- 标题
- 摘要
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- 实验方案
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Forecasting the Performance of a Photovoltaic Solar System Installed in other Locations using Artificial Neural Networks
摘要： Photovoltaic solar energy has been spread all over the world, and in Brazil this energy source has been getting considerable space in the last years, being driven mainly by the energy crises. However, when installed in regions with low incidence of solar irradiation, this technology presents a loss of efficiency in the generation of energy. As an alternative to this consideration, a power prediction study could be conducted prior to its installation, based on local climate information that directly influences power generation, verifying the feasibility of system implementation and avoiding unrewarded investment. Therefore, the objective of this work is to predict the viability of the installation of a photovoltaic system of 3kWp in different places, with the assist of an Artificial Neural Network. Thus, the feedforward network was used for the training, being trained and validated with the support of MatlabVR , and inserting samples of temperature and solar irradiation as input variables. Through the performance methods, the results are favorable for this application, presenting validations with RMSE% in the range of 13-20% and R of not less than 0.93. The predictions presented RMSE% around 19-25% and average powers close to the real values generated by the PV system.
关键词： solar irradiation,renewable energy,electrical systems,energy efficiency,power forecasting,feedforward,artificial neural network,root mean square error,solar photovoltaic system,distributed generation
更新于2025-09-19 17:13:59
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Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models
摘要： Laser-based surface texturing provides highly controlled interference fit between two parts. In this work, artificial intelligence-based models were used to predict the surface properties of laser processed stainless steel 316 samples. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the characteristics of laser surface texturing. The models based on feedforward neural network (FFNN) were developed to examine the effect of the laser process parameters for surface texturing on 316L cylindrical pins. The accuracy of the models was measured by calculating the root mean square error and mean absolute error. The reliability of the ANFIS and FFNN models for the output prediction of the laser surface texturing (LST) system were investigated by using the data measured from experiments based on a 3^3 factorial design, with main processing parameters set as laser power, pulse repetition frequency, and percentage of laser spot overlap. The relative assessment of the models was performed by comparing percentage error prediction. Finally, the impact of input data was examined using predicted response surface plots. Results showed that ANFIS prediction was 48% more accurate compared with that provided by the FFNN model.
关键词： Artificial neural network,Adaptive neuro-fuzzy inference system,Laser texturing
更新于2025-09-19 17:13:59
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Octane prediction from infrared spectroscopic data
摘要： A model for the prediction of research octane number (RON) and motor octane number (MON) of hydrocarbon mixtures and gasoline-ethanol blends has been developed based on infrared spectroscopy data of pure components. Infrared spectra for 61 neat hydrocarbon species were used to generate spectra of 148 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 38 FACE (Fuels for Advanced Combustion Engines) gasoline blends were calculated using PIONA (Paraffin, Isoparaffin, Olefin, Naphthene, and Aromatic) class averages of the pure components. The study sheds light on the significance of dimensional reduction of spectra and shows how it can be used to extract scores with linear correlations to the following important features: molecular weight, paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, ethanolic OH groups, and branching index. Both scores and features can be used as input to predict octane numbers through nonlinear regression. Artificial Neural Network (ANN) was found to be the optimal method where the mean absolute error on a randomly selected test set was within the experimental uncertainty of RON, MON, and octane sensitivity.
