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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - On-demand design of nanophotonic gratings using artificial neural network
摘要： The design of nanophotonic structures relies on solving the Maxwell’s equation and sweeping parameters in order to obtain the desired properties. The inverse problem, the retrieval of the appropriate parameters from the required electromagnetic responses, remains challenging and time consuming. Here we report on-demand design of photonic gratings using a recurrent neural network. By using back propagation neural network model, we show that a nanophotonic grating can be designed and optimized with predesignated responses. We expect this work will advance the applications of deep learning algorithms in the design of nanophotonic devices.
关键词： nanophotonic structures,nanophotonic grating,back propagation neural network
更新于2025-09-16 10:30:52
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Prediction of Photovoltaic Power Generation Based on General Regression and Back Propagation Neural Network
摘要： Based on the general regression (GR) and back propagation (BP) neural network prediction method, this work forecasts the generated power of photovoltaic (PV) power station. First, the Pearson correlation coefficient method was used to analyze the meteorological factors. The degree of correlation between complex weather factors and PV power output was differentiated and the irradiance and battery temperature were selected as the important influencing variables. Second, the weather was classified according to the certain classification criteria. Then, we established the model by using GR and BP neural network prediction methods. The relative errors were within acceptable limits. The former model is more convenient while the latter model has better nonlinear fitting capacity. The results of the two models are compared and analyzed. We find out that the BP neural prediction method have better prediction results than GR method on PV power generation. Our findings can not only provide valuable information for the optimal dispatching of micro-grid and photovoltaic power, but also be of great significance in energy management and hierarchical control of micro-grid.
关键词： back propagation neural network,photovoltaic power generation,general regression neural network,Pearson correlation coefficient
更新于2025-09-12 10:27:22
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Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation
摘要： Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.
关键词： Photovoltaic generators,artificial neural networks,long short-term memory,power forecasting,long short-term memory-back-propagation neural network
更新于2025-09-11 14:15:04
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Intelligent approach-based hybrid control algorithm for integration of solar photovoltaic system in smart grid environment
摘要： Integration of solar PV as a distributed generator (DG) require efficient and coordinated control measures for the proper synchronization. In this paper, a hybrid control algorithm for single stage solar photovoltaic (PV) system integrated with low voltage (LV)/medium voltage (MV) grid is proposed. The hybrid algorithm utilizes I cos ? technique and quasi-Newton back-propagation (QNBP) neural network (NN). The main contributions of the present work are: i) support the utility grid by feeding power and connected loads, ii) provide harmonics elimination, reactive power compensation, load balancing, iii) also works in power factor correction (PFC) and zero voltage regulation modes, iv) provides power quality improvement. The proposed control of grid tied PV system provides very fast response during static and dynamic conditions. The obtained results are compared with other well-established algorithms available in the literature. The comparison of the proposed algorithm is done on the basis of various parameters such as DC voltage undershoot and overshoot, settling time and THD in grid currents. The developed system is demonstrated in MATLAB/SIMULINK platform. Using the proposed algorithms there is significant improvement of 1.1% in total harmonic distortion (THD) of source current. The results of proposed controllers are experimentally validated on a developed laboratory protype.
关键词： hybrid control algorithm,power quality improvement,quasi-Newton back-propagation neural network,smart grid,solar photovoltaic system
更新于2025-09-11 14:15:04
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Rapid prediction of acid detergent fiber content in corn stover based on NIR-spectroscopy technology
摘要： Prediction of acid detergent fiber (ADF) content in corn stover depends on precise data and appropriate analytical methods. In this paper, the optimal PLSR-BPNN model was created for rapidly getting ADF content based on the optimal selection of crucial parameters and the combination of partial least squares regression (PLSR) and back propagation neural network (BPNN). Herein, Mahalanobis distance (MD) was proposed as a tool to recognize and remove outliers. Additionally, on the basis of the characteristic bands extracted by correlation coefficient method (CC), principal component analysis (PCA) was performed to select principal components (PCs) to further compress data of bands for obtaining few characteristic wavelengths. It turned out that the performance of PLSR calibration model based on the selected 10 wavelengths was best. The correlation coefficient (R2), root mean square error of prediction (RMSEP), residual predictive deviation (RPD) and relative standard deviation (RSD) of test set successively were 0.9936, 0.3765, 12.5869, and 0.0087. Besides, BPNN was proposed to cut down the nonlinear regression residual of PLSR model. Genetic algorithm (GA) was applied to avoid the problem of local minimum in network. When RMSEP decreased to the minimum value of 0.2181, PLSR-BPNN model was proven to further improve performance and reached for the best level. Finally, the result of external validation shown that the R2, RMSEP, RPD, RSD were 0.9856, 0.4590, 8.3264, 0.0110, respectively, the created model presented the best predictive performance. Hence, the proposed methods combining with NIR-spectroscopy technology can be used to determine ADF content in corn stover.
关键词： Principal component analysis,Corn stover,Acid detergent fiber,Back propagation neural network,Genetic algorithm,Partial least squares regression
更新于2025-09-04 15:30:14