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- 实验方案
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Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network
摘要： This paper presents an intelligent method to extract the maximum power from the photovoltaic panel using artificial neural network (ANN). The inputs data required for training the ANN controller are obtained from real weather conditions and the desired output is obtained from perturb and observe (P&O) method. The proposed model is capable to improve the dynamic response and steady-state performance of the system, provides an accurate identification of the optimal operating point and an accurate estimation of the maximum power from the photovoltaic panels. The proposed ANN model is compared with conventional P&O model and shown that ANN controller could increase the power output by approximately 20%. The system is simulated and studied using MATLAB software.
关键词： Artificial Neural Network,Maximum Power Point tracking,photovoltaic system,P&O method,MATLAB
更新于2025-09-23 15:21:01
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Improved cooperative artificial neural network <scp>a??</scp> particle swarm optimization approach for solar photovoltaic systems using maximum power point tracking
摘要： Photovoltaic (PV) energy represents one of the most important renewable energies, but its disadvantage resides in its maximum power point, which varies according to meteorological changes that make the efficiency low. Intelligent techniques, using the maximum power point tracking (MPPT) method, can achieve an efficient real-time tracking of this point in order to ensure optimal functioning of the system. The output power of the PV system is removed from solar irradiation and cell temperature of the PV panel type SOLON 55W. Therefore, it is essential to harvest the generated power of the PV system and optimally exploit the collected solar energy. For this objective, this work treats on a new artificial neural network-particle swarm optimization approach (ANN-PSO). The ANN is used to predict the solar irradiation level and cell temperature followed by PSO to optimize the power generation and optimally track the solar power of the PV panel type SOLON 55W based on various operation conditions under changes in environmental conditions. The simulation results of the proposed approach give a minimum error with a relevant efficiency, that is, the power provided by ANN-PSO approach is optimal and closer to the PV power. Consequently, this novel approach ANN-PSO shows its major capability to extract the optimal power with excellent efficiency up of 97%. For this objective, this work treats a new hybrid ANN-PSO approach.
关键词： photovoltaic system,particle swarm optimization,maximum power point tracking,artificial neural network
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Using Artificial Neural Network for Compensation of Semiconductor Thermistor Nonlinearity
摘要： A method for correcting the transformation function of a semiconductor thermistor using a nonlinearity compensator based on a three-layer perceptron is proposed. Using of computer simulation, the operability of the proposed method has been investigated, a comparative analysis has been carried out with polynomial compensators of nonlinearity.
关键词： compensation of nonlinearity,learning,perceptron,artificial neural network
更新于2025-09-23 15:21:01
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[IEEE 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) - Ghaziabad (2018.2.9-2018.2.10)] 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) - Development of a Decision-Based Neural Network for a Day-Ahead Prediction of Solar PV Plant Power Output
摘要： Day-ahead photovoltaic power prediction is vital for policy making and providing necessary backup capacities. Previous researchers include the implementation of time series, auto-regression and Soft computing techniques like Artificial Neural Networks and Fuzzy Logic. Artificial Neural Networks provides a better fit to complex, non-linear and error-prone data. The paper shows a comparative study of a Radial Basis Neural Network Schema (exact fit), a ‘k-means’ Radial Neural Network, and a Feed Forward Neural Network with Levenberg-Marquardt error backpropagation designed for the prediction of power output at an hourly resolution. The ability of the Neural Network to be trained to adapt to a previous set of data and then interpolate or extrapolate to the new data set has been exploited. The proposed model uses five meteorological variables and uses recorded data collected from the SN Mohanty PV Power Plant. Training of neural network is done on a monthly basis so that normalization constants of variables can be lower and better mapping can be produced. An improved decision-based schematic using Neural Networks is proposed which combines the advantages of both Radial Basis Function (exact fit) and FFNN.
关键词： solar photovoltaic power plant,Radial Basis,Artificial Neural Network,Decision-based,ANN
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 31st Canadian Conference on Electrical & Computer Engineering (CCECE) - Quebec City, QC (2018.5.13-2018.5.16)] 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) - Solar Forecasting Using Remote Solar Monitoring Stations and Artificial Neural Networks
摘要： The need to accurately forecast available solar irradiance is a significant issue for the power industry and poses special challenges for utilities who serve customers in isolated regions where weather forecast data may not be abundant. This paper proposes a method to forecast two hour ahead solar irradiance levels at a site in Northwestern Alberta, Canada using real-time solar irradiance measured both locally and at remote monitoring stations. This paper makes use of an Artificial Neural Network (ANN) to forecast the solar irradiance levels and uses the genetic algorithm to determine the optimal array size and positioning of solar monitoring stations to obtain the most accurate forecast from the ANN. The findings of this paper are that it is possible to use as few as five remote monitoring stations to obtain a near-peak forecasting accuracy from the algorithm and that providing adequate geospatial separation of the remote monitoring sites around the target site is more desirable than clustering the sites in the strictly upwind directions.
