scholarly journals A Study on the Improvement of Efficiency by Detection Solar Module Faults in Deteriorated Photovoltaic Power Plants

2021 ◽  
Vol 11 (2) ◽  
pp. 727 ◽  
Author(s):  
Myeong-Hwan Hwang ◽  
Young-Gon Kim ◽  
Hae-Sol Lee ◽  
Young-Dae Kim ◽  
Hyun-Rok Cha
Keyword(s):  
Power Generation ◽  
Power Plants ◽  
Detection System ◽  
Failure Detection ◽  
Solar Module ◽  
Thermal Images ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Manufacturing Technologies ◽  
Pv Power Generation

In recent years, photovoltaic (PV) power generation has attracted considerable attention as a new eco-friendly and renewable energy generation technology. With the recent development of semiconductor manufacturing technologies, PV power generation is gradually increasing. In this paper, we analyze the types of defects that form in PV power generation panels and propose a method for enhancing the productivity and efficiency of PV power stations by determining the defects of aging PV modules based on their temperature, power output, and panel images. The method proposed in the paper allows the replacement of individual panels that are experiencing a malfunction, thereby reducing the output loss of solar power generation plants. The aim is to develop a method that enables users to immediately check the type of failures among the six failure types that frequently occur in aging PV panels—namely, hotspot, panel breakage, connector breakage, busbar breakage, panel cell overheating, and diode failure—based on thermal images by using the failure detection system. By comparing the data acquired in the study with the thermal images of a PV power station, efficiency is increased by detecting solar module faults in deteriorated photovoltaic power plants.

Accommodation Capacity Analysis of the Large Scale PV Power Generation Access to Regional Power Grid

2013 ◽  
Vol 772 ◽  
pp. 630-633
Author(s):  
Tao Shi ◽  
Hua Ling Han
Keyword(s):  
Power Generation ◽  
Power Plants ◽  
Large Scale ◽  
Peak Load ◽  
Load Level ◽  
Analysis Method ◽  
Regional Power ◽  
Photovoltaic Power ◽  
Grid Planning ◽  
Pv Power Generation

In this paper, the factors are analyzed,which affect the accommodation capacity of large scale PV power generation access to the grid . The analysis method mainly consider the peak load adjustment ability of the whole power system. Then a real case based on provincial grid is calculated and analyzed. The results show that: As an important technical style ,the generation and acommodation of the large scale photovoltaic power generation are influenced by the light resource, load level, adjustment ability, transmission channel. The planning of large-scale photovoltaic power plants must keep pace with the conventional power and grid planning.

scholarly journals Hydropower Generation on The Nysa Klodzka River

2014 ◽  
Vol 21 (2) ◽  
pp. 327-336 ◽  
Author(s):  
Robert Kasperek ◽  
Mirosław Wiatkowski
Keyword(s):  
Power Generation ◽  
Electric Power ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Primary Energy ◽  
Energy Sources ◽  
Hydropower Generation ◽  
Electric Power Plants ◽  
Power Stations ◽  
Total Capacity

Abstract Adopted in 2009, the Directive of the European Parliament and of the Council on the promotion of the use of energy from renewable sources sets out the rules for how Poland is to achieve the 15% target of total primary energy from renewables by 2020. However, there are fears that the goals set out in this Directive may not be met. The share of Renewable Energy Sources (RES) in national energy consumption (150 TWh) is estimated at 8.6 TWh in 2009 and 12 TWh in 2011 (5.7 and 8% respectively). The level of RES in Poland until 2005 was approx. 7.2%. The analysis of RES technologies currently in use in Poland shows that in terms of the share in the total capacity, the 750 hydro-electric power plants which are currently in operation (with the overall capacity of almost 0.95 GW) are second only to wind power stations (2 GW). The authors have studied the Nysa Klodzka River in terms of possible locations for hydro-electric facilities. Eight locations have been identified where power plants might be constructed with installed capacities ranging from 319 to 1717 kW. The expected total annual electric power generation of these locations would stand at approx. 37.5 GWh.

