Observer-based neural network dynamic surface control of single-phase LCL-filtered grid-connected inverters under parametric uncertainties and weak grid condition
Restricted accessResearch articleFirst published online November, 2021
Observer-based neural network dynamic surface control of single-phase LCL-filtered grid-connected inverters under parametric uncertainties and weak grid condition
This paper focuses on the current control of single-phase LCL-filtered grid-connected inverters in the presence of parametric uncertainties and weak grid condition. Therefore, a novel neural network dynamic surface control (NNDSC) method is proposed by overcoming the problem of ‘explosion of complexity’. In addition, radial basis function neural networks (RBFNNs) are employed to approximate the system parametric uncertainties. Furthermore, by considering practical considerations, a novel state observer (SO) is designed to estimate the inverter-side current and capacitor voltage. As a result, additional current and voltage sensors are not required, which makes the implementation of the proposed approach straightforward and reliable. The origin neighbourhood convergence of estimated and tracking errors is assured through Lyapunov stability theorem and Young’s inequality. The effectiveness and performance of the proposed NNDSC+SO approach is demonstrated through MATLAB/Simpower simulations in view of the reference current changes, LCL filter parametric uncertainties and weak grid condition.
AltinNOzdemirSKomurcugilHSefaI (2019) Sliding-mode control in natural frame with reduced number of sensors for three-phase grid-tied LCL-interfaced inverters. IEEE Transactions on Industrial Electronics66(4): 2903–2913.
2.
BaoCLRuanXBWangXH, et al. (2014) Step-by-step controller design for LCL-type grid-connected inverter with capacitor-current feedback active damping. IEEE Transactions Power Electronics29(3): 1239–1253.
3.
BeresRNWangXLiserreM, et al. (2015) A review of passive power filters for three-phase grid-connected voltage-source converters. Electric Journal of Emerging and Selected Topics in Power Electronics4(1): 54–69.
4.
BüyükMTanATümayM, et al. (2016) Topologies, generalized designs, passive and active damping methods of switching ripple filters for voltage source inverter: A comprehensive review. Renewable and Sustainable Energy Reviews62: 46–69.
5.
DannehJFuchsFWThøgersenPB (2010) PI state space current control of grid-connected PWM converters with LCL filter. IEEE Trans Power Electron25(9): 2320–2330.
6.
DannehJLiserreMFuchsFW (2011) Filter-based active damping of voltage source converters with LCL filter. IEEE Trans Ind Electron58(8): 3623–3633.
7.
DannehJWesselsCFuchsFW (2009) Limitations of voltage-oriented PI current control of grid-connected PWM rectifiers with LCL filters. IEEE Trans Ind Electron56(2): 380–388.
8.
DelghaviMBShoja-MajidabadSYazdaniA (2016) Fractional-order sliding-mode control of islanded distributed energy resource systems. IEEE Transactions on Sustainable Energy7: 1482–1491.
9.
DharSDashPK (2016) Adaptive backstepping sliding mode control of a grid interactive PV-VSC system with LCL filter. Sustainable Energy, Grids and Networks6: 109–124.
10.
EldeebHMassoudAAbdel-KhalikAS, et al. (2017) A sensorless Kalman filter-based active damping technique for grid-tied VSI with LCL filter. Electrical Power and Energy Systems93: 146–155.
11.
FangJLiXYangX, et al. (2017) An integrated trap-LCL filter with reduced current harmonics for grid-connected converters under weak grid conditions. IEEE Transactions on Power Electronics32: 8446–8457.
12.
GuoBSuMWangH, et al. (2020) Observer-based second-order sliding mode control for grid-connected VSI with LCL-type filter under weak grid. Electric Power Systems Research183: 106270.
13.
GuoQZhangYCellerBG, et al. (2019) Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint. IEEE Trans on Neural Networks and Learning Systems30(12): 3572–3583.
14.
GuoQChenZ (2021) Neural adaptive control of single-rod electrohydraulic system with lumped uncertainty. Mechanical Systems and Signal Processing146: 106869.
15.
