scholarly journals An Efficient Intelligent Power Detection Method for Photovoltaic System

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Detection Method ◽  
Peak Load ◽  
Weather Conditions ◽  
Photovoltaic System ◽  
Weather Data ◽  
Weather Factors ◽  
Pv System ◽  
Intelligent Technique ◽  
Highly Nonlinear ◽  
Validation Set

Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.

scholarly journals A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yuan-Kang Wu ◽  
Chao-Rong Chen ◽  
Hasimah Abdul Rahman
Keyword(s):  
Power Output ◽  
Solar Power ◽  
Peak Load ◽  
Weather Conditions ◽  
Green Energy ◽  
Weather Data ◽  
Stable Operation ◽  
Short Term ◽  
Pv System ◽  
Experimental Database

The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

scholarly journals PVcheck—A Software to Check Your Photovoltaic System

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6757
Author(s):  
Markus Rinio
Keyword(s):  
Peak Power ◽  
Weather Conditions ◽  
Data File ◽  
Photovoltaic System ◽  
Weather Data ◽  
Energy Yield ◽  
Pv System ◽  
Time Operation ◽  
Long Time ◽  
Module Efficiency

Having a photovoltaic (PV) system raises the question of whether it runs as expected. Measuring its energy yield takes a long time and the result still contains uncertainties from varying weather conditions and possible shading of the modules. Here, a free software PVcheck to measure the peak power of the system is announced, using the power data of a single sunny day. The software loads a data file of the generated power as a function of time from this day. This data file is provided by typical inverters. The software then simulates this power curve using known parameters like angle and location of the PV system. The assumed peak power of the simulation can then be adjusted so that the simulated curve matches the measured one. The software runs under Microsoft Windows™ and makes use of the free library pvlib python. The simulation can be refined by importing weather data like temperature, wind speed, and insolation. Furthermore, curves describing the nominal module efficiency as a function of the illumination intensity as well as the power-dependent inverter efficiency can be included in the simulation. First results reveal a good agreement of the simulation with experimental data. The software can be used to detect strong problems in PV systems after installation and to monitor their long-time operation.

Impact of Storage Integrated Solar Photovoltaics (PV) Power System on Utility Peak Loads

2011 ◽  
Author(s):  
K. Agyenim-Boateng ◽  
R. F. Boehm
Keyword(s):  
Large Scale ◽  
Peak Load ◽  
Average Energy ◽  
Transient Behavior ◽  
Climatic Conditions ◽  
Photovoltaic System ◽  
Solar Pv ◽  
Pv System ◽  
Grid Load ◽  
The Impact

The promise of large-scale use of renewables such as wind and solar for supplying electrical power is tempered by the sources’ transient behavior and the impact this would have on the operation of the grid. One way of addressing this is through the use of supplemental energy storage. While the technology for the latter has not been proven on a large scale or to be economical at the present time, some assessments of what magnitude is required can be made. In performing this work we have used NREL’s Solar Advisor Model (SAM 2010) with TMY3 solar data to estimate the photovoltaic system power generation. Climatic conditions close to load centers were chosen for the simulations. Then the PV output for varying sizes of arrays were examined and the impact of varying amounts of storage investigated. The storage was characterized by maximum limiting energy and power capacities based on annual hourly peak load, as well as its charging and discharging efficiencies. The simulations were performed using hourly time steps with energy withdrawn from, or input to, storage only after considering base generation and the PV system output in serving the grid load. In this work, we examined the load matching capability of solar PV generation (orientated for maximum summer output) for a sample Southwestern US utility grid load of 2008. Specifically we evaluated the daily and seasonal peak load shifting with employing varying storage capacities. The annual average energy penetration based on the usable solar PV output is also examined under these conditions and at different levels of system flexibility.

