Voltage Regulation for Photovoltaics-Battery-Fuel Systems Using Adaptive Group Method of Data Handling Neural Networks (GMDH-NN)

被引：13

作者：

Band, Shahab S. ^{[1
]}

Mohammadzadeh, Ardashir ^{[2
]}

Csiba, Peter ^{[3
]}

Mosavi, Amirhosein ^{[4
,5
]}

Varkonyi-Koczy, Annamaria R. ^{[3
,6
]}

机构：

[1] Natl Yunlin Univ Sci & Technol, Coll Future, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan

[2] Univ Bonab, Dept Elect Engn, Fac Engn, Bonab, Iran

[3] J Selye Univ, Dept Informat, Komarno 94501, Slovakia

[4] Ton Duc Thang Univ, Environm Qual Atmospher Sci & Climate Change Res, Ho Chi Minh City, Vietnam

[5] Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam

[6] Obuda Univ, Kalman Kando Fac Elect Engn, H-1034 Budapest, Hungary

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Adaptive control; GMDH; adaptive learning; energy management; PV panels; solar energy; machine learning; CONTROL STRATEGIES; ENERGY; POWER; OPTIMIZATION; ALLOCATION; CONTROLLER;

D O I：

10.1109/ACCESS.2020.3037134

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper a new control system on basis of group method for data handling neural networks (GMDH-NNs) is designed for voltage and power regulation in the photovoltaic (PV)/Fuel/Battery systems. The dynamics of all subsystems are considered to be fully uncertain. The suggested GMDH-NN is learned using online tuning rules that are concluded through the robustness investigation. The challenging operation conditions such as variable unknown dynamics, unknown temperature and irradiation and suddenly changes in output load are taken into account and are handled by suggested control system. The superiority of the suggested method is shown by simulation in several scenarios and comparison with other techniques.

引用

页码：213748 / 213757

页数：10

共 31 条

[1] A contingency based energy management strategy for multi-microgrids considering battery energy storage systems and electric vehicles [J].

Afrakhte, Hossein ;

Bayat, Peyman .

JOURNAL OF ENERGY STORAGE, 2020, 27

[2]

Allahvirdizadeh Y, 2017, INT J RENEW ENERGY R, V7, P1495

[3] Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm [J].

Ardakani, Alireza ;

Kordnaeij, Afshin .

EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING, 2019, 23 (04) :449-462

[4] GMDH-based networks for intelligent intrusion detection [J].

Baig, Zubair A. ;

Sait, Sadiq M. ;

Shaheen, AbdulRahman .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2013, 26 (07) :1731-1740

[5] Physics-based models in PV-battery hybrid power systems: Thermal management and degradation analysis [J].

Bonkile, Mayur P. ;

Ramadesigan, Venkatasailanathan .

JOURNAL OF ENERGY STORAGE, 2020, 31

[6] An optimal control strategy for standalone PV system with Battery-Supercapacitor Hybrid Energy Storage System [J].

Chong, Lee Wai ;

Wong, Yee Wan ;

Rajkumar, Rajprasad Kumar ;

Isa, Dino .

JOURNAL OF POWER SOURCES, 2016, 331 :553-565

[7]

Dag O, 2016, R J, V8, P379

[8] Implementation of a novel hybrid BAT-Fuzzy controller based MPPT for grid-connected PV-battery system [J].

Ge, Xun ;

Ahmed, Faraedoon Waly ;

Rezvani, Alireza ;

Aljojo, Nahla ;

Samad, Sarminah ;

Foong, Loke Kok .

CONTROL ENGINEERING PRACTICE, 2020, 98

[9] Microgrid management using hybrid inverter fuzzy-based control [J].

Habib, Mustapha ;

Ladjici, Ahmed Amine ;

Harrag, Abdelghani .

NEURAL COMPUTING & APPLICATIONS, 2020, 32 (13) :9093-9111

[10] A new development of ANFIS-GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets [J].

Harandizadeh, Hooman ;

Jahed Armaghani, Danial ;

Khari, Mahdy .

ENGINEERING WITH COMPUTERS, 2021, 37 (01) :685-700

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