Artificial intelligence power controller of fuel cell based DC nanogrid

被引:14
作者
Kumar, Saurabh [1 ]
Krishnasamy, Vijayakumar [2 ]
Neeli, Satyanarayana [1 ]
Kaur, Rajvir [3 ]
机构
[1] MNIT, EED, Jaipur, Rajasthan, India
[2] IITDM, ECED, Kancheepuram, India
[3] SUTD, ISTD, Singapore, Singapore
关键词
SLIDING-MODE; GENETIC ALGORITHM; OPTIMIZATION;
D O I
10.1016/j.ref.2020.05.004
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The expansion of DC nanogrid is projected in rural and remote areas for electrification. Conventional way of powering the rural areas is to extend the central grid to that area. However, extending the network of central grid is a cumbersome process and it involves huge capital cost. Further, with the development of power electronics and evolution of renewable resource, a standalone nanogrid is a simple and feasible solution to power the rural areas. Therefore, a clean and sustainable fuel cell based nanogrid is proposed as a step towards rural electrification. The output voltage of the fuel cell is varying in nature which could damage the voltage sensitive electronic equipment of the house hold. Therefore, boost converter based interfacing unit with an intelligent controller is proposed in this paper to regulate the voltage. The intelligent controller improves the dynamic response of the system and regulates the voltage at the DC distribution bus irrespective of source side or load side disturbances. The proposed intelligent proportional integral and derivative controller parameters are tuned using a binary genetic algorithm and real-coded genetic algorithm by formulating an optimal control problem. The proposed controller is model independent, simple in implementation and gives an excellent dynamic response.A laboratory scaled hardware prototype is developed to validate the performance of proposed controller.
引用
收藏
页码:120 / 128
页数:9
相关论文
共 24 条
[1]   Nonlinear control of fuel cell hybrid power sources: Part II - Current control [J].
Bizon, N. .
APPLIED ENERGY, 2011, 88 (07) :2574-2591
[2]  
Cell F., 2015, IEEE T CONTR SYST T, V23, P1098
[3]   Sliding mode voltage control of boost converters in DC microgrids [J].
Cucuzzella, Michele ;
Lazzari, Riccardo ;
Trip, Sebastian ;
Rosti, Simone ;
Sandroni, Carlo ;
Ferrara, Antonella .
CONTROL ENGINEERING PRACTICE, 2018, 73 :161-170
[4]   Network-constrained economic, dispatch using real-coded genetic algorithm [J].
Damousis, IG ;
Bakirtzis, AG ;
Dokopoulos, PS .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2003, 18 (01) :198-205
[5]   POWERING MOBILE NETWORKS WITH GREEN ENERGY [J].
Han, Tao ;
Ansari, Nirwan .
IEEE WIRELESS COMMUNICATIONS, 2014, 21 (01) :90-96
[6]  
Haupt L.R., 2004, PRACTICAL GENETIC AL
[7]  
International Energy Agency, 2016, Outlook Int. Energy Outlook 2016
[8]   Characterization of PV panel and global optimization of its model parameters using genetic algorithm [J].
Ismail, M. S. ;
Moghavvemi, M. ;
Mahlia, T. M. I. .
ENERGY CONVERSION AND MANAGEMENT, 2013, 73 :10-25
[9]   A Fast Fixed-Frequency Adaptive-On-Time Boost Converter With Light Load Efficiency Enhancement and Predictable Noise Spectrum [J].
Jing, Xiaocheng ;
Mok, Philip K. T. .
IEEE JOURNAL OF SOLID-STATE CIRCUITS, 2013, 48 (10) :2442-2456
[10]   Optimal sizing of wind-PV-based DC microgrid for telecom power supply in remote areas [J].
Kaur, Rajvir ;
Krishnasamy, Vijayakumar ;
Kandasamy, Nandha Kumar .
IET RENEWABLE POWER GENERATION, 2018, 12 (07) :859-866