Performance and Energy Saving Analysis of Grid Connected Photovoltaic in West Sumatera

2016 ◽  
Vol 7 (4) ◽  
pp. 1348 ◽  
Author(s):  
Syafii Ghazali ◽  
Refdinal Nazir
Keyword(s):  
Energy Saving ◽  
Weather Condition ◽  
Weather Conditions ◽  
Photovoltaic System ◽  
Weather Data ◽  
Solar Pv ◽  
Clear Sky ◽  
Cloudy Weather ◽  
West Sumatra ◽  
Bad Weather

<p>The paper presents performance and energy saving analysis of 1.25 kWp grid connected Photovoltaic system under difference weather condition in West Sumatera.  The measured data were performed during weather data that often occur in West Sumatra i.e. sunny, overcast, raining and cloudy. The synchronizing process successfully done even bad weather conditions when sunlight was low automatically. Photovoltaic in average start producing power from 7:00 AM to 6:00 PM for normal or clear sky, however under overcast, raining and cloudy weather, the PV power decreased and disconnected earlier before sunset. During intermittent raining, overcast and cloud covered the PV power output show an irregular profile. The PV energy saving performed for three residential connection cases: 1300 VA, 900 VA with subsidized and 900 VA without subsidized. The solar PV installation have more benefits and energy saving for 1300 VA, 900 VA without subsidized with payback period around 8.5 years. However, the 900 VA with subsidized take longer 20.8 years, but still in PV lifespan 25 years. In the future, household subsidies may be reduced or eliminated, the solar energy will be viable alternative of energy resources when it can produce electricity at a cost equivalent to utility grid PLN rate. </p>

scholarly journals Weather Prediction using Advanced Machine Learning Techniques

2021 ◽  
Vol 2089 (1) ◽  
pp. 012059
Author(s):  
G. Hemalatha ◽  
K. Srinivasa Rao ◽  
D. Arun Kumar
Keyword(s):  
Linear Relationship ◽  
Weather Prediction ◽  
Weather Condition ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Learning Techniques ◽  
Proposed Model ◽  
Non Linear ◽  
The Relationship

Abstract Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..

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

Classification of Failures in Photovoltaic Systems using Data Mining Techniques

Big Data ◽  
2016 ◽  
pp. 1347-1366
Author(s):  
Lucía Serrano-Luján ◽  
Jose Manuel Cadenas ◽  
Antonio Urbina
Keyword(s):  
Data Mining ◽  
Environmental Parameters ◽  
Weather Conditions ◽  
Environmental Data ◽  
Photovoltaic System ◽  
Data Mining Techniques ◽  
Learning Techniques ◽  
And Performance ◽  
Using Data ◽  
Bad Weather

Data mining techniques have been used on data collected from a photovoltaic system to predict its generation and performance. Nevertheless, up to date, this computing approach has needed the simultaneous measurement of environmental parameters that are collected by an array of sensors. This chapter presents the application of several computing learning techniques to electrical data in order to detect and classify the occurrence of failures (i.e. shadows, bad weather conditions, etc.) without using environmental data. The results of a 222kWp (CdTe) case study show how the application of computing learning algorithms can be used to improve the management and performance of photovoltaic generators without relying on environmental parameters.

Classification of Failures in Photovoltaic Systems using Data Mining Techniques

2015 ◽  
pp. 300-319 ◽  
Author(s):  
Lucía Serrano-Luján ◽  
Jose Manuel Cadenas ◽  
Antonio Urbina
Keyword(s):  
Data Mining ◽  
Environmental Parameters ◽  
Weather Conditions ◽  
Environmental Data ◽  
Photovoltaic System ◽  
Data Mining Techniques ◽  
Learning Techniques ◽  
And Performance ◽  
Using Data ◽  
Bad Weather

Data mining techniques have been used on data collected from a photovoltaic system to predict its generation and performance. Nevertheless, up to date, this computing approach has needed the simultaneous measurement of environmental parameters that are collected by an array of sensors. This chapter presents the application of several computing learning techniques to electrical data in order to detect and classify the occurrence of failures (i.e. shadows, bad weather conditions, etc.) without using environmental data. The results of a 222kWp (CdTe) case study show how the application of computing learning algorithms can be used to improve the management and performance of photovoltaic generators without relying on environmental parameters.

scholarly journals Power Prediction and Technoeconomic Analysis of a Solar PV Power Plant by MLP-ABC and COMFAR III, considering Cloudy Weather Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
M. Khademi ◽  
M. Moadel ◽  
A. Khosravi
Keyword(s):  
Power Plant ◽  
Output Power ◽  
Weather Conditions ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Solar Pv ◽  
Economic Point ◽  
Cloudy Weather ◽  
The Mean

The prediction of power generated by photovoltaic (PV) panels in different climates is of great importance. The aim of this paper is to predict the output power of a 3.2 kW PV power plant using the MLP-ABC (multilayer perceptron-artificial bee colony) algorithm. Experimental data (ambient temperature, solar radiation, and relative humidity) was gathered at five-minute intervals from Tehran University’s PV Power Plant from September 22nd, 2012, to January 14th, 2013. Following data validation, 10665 data sets, equivalent to 35 days, were used in the analysis. The output power was predicted using the MLP-ABC algorithm with the mean absolute percentage error (MAPE), the mean bias error (MBE), and correlation coefficient (R2), of 3.7, 3.1, and 94.7%, respectively. The optimized configuration of the network consisted of two hidden layers. The first layer had four neurons and the second had two neurons. A detailed economic analysis is also presented for sunny and cloudy weather conditions using COMFAR III software. A detailed cost analysis indicated that the total investment’s payback period would be 3.83 years in sunny periods and 4.08 years in cloudy periods. The results showed that the solar PV power plant is feasible from an economic point of view in both cloudy and sunny weather conditions.