关键词： octane prediction,infrared spectroscopy,hydrocarbon blends,artificial neural network,gasoline-ethanol blends,dimensional reduction
更新于2025-09-19 17:13:59
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Preheating and thermal behaviour of a rotating cylindrical workpiece in laser-assisted machining
摘要： Laser-assisted machining is a widely used technique for preheating workpiece to reduce cutting forces and promote machinability in metal machining, thereby enhancing manufacturing quality and productivity. In setting laser-assisted machining parameters, the current practice typically relies on trial-and-error approaches. The uncertainties thereof could lead to adverse outcomes in product manufacturing, thus negating the potential benefits of this machining method. A clear understanding of workpiece thermal behaviour under laser spot heating is pivotal to developing a systematic basis for determining required preheating levels and optimised cutting variables for laser-assisted machining. In achieving this, the experimental methods are recognised to be largely impractical, if not tedious, due to instrument limitations and practicality of suitable non-intrusive measuring methods. Conversely, numerical methodologies do provide precise, flexible and cost-effective analytical options, warranting potential for insightful understanding on the transient thermal impact from laser preheating on rotating workpiece. Presenting such an investigation, this article presents a finite volume-based numerical simulation that examines and analyses the thermal response imparted by laser spot preheating on a rotating cylinder surface. On a rotating frame of reference using the ANSYS Fluent solver, the numerical model is formulated, accounting for transient heat conduction into the cylinder body and the combined convection and radiation loses from the cylinder surface. The model is comprehensively validated to ascertaining its high predictive accuracy and the applicability under reported laser-assisted machining operating conditions. The extensive parametric analyses carried out deliver clear insight into the dynamics of thermal penetration occurring within the workpiece due to laser spot pre-heating. This facilitates appropriate consideration of laser preheating intensity in relation to other operating variables to achieve necessary material softening depth at the workpiece surface prior to setting out on the subsequent machining process. Building upon the data generated, a practically simpler and cost-effective preheating parametric predictor is synthesised for laser-assisted machining using neural network principles incorporating the Levenberg–Marquardt algorithm. This predictive tool is trained and verified as a practical preheating guide for laser-assisted machining for a range of operating conditions.
关键词： numerical thermal modelling,Laser-assisted machining,artificial neural network,validation,thermal treatment
更新于2025-09-19 17:13:59
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Non-communication and artificial neural network based photovoltaic monitoring using the existing impedance relay
摘要： This paper deals with developing a new technique based on artificial neural networks (ANN) for monitoring of the remote grid connected photovoltaic (PV) plant. An ANN utilizes the existing impedance relays’ measurements located at switchgear panel to monitor the PV power generated. Also, the proposed technique is able to monitor the power consumed by the load at the distribution side. The simple proposed technique can monitor and decide the reverse power flow in the distribution feeder. Furthermore, the proposed method identifies an index which diagnosis the PV plant performance. The estimated power from the ANN is compared with the PV generation from real time recorded weather data at the distribution site, and the performance index is obtained accordingly. This technique does not employ any communication infrastructure as usually used in the classical monitoring techniques available in the literature. This advantage makes the proposed scheme very highly attractive from the economical point of view.
关键词： Distance relay,Non-communication monitoring,Photovoltaic distributed generation,Photovoltaic array,Artificial neural network
更新于2025-09-19 17:13:59
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Artificial neural network based assessment of grid-connected photovoltaic thermal systems in heating dominated regions of Iran
摘要： In this paper, an artificial neural network (ANN) is developed to assess hybrid photovoltaic thermal (PVT) systems for grid-connected (GC) electricity generation, space heating and domestic hot water providing in heating dominated regions of Iran. To do so, monthly and annual performance of a 5 kWp GCPVT system is simulated for a single-family house. The simulation results show that the GCPVT system is very promising whereas the annual yield factor varies from 1506 kWh/kWp to 1891 kWh/kWp. Also, an appropriate solar fractions for covering hot water are achieved in a range from 74.5% to 49.4%. A multilayered perceptron feed-forward neural network which is trained by Levenberg-Marquardt algorithm is used to predict AC electrical energy and solar thermal output of the GCPVT system. The developed ANN is based on global horizontal irradiance, ambient temperature, ambient relative humidity and wind speed as inputs. The proposed configuration of ANN presents a high accuracy in predicting output energy of the GCPVT system according to minimum mean square error and maximum correlation coefficient. Analysis of variance is performed to determine the significant control parameters influencing the output energy of the GCPVT system.
