vol. 46, no. 5, pp. 960–971, Oct. 1999.

[3] M. Popescu, A. Bitoleanu, and V. Suru, “A DSP-based implementation of the p–q theory in active power filtering under nonideal voltage conditions,” IEEE Trans. Ind. Informat., vol. 9, no. 2, pp. 880–889, May 2013.

[4] V. Silva, J. G. Pinto, J. Cabral, J. L. Afonso, and A. Tavares, “Real time digital control system for a single-phase shunt active power filter,” in Proc. Conf. Rec. INDIN, 2012, pp. 869–874.

[5] A. Hamadi, S. Rahmani, and K. Al-Haddad, “Digital control of a shunt hybrid power filter adopting a nonlinear control approach,” IEEE Trans. Ind. Informat., vol. 9, no. 4, pp. 2092–2104, Nov. 2013.

Solar Grid-Tied Inverter, with Battery Back-up, for Efficient Solar Energy Harvesting

ABSTRACT:

Solar Grid-Tied Inverter system is an electricity generating system that is connected to the utility grid. This paper discusses the design of a Grid-Tied Inverter (GTI). The first stage is Maximum Power Point Tracking (MPPT) which is implemented using perturb and observe algorithm. Then push pull converter is used to convert DC output from MPPT stage to 330V dc. This DC voltage is then converted into AC voltage using full-wave inverter topology employing unipolar SPWM technique. Then synchronization is achieved between grid and photovoltaic system. Finally, power flow control mechanism controls the power flow from GTI system to the grid and the house load.

KEYWORDS:

1.      Grid-tied inverter

2.       SPWM

3.       Power flow

4.       MPPT

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

 

Figure 1. Cuk converter.

 

Figure 2. Full wave inverter topology .

EXPECTED SIMULATION RESULTS:

Figure 3. Inverter output before filter

Figure 4. Inverter output after filter.

Figure 5. Simulink circuit to demonstrate power flow.

Figure 6. GTI output power.

Figure 7. Grid output power.

Figure 8. Power through inductor.

Figure 9. GTI output power.

Figure 10. Grid output power.

Figure 11. Power through inductor.

             

REFERENCES:

[1] Power Electronics: Circuits, Devices and Applications, 3/E by M. H. Rashid, Prentice Hall, 2004J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.

[2] Electric Machinery Fundamentals by Stephen J. Chapman, 4^th Edition, McGraw-Hill, 2005K. Elissa, “Title of paper if known,” unpublished.

[3] M. A. Salam, Fundamentals of Power Systems, Alpha Science Oxford, UK International Ltd., 2009.

[4] T. Kwang and S. Masri, “Grid Tie Photovoltaic Inverter for Residential Application,” Modern Applied Science, vol. 5, No. 4, Aug. 2011, pp. 3-4, doi:10.5539/mas.v5n4p200

Sliding-Mode Control of Quasi-Z-Source Inverter with Battery for Renewable Energy System

ABSTRACT:

 

In order to meet the energy storage requirements, a battery unit is required for the voltage-fed quasi Z-source inverter (q ZSI) system in renewable energy applications. However, the order of the system will be increased accordingly, which make the control of the high order nonlinear systems more complicated. This paper presents a sliding mode current control based on fixed frequency operating with fast response and improved stability. Unlikely the conventional sliding mode control (SMC), the proposed controller engaged a fixed frequency SMC based on the equivalent control theory to cooperate the modulation index and shoot through duty ratio. By establishing the large-signal dynamic model, the system will obtain a wide operating range to adapt to the renewable energy system. Using linear approximation, the small-signal model near steady-state operating point will be obtained to analysis the stable working conditions of the control system. Compared to the conventional current mode controller, the proposed controller can achieve a faster response, lower current ripple and better stability for q ZSI when the supply and load variation is large. Experimental results are presented to validate the theoretical design and the effectiveness of the proposed controller.

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig. 1: Proposed q ZSI with battery energy storage system configuration

EXPECTED SIMULATION RESULTS:

Figure 2: Waveform of the output voltage Vout and the battery charging current Ibat of the qZSI with the proposed SM controller operating at input voltage Vin=100 V. (a) Simulation results, (b) experiment results

Figure 3: Waveform of the battery charging current Ibat response to a step change in the load current Ic from 0 A to 5 A. (a) Simulation results, (b) experiment results

Figure 4: Experiment results of the output voltage Vout and battery charging current Ibat of the qZSI (a) with the SM controller operating at the input voltage Vin=200 V, (b) with the PI controller operating at the input voltage Vin=100 V.

Figure 5: Experiment results of the battery charging current Ibat response of the qZSI with the proposed SM controller to a slowly change in the input voltage Vin. (A) With the proposed SM controller from 100 V to 200 V. (b) With the PI controller from 200 V to 100 V.

             

CONCLUSION:

A fast-response sliding mode controller operating at a fixed frequency has been proposed for the voltage-fed quasi Z-source inverter with battery energy storage unit. Various aspects of the controller are discussed in the paper, which includes the selection method of the sliding surface, the existence condition and stability properties analysis, and the control parameters design.

Since the SM controller is designed from the large-signal converter model, it is stable and robust to large parameter, line and load variation. This is also a major advantage over conventional current mode and voltage mode controllers which often fail to perform satisfactorily under parameter or large load variation because they are designed based on the linearized small-signal models. It is experimentally demonstrated that, with the proposed SM controller, the battery charging current of the qZSI has a faster response with a lower ripple over a wide range of operating conditions than the traditional PI controller.

Furthermore, the simulation and experimental results presented in the paper are in close agreement and have shown the achievement of a qZSI with a good charging current control accuracy and fast response for battery energy storage unit, as well as robustness under input voltage and load perturbation, thus validating the proposed design methodology. In this sense, the approach presented in this paper can be applied for a robust and accurate high order Quasi Z-Source conversion involving other output voltage amplitudes and frequencies by applying the design procedure presented in the paper, and changing the converter sinusoidal voltage reference accordingly

REFERENCES:

[1] P. Fang Zheng, "Z-source inverter," Industry Applications, IEEE Transactions on, vol. 39, pp. 504-510, 2003.

[2] P. Fang Zheng, et al., "Maximum boost control of the Z-source inverter," Power Electronics, IEEE Transactions on, vol. 20, pp. 833- 838, 2005.

[3] J. Anderson and F. Z. Peng, "Four quasi-Z-Source inverters," in Power Electronics Specialists Conference, 2008. PESC 2008. IEEE, 2008, pp. 2743-2749.

[4] L. Yuan, et al., "Quasi-Z-Source Inverter for Photovoltaic Power Generation Systems," in Applied Power Electronics Conference and Exposition, 2009. APEC 2009. Twenty-Fourth Annual IEEE, 2009, pp. 918-924.

[5] Bagen and R. Billinton, "Evaluation of Different Operating Strategies in Small Stand-Alone Power Systems," Energy Conversion, IEEE Transactions on, vol. 20, pp. 654-660, 2005.