Search

Abstract Accurate parameter identification plays a crucial role in realizing precise modelling, design optimization, condition monitoring, and fault diagnosis of photovoltaic systems. However, due to the nonlinear, multivariate, and multistate characteristics of PV models, it is difficult to identify perfect model parameters using traditional analytical and numerical methods. Besides, some existing methods may stick in local optimum and have slow convergence speed. To address these challenges, this paper proposes an enhanced nature‐inspired OLARO algorithm for PV parameter identification under different conditions. OLARO is improved from ARO incorporating existing opposition‐based learning, Lévy flight and roulette fitness‐distance balance to improve global search capability and avoid local optima. Firstly, a novel data smoothing measure is taken to reduce the noises of I–V curves. Then, OLARO is compared with several common algorithms on different solar cells and PV modules using robustness analysis and statistical tests. The results indicate that OLARO has better ability than others to extract parameters from PV models such as single diode, double diode, and PV module models. Moreover, the convergence performance of OLARO is more excellent than the other algorithms. Additionally, the I–V curves of two PV modules under different irradiance and temperature conditions are applied to verify the robustness of the proposed parameter extraction algorithm. Besides, OLARO is successfully applied to two real operating PV modules, and it is compared with two recent well‐known methods improved by FDB. Finally, sensitivity analysis, stability analysis, and discussion of practical challenges are provided.

Abstract The acceptance check is a key step to decide whether an overhead transmission line can be put into use. However, for the acceptance check of towers, the manual means is still adopted mostly nowadays, which is dangerous and inefficient. The main challenges for intelligent check technique are the automatic tower segmentation and intelligent requirements of some detection items. Here, based on point clouds from lidars, some intelligent methods are proposed for tower related acceptance check items. The geometric structural changing rules for towers are found and analyzed to identify and segment towers from the scene point cloud automatically. A model‐constrained method is proposed for fine filtering of towers. With point cloud registration strategies, the tilt angle of a tower is calculated intelligently and the defect detection of a tower is conducted successfully. Experiments show that the results of the proposed methods are accurate, efficient and automatic, which have potential for real applications.

The genus Ilex belongs to the sole family and is the single genus within the order Aquifoliales, exhibiting significant phenotypic diversity. However, the genetic differences underlying these phenotypic variations have rarely been studied. In this study, collinearity analyses of three Ilex genomes, Ilex latifolia Thunb., Ilex polyneura (Hand.-Mazz.) S. Y. Hu, and Ilex asprella Champ. ex Benth., indicated a recent fusion event contributing to the reduction of chromosomes in I. asprella. Comparative genome analyses showed slight differences in gene annotation among the three species, implying a minimal disruption of genes following chromosomal fusion in I. asprella. Comprehensive annotation of transposable elements (TEs) revealed that TEs constitute a significant portion of the Ilex genomes, with LTR transposons being predominant. TEs exhibited an inverse relationship with gene density, potentially influencing gene regulation and chromosomal architecture. TE insertions were shown to affect the conformation and binding sites of key genes such as 7-deoxyloganetin glucosyltransferase and transmembrane kinase (TMK) genes, highlighting potential functional impacts. The structural variations caused by TE insertions suggest significant roles in the evolutionary dynamics, leading to either loss or gain of gene function. This study underscores the importance of TEs in shaping the genomic landscape and evolutionary trajectories of Ilex species.

Abstract A convenient and effective method based on vector analysis was proposed in this paper to quantitatively describe the pattern of the wind speed distribution curve. This method can accurately describe the steepness of the single‐peak curve by using two parameters of concentration degree (CD) and concentration period (CP). Through the analysis of wind speed data in China over the past 40 years, this paper found that regional wind resources with larger CD were poorer, while regional wind resources with larger CP were richer. In addition, there were obvious cubic relationships between the CP and the average wind power density. These two parameters could reflect the richness of wind energy and realize the comparisons of wind resources across time and scales. In comparison with conventional approaches, this method is simpler and avoids fitting process, which has broad promotion prospects in the field of power grids.

Abstract Irradiance prediction is a vital task in the renewable energy field. Its aim is to forecast the irradiance or power of a photovoltaic plant and thus provide a reference for stabilizing the power grid. In the real scenarios, missing data can significantly reduce the accuracy of the prediction. Meanwhile, due to the unawareness of the distribution of datasets, it is difficult to choose a suitable imputation method before modeling. Also, different imputation methods do not have the same good effects on different datasets. In this article, a recurrent neural network with an adaptive neural imputation module is proposed for forecasting direct solar irradiance using missing data. The model predicts future 4‐h irradiance based on the missing historical climate and irradiance data without imputing the data in pre‐processing stage. The proposed model is tested on the open access datasets, with missing values generated randomly in all input series. The model performance is compared under various missing rates and different input factors with other imputation methods. The results demonstrate that the proposed methods outperform other methods under different evaluation metrics. Furthermore, the authors integrate the model with the attention mechanism and find it has better performance at high irradiance.

