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

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Adel Ab-BelKhair ◽  
Javad Rahebi ◽  
Abdulbaset Abdulhamed Mohamed Nureddin
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Energy ◽  
Power Quality ◽  
Alternative Energy ◽  
Deep Neural Network ◽  
Weather Conditions ◽  
Energy Sources ◽  
Maximum Power Point ◽  
Power Point

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.

Incremental Learning on a Budget and its Application to Quick Maximum Power Point Tracking of Photovoltaic Systems

2014 ◽  
Vol 18 (4) ◽  
pp. 682-696 ◽  
Author(s):  
Koichiro Yamauchi ◽  
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Incremental Learning ◽  
Maximum Power ◽  
General Regression Neural Network ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
General Regression

Recent improvements in embedded systems has enabled learning algorithms to provide realistic solutions for system identification problems. Existing learning algorithms, however, continue to have limitations in learning on embedded systems, where physical memory space is constrained. To overcome this problem, we propose a Limited General Regression Neural Network (LGRNN), which is a variation of general regression neural network proposed by Specht or of simplified fuzzy inference systems. The LGRNN continues incremental learning even if the number of instances exceeds the maximum number of kernels in the LGRNN. We demonstrate LGRNN advantages by comparing it to other kernel-based perceptron learning methods. We also propose a light-weighted LGRNN algorithm, -LGRNNLight- for reducing computational complexity. As an example of its application, we present a Maximum Power Point Tracking (MPPT) microconverter for photovoltaic power generation systems. MPPT is essential for improving the efficiency of renewable energy systems. Although various techniques exist that can realize MPPT, few techniques are able to realize quick control using conventional circuit design. The LGRNN enables the MPPT converter to be constructed at low cost using the conventional combination of a chopper circuit and microcomputer control. The LGRNN learns the Maximum Power Point (MPP) found by Perturb and Observe (P&O), and immediately sets the converter reference voltage after a sudden irradiation change. By using this strategy, the MPPT quickly responds without a predetermination of parameters. The experimental results suggest that, after learning, the proposed converter controls a chopper circuit within 14 ms after a sudden irradiation change. This rapid response property is suitable for efficient power generation, even under shadow flicker conditions that often occur in solar panels located near large wind turbines.

Simulation of the Wind Turbine Yaw Control System

Vestnik MEI ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 44-55
Author(s):  
Yusong Yang ◽  
◽  
Evgeniy V. Solomin ◽  
Gleb N. Ryavkin ◽  
◽  
...  
Keyword(s):  
Power Generation ◽  
Control System ◽  
Wind Energy ◽  
Wind Turbine ◽  
Maximum Power Point ◽  
Wind Turbine Control ◽  
Yaw Control ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Owing to its being an important component of renewable energy, wind energy is a kind of power generation method with the most mature, highly developed technologies and broad commercial prospects. The stable and efficient conversion of wind energy by wind turbines depends not only on the reliability of the wind power generation equipment itself, but also on the wind turbine control system, which, in turn, contributes to long-term safe and reliable operation of the wind farm fleet. It is exactly the wind turbine control system that is the main subject of this study. The key to efficient and stable operation of the entire wind energy conversion system is the control technology, which includes yaw control, pitch angle control, and maximum power point tracking control. The active yaw control system is one of the important components of a horizontal axis wind turbine's control system. To eliminate the uncertainty of wind direction influence on the turbine power output, a composite yaw control system has been checked. By using an active yaw system and maximum power point tracking system, the turbine position and its rotation speed are adjusted to enable the wind turbine to accurately track the wind direction and capture the wind energy to the fullest extent.

scholarly journals Comparison of simple Maximum Power Point Tracking algoritms and simulation by PSIM for a standalone photovoltaic system

2021 ◽  
pp. 344-354
Keyword(s):  
Alternative Energy ◽  
Solar Power ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Energy Sources ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Traditional fossil energy sources are increasingly exhausted, leading to the need for mankind to exploit alternative energy sources; and solar energy can be viewed as infinite. Solar photovoltaic and its applications are increasingly widely studied. However, due to its nonlinearity and unstable nature, high technology is required to achieve good conversion efficiency. One of the techniques to optimize solar cell efficiency is to use the Maximum Power Point Tracking algorithm (MPPT) and P&O is a relatively easy algorithm to implement. This article will present some problems about photovoltaic cells, power converters in solar power systems and using PSIM software to simulate an independent solar system with several harvesting solutions for solar power and compare the efficiency of them.

