Stand-alone Micro Grid based on Artificially Intelligent Neural Network (AI-NN)

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2023-06-22 DOI:10.4108/ew.v9i6.147

Jenitha R., K. Rajesh

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引用次数: 0

Abstract

INTRODUCTION: Hybrid stand-alone Small Wind Solar Energy System offers a feasible solution in remote areas where grid connectivity is either financially or physically unavailable. A small wind turbine (SWT) and a solar photovoltaic system are part of the hybrid energy system, which is effectively employed to meet the energy needs of rural household loads. OBJECTIVE: This research suggests an effective analysis of wind solar hybrid system controllers taking energy demands into account. The controller should be designed in such a way as to intelligently monitor the availability of wind energy and solar energy and store the energy without spilling it out. METHODS: In order to cope with the challenging factors involved in designing the controller, intelligent power tracking with an artificially intelligent neural network (AI-NN) is designed. Added to that, the whole process has been designed and analysed with the MATLAB SIMULINK tool. RESUSTS: The results of the simulation, infer that AI-NN achieved the regression value of 0.99 when compared with the Perturb & Observe algorithm (P&O), and the Fuzzy Logic Control (FLC) algorithm, and has a higher tracking speed. Also, the AI-NN attained 2.62kW whereas the P&O has attained 2.52kW and Fuzzy logic has attained 2.43W of power which is 3.89% higher than P&O algorithm and 7.52% higher than fuzzy MPPT algorithm. CONCLUSION: The designed controller module enhances the system by artificially intelligent algorithm. The AI-NN attains the better power performance with lesser tracking time and higher efficiency. Thus, it is evident that AI-NN MPPT suits well for the hybrid system.

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基于人工智能神经网络的单机微电网

简介:在经济上或物理上无法连接电网的偏远地区，混合独立小型风能太阳能系统提供了可行的解决方案。小型风力发电机组和太阳能光伏发电系统作为混合能源系统的组成部分，有效地满足了农村家庭负荷的能源需求。目的:本研究提出了考虑能源需求的风能太阳能混合系统控制器的有效分析。控制器的设计应能够智能地监测风能和太阳能的可用性，并将其储存起来而不泄漏。方法:为了应对控制器设计中所涉及的挑战性因素，设计了一种人工智能神经网络(AI-NN)的智能功率跟踪。在此基础上，利用MATLAB SIMULINK工具对整个过程进行了设计和分析。结果:仿真结果表明，AI-NN与扰动与观察算法(P&O)和模糊逻辑控制算法(FLC)相比，回归值达到0.99，并且具有更高的跟踪速度。AI-NN的功率为2.62kW，而P&O的功率为2.52kW，模糊逻辑的功率为2.43W，比P&O算法高3.89%，比模糊MPPT算法高7.52%。结论:所设计的控制器模块通过人工智能算法对系统进行了增强。人工智能神经网络以更短的跟踪时间和更高的效率获得了更好的功率性能。由此可见，AI-NN MPPT算法非常适合于混合系统。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.