Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults

This study presents a reactive power controller using Probabilistic Wavelet Fuzzy Neural Network (PWFNN) for grid-connected three-phase PhotoVoltaic (PV) system during grid faults. The controller also considers the ratio of the injected reactive current to meet the Low Voltage Ride Through (LVRT) regulation. Moreover, the balance of the active power between the PV panel and the grid-connected inverter during grid faults is controlled by the dc-link bus voltage. Furthermore, to reduce the risk of over-current during LVRT operation, a current limit is predefined for the injection of reactive current. The main contribution of this study is the introduction of the PWFNN controller for reactive and active power control that provides LVRT operation with power balance under various grid fault conditions. Finally, some experimental tests are realized to validate the effectiveness of the proposed controller.

[1]  Whei-Min Lin,et al.  A New Elman Neural Network-Based Control Algorithm for Adjustable-Pitch Variable-Speed Wind-Energy Conversion Systems , 2011, IEEE Transactions on Power Electronics.

[2]  Fang Zheng Peng,et al.  Reactive power and harmonic compensation based on the generalized instantaneous reactive power theory for three-phase power systems , 1996 .

[3]  Xiaoqiang Guo,et al.  Asymmetrical Grid Fault Ride-Through Strategy of Three-Phase Grid-Connected Inverter Considering Network Impedance Impact in Low-Voltage Grid , 2014, IEEE Transactions on Power Electronics.

[4]  M. Liserre,et al.  Reactive Power Control for Improving Wind Turbine System Behavior Under Grid Faults , 2009, IEEE Transactions on Power Electronics.

[5]  Francesco Grimaccia,et al.  Hybrid model for hourly forecast of photovoltaic and wind power , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[6]  Z. Gaing.  Wavelet-based neural network for power disturbance recognition and classification , 2004 .

[7]  Chi-Huang Lu,et al.  Wavelet Fuzzy Neural Networks for Identification and Predictive Control of Dynamic Systems , 2011, IEEE Transactions on Industrial Electronics.

[8]  Faa-Jeng Lin,et al.  DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger , 2012, IEEE Transactions on Power Electronics.

[9]  Vinod Khadkikar,et al.  Application of Artificial Neural Networks for Shunt Active Power Filter Control , 2014, IEEE Transactions on Industrial Informatics.

[10]  S. Bacha,et al.  Space Vector Method for Voltage Dips and Swells Analysis , 2009, IEEE Transactions on Power Delivery.

[11]  De-Shuang Huang,et al.  A Hybrid Forward Algorithm for RBF Neural Network Construction , 2006, IEEE Transactions on Neural Networks.

[12]  Juan C. Vasquez,et al.  Flexible Voltage Support Control for Three-Phase Distributed Generation Inverters Under Grid Fault , 2013, IEEE Transactions on Industrial Electronics.

[13]  Miguel Castilla,et al.  Control Scheme With Voltage Support Capability for Distributed Generation Inverters Under Voltage Sags , 2013, IEEE Transactions on Power Electronics.

[14]  Faa-Jeng Lin,et al.  Fault-Tolerant Control of a Six-Phase Motor Drive System Using a Takagi–Sugeno–Kang Type Fuzzy Neural Network With Asymmetric Membership Function , 2013, IEEE Transactions on Power Electronics.

[15]  P. Rodriguez,et al.  Enhanced Decoupled Double Synchronous Reference Frame Current Controller for Unbalanced Grid-Voltage Conditions , 2012, IEEE Transactions on Power Electronics.

[16]  Frede Blaabjerg,et al.  A modified P&O MPPT algorithm for single-phase PV systems based on deadbeat control , 2012 .

[17]  Frede Blaabjerg,et al.  Flexible Active Power Control of Distributed Power Generation Systems During Grid Faults , 2007, IEEE Transactions on Industrial Electronics.

[18]  Marco Liserre,et al.  Grid Converters for Photovoltaic and Wind Power Systems , 2011 .

[19]  N. Pindoriya,et al.  An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets , 2008, IEEE Transactions on Power Systems.

[20]  J. Miret,et al.  Control Scheme for Photovoltaic Three-Phase Inverters to Minimize Peak Currents During Unbalanced Grid-Voltage Sags , 2012, IEEE Transactions on Power Electronics.

[21]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[22]  Zhi Liu,et al.  A probabilistic fuzzy logic system for modeling and control , 2005, IEEE Transactions on Fuzzy Systems.

[23]  Zhi Liu,et al.  A Probabilistic Neural-Fuzzy Learning System for Stochastic Modeling , 2008, IEEE Transactions on Fuzzy Systems.

[24]  Stavros A. Papathanassiou,et al.  A review of grid code technical requirements for wind farms , 2009 .

[25]  Rong-Jong Wai,et al.  Sliding-mode-controlled slider-crank mechanism with fuzzy neural network , 2001, IEEE Trans. Ind. Electron..

[26]  M Castilla,et al.  Grid-Fault Control Scheme for Three-Phase Photovoltaic Inverters With Adjustable Power Quality Characteristics , 2010, IEEE Transactions on Power Electronics.

[27]  Hirofumi Akagi,et al.  Instantaneous Reactive Power Compensators Comprising Switching Devices without Energy Storage Components , 1984, IEEE Transactions on Industry Applications.

[28]  S. Sathiyamoorthy,et al.  Design and implementation of probabilistic fuzzy logic control system , 2012, 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET).

[29]  Y.S. Abu-Mostafa,et al.  Information theory, complexity and neural networks , 1989, IEEE Communications Magazine.

[30]  F. Sheikholeslam,et al.  Fuzzy Wavelet Neural Network With an Accelerated Hybrid Learning Algorithm , 2012, IEEE Transactions on Fuzzy Systems.