scholarly journals Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

2020 ◽  
Vol 185 ◽  
pp. 01052
Author(s):  
Runjie Shen ◽  
Ruimin Xing ◽  
Yiying Wang ◽  
Danqiong Hua ◽  
Ming Ma
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Random Forest ◽  
Prediction Model ◽  
Power Plants ◽  
Time Series Prediction ◽  
Cloud Amount ◽  
Continuous Model ◽  
Short Term ◽  
Photovoltaic Power

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

scholarly journals Forecast of photovoltaic power generation based on DBSCAN

2021 ◽  
Vol 236 ◽  
pp. 02016
Author(s):  
Jiaying Zhang ◽  
Yingfan Zhang
Keyword(s):  
Power Generation ◽  
Prediction Model ◽  
Power Plants ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Renewable Energy Sources ◽  
Stable Operation ◽  
Power Prediction ◽  
Photovoltaic Power ◽  
Bidirectional Lstm

The power output of the photovoltaic power generation has prominent intermittent fluctuation characteristics. Large-scale photovoltaic power generation access will bring a specific impact on the safe and stable operation of the power grid. With the increase in the proportion of renewable energy sources such as wind power and photovoltaics, the phenomenon of wind abandonment and light abandonment has further increased. The photovoltaic power generation prediction is one of the critical technologies to solve this problem. It is of outstanding academic and application value to research photovoltaic power generation prediction methods and systems. Therefore, accurately carrying out the power forecast of photovoltaic power plants has become a research hot point in recent years. It is favored by scholars at home and abroad. First, this paper builds a simulation model of the photovoltaic cell based on known theoretical knowledge. Then it uses the density clustering algorithm (DBSCAN) in the clustering algorithm and classifies the original data. Finally, according to a series of problems such as the slow modeling speed of photovoltaic short-term power prediction, the bidirectional LSTM photovoltaic power prediction model, and CNN-GRU photovoltaic power prediction model based on clustering algorithm are proposed. After comparing the two models, it is concluded that the bidirectional LSTM prediction model is more accurate.

Efficient Power Generation From Large 750°C Heat Sources: Application to Coal-Fired and Nuclear Power Stations

1980 ◽  
Author(s):  
Z. P. Tilliette ◽  
B. Pierre
Keyword(s):  
Power Generation ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Energy ◽  
Inlet Temperature ◽  
Heat Sources ◽  
Turbine Inlet Temperature ◽  
Power Stations ◽  
Efficient Power ◽  
Steam Cycle

Considering the concern about a more efficient, rational use of heat sources, and a greater location flexibility of power plants owing to cooling capability, closed gas cycles can offer new solutions for fossil or nuclear energy. An efficient heat conversion into power is obtained by the combination of a main non-intercooled helium cycle with a flexible, superheated, low-pressure bottoming steam cycle. Emphasis is placed on the matching of the two cycles and, for that, a recuperator bypass arrangement is used. The operation of the main gas turbocompressor does not depend upon the operation of the small steam cycle. Results are presented for a conservative turbine inlet temperature of 750 C. Applications are made for a coal-fired power plant and for a nuclear GT-HTGR. Overall net plant efficiencies of 39 and 46 percent, respectively, are projected.

scholarly journals Forecasting Photovoltaic Power Generation Using Satellite Images

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6603
Author(s):  
Dukhwan Yu ◽  
Seowoo Lee ◽  
Sangwon Lee ◽  
Wonik Choi ◽  
Ling Liu
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Power Plants ◽  
Satellite Images ◽  
Cloud Amount ◽  
Weather Forecast ◽  
Percentage Error ◽  
Virtual Power Plants ◽  
Forecast Data ◽  
Pv Power Generation