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional approaches commonly collect training and testing samples from the same patient due to inter-individual variability. However, the challenging problem of domain shift between various subjects remains unsolved, resulting in a low conversion rate to the clinic. In this work, a domain adaptation (DA)-based model is proposed to circumvent this issue. The short-time Fourier transform (STFT) is employed to extract the time-frequency features from raw EEG data, and an autoencoder is developed to map these features into high-dimensional space. By minimizing the inter-domain distance in the embedding space, this model learns the domain-invariant information, such that the generalization ability is improved by distribution alignment. Besides, to increase the feasibility of its application, this work mimics the data distribution under the clinical sampling situation and tests the model under this condition, which is the first study that adopts the assessment strategy. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can minimize the domain gap effectively compared with previous approaches.

A variable fractional-order equivalent circuit model is proposed to accurately describe the dynamic characteristics of lithium-ion batteries (LIBs). Firstly, a fractional impedance model (FIM) is established, such that the fractional-order (FO) is a polynomial function of the LIB state of charge (SOC). Then, a chaotic adaptive fractional particle swarm optimization (CAFPSO) method is derived to identify the parameters of the FIM. Experiments reveal the reliability of the novel approach through the root-mean-squared error (RMSE) and the mean absolute error (MAE) of the LIB terminals voltage, yielding the values 8.99 mV and 4.56 mV, respectively. This translates into accuracy improvements of 22.5% and 34.4% for the particle swarm optimization (PSO) algorithm and 57.9% and 72.8% for the adaptive fractional particle swarm optimization (AFPSO) algorithm, respectively.

Extensive research has been paid for event detection in the past decades. However, the timeliness requirement of real-world applications cannot be satisfied by these approaches. Early event detector is thus proposed recently to deal with this issue. Early detection aims to recognize the target as early as possible, i.e., it can detect partial events and create a monotonous function to rank them. Although important and practical, few studies have been given for early detection due to its complexity. Max-margin Early Event Detector (MMED) is a well-known approach, which achieves satisfied performance in identifying partial events. However, the MMED works in an offline manner and may fail in this era of streaming sequence. In addition, the large memory consumption and high retraining time cost of the MMED are hard to be satisfied in general platform conditions. In this paper, we introduce an online learning technique with max-margin to early event detection. The proposed model could be adapted to the changing data distribution of the streaming sequences. No historical data need to be stored. Therefore, both the memory requirement and retraining time cost are decreased significantly. We evaluate the proposed approach on three benchmark datasets with various complexities. The extensive results demonstrate both the effectiveness and efficiency of the proposed framework.

Boosted by a strong solar power market, the electricity grid is exposed to risk under an increasing share of fluctuant solar power. To increase the stability of the electricity grid, an accurate solar power forecast is needed to evaluate such fluctuations. In terms of forecast, solar irradiance is the key factor of solar power generation, which is affected by atmospheric conditions, including surface meteorological variables and column integrated variables. These variables involve multiple numerical time-series and images. However, few studies have focused on the processing method of multiple data types in an inter-hour direct normal irradiance (DNI) forecast. In this study, a framework for predicting the DNI for a 10-min time horizon was developed, which included the nondimensionalization of multiple data types and time-series, development of a forecast model, and transformation of the outputs. Several atmospheric variables were considered in the forecast framework, including the historical DNI, wind speed and direction, relative humidity time-series, and ground-based cloud images. Experiments were conducted to evaluate the performance of the forecast framework. The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41% and a normalized root mean square error (nRMSE) of 20.53%, and outperforms the persistent model with an improvement of 34% in the nRMSE.

Hexokinase (HXK) family proteins exert critical roles in catalyzing hexose phosphorylation, sugar sensing, and modulation of plant growth and stress adaptation. Nevertheless, a large amount remains unknown about the molecular profile of HXK enzymes in Populus trichocarpa, a woody model tree species. A genome-wide survey of HXK-encoding genes, including phylogenies, genomic structures, exon/intron organization, chromosomal distribution, and conserved features, was conducted, identifying six putative HXK isogenes (PtHXK1-6) in the Populus genome. The evolutionary tree demonstrated that 135 homologous HXKs between 17 plant species were categorized into four major subfamilies (type A, B, C, and D), clustering one plastidic (PtHXK3) and five mitochondrial PtHXKs grouped into type A and B, respectively. The in silico deduction prompted the presence of the conserved sugar-binding core (motif 4), phosphorylation sites (motif 2 and 3), and adenosine-binding domains (motif 7). The transcriptomic sequencing (RNA-seq) and the quantitative real-time PCR (qRT-PCR) assays revealed that three isogenes (PtHXK2, 3, and 6) were abundantly expressed in leaves, stems, and roots, while others appeared to be dominantly expressed in the reproductive tissues. Under the stress exposure, PtHXK2 and 6 displayed a significant induction upon the pathogenic fungi (Fusarium solani) infection and marked promotions by glucose feeding in roots. In contrast, the PtHXK3 and 6 are ABA-responsive genes, following a dose-dependent manner. The comprehensive analyses of the genomic patterns and expression profiling provide theoretical clues and lay a foundation for unraveling the physiological and signaling roles underlying the fine-tuned PtHXKs responding to diverse stressors.