Design and Analysis of a Low Reflecting Concentrating Photovoltaic System

2012 ◽  
Vol 517 ◽  
pp. 791-796
Author(s):  
Cheng Yao Wang ◽  
Yin Xu ◽  
Yao Ming Zhang ◽  
Yong Ming Hua
Keyword(s):  
Electric Power ◽  
Concentration Ratio ◽  
Tracking System ◽  
Weather Conditions ◽  
Mathematic Model ◽  
Photovoltaic System ◽  
Voltage Profile ◽  
Pv System ◽  
Tracking Errors ◽  
Flux Concentration

In this paper, a concentrating photovoltaic (CPV) system with low ratio was successfully developed. In the design of CPV concentrator, a quasi-parabolic reflector was adopted. With the research of basic optical mechanisms, a mathematic model was built with the corresponding program. In addition, the width of light spot was analyzed with considering the symmetry of tracking errors and glass deformation in manufacture to identify reasonable values. The system was designed with a reflector of 10 flat mirrors, which has a geometrical concentration ratio of 8.18 and a flux concentration ratio of 5. The concentrating photovoltaic system was investigated experimentally under the various weather conditions. The output voltage profile and the output power of the flat PV system and the CPV system were presented to analyze the concentration ratio and the electric power. And the influence of soiling was also discussed. The results showed that the performance of tracking system was good in a clear day. Compared to the flat cell with the same system, the electric power was nearly increased by 4-5 times.

scholarly journals A Cuckoo MPPT based SMC Control for Grid tied PV System

2020 ◽  
pp. 22-26
Author(s):  
G Vaddikasulu , Meneni Saigeetha
Keyword(s):  
Output Power ◽  
Weather Conditions ◽  
Atmospheric Temperature ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Solar Irradiation ◽  
Pv System ◽  
Pv Array ◽  
Cloudy Weather ◽  
Pv Cell

Maximum power point techniques (MPPT) are used in photovoltaic system to make full utilization of PV array output power. The output power of PV array is always changing with weather conditions i.e., solar irradiation and atmospheric temperature. PV cell generates power by converting sunlight into electricity. The electric power generated is proportional to solar radiation. PV cell can generate around 0.5 to 0.8 volts. During cloudy weather due to varying insolation levels the output of PV array varies. The MPPT is a process which tracks the maximum power from array and by increasing the duty cycle of the DC-DC boost converter, the output voltage of the system is increased. This paper presents the cuckoo mppt technique for PV system along with SMC controller methods in grid connected photovoltaic (PV) systems for optimizing the solar energy efficiency

Weather factors affecting the development of maize from sowing to flowering

1972 ◽  
Vol 78 (2) ◽  
pp. 325-331 ◽  
Author(s):  
M. N. Hough
Keyword(s):  
Soil Moisture ◽  
Correlation Analysis ◽  
Linear Correlation ◽  
Weather Conditions ◽  
Weather Data ◽  
Weather Factors ◽  
Mean Values ◽  
Rate Of Development ◽  
Linear Correlation Analysis ◽  
The Mean

SUMMARYThe phenological development from sowing to flowering of the eaxly maize hybrid INRA 200 is related to the weather conditions. Plot trial data from Wytham, near Oxford, England, and weather information from that and nearby sites formed the basic data.The mean rate of development per day from sowing to emergence is related by linear correlation analysis to the mean values of soil temperature at 5 cm depth and soil moisture deficit. A range of temperature thresholds for emergence development exist, which depend upon the soil moisture, and which differ from the true physiological threshold.Between omergence and flowering the mean rate of development per day is related by linear correlation analysis to mean air temperature, solar radiation and potential transpiration estimated from weather data. All correlations are significant, but the parameters which combine radiation and temperature are statistically better.

Prediction of Photovoltaic Power Output Considering Weather Conditions

Solar Energy ◽  
2006 ◽  
Author(s):  
Kaoru Furushima ◽  
Yutaka Nawata ◽  
Michio Sadatomi
Keyword(s):  
Heat Transfer ◽  
Silicon Solar Cell ◽  
Weather Conditions ◽  
Weather Data ◽  
Cell Temperature ◽  
Pv System ◽  
Voltage Current Characteristic ◽  
Current Characteristics ◽  
Modified Equation ◽  
Electrical Output

