scholarly journals A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3817 ◽  
Author(s):  
Fu ◽  
Yang ◽  
Yao ◽  
Jiao ◽  
Zhu
Keyword(s):  
Power Generation ◽  
Hierarchical Clustering ◽  
Power Plants ◽  
Large Scale ◽  
Prediction Method ◽  
Entire Region ◽  
Elman Neural Network ◽  
Large Power ◽  
Power Dispatching ◽  
Pv Power Generation

Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

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 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.

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.

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.

Ultimate Trough: The New Parabolic Trough Collector Generation for Large Scale Solar Thermal Power Plants

2011 ◽  
Author(s):  
Klaus-Ju¨rgen Riffelmann ◽  
Daniela Graf ◽  
Paul Nava
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Thermal Power ◽  
Thermal Storage ◽  
Solar Thermal ◽  
Thermal Power Plants ◽  
Large Power ◽  
Solar Thermal Power ◽  
Aperture Width ◽  
Solar Field

From 1984 to 1992, the first commercial solar thermal power plants — SEGS I to IX — were built in the Californian Mojave desert. The first generation of trough collectors (LS1) used in SEGS I showed an aperture area of about 120 m2 (1’292 ft2), having an aperture width of 2.5 m (8.2 ft). With the second generation collector (LS2), used in SEGS II to VI, the aperture width was doubled to 5 m (16.4 ft). The third generation (LS3) has been increased regarding width (5.76 m or 18.9 ft) and length (96 m or 315 ft) to about 550 m2 (5’920 ft2) aperture. It was used in the last SEGS plants VIII and IX, those plants having a capacity of 80 MW each. After more than 10 years stagnancy, several commercial plants in the US (the 64 MW Nevada Solar One project) and Spain (the ANDASOL projects, 50 MW each with 8 h thermal storage) started operation in 2007/2008. New collectors have been developed, but all are showing similar dimensions as either the LS2 or the LS3 collector. One reason for this is the limited availability of key components, mainly the parabolic shaped mirrors and heat collection elements. However, in order to reduce cost, solar power projects are getting larger and larger. Several projects in the range of 250 MW, with and without thermal storage system, are going to start construction in 2011, requiring solar field sizes of 1 to 2.5 Million m2. FLABEG, market leader of parabolic shaped mirrors and e.g. mirror supplier for all SEGS plants and most of the Spanish plants, has started the development of a new collector generation to serve the urgent market needs: lower cost and improved suitability for large solar fields. The new generation will utilize accordingly larger reflector panels and heat collection elements attended by advanced design, installation methods and control systems at the same time. The so-called ‘Ultimate Trough’ collector is showing an aperture area of 1’667 m2 (17’944 ft2), with an aperture width of 7.5 m (24.6 ft). Some design features are presented in this paper, showing how the new and huge dimensions could be realized without compromising stiffness, and bending of the support structure and improving the optical performance at the same time. Solar field layouts for large power plants are presented, and solar field cost savings in the range of 25% are disclosed.

Large-scale PV power generation in China: A grid parity and techno-economic analysis

Energy ◽  
2017 ◽  
Vol 134 ◽  
pp. 256-268 ◽  
Author(s):  
Hongyang Zou ◽  
Huibin Du ◽  
Marilyn A. Brown ◽  
Guozhu Mao
Keyword(s):  
Power Generation ◽  
Economic Analysis ◽  
Large Scale ◽  
Grid Parity ◽  
Pv Power Generation

Effect Study of Large-Scale PV Power Access to HVDC System

2013 ◽  
Vol 772 ◽  
pp. 640-645
Author(s):  
Hua Ling Han ◽  
Zhen Li ◽  
Tao Shi ◽  
Ning Chen
Keyword(s):  
Power Generation ◽  
Large Scale ◽  
Power Grid ◽  
Operating Mode ◽  
Effect Study ◽  
Stable Operation ◽  
Long Distance ◽  
Hvdc System ◽  
Set Up ◽  
Pv Power Generation

In order to realize electric power high voltage, large capacity, long distance transmission and regional power grid interconnection, dc transmission system is an important technical means, and it plays an important role for cross-regional given of new large-scale energy power generation. Based on large scale photovoltaic (PV) power generation access to the northwest power grid as the object, this article set up PV power station model, analysis the influence of large-scale PV power access to HVDC system under different PV station operating mode and the grid disturbance situation. At last this paper puts forward suggestions and measures to guarantee stable operation of large scale PV power generation access to the system.

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