This work was supported in part by the National Natural Science Fund of China under Grant 61233011, Grant 61374006, Grant 61473086, Grant 61463008, Grant 61703100, Grant 61773118, and Grant 61773117, in part by the Major Program of National Natural Science Foundation of China under Grant 11190015, in part by the Natural Science Foundation of Jiangsu under Grant BK20131300 and Grant BK20170692, in part by the Innovation Fund of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, under Grant JYB201601, in part by the Innovation Fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University, under Grant MCCSE2017B01, and in part by the Fundamental Research Funds for the Central Universities under Grant 2242016k30009.

The existing overlap singularities in the parameter space significantly affect the learning dynamics of the multilayer perceptrons. From the obtained theoretical learning trajectories near overlap singularity, when the learning process has been affected by the overlap singularity, the influence area of the overlap singularity is just the line space where the two hidden units equal to each other. However, in the practical applications, different case has been observed and the influence area of such singularity may be larger. By analyzing the generalization error of multilayer perceptrons, we find that the error surface is much flatter near overlap singularity and the singularity would have much larger influence area. Finally, the validity of the obtained results are verified by taking an artificial experiment and two real-data experiments.

Fusarium solani (Fs) is one of the notorious necrotrophic fungal pathogens that cause root rot and vascular wilt, accounting for the severe loss of Populus production worldwide. The plant–pathogen interactions have a strong molecular basis. As yet, the genomic information and transcriptomic profiling on the attempted infection of Fs remain unavailable in a woody model species, Populus trichocarpa. We used a full RNA-seq transcriptome to investigate the molecular interactions in the roots with a time-course infection at 0, 24, 48, and 72 h post-inoculation (hpi) of Fs. Concomitantly, the invertase and invertase inhibitor-like gene families were further analyzed, followed by the experimental evaluation of their expression patterns using quantitative PCR (qPCR) and enzyme assay. The magnitude profiles of the differentially expressed genes (DEGs) were observed at 72 hpi inoculation. Approximately 839 genes evidenced a reception and transduction of pathogen signals, a large transcriptional reprogramming, induction of hormone signaling, activation of pathogenesis-related genes, and secondary and carbohydrate metabolism changes. Among these, a total of 63 critical genes that consistently appear during the entire interactions of plant–pathogen had substantially altered transcript abundance and potentially constituted suitable candidates as resistant genes in genetic engineering. These data provide essential clues in the developing new strategies of broadening resistance to Fs through transcriptional or translational modifications of the critical responsive genes within various analyzed categories (e.g., carbohydrate metabolism) in Populus.

AAOISE, the Antarctic Astronomical Observations Intelligent Support Equipment, is an autonomous control equipment serving for energy support and environment thermal preservation, which is used for astronomical science observations in the Antarctic “Dome A”. It was deployed to “Dome A” and had an unattended run until now. The AAOISE stressed on the ways to adapt to adverse circumstances of “Dome A” and to have as little influence on the environment as possible. Its shape and structure are fully qualified for transportation and thermal insulation demands. The power generation and control systems are designed to provide continuous power and heat. Its communication system can support high-reliability data transmission and communications. It offers a possibility for developing “Dome A” scientific activities and remote monitoring of the running situation of the science instruments. This paper presents a detailed description of the power generation, power control, thermal management, instrument interface, and communications systems for AAOISE.

This paper considers an adaptive fault-tolerant control problem for a class of uncertain strict feedback nonlinear systems, in which the actuator has an unknown drift fault and the loss of effectiveness fault. Based on the event-triggered theory, the adaptive backstepping technique, and Lyapunov theory, a novel fault-tolerant control strategy is presented. It is shown that an appropriate comprise between the control performance and the sensor data real-time transmission consumption is made, and the fault-tolerant tracking control problem of the strict feedback nonlinear system with uncertain and unknown control direction is solved. The adaptive backstepping method is introduced to compensate the actuator faults. Moreover, a new adjustable event-triggered rule is designed to determine the sampling state instants. The overall control strategy guarantees that the output signal tracks the reference signal, and all the signals of the closed-loop systems are convergent. Finally, the fan speed control system is constructed to demonstrate the validity of the proposed strategy and the application of the general systems.