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 An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network

2020 ◽  
Vol 10 (1) ◽  
pp. 630-641 ◽  
Author(s):  
Debasish Pattanaik ◽  
Sanhita Mishra ◽  
Ganesh Prasad Khuntia ◽  
Ritesh Dash ◽  
Sarat Chandra Swain
Keyword(s):  
Solar Power ◽  
Weather Condition ◽  
Photovoltaic System ◽  
Design Stage ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Time Data ◽  
Pv System ◽  
Simulink Model ◽  
Solar Photovoltaic System

AbstractAnalysing the Output Power of a Solar Photo-voltaic System at the design stage and at the same time predicting the performance of solar PV System under different weather condition is a primary work i.e. to be carried out before any installation. Due to large penetration of solar Photovoltaic system into the traditional grid and increase in the construction of smart grid, now it is required to inject a very clean and economic power into the grid so that grid disturbance can be avoided. The level of solar Power that can be generated by a solar photovoltaic system depends upon the environment in which it is operated and two other important factor like the amount of solar insolation and temperature. As these two factors are intermittent in nature hence forecasting the output of solar photovoltaic system is the most difficult work. In this paper a comparative analysis of different solar photovoltaic forecasting method were presented. A MATLAB Simulink model based on Real time data which were collected from Odisha (20.9517∘N, 85.0985∘E), India. were used in the model for forecasting performance of solar photovoltaic system.

scholarly journals Analysis of Peak Power Capacity on Rooftop Solar PV 1.25 kWp at Sun Conditions 90 Degrees

2021 ◽  
Vol 4 (3) ◽  
pp. 173-178
Author(s):  
Habib Satria ◽  
Syafii Syafii ◽  
Aswardi Aswardi
Keyword(s):  
Solar Radiation ◽  
Peak Power ◽  
Weather Conditions ◽  
Data Retrieval ◽  
Solar Pv ◽  
Solar Panels ◽  
Minimal Impact ◽  
Dc Power ◽  
West Sumatra ◽  
Dc Current

This paper describes the optimization of energy conversion when solar radiation occurs at peak power conditions, namely at 11.00 am to 2.00 pm where the position of the sun is parallel to the layout of the PV Rooftop installation. The panels used are 5 units with the type of polycrystalline with a capacity of 1 panel unit consisting of 250 Wp. The position of the panels installed on the roof of the Andalas University building is based on an angle of 90o degrees with a position of ±255m above sea level. The advantages obtained when placed on the roof of the building are due to the minimal impact of shadow effects and environmental disturbances. Data retrieval using DC current and voltage sensors is then connected to the Arduino Uno microcontroller which is then interfaced in graphic form. Considerations in the installation of PV by reviewing the weather conditions at that time where the conditions were sunny and the air was clean with the aim that the performance when solar radiation entered the solar cells could be produced more optimally. Based on the data obtained at peak power, PV can convert DC power to 972.56 Wp. In the final stage of collecting data recorded on this PC, it can later be used as a reference for installing solar panels for household electricity scales in the West Sumatra region.

scholarly journals Weather Analysis of Flight Delay at Husein Sastranegara Airport

2019 ◽  
Vol 4 (2) ◽  
pp. 89-98
Author(s):  
Yedi Dermadi ◽  
Shinta Devi Lukitasari ◽  
Annisaa Nurhayati
Keyword(s):  
Weather Conditions ◽  
Air Transportation ◽  
Weather Data ◽  
Flight Delays ◽  
Weather Forecasts ◽  
Weather Information ◽  
Directorate General ◽  
Flight Delay ◽  
Flight Cancellations ◽  
Bad Weather

Flight is an activity that is very vulnerable to weather conditions. The accuracy of weather information strongly supports flight activities. The effects of bad weather on flights include flight delays and flight cancellations. Based on data on flight delays from the Directorate General of Air Transportation of the Ministry of Transportation from January to March 2019 at Husein Sastranegara Airport, it is known that 20-30% of flight delays are caused by weather constraints. To estimate flight delays based on weather forecasts, weather data analysis is carried out to determine the type of weather that is endangering flights and causing flight delays. The analysis was carried out using the K-NN and Random Forest algorithms

Study on Desorption Temperature of Adsorption Bed in Typical Weather Conditions

2014 ◽  
Vol 986-987 ◽  
pp. 891-894
Author(s):  
Hua Zhang ◽  
Xu Ji ◽  
Ming Li ◽  
Jie Qing Fan ◽  
Bin Luo ◽  
...  
Keyword(s):  
Weather Condition ◽  
Weather Conditions ◽  
Adsorption Refrigeration ◽  
Desorption Temperature ◽  
Cloudy Weather

In this paper, the desorption temperature of adsorption bed in typical weather condition is studied though the solar adsorption refrigeration experiment with conditions of different weather. The results showed that: the better the weather condition is, the higher the highest temperature of the adsorption bed is, so the better the effect of desorption is. No matter in cloudless sunny or partly cloudy sunny conditions, the desorption temperature of adsorption bed can reach 93.4°C, and the amount of desorption also will be larger; however, in cloudy weather conditions, desorption temperature can reach 88.5°C, and desorption quantity is relatively fewer than it in cloudless sunny days.

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