Abstract
Intelligent inverters have the capability to interact with the grid and supply supplemental services. Solar inverters designed for the future will have the ability to self-govern, self-adapt, self-secure, and self-heal themselves. Based on the available capacity, the ancillary service rendered by a solar inverter is referred to as moonlighting. Inverters that communicate with the grid but are autonomous can switch between the grid forming mode and the grid following control mode as well. Self-adaptive grid-interactive inverters can keep their dynamics stable with the assistance of adaptive controllers. Inverters that interact with the grid are also capable of self-adaptation Grid-interactive inverters may be vulnerable to hacking in situations in which they are forced to rely on their own self-security to determine whether malicious setpoints have been entered. To restate, an inverter can be referred to as a “smart inverter” when it is self-tolerant, self-healing, and provides ancillary services. The use of artificial intelligence in solar plants in addition to moon-lighting capabilities further paves the way for its flexibility in an environment containing a smart grid. This perspective paper presents the present as well as a more futuristic outlook of solar plants that utilize artificial intelligence while moonlighting advanced capabilities as smart inverters to form the core of a smart grid. For the first time, this perspective paper presents all the novel ancillary applications of a smart inverter while employing Artificial intelligence on smart inverters. The paper’s emphasis on the Artificial Intelligence associated with PV inverters further makes them smarter in addition to ancillary services.
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference79 articles.
1. Smart inverter voltwatt control design in high PV-penetrated distribution systems;Kashani;IEEE Trans. Ind. Appl.,2019
2. Arbab-Zavar, B., Palacios-Garcia, E., Vasquez, J., and Guerrero, J. (2019). Smart Inverters for Microgrid Applications: A Review. Energies, 12.
3. Multilayer Resilience Paradigm Against Cyber Attacks in DC Microgrids;Sahoo;IEEE Trans. Power Electron.,2020
4. Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison;Renew. Sustain. Energy Rev.,2020
5. Rajagukguk, R.A., Ramadhan, R.A., and Lee, H.-J. (2020). A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power. Energies, 13.
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