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

Wind speed prediction is the key to wind power prediction, which is very important to guarantee the security and stability of the power system. Due to dramatic changes in wind speed, it needs high-frequency sampling to describe the wind. A large number of samples are generated and affect modeling time and accuracy. Therefore, two novel active learning methods with sample selection are proposed for short-term wind speed prediction. The main objective of active learning is to minimize the number of training samples and ensure the prediction accuracy. In order to verify the validity of the proposed methods, the results of support vector regression (SVR) and artificial neural network (ANN) models with different training sets are compared. The experimental data are from a wind farm in Jiangsu Province. The simulation results show that the two novel active learning methods can effectively select typical samples. While reducing the number of training samples, the prediction performance remains almost the same or slightly improved.

A novel sufficient condition is developed to obtain the discrete-time analogues of cellular neural network (CNN) with periodic coefficients in the three-dimensional space. Existence and global stability of a periodic solution for the discrete-time cellular neural network (DT-CNN) are analysed by utilizing continuation theorem of coincidence degree theory and Lyapunov stability theory, respectively. In addition, an illustrative numerical example is presented to verify the effectiveness of the proposed results.

The existence of singularities often affects the learning dynamics in feedforward neural networks. In this paper, based on theoretical analysis results, we numerically analyze the learning dynamics of radial basis function (RBF) networks near singularities to understand to what extent singularities in uence the learning dynamics. First, we show the explicit expression of the Fisher information matrix for RBF networks. Second, we demonstrate through numerical simulations that the singularities have a significant impact on the learn- ing dynamics of RBF networks. Our results show that overlap singularities mainly have in uence on the low dimensional RBF networks and elimination singularities have a more significant impact to the learning processes than overlap singularities in both low and high dimensional RBF networks, whereas the plateau phenomena are mainly caused by the elimination singularities. The results can also be the foundation to investigate the singular learning dynamics in deep feedforward neural networks. [ABSTRACT FROM AUTHOR]

The alternating direction method has been used widely in the power systems field for solving the multiarea dispatch problem. However, experience with applications has shown that the convergence rate of the alternating direction method depends significantly on the selection of the penalty parameter of the linear consistency constraint. Typically, it is difficult to obtain the optimal penalty parameter in advance. In this paper, for the purpose of solving this problem, we propose centralized and distributed self-adaptive penalty parameter strategies that allow the value of the penalty parameter to increase or decrease based on the information from each iteration. Simulation results illustrate that the proposed centralized and distributed self-adaptive methods are superior to the traditional alternating direction method in terms of robustness and convergence rate. [ABSTRACT FROM AUTHOR]

Model structure selection with respect to short-term wind speed forecasting is relatively difficult due to the stochastic and intermittent nature of the wind speed distribution. In order to overcome the disadvantages in traditional approaches such as computing burden and low accuracy, a novel model structure selection technique about short-term wind speed forecasting is proposed in order to improve the computational efficiency and forecasting accuracy using the model variable selection, variable order estimation, model structure optimization techniques, and so on. The detailed and complete process flow associated to the theoretical analysis of the proposed model structure selection technique is described. Moreover, both the so-called overkill in the data filtering and so-called overfitting in the learning processing are avoided by a proper technique in the design of proposed approach. In order to verify the effectiveness of proposed strategy in a practical application, all the experimental results are evaluated based on the real data provided by a sampling device with respect to a low-wind-speed wind turbine (i.e., FD-77) of a wind power plant of north China. Finally, the developed model structure selection technique is verified by the cross-validation method. [ABSTRACT FROM AUTHOR]

Radial basis function (RBF) networks are one of the most widely used models for function approximation and classification. There are many strange behaviors in the learning process of RBF networks, such as slow learning speed and the existence of the plateaus. The natural gradient learning method can overcome these disadvantages effectively. It can accelerate the dynamics of learning and avoid plateaus. In this letter, we assume that the probability density function (pdf) of the input and the activation function are gaussian. First, we introduce natural gradient learning to the RBF networks and give the explicit forms of the Fisher information matrix and its inverse. Second, since it is difficult to calculate the Fisher information matrix and its inverse when the numbers of the hidden units and the dimensions of the input are large, we introduce the adaptive method to the natural gradient learning algorithms. Finally, we give an explicit form of the adaptive natural gradient learning algorithm and compare it to the conventional gradient descent method. Simulations show that the proposed adaptive natural gradient method, which can avoid the plateaus effectively, has a good performance when RBF networks are used for nonlinear functions approximation. [ABSTRACT FROM AUTHOR]