An Intelligent Location Prediction System for Wind Farms Using Belief Networks

2020 ◽  
Vol 17 (8) ◽  
pp. 3412-3415
Author(s):  
P. Sardar Maran ◽  
K. Ashokkumar ◽  
J. Refonaa ◽  
Jany Shabu ◽  
A. Jesudoss ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Energy ◽  
Soft Computing ◽  
Alternative Energy ◽  
Machine Language ◽  
Energy Output ◽  
Energy Sources ◽  
Soft Computing Techniques ◽  
Ecological Parameters ◽  
The Impact

An energy crisis is a major problem India is facing today. Alternative energy sources are very important today to overcome the impact of rising oil prices. The main alternative energy source is the power from wind energy sources having 10% in India’s total energy consumption. The wind farm location planning to achieve the best energy output generation is in need of a finding solution. In this article, together with the belief network, a challenge is made exactly to prospects the energy from wind resources with and applied a statistical approach. Soft Computing techniques play a major role in the research applications especially in the multi-disciplinary areas. According to the technological development various soft computing techniques can be used in many areas to analyze the problem including Artificial Neural Network, Fuzzy logic, Adaptive Neuro and machine language. In this study the potential location of wind energy is identified by the belief neural network technique. The basic concept of Bayesian uncertainty treatment is that this analysis analyzes the conditional probability of occurrence. In addition, a particular region’s ecological parameters and environmental issues are also important. The ecological parameters and environmental factors also involved in the wind velocity of a particular region.

scholarly journals Manufacturing Cost Optimization of Photovoltaic Enterprises Based on Neural Network

2018 ◽  
pp. 167-173
Author(s):  
Weiping ZHANG ◽  
Shuming LI ◽  
Junfeng YU ◽  
Yihua MAO
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power Generation ◽  
Rbf Neural Network ◽  
Cost Optimization ◽  
Maximum Power ◽  
Manufacturing Cost ◽  
Maximum Power Point ◽  
Photovoltaic Power ◽  
Power Point

How to reduce the cost of photovoltaic power generation is the core issue of the survival and development of photovoltaic enterprises. Based on this, the manufacturing cost optimization of photovoltaic enterprises is studied based on neural network. Through the design of cost accounting control of photovoltaic enterprises, a genetic algorithm is proposed to optimize the manufacturing cost of photovoltaic enterprises, which is predicted at the maximum power point of the same photovoltaic power generation system. The results show that the RBF neural network optimized by genetic algorithm not only improves the prediction speed, but also improves the prediction accuracy. Thus, the maximum power point tracking control of photovoltaic power generation can be achieved better, and the manufacturing cost of photovoltaic enterprises can be optimized.

scholarly journals Comprehensive Examination on Solar -Wind Energy Systems Grid Integration and Emerging Power Quality challenges

2019 ◽  
Vol 8 (6S3) ◽  
pp. 733-737
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Wind Energy ◽  
Power Quality ◽  
Renewable Energy Sources ◽  
Atmospheric Condition ◽  
Energy Systems ◽  
Green Energy ◽  
Energy Sources ◽  
Wind Energy Systems

