Online learning control for harmonics reduction based on current controlled voltage source power inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components. Shunt active filters U+0028 SAF U+0029 with current controlled voltage source inverters U+0028 CCVSI U+0029 are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents. However, CCVSI with traditional controllers have a limited transient and steady state performance. In this paper, we propose an adaptive dynamic programming U+0028 ADP U+0029 controller with online learning capability to improve transient response and harmonics. The proposed controller works alongside existing proportional integral U+0028 PI U+0029 controllers to efficiently track the reference currents in the d-q domain. It can generate adaptive control actions to compensate the PI controller. The proposed system was simulated under different nonlinear U+0028 three-phase full wave rectifier U+0029 load conditions. The performance of the proposed approach was compared with the traditional approach. We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison. The online learning based ADP controller not only reduced average total harmonic distortion by 18.41 U+0025, but also outperformed traditional PI controllers during transients.

[1]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[2]  Haibo He,et al.  Power System Stability Control for a Wind Farm Based on Adaptive Dynamic Programming , 2015, IEEE Transactions on Smart Grid.

[3]  Haibo He,et al.  A Theoretical Foundation of Goal Representation Heuristic Dynamic Programming , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Qinglai Wei,et al.  Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Haibo He,et al.  Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources , 2015, Neurocomputing.

[6]  Patrice Wira,et al.  A Unified Artificial Neural Network Architecture for Active Power Filters , 2007, IEEE Transactions on Industrial Electronics.

[7]  Changyin Sun,et al.  An Event-Triggered Approach for Load Frequency Control With Supplementary ADP , 2017, IEEE Transactions on Power Systems.

[8]  Derong Liu,et al.  Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems , 2016, IEEE Transactions on Cybernetics.

[9]  Frank L. Lewis,et al.  Reinforcement Learning and Approximate Dynamic Programming for Feedback Control , 2012 .

[10]  Hirofumi Akagi,et al.  Control Strategy of Active Power Filters Using Multiple Voltage-Source PWM Converters , 1986, IEEE Transactions on Industry Applications.

[11]  Marcelo A. Pérez,et al.  Analysis of Finite-Control-Set Model Predictive Current Control With Model Parameter Mismatch in a Three-Phase Inverter , 2016, IEEE Transactions on Industrial Electronics.

[12]  Xiong Luo,et al.  Stability of direct heuristic dynamic programming for nonlinear tracking control using PID neural network , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[13]  Shuhui Li,et al.  Training Recurrent Neural Networks With the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Paolo Mattavelli,et al.  Comparison of current control techniques for active filter applications , 1998, IEEE Trans. Ind. Electron..

[15]  Huaguang Zhang,et al.  Leader-Based Optimal Coordination Control for the Consensus Problem of Multiagent Differential Games via Fuzzy Adaptive Dynamic Programming , 2015, IEEE Transactions on Fuzzy Systems.

[16]  Shuhui Li,et al.  Nested-loop neural network vector control of permanent magnet synchronous motors , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[17]  Feng Liu,et al.  Incorporating approximate dynamic programming-based parameter tuning into PD-type virtual inertia control of DFIGs , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[18]  Derong Liu,et al.  Multibattery Optimal Coordination Control for Home Energy Management Systems via Distributed Iterative Adaptive Dynamic Programming , 2015, IEEE Transactions on Industrial Electronics.

[19]  Zhen Ni,et al.  Current control techniques for applications in virtual synchronous machines , 2016, 2016 IEEE 6th International Conference on Power Systems (ICPS).

[20]  Chao Lu,et al.  Direct Heuristic Dynamic Programming for Damping Oscillations in a Large Power System , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Haibo He,et al.  GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Mohd Amran Mohd Radzi,et al.  Neural Network and Bandless Hysteresis Approach to Control Switched Capacitor Active Power Filter for Reduction of Harmonics , 2009, IEEE Transactions on Industrial Electronics.