GuzmanRGarcia de VicunaLCastillaM, et al. (2018) Variable structure control for three-phase LCL-filtered inverters using a reduced converter model. IEEE Trans on Industrial Electronics65(1): 5–15.
16.
GuzmanRGarcia de VicunaLMoralesJ, et al. (2016) Sliding-mode control for a three-phase unity power factor rectifier operating at fixed switching frequency. IEEE Trans Power Electron31(1): 758–769.
17.
GuzmanRGarcia de VicunaLMoralesJ, et al. (2017) Model-based active damping control for three-phase voltage source inverters with LCL filter. IEEE Transactions on Power Electronics32(7): 5637–5650.
18.
HaddadAGHumaisyMAAl-DurraA (2019) Investigation of different controllers and observers combinations for grid-tied LCL filter. IEEE 28th International Symposium on Industrial Electronics (ISIE). IEEE, 896–901.
19.
HaoXYangXLiuT, et al. (2013) A sliding-mode controller with multiresonant sliding surface for single-phase grid-connected VSI with an LCL filter. IEEE Trans Power Electron28(5): 2259–2268.
20.
HuangMLiHWuW, et al. (2019) Observer-based sliding mode control to improve stability of three-phase LCL-filtered grid-connected VSIs. Energies12: 1421.
21.
HuJShangLHeY, et al. (2011) Direct active and reactive power regulation of grid-connected DC/AC converters using sliding mode control approach. IEEE Transactions on Power Electronics26(1): 210–222.
22.
IEEE Guide (1997) IEEE guide for planning DC links terminating at AC locations having low short-circuit capacities. IEEE Std. 1204–1997, Tech. Rep.
23.
KeighobadiJHosseini-PishrobatMFarajiJ (2020) Adaptive neural dynamic surface control of mechanical systems using integral terminal sliding mode. Neurocomputing379: 141–151.
24.
KukkolaJHinkkanenM (2014) Observer-based state-space current control for a three-phase grid-connected converter equipped with an LCL filter. IEEE Transactions on Industry Application50(4): 2700–2709.
25.
LiSFuXRamezaniM, et al. (2015a) A novel direct-current vector control technique for single-phase inverter with L, LC and LCL filters. Electric Power Systems Research125: 235–244.
26.
LiSJaithwaISuftahR, et al. (2015b) Direct-current vector control of three-phase grid-connected converter with L, LC, and LCL filters. Electric Power Components and Systems43(14): 1644–1655.
27.
LongLZhaoJ (2017) Switched-observer-based adaptive neural control of MIMO switched nonlinear systems with unknown control gains. IEEE Trans on Neural Networks and Learning Systems28(7): 1696–1709.
28.
Mahdian DehkordiNSadatiNHamzehM (2017a) A backstepping high-order sliding mode voltage control strategy for an islanded microgrid with harmonic/interharmonic loads. Control Engineering Practice58: 150–160.
29.
Mahdian DehkordiNSadatiNHamzehM (2017b) A robust backstepping high-order sliding mode control strategy for grid-connected DG units with harmonic/interharmonic current compensation capability. IEEE Transactions on Sustainable Energy8(2): 562–571.
30.
Mahdian DehkordiNSadatiNHamzehM (2017c) Robust backstepping control of an interlink converter in a hybrid AC/DC microgrid based on feedback linearization method. International Journal of Control90(9): 1990–2004.
31.
MaoYYangY (2016) Backstepping sliding mode control of grid connected inverters. International Journal of Electronics Letters 10.1080/21 681724.2016.1216174.
32.
MalinowskiMBernetS (2008) A simple voltage sensorless active damping scheme for three-phase PWM converters with an LCL filter. IEEE Trans Ind Electron55(4): 1876–1880.
33.
Peña-AlzolaRLiserreMBlaabjergF, et al. (2014) A self-commissioning notch filter for active damping in a three-phase LCL filter-based grid-tie converter. IEEE Transactions Power Electronics29(12): 6754–6761.
34.