Sources of energy for conventional power generation are limited and depleting ceaselessly owing to rising demand of power because of the social modernization, rising industrial growth, quick rate of infrastructure development and also technological innovation. Several developed countries have started the employment of renewable energy sources considerably to attenuate the greenhouse gases effects within the atmosphere and harmful emission. The rising demand of the power without any harmful and damaging issue, forces the eye of researchers towards renewable sources (like wind and solar) of energy. Therefore, it's minimum impact on the atmosphere. Renewable Energy sources are becoming the key contributors in the present society due to the increasing cost of oil products and decrease in the price of RES. By using natural resources energy sources like Solar and wind are providing green energy. Renewable Energy penetration is increasing worldwide day by day. Renewable power generation will introduce noticeable power quality challenges when integrated to power grid. From the aspect of RES, renewable energy generation is intermittent and non-dispatchable because of varied nature of RES. The most common PQ challenges on RE integration are frequency and voltage fluctuations in the power system caused by noncontrollable atmospheric condition and Harmonics that are introduced because of power electronic converters used in RE power generation. This paper presents an intensive literature review, conducted on emerging PQ issues owing to Solar and Wind energy systems integration and existing mitigation methods.

scholarly journals Power Quality Enhancement of Solar PV Interfaced Distribution System by Delta-Bar-Delta Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 4293-4308
Author(s):  
Kannan Kaliappan
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Control Algorithm ◽  
Maximum Power ◽  
Control Technique ◽  
Solar Pv ◽  
Maximum Power Point ◽  
Incremental Conductance ◽  
Conductance Method ◽  
Power Point

This project presents incremental conductance method for maximum power point tracker (MPPT). Main reason to develop solar photovoltaic energy source is to reduce deterioration in power. Power quality cannot be increases by the week distribution of grid. To increases the power of the grid solar energy conversion system paramount by implementation of a robust control technique. In this project we are we have control algorithm such as delta bar delta but neural network which is in control technique by this active power will be fed to the loads and remaining power to the grid as a function of distribution static compensator it has the capability of mitigating harmonics and balancing of loss and improving power factor. The delta bar delta neural network control algorithm has a capability to adjust weight adaptively in an independent manner and it offers alleviation in the model complexity predominant during an abnormal great conditions along with a reduction in complexion time. This model is efficient utilization accomplished with incremental conductance based on maximum power point tracking techniques for validating the behaviour of proposed system and we can expect this results in the simulation via MATLAB. The realization of MPPT controller can be based on different methods and algorithms. The results and technique include incremental conductance techniques due to the reduced oscillations while determining the Maximum PowerPoint is preferred here and it is also suitable for commercial purpose.

scholarly journals Power Management Controller for Microgrid Integration of Hybrid PV/Fuel Cell System Based on Artificial Deep Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Abdulbaset Abdulhamed Mohamed Nureddin ◽  
Javad Rahebi ◽  
Adel Ab-BelKhair
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Renewable Energy ◽  
Fuel Cell ◽  
Deep Neural Network ◽  
Consumer Demand ◽  
Renewable Energy Sources ◽  
Cell System ◽  
Energy Sources ◽  
Fuel Cell System

Nowadays, the power demand is increasing day by day due to the growth of the population and industries. The conventional power plant alone is incompetent to meet the consumer demand due to environmental concerns. In this present situation, the essential thing is to be find an alternate way to meet the consumer demand. In present days most of the developed countries concentrate to develop alternative resources and invest huge money for its research and development activities. Most renewable energy sources are naturally friendly sources such as wind, solar, fuel cell, and hydro/water sources. The results of power generation using renewable energy sources only depend on the availability of the resources. The availability of renewable energy sources throughout the day is variable due to fluctuations in the natural resources. This research work discusses two major renewable energy power generating sources: photovoltaic (PV) cell and fuel cell. Both of them provide foundations for power generation, so they are very popular because of their impressive performance mechanisms. The mentioned renewable energy-based power generating systems are static devices, so the power losses are generally ignorable as compared to line losses in the main grid. The PV and fuel cell (FC) power systems need a controller for maximum power generation during fluctuations in the input resources. Based on the investigation report, an algorithm is proposed for an advanced maximum power point tracking (MPPT) controller. This paper proposes a deep neural network- (DNN-) based MPPT algorithm, which has been simulated using MATLAB both for PV and for FC. The main purpose behind this paper has been to develop the latest DNN controller for improving the output power quality that is generated using a hybrid PV and fuel cell system. After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system’s effectiveness.

Sign in / Sign up

Export Citation Format

Share Document