A reasonable construction of photovoltaic (PV) system would be possible if the electrical output from the PV can be predicted accurately from weather data before the construction. The electrical output can be calculated theoretically from the voltage-current characteristics equation for a silicon solar cell, if the solar irradiance and the cell temperature are known. However, it is not easy to predict the cell temperature because it depends on several physical and environment factors; in particular, it is difficult to estimate a heat transfer from PV surface to surrounds. In this study, firstly, we confirmed that the electrical output can be predicted accurately by a modified equation of traditional voltage-current characteristic equation when the irradiance and the cell temperature are given. Secondly, we estimated the average heat transfer coefficient from PV surface as a function of wind speed using the experimental data on the PV system obtained in this study and compared it with three kinds of data in references. As a result, the estimated values agree with the references’ results in their accuracy of the data.

scholarly journals MPPT for Photovoltaic System Using Nonlinear Controller

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ramsha Iftikhar ◽  
Iftikhar Ahmad ◽  
Muhammad Arsalan ◽  
Neelma Naz ◽  
Naghmash Ali ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Weather Conditions ◽  
Buck Converter ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Nonlinear Controller ◽  
Pv System ◽  
Pv Array ◽  
Increasing Demand ◽  
Power Point

Photovoltaic (PV) system generates energy that varies with the variation in environmental conditions such as temperature and solar radiation. To cope up with the ever increasing demand of energy, the PV system must operate at maximum power point (MPP), which changes with load as well as weather conditions. This paper proposes a nonlinear backstepping controller to harvest maximum power from a PV array using DC-DC buck converter. A regression plane is formulated after collecting the data of the PV array from its characteristic curves to provide the reference voltage to track MPP. Asymptotic stability of the system is proved using Lyapunov stability criteria. The simulation results validate the rapid tracking and efficient performance of the controller. For further validation of the results, it also provides a comparison of the proposed controller with conventional perturb and observe (P&O) and fuzzy logic-based controller (FLBC) under abrupt changes in environmental conditions.

scholarly journals Simulation of Grid-Tied Photovoltaic System Based on Solar Irradiance and Temperature Data in Semarang

2019 ◽  
Vol 125 ◽  
pp. 14006
Author(s):  
Ahmed Jumui Sumoi Fomba ◽  
Hermawan Hermawan ◽  
Trias Andromeda ◽  
Mochammad Facta ◽  
Iwan Setiawan
Keyword(s):  
Power Distribution ◽  
Solar Irradiance ◽  
Distribution System ◽  
Photovoltaic System ◽  
Weather Data ◽  
Voltage Source ◽  
Pv System ◽  
Matlab Simulink ◽  
Power Distribution System ◽  
Current Voltage

This paper presents a simulation of a grid-connected photovoltaic power system. A complex model of power distribution system is developed in MATLAB Simulink, then it will be simulated to determine an amount of power delivered to the grid based on irradiance and temperature. Solar irradiance data collection is conducted using a solar irradiance meter. These weather data (solar irradiances and temperatures) are transformed into signal inputs and model through a grid-tied Photovoltaic (PV) model system which consists of PV, incremental conductance Maximum Power Point Tracking (MPPT) method, DC-DC boost converter, inverter, voltage source converter (VSC) control algorithms, and grid equipment. The output variables can be related to current, voltage or power. However, tracing of the current-voltage (I-V) characteristics or power-voltage (P-V) characteristics are the vital need to grid-tied PV system operation. Changes in solar irradiance and temperature imply changes in output variables. Detailed modelling of the effect of irradiance and temperature, on the parameters of the PV module and the output parameters will be discussed. With the aid of this model, one can have a feasible idea about the solar energy generation potential at given locations. This comprehensive model is simulated using MATLAB/Simulink software.

scholarly journals Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Mohammad Alsaffar ◽  
Mohamed El-Sayed ◽  
Bader Alajmi
Keyword(s):  
System Identification ◽  
System Modeling ◽  
Nonlinear System Identification ◽  
Power Converter ◽  
Photovoltaic System ◽  
Pv System ◽  
Potential Growth ◽  
Power Sources ◽  
Pv Panel ◽  
Highly Nonlinear

Solar photovoltaic (PV) energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.

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