As the relative importance of renewable energy in electric power systems increases, the prediction of photovoltaic (PV) power generation has become a crucial technology, for improving stability in the operation of next-generation power systems, such as microgrid and virtual power plants (VPP). In order to improve the accuracy of PV power generation forecasting, a fair amount of research has been applied to weather forecast data (to a learning process). Despite these efforts, the problems of forecasting PV power generation remains challenging since existing methods show limited accuracy due to inappropriate cloud amount forecast data, which are strongly correlated with PV power generation. To address this problem, we propose a PV power forecasting model, including a cloud amount forecasting network trained with satellite images. In addition, our proposed model adopts convolutional self-attention to effectively capture historical features, and thus acquire helpful information from weather forecasts. To show the efficacy of the proposed cloud amount forecast network, we conduct extensive experiments on PV power generation forecasting with and without the cloud amount forecast network. The experimental results show that the Mean Absolute Percentage Error (MAPE) of our proposed prediction model, combined with the cloud amount forecast network, are reduced by 22.5% compared to the model without the cloud amount forecast network.

A technoeconomic analysis of perovskite solar module manufacturing with low-cost materials and techniques

2017 ◽  
Vol 10 (6) ◽  
pp. 1297-1305 ◽  
Author(s):  
Zhaoning Song ◽  
Chad L. McElvany ◽  
Adam B. Phillips ◽  
Ilke Celik ◽  
Patrick W. Krantz ◽  
...  
Keyword(s):  
Power Generation ◽  
Solar Cells ◽  
Low Cost ◽  
Perovskite Solar Cells ◽  
Solar Module ◽  
Technoeconomic Analysis ◽  
Photovoltaic Power

This technoeconomic analysis shows that perovskite solar cells can emerge as a cost leader in photovoltaic power generation.

An Information System for Multiple Criteria Assessment of Renewable Energy Power Plants

2000 ◽  
Vol 24 (2) ◽  
pp. 81-99
Author(s):  
Stelios A. Papazis ◽  
Maria G. Ioannides ◽  
Panayotis N. Fotilas
Keyword(s):  
Information System ◽  
Renewable Energy ◽  
Power Plants ◽  
Solar Power ◽  
National Level ◽  
Payback Period ◽  
Rate Of Return ◽  
Internal Rate Of Return ◽  
Power Stations ◽  
Photovoltaic Power

An information system, built-up from a database and a multiple criteria decision support system, was developed to collect, process, store, assess and disseminate information on renewable energy power stations in Greece. This system is used to support decision-making, control, analysis and visualization regarding the existent installations, and the middle term forecasting of similar ones. The results show that, at national level, the wind and solar photovoltaic power stations, except the solar systems of private producers, are acceptable investments. The low national level of the capacity factor, 18 % for wind and 11% for photovoltaic power stations, is one of the reasons for their low productivity. The study relates to the prices paid for renewables electricity in Greece. The cost of the electric energy generated, and also the total cost of all wind and solar power stations, are high as compared to those of conventional fuels based systems. The wind power systems have acceptable return of investment (9%/y), internal rate of return (20%/y) and payback period (6-11 y). The solar stations have low internal rate of return (<9%/y) and return of investment (0.57%/y) and long-term payback period (22-35 y). Although the renewables are attractive investments with acceptable nationwide levels of financial indexes, they present high variations from one power station to another. Most wind and solar power plants must increase their competitiveness, and some must revise their feasibility criteria and design of installations. Thus, units with higher profits must be developed to an optimal installed rated power, while others must be reconsidered.

The Study on a Simplified Model of Photovoltaic Power System

2013 ◽  
Vol 860-863 ◽  
pp. 13-17
Author(s):  
Xin Shu ◽  
Shi Ping Zhou ◽  
Yong Jun Xia ◽  
Gang Hu ◽  
Yang Lei
Keyword(s):  
Power Generation ◽  
Power System ◽  
Flow Model ◽  
Simplified Model ◽  
Generation System ◽  
Photovoltaic Power ◽  
Power Generation System ◽  
Pv Generation ◽  
The Impact ◽  
Pv Power Generation

As the proportion of photovoltaic power increasing, the impact on the power system is evident. An accurate mathematical model for PV power generation system is necessary. This paper demonstrates the detailed mathematic models for PV power generation system consisted of PV cell, converters, flow model are summarized. This work might provide a generic PV power system models for the research of characteristics of PV generation system and its grid-connected operation.

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