A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions

Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.

A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

Theoretical Model for Calculating Electric-Power Curves Depicting Accelerated Flyer by Exploding Foil

A theoretical model for calculating electric-power curves of small-size foil during its electrical explosion is given.This technique is based on temperature dependence of foil conductivity.After taking into account the energy conversion of the foil explosion,the power-time curve is applied to the hydrodynamic code.One-dimensional numerical simulations of electric-explosion driving flyers are performed using this code.Calculated flyer velocities lie within ±8% of experimental data from Lawrence Livermore National Laboratory （LLNL）,and simulated history curves of flyer velocities coincide well with those measured using velocity interferometer system for any reflector （VISAR）,indicating a helpful work for design optimization of slapper detonators.

General solutions of plane problem for power function curved cracks

A new exact and universal conformal mapping is proposed. Using Muskhelishvili＇s complex potential method, the plane elasticity problem of power function curved cracks is investigated with an arbitrary power of a natural number, and the general solutions of the stress intensity factors （SIFs） for mode I and mode II at the crack tip are obtained under the remotely uniform tensile loads. The present results can be reduced to the well-known solutions when the power of the function takes different natural numbers. Numerical examples are conducted to reveal the effects of the coefficient, the power, and the projected length along the x-axis of the power function curved crack on the SIFs for mode I and mode II.

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm

Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.

A Novel Power Curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks

Accurate prediction of wind turbine power curve is essential for wind farm planning as it influences the expected power production.Existing methods require detailed wind turbine geometry for performance evaluation,which most of the time unattainable and impractical in early stage of wind farm planning.While significant amount of work has been done on fitting of wind turbine power curve using parametric and non-parametric models,little to no attention has been paid for power curve modelling that relates the wind turbine design information.This paper presents a novel method that employs artificial neural network to learn the underlying relationships between 6 turbine design parameters and its power curve.A total of 198 existing pitch-controlled and active stall-controlled horizontal-axis wind turbines have been used for model training and validation.The results showed that the method is reliable and reasonably accurate,with average R^(2)score of 0.9966.

Comments on “General solutions of plane problem for power function curved cracks”

An error has been found in Ref. [1]. In the case of n = 2, authors of Ref. [1] suggested the following conformal mapping function：

Study on Prediction Method of Structural Three-parameter Power Function P-S-N Curve

This paper applies weighted least square method to estimate the three-parameter power function equation of the fatigue life curve, and uses comprehensive fatigue life coefficient to correct the equation, and at the same time combines probability statistics method to bring out the prediction method of structure＇s three- parameter power function P-S-N curve, finally applies the prediction method to a ship＇s frame-type elevate, based on the fatigue test data of it＇s material-SA06 aluminium alloy, to obtain it＇s structure＇s three-parameter power function P-S-N curve. Compared with the conventional least square method, the presented method can give

An explainable AI framework for robust and transparent data-driven wind turbine power curve models

In recent years,increasingly complex machine learning methods have become state-of-the-art in modelling wind turbine power curves based on operational data.While these methods often exhibit superior performance on test sets,they face criticism due to a perceived lack of transparency and concerns about their robustness in dynamic,non-stationary environments encountered by wind turbines.In this work,we address these issues and present a framework that leverages explainable artificial intelligence methods to gain systematic insights into data-driven power curve models.At its core,we propose a metric to quantify how well a learned model strategy aligns with the underlying physical principles of the problem.This novel tool enables model validation beyond the conventional error metrics in an automated manner.We demonstrate,for instance,its capacity as an indicator for model generalization even when limited data is available.Moreover,it facilitates understanding how decisions made during the machine learning development process,such as data selection,pre-processing,or training parameters,affect learned strategies.As a result,we obtain physically more reasonable models,a prerequisite not only for robustness but also for meaningful insights into turbine operation by domain experts.The latter,we illustrate in the context of wind turbine performance monitoring.In summary,the framework aims to guide researchers and practitioners alike toward a more informed selection and utilization of data-driven wind turbine power curve models.

Secure and efficient elliptic curve cryptography resists side-channel attacks

An embedded cryptosystem needs higher reconfiguration capability and security. After analyzing the newly emerging side-channel attacks on elliptic curve cryptosystem （ECC）, an efficient fractional width-w NAF （FWNAF） algorithm is proposed to secure ECC scalar multiplication from these attacks. This algorithm adopts the fractional window method and probabilistic SPA scheme to reconfigure the pre-computed table, and it allows designers to make a dynamic configuration on pre-computed table. And then, it is enhanced to resist SPA, DPA, RPA and ZPA attacks by using the random masking method. Compared with the WBRIP and EBRIP methods, our proposals has the lowest total computation cost and reduce the shake phenomenon due to sharp fluctuation on computation performance.