[23]  Haibo He,et al.  Goal Representation Heuristic Dynamic Programming on Maze Navigation , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Warren B. Powell,et al.  Handbook of Learning and Approximate Dynamic Programming , 2006, IEEE Transactions on Automatic Control.

[25]  Lorenzo Marconi,et al.  Robust nonlinear control of shunt active filters for harmonic current compensation , 2007, Autom..

[26]  Naresh Malla,et al.  Supplementary control for virtual synchronous machine based on adaptive dynamic programming , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[27]  Naresh Malla,et al.  Design of online supplementary adaptive dynamic programming for current control in power electronic systems , 2017, 2017 IEEE Energy Conversion Congress and Exposition (ECCE).

[28]  Derong Liu,et al.  Infinite Horizon Self-Learning Optimal Control of Nonaffine Discrete-Time Nonlinear Systems , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Derong Liu,et al.  Adaptive Dynamic Programming for Discrete-Time Zero-Sum Games , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Naresh Malla,et al.  Real-time cyber physical system testbed for power system security and control , 2017 .

[31]  Feng Liu,et al.  Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Haibo He,et al.  Adaptive Modulation for DFIG and STATCOM With High-Voltage Direct Current Transmission , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Jennie Si,et al.  Online learning control by association and reinforcement , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[34]  Frank L. Lewis,et al.  Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Convergence Analysis , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[35]  Haibo He,et al.  A three-network architecture for on-line learning and optimization based on adaptive dynamic programming , 2012, Neurocomputing.

[36]  Loi Lei Lai,et al.  Intelligent System Applications in Power Engineering: Evolutionary Programming and Neural Networks , 1998 .

[37]  Huaguang Zhang,et al.  An Overview of Research on Adaptive Dynamic Programming , 2013, Acta Automatica Sinica.

[38]  Jinyu Wen,et al.  Adaptive Learning in Tracking Control Based on the Dual Critic Network Design , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[39]  Haibo He,et al.  Multi-machine power system control based on dual heuristic dynamic programming , 2014, 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG).

[40]  Shuhui Li,et al.  Vector control of a grid-connected rectifier/inverter using an artificial neural network , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[41]  J. R. Vazquez,et al.  Active power filter control using neural network technologies , 2003 .

[42]  Bin Wu,et al.  Selective Harmonic Elimination Model Predictive Control for Multilevel Power Converters , 2017, IEEE Transactions on Power Electronics.

[43]  Jinyu Wen,et al.  An adaptive neuro-control approach for multi-machine power systems , 2016 .

[44]  Haibo He,et al.  Reactive power control of grid-connected wind farm based on adaptive dynamic programming , 2014, Neurocomputing.

[45]  Lei Yang,et al.  Direct Heuristic Dynamic Programming for Nonlinear Tracking Control With Filtered Tracking Error , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[46]  F. Blaabjerg,et al.  Detection is key - Harmonic detection methods for active power filter applications , 2007, IEEE Industry Applications Magazine.

[47]  Shuhui Li,et al.  An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances , 2014, Neural Networks.

[48]  Derong Liu,et al.  Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[49]  Gil D. Marques,et al.  An instantaneous active and reactive current component method for active filters , 2000 .

[50]  Eidy Hadadi.  Active Shunt Filter for Harmonic Mitigation in Wind Turbines Generators , 2013 .

[51]  Shuhui Li,et al.  Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[52]  J. Na,et al.  Online adaptive approximate optimal tracking control with simplified dual approximation structure for continuous-time unknown nonlinear systems , 2014, IEEE/CAA Journal of Automatica Sinica.

[53]  Feng Liu,et al.  A boundedness result for the direct heuristic dynamic programming , 2012, Neural Networks.

[54]  Feng Liu,et al.  Approximate dynamic programming based supplementary reactive power control for DFIG wind farm to enhance power system stability , 2015, Neurocomputing.