关键词： GHI,remote sensing,solar,PV,isolated generation,forecasting,irradiance,artificial neural network
更新于2025-09-23 15:21:01
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Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging
摘要： Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.
关键词： granite soils,artificial neural network,hyperspectral camera,soil water characteristic curve,water content,visible and near-infrared
更新于2025-09-23 15:19:57
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Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems
摘要： The solar water pumping system is one of the brightest applications of solar energy for its environmental and economic advantages. It consists of a photovoltaic panel which converts solar energy into electrical energy to operate a DC or AC motor and a battery bank. The photovoltaic power fluctuation can affect the water pumping system performances. Thus, the photovoltaic power prediction is very important to ensure a balance between the produced energy and the pump requirements. The prediction of the generated power depends on solar irradiation and ambient temperature forecasting. The purpose of this study was to evaluate various methodologies for weather data estimation namely: the empirical models, the feed forward neural network and the adaptive neuro-fuzzy inference system. The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The predicted energy was used for the solar water pumping management algorithm.
关键词： water pumping system management,photovoltaic power,empirical models,forecast,artificial neural network,neuro fuzzy inference system
更新于2025-09-23 15:19:57
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Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II)
摘要： This work focused on the combination of CdTe and AgInS2 quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS2 combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R2P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7e9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest.
关键词： CdTe/AgInS2 combined nanoprobe,Artificial neural network,Iron,Partial least-squares,Folic acid
更新于2025-09-23 15:19:57
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RAMS: Remote and automatic mammogram screening
摘要： About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, then the chances of successful treatment can be high. Typically, mammograms are screened by radiologists who determine whether a biopsy is necessary to ascertain the presence of cancer. Although historical screening methods have been effective, recent advances in computer vision and web technologies may be able to improve the accuracy, speed, cost, and accessibility of mammogram screenings. We propose a total screening solution comprised of three main components: a web service for uploading images and reviewing results, a machine learning algorithm for accepting or rejecting images as valid mammograms, and an artificial neural network for locating potential malignancies. Once an image is uploaded to our web service, an image acceptor determines whether or not the image is a mammogram. The image acceptor is primarily a one-class SVM built on features derived with a variational autoencoder. If an image is accepted as a mammogram, the malignancy identifier, a ResNet-101 Faster R-CNN, will locate tumors within the mammogram. On test data, the image acceptor had only 2 misclassifications out of 410 mammograms and 2 misclassifications out of 1,640 non-mammograms while the malignancy identifier achieved 0.951 AUROC when tested on BI-RADS 1, 5, and 6 images from the INbreast dataset.
关键词： Faster R-CNN,SVM,Deep Learning,DDSM,Convolutional,TensorFlow,INbreast,Mammograms,Telemedicine,Artificial Neural Network
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) - Sonderborg, Denmark (2019.4.23-2019.4.25)] 2019 IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) - An AN-GA Controlled SEPIC Converter for Photovoltaic Grid Integration
摘要： In this paper, Artificial Neural Network (ANN) optimization with Genetic Algorithm (GA) is implemented. The optimized training to ANN is provide using Bayesian regulation. For this study, a Photovoltaic (PV) system has considered and optimal power tracking been interpreted with proper adjustment of ANN weights using GA approach, which reduces the Root Mean Square Error (RMSE). In this work, the single-ended primary – inductor converter (SEPIC) has been utilized for better power tracking from PV modules. SEPIC Converter accomplish with impedance matching power device and provides utmost PV power tracking. Space vector pulse width modulation-dSPACE interface been utilized as an inverter control. Simulated responses show the potency of the proposed system under sag, swell and varying loading conditions.
关键词： Root Mean Square Error (RMSE),SEPIC.,Grid,Photovoltaic,Artificial neural network (ANN),Genetic Algorithm (GA)
更新于2025-09-19 17:13:59