Unraveling fundamental properties of power system resilience curves using unsupervised machine learning

Power system is vital to modern societies,while it is susceptible to hazard events.Thus,analyzing resilience characteristics of power system is important.The standard model of infrastructure resilience,the resilience triangle,has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades.However,the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves(e.g.,residual performance and duration of recovery).Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data.Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance.There is a dire dearth of empirical studies in the field,which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems.To address this gap,this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States.Through the use of unsupervised machine learning,we examined different curve archetypes,as well as the fundamental properties of each resilience curve archetype.The results show two primary archetypes for power grid resilience curves,triangular curves,and trapezoidal curves.Triangular curves characterize resilience behavior based on three fundamental properties:1.critical functionality threshold,2.critical functionality recovery rate,and 3.recovery pivot point.Trapezoidal archetypes explain resilience curves based on 1.duration of sustained function loss and 2.constant recovery rate.The longer the duration of sustained function loss,the slower the constant rate of recovery.The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.

Dynamic Setting Method of Assessment Indicators for Power Curves of Renewable Energy Sources Considering Scarcity of Reserve Resources

With the increasing proportion of renewable energy sources(RESs)in power grid,the reserve resource(RR)scarcity for correcting power deviation of RESs has become a potential issue.Consequently,the power curve of RES needs to be more rigorously assessed.The RR scarcity varies during different time periods,so the values of assessment indicators should be dynamically adjusted.The assessment indicators in this paper include two aspects,i.e.,deviation exemption ratio and penalty price.Firstly,this paper proposes a method for dynamically calculating the supply capacity and RR cost,primarily taking into account the operating status of thermal units,forecast information of RES,and load curve.Secondly,after clarifying the logical relationship between the degree of RR scarcity and the values of assessment indicators,this paper establishes a mapping function between them.Based on this mapping function,a dynamic setting method for assessment indicators is proposed.In the future,RES will generally be equipped with battery energy storage systems(BESSs).Reasonably utilizing BESSs to reduce the power deviation of RESs can increase the expected income of RESs.Therefore,this paper proposes a power curve optimization strategy for RESs considering self-owned BESSs.The case study demonstrates that the dynamic setting method of assessment indicators can increase the revenue of RESs while ensuring that the penalty fees paid by RESs to the grid are sufficient to cover the RR costs.Additionally,the power curve optimization strategy can help RESs further increase income and fully utilize BESSs to reduce power deviation.

TWO PRIVACY-PRESERVING PROTOCOLS FOR POINT-CURVE RELATION

Numerous privacy-preserving issues have emerged along with the fast development of Internet, both in theory and in real-life applications. To settle the privacy-preserving problems, secure multi-party computation is essential and critical. In this paper, we have solved two problems regarding to how to determine the position relation between points and curves without revealing any private information. Two protocols have been proposed in order to solve the problems in different conditions. In addition, some building blocks have been developed, such as scalar product protocol, so that we can take advantage of them to settle the privacy-preserving computational geometry problems which are a kind of special secure multi-party computation problems. Moreover, oblivious transfer and power series expansion serve as significant parts in our protocols. Analyses and proofs have also been given to argue our conclusion.

S-curve networks and an approximate method for estimating degree distributions of complex networks

In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 （Internet Protocol version 4） addresses, this paper proposes a forecasting model by using S curve （logistic curve）. The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions （i.e., Barabasi-Albert method） is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabasi-Albert method commonly used in current network research.

Fractal Reconstruction of Log Curve

Sedimentary cyclothems at different scales show formations’ fractal structure which can be reflected on logs. The slope of the power spectrum of log is related to the fractal dimension of formations. The fractal dimensions from two logs with similar vertical resolutions are the same. Using fractal interpolating algorithm density log can be reconstructed. The reconstructed log can be compared with core density in washout intervals.

