A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter

Abstract

Presently, climate change and global warming are the most uncontrolled global challenges due to the extensive fossil fuel usage for power generation and transportation. Nowadays, most of the developed countries are concentrating on developing alternative resources; consequently, they did huge investments in research and development. In general, alternative energy resources including hydropower, solar power, and wind energy are not harmful to nature. Today, solar power and wind power are very popular alternative energy sources due to their enormous availability in nature. In this paper, the photovoltaic cell and wind energy systems are investigated under various weather conditions. Based on the findings, we developed an advanced intelligent controller system that tracks the maximum power point. The MPPT controller is a must for the renewable energy sources due to unpredictable weather conditions. The main objective of this paper is to propose a new algorithm that is based on deep neural network (DNN) and maximum power point tracking (MPPT), which was simulated in a MATLAB environment for photovoltaic (PV) and wind-based power generation systems. The development of an advanced DNN controller that improves the power quality and reduces THD value for the microgrid integration of hybrid PV/wind energy system was performed. The MATLAB simulation tool has been used to develop the proposed system and tested its performance in different operating situations. Finally, we analyzed the simulation results applying the IEEE 1547 standard.

Belirsiz Yük Koşullarında Bir Dek Sisteminin Pratik Radyal Dağıtımlı Besleyicide Tekno-ekonomik ve Çevresel Analizi

“…The solar irradiance (kW/m 2 ) is denoted by s, and α and β are the parameters of the beta distribution function fb(s), which can be determined using ( 16) and (17).…”

“…In [16] [17], solar energy and wind turbines were used to evaluate the energy in renewable energy systems based on the convolutional neural network.…”

Spotted Hyena-Bat Optimized Extreme Learning Machine for Solar Power Extraction

“…A computation for an ANN-based MPPT controller for wind energy structure and mutt PV/wind is discussed in [27]. The fundamental mark of this investigation is to show the Total Harmonic Distortion (THD) potential gains of PV, wind, and scattered lattice voltage and current profiles, all of which show redesigns in power quality when the micro grid is joined to harmless to the ecosystem power sources.…”

Power Quality Enhancement For Decentralized Microgrids in Island Mode

“…Many researchers have designed the unique MPPT algorithm using the incremental drive, Fuzzy, ANN, P&O, PSO, ANFIS, feedback voltage/current, and other controllers during the past decade [22,23]. In this study, an MPPT algorithm for a PV system is designed, employing a deep neural network controller (DNN) similar to [24]. In DNN learning algorithm, more than 80000 sample points are employed to train the MPPT algorithm.…”