Said-RomdhaneMBNaouarMWBelkhodjaIS, et al. (2017) An improved LCL filter design in order to ensure stability without damping and despite large grid impedance variations. Energies10: 336.
35.
ShiXChengYYinC, et al. (2019) Design of adaptive backstepping dynamic surface control method with RBF neural network for uncertain nonlinear system. Neurocomputing330: 490–503.
36.
Shoja-MajidabadSHajizadehA (2020) Decentralized adaptive neural network control of cascaded DC–DC converters with high voltage conversion ratio. Applied Soft Computing Journal86: 105878.
37.
SongSZhangBSongX, et al. (2019) Adaptive neuro-fuzzy backstepping dynamic surface control for uncertain fractional-order nonlinear systems. Neurocomputing360: 172–184.
38.
SuMChengBSunY, et al. (2019) Single-sensor control of LCL-filtered grid-connected inverters. IEEE Access7: 38481–38494.
39.
SwaroopSHedrickJKYipPP, et al. (2000) Dynamic surface control for a class of nonlinear systems. IEEE Trans Autom Control45(10): 1893–1899.
40.
TangYLohPCWangP, et al. (2012) Generalized design of high performance shunt active power filter with output LCL filter. IEEE Trans Ind Electron59(3): 1443–1452.
41.
TranTVChunTVLeeHH, et al. (2014) PLL-based seamless transfer control between grid-connected and islanding modes in grid-connected inverters. IEEE Transactions on Power Electronics29: 5218–5228.
42.
WangBXuYShenZ, et al. (2017) Current control of grid-connected inverter with LCL filter based on extended-state observer estimations using single sensor and achieving improved robust observation dynamics. IEEE Trans Ind Electron64(7): 5428–5439.
43.
WangDHuangJ (2005) Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Trans Neural Network16(1): 195–202.
44.
WuELehnPW (2006) Digital current control of a voltage source converter with active damping of LCL Resonance. IEEE Trans Power Electron21(5): 1364–1373.
45.
WuWHeYTangT, et al. (2013) A new design method for the passive damped LCL - and LLCL-filter based single-phase grid-tied inverter. IEEE Transactions Power Electronics60(10): 4339–4350.
46.
YaoWYangYZhangX, et al. (2017) Design and analysis of robust active damping for LCL filters using digital notch filters. IEEE Transactions Power Electronics32(3): 2360–2375.
47.
YinHDaiZLeiX, et al. (2019) Grid current low-order harmonics suppression of the three-phase grid converter with an LCL filter under the distorted grid voltage. J Eng4675–4680.
48.
YoonSJLaiNBKimKH (2018) A systematic controller design for a grid-connected inverter with LCL filter using a discrete-time integral state feedback control and state observer. Energies11: 437.
49.
YuJShiPDongW, et al. (2014) Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors. IEEE Trans on Neural Networks and Learning Systems26: 640–645.
50.
YuZLiS (2014) Neural-network-based output-feedback adaptive dynamic surface control for a class of stochastic nonlinear time-delay systems with unknown control directions. Neurocomputing129: 540–547.
51.
ZhangJJ (2019) State observer-based adaptive neural dynamic surface control for a class of uncertain nonlinear systems with input saturation using disturbance observer. Neural Comput Appl 10.1007/s00521-018-03993-x.
52.
ZhangGLiuJLiuZ, et al. (2020a) Adaptive fuzzy discrete-time fault-tolerant control for permanent magnet synchronous motors based on dynamic surface technology. Neurocomputing404: 145–153.
53.
ZhangHXianJShiJ, et al. (2020b) High performance decoupling current control by linear extended state observer for three-phase grid-connected inverter with an LCL filter. IEEE Access8: 13119–13127.
54.
ZhouQWuCJingX, et al. (2016) Adaptive fuzzy backstepping dynamic surface control for nonlinear Input-delay systems. Neurocomputing199: 58–65.
55.
ZhuDZouXZhaoY, et al. (2019) Systematic controller design for digitally controlled LCL-type grid-connected inverter with grid-current-feedback active damping. International Journal of Electrical Power & Energy Systems110: 642–652.