Analysis of the generation mechanism of the S-shaped J–V curves of MoS_(2)/Si-based solar cells

Amorphous–microcrystalline MoS_(2)thin films are fabricated using the sol-gel method to produce MoS_(2)/Si-based solar cells. The generation mechanisms of the S-shaped current density–voltage(J–V) curves of the solar cells are analyzed. To improve the performance of the solar cells and address the problem of the S-shaped J–V curve, a MoS_(2)film and a p^(+) layer are introduced into the front and back interfaces of the solar cell, respectively, which leads to the formation of a p–n junction between the p-Si and the MoS_(2)film as well as ohmic contacts between the MoS_(2)film and the ITO, improving the S-shaped J–V curve. As a result of the high doping characteristics and the high work function of the p^(+) layer, a high–low junction is formed between the p;and p layers along with ohmic contacts between the p;layer and the Ag electrode. Consequently,the S-shaped J–V curve is eliminated, and a significantly higher current density is achieved at a high voltage. The device exhibits ideal p–n junction rectification characteristics and achieves a high power-conversion efficiency(CE) of 7.55%. The findings of this study may improve the application of MoS_(2)thin films in silicon-based solar cells, which are expected to be widely used in various silicon-based electronic and optical devices.

Stability and Leakage Analysis of a Novel PP Based 9T SRAM Cell Using N Curve at Deep Submicron Technology for Multimedia Applications

Due to continuous scaling of CMOS, stability is a prime concerned for CMOS SRAM memory cells. As scaling will increase the packing density but at the same time it is affecting the stability which leads to write failures and read disturbs of the conventional 6T SRAM cell. To increase the stability of the cell various SRAM cell topologies has been introduced, 8T SRAM is one of them but it has its limitation like read disturbance. In this paper we have analyzed a novel PP based 9T SRAM at 45 nm technology. Cell which has 33% increased SVNM (Static Voltage Noise Margin) from 6T and also 22%.reduced leakage power. N curve analysis has been done to find the various stability factors. As compared to the 10T SRAM cell it is more area efficient.

城市轨道交通轨面短波不平顺谱实测研究

针对城市轨道交通诱发的振动噪声问题,对轨面短波不平顺进行研究.首先,对上海市轨道交通线路轨面短波不平顺实测数据进行零均值化、剔除异常值、消除趋势项等预处理.其次,利用改进的协方差法计算得到轨面短波不平顺功率谱,分析轨道类型、曲线半径及钢轨接头对轨面短波不平顺功率谱分布规律的影响.然后,基于非线性最小二乘法对轨面短波不平顺谱进行拟合.最后,提出我国城市轨道交通轨面短波不平顺统计谱表达式.研究结果表明:直线段接头区轨面短波不平顺功率谱密度曲线在整个分析波长范围内具有较多特征峰值;直线段接头区上、下行短波不平顺功率谱密度值随波长的分布特征和数值相差不大,说明上、下行的轨面不平顺状态基本相同;钢轨的磨耗程度与曲线半径呈负相关,内、外轨面不平顺功率谱密度的差异随曲线半径的增大而减小,曲线半径是影响轨面不平顺状态的主要因素之一;线路类型对轨面短波不平顺功率谱的影响不大,不同曲线半径、焊接接头影响明显,在对轨道进行维护和养护时需关注小曲线半径区段以及焊接接头区段的不平顺状态.

电动助力转向系统Carsim-Simulink联合仿真控制研究

电动助力转向系统作为电动汽车的主流配置,其性能好坏对整车转向操纵稳定性能有重要影响。以某款自主品牌电动汽车EPS系统为研究对象,进行动力学分析并依次建立Simulink子系统模型。根据实车参数,设计直线型助力特性曲线并输入前述模型,最终建立Carsim/Simulink联合仿真运动学模型并开展国标操纵稳定性仿真试验。结果表明:在方向盘转向角脉冲和角阶跃试验中,较无EPS助力控制车型,有EPS助力控制车型具有良好的车辆瞬态响应度和操纵稳定性;在转向轻便性试验中,验证本款车型助力特性合理,车辆具有较好的助力效果。

基于DCGCN模型的海上风电场超短期功率预测

图卷积网络(GCN)具有很强的数据关联挖掘能力,近年来在风电功率预测领域获得了广泛关注。然而,传统的基于GCN模型的超短期风电功率预测难以同时处理影响风电功率的两大核心因素(风速与机组状态信息)的双模态问题,基于此,提出了一种基于双通道图卷积网络(DCGCN)的海上风电场超短期功率预测模型。首先,建立以理论功率曲线为基准的机组状态指标模型,定量表征机组状态变化对其发电能力的影响;其次,构建海上风电场图拓扑,建立基于风速和状态邻接矩阵的风电场各机组捕获的风速与机组状态信息的关联关系模型;最后,建立基于DCGCN的风电场超短期功率预测方法。算例结果表明,所提模型有助于提高风电场功率预测模型的训练效率和预测精度。