关键词： Solar heating,AC electricity,Grid-connected PVT,Artificial neural network,Performance assessment
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE International Ultrasonics Symposium (IUS) - Glasgow, United Kingdom (2019.10.6-2019.10.9)] 2019 IEEE International Ultrasonics Symposium (IUS) - Integrated Ultrasound and Photoacoustic Imaging for Effective Endovenous Laser Ablation: A Characterization Study
摘要： A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural network (ANN) is used to model and optimize the chemical composition of a steel bar. The novel contribution of a TPSO is the use of a Taguchi method mechanism to exploit better solutions in the search space through iterations, the use of the conventional non-linear PSO to increase convergence speed, and the use of random movement for particle diversity. The exploration and exploitation capability of the TPSO were confirmed by performance comparisons with other PSO-based algorithms in solving high-dimensional global numerical optimization problems. Experiments in this paper showed that the TPSO provides higher computational efficiency and higher robustness when solving problems involving seven non-linear benchmark functions, including three unimodal functions, one multimodal functions, two rotated functions, and one shifted functions. The results for the computational experiments show that the TPSO outperforms other PSO-based algorithms reported in the literature. Finally, the results obtained by a TPSO-based ANN model of the chemical composition of the steel bar were consistent with the actual data. That is, the proposed TPSO with three-layer feedforward ANN can be used in practical applications.
关键词： yield point,feedforward artificial neural network,tensile strength,particle swarm optimization,chemical composition of steel bar,Taguchi method
更新于2025-09-19 17:13:59
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Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths
摘要： Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x‐loading weights (x‐LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of “Fuji” and “Qinguan” apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for “Fuji” and “Qinguan” apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the “Fuji” apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x‐LW for the “Qinguan” apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for “Fuji” and “Qinguan” apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both “Fuji” and “Qinguan” apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection.
关键词： optical fiber spectroscopy,artificial neural network,apple crispness,partial least squares method,effective wavelengths,successive projections algorithm
更新于2025-09-19 17:13:59
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A spectroscopic method based on support vector machine and artificial neural network for fiber laser welding defects detection and classification
摘要： Diverse welding processes have been utilized in manufacturing industry for years. But up to date, welding quality still cannot be guaranteed, due to the lack of an efficient and on-line welding defects monitoring method, and this leads to increased manufacturing costs. In this paper, a method based on feature extraction and machine learning algorithm for on-line quality monitoring and defects classification was presented. Plasma radiation was captured by an optical fiber probe, and delivered by an optical fiber to the spectrometer. The captured spectral signal was processed by selecting sensitive emission lines and extracting features of spectral data’s evolution, which realized spectral data compression with low computational cost. After selecting the proper training data set, the designed ANN and SVM allows automatic detection and classification of welding defects. The validity of proposed method was successfully approved by test data set in welding experiments. Welding experiments on galvanized steel sheets showed the corresponding relationship between the output of classifiers and welding defects. Finally, the two classifiers were compared. Experiments indicated the performance of ANN is slightly better than that of SVM, while both of them have its own advantages.
关键词： Laser welding,Support vector machine,Plasma spectral analysis,Artificial neural network
更新于2025-09-19 17:13:59
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[IEEE 2019 International Conference on Applied Automation and Industrial Diagnostics (ICAAID) - Elazig, Turkey (2019.9.25-2019.9.27)] 2019 International Conference on Applied Automation and Industrial Diagnostics (ICAAID) - An Intelligent Faults Diagnosis and Detection Method Based an Artificial Neural Networks for Photovoltaic Array
摘要： At present, renewable energy has many sources, most important of which are PV systems. It is therefore necessary to contribute to the diagnosis of the state of the photovoltaic system and the detection and diagnosis of malfunction. And identify and resolve failures in photovoltaic systems as quickly as possible, so that the system will operate at the expected levels of performance and reliability, thereby ensuring the expected return on investment. This article proposes modeling, detection and classification of photovoltaic system faults by Artificial neural network: ANN using measured values of the PV system voltage (v) and the current of the PV system (I). The method allows the classification of the PV state into several possible situations: normal operation and some different faults, modeling or simulations of MatLab. This method has proved to be able to detect and identify the faults in the PV array accurately and efficiently.
关键词： photovoltaic generator,Matlab / Simulink,artificial neural network,faults detection and diagnosis
更新于2025-09-16 10:30:52