POWER VARIATION OF SUBFRACTIONAL BROWNIAN MOTION AND APPLICATION

In this paper, we consider the power variation of subfractional Brownian mo- tion. As an application, we introduce a class of estimators for the index of a subfractional Brownian motion and show that they are strongly consistent.

Potential Power of the Pyramidal Structure III: Discovery of Pyramid Effects with and without Seasonal Variation

Since October 2007, we have been conducting rigorous scientific research on the unexplained “power” of a pyramidal structure (PS). From our research results so far, we could classify the pyramid effects by the PS into the following two types. (i) The pyramid effects in which the PS converted the test subject’s unexplained energy to affect biosensors when the test subject entered the PS and meditated. (ii) The pyramid effects in which the potential power of the PS affected biosensors if the test subject had not been inside the PS for at least 20 days and the test subject’s unexplained energy was excluded. In this paper, we report new results regarding (ii). As a result of dividing a year according to the four seasons of winter, spring, summer, and autumn and analyzing the pyramid effect of each period, the following points were found. 1) There was a pyramid effect without seasonal variation. The pyramid effect on the lower and upper layers was different throughout the year for the biosensors placed at the PS apex in two layers, regardless of the season. 2) There was a pyramid effect with seasonal variation. The value of the psi index, which indicates the magnitude of the pyramid effect, changed as the seasons changed, while different pyramid effects were maintained on the lower and upper layers. Regarding the change in the pyramid effect depending on the season, the psi index in summer was larger than that in winter in both the lower and upper layers. From these results, we found that there are two types of potential power at the PS apex: seasonal potential power and non-seasonal potential power.

A Wind Power Prediction Framework for Distributed Power Grids

To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.

Improved AVOA based on LSSVM for wind power prediction

Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.

Investigation of power-type variational principles in liquid-filled system

Starting from the basic equations of hydrodynamics, the maximum power- type variational principle of the hydrodynamics of viscous fluids was established by Weizang CHIEN in 1984. Through long-term research, it is clarified that the maximum power-type variational principle coincides with the Jourdian principle, which is one of the common principles for analytical mechanics. In the paper, the power-type variational principle is extended to rigid-body dynamics, elasto-dynamics, and rigid-elastic：liquid coupling dynamics. The governing equations of the rigid-elastic-liquid coupling dynamics in the liquid-filled system are obtained by deriving the stationary value conditions. The results show that, with the power-type variational principles studied directly in the state space, some transformations in the time domain space may be omitted in the establishing process, and the rigid-elastic-liqUid coupling dynamics can be easily numerically modeled. Moreover, the analysis of the coupling dynamics in the liquid-filled system in this paper agrees well with the numerical analyses of the coupling dynamics in the liquid-filled system offered in the literatures.

Seasonal Performance of Solar Power Plants in the Sahel Region: A Study in Senegal, West Africa

The main objective of this study is to evaluate the seasonal performance of 20 MW solar power plants in Senegal. The analysis revealed notable seasonal variations in the performance of all stations. The most significant yields are recorded in spring, autumn and winter, with values ranging from 5 to 7.51 kWh/kWp/day for the reference yield and 4.02 to 7.58 kWh/kWp/day for the final yield. These fluctuations are associated with intense solar activity during the dry season and clear skies, indicating peak production. Conversely, minimum values are recorded during the rainy season from June to September, with a final yield of 3.86 kWh/kW/day due to dust, clouds and high temperatures. The performance ratio analysis shows seasonal dynamics throughout the year with rates ranging from 77.40% to 95.79%, reinforcing reliability and optimal utilization of installed capacity. The results of the capacity factor vary significantly, with March, April, May, and sometimes October standing out as periods of optimal performance, with 16% for Kahone, 16% for Bokhol, 18% for Malicounda and 23% for Sakal. Total losses from solar power plants show similar seasonal trends standing out for high loss levels from June to July, reaching up to 3.35 kWh/kWp/day in June. However, using solar trackers at Sakal has increased production by up to 25%, demonstrating the operational stability of this innovative technology compared with the plants fixed panel. Finally, comparing these results with international studies confirms the outstanding efficiency of Senegalese solar power plants, other installations around the world.

Protection of Zero-Sequence Power Variation in Mountain Wind Farm Collector Lines Based on Multi-Mode Grounding

The arc-suppression coil(ASC)in parallel low resistance(LR)multi-mode grounding is adopted in the mountain wind farm to cope with the phenomenon that is misoperation or refusal of zero-sequence protection in LR grounding wind farm.If the fault disappears before LR is put into the system,it is judged as an instantaneous fault;while the fault does not disappear after LR is put into the system,it is judged as a permanent fault;the single-phase grounding fault(SLG)protection criterion based on zerosequence power variation is proposed to identify the instantaneous-permanent fault.Firstly,the distribution characteristic of zero-sequence voltage(ZSV)and zero-sequence current(ZSC)are analyzed after SLGfault occurs in multi-mode grounding.Then,according to the characteristics that zero-sequence power variation of non-fault collector line is small,while the zero-sequence power variation of fault collector line can reflect the active power component of fault resistance,the protection criterion based on zero-sequence power variation is constructed.The theoretical analysis and simulation results show that the protection criterion can distinguish the property of fault only by using the single terminal information,which has high reliability.

FEATURES AND COMPARISONS OF THE QUASI-BIENNIAL VARIATIONS IN THE ASIA-PACIFIC MONSOON SUBSYSTEMS

The National Centers for Environmental Prediction (NCEP) reanalysis data, Climate Diagnostics Center Merged Analysis of Precipitation (CMAP) results, and NOAA Extended Reconstructed Sea Surface Temperature (SST), have been utilized in this paper to study the quasi-biennial variations in Asia-Pacific monsoon subsystems and associated SST anomalies (SSTA) and wind anomalies. Four monsoon indices are computed from NCEP/ National Center for Atmospheric Research (NCAR) reanalysis to represent the South Asian monsoon (SAM), South China Sea summer monsoon (SCSSM), Western North Pacific monsoon (WNPM) and East Asian monsoon (EAM), respectively. The quasi-biennial periods are very significant in Asia-Pacific monsoons (as discovered by power spectrum analysis), and for SAM and EAM---with moderate effects by El Ni?o-Southern Oscillation (ENSO)---the quasi-biennial periods are the most important factor. For SCSSM and WNPM (once again due to the effects of ENSO), the quasi-biennial periods are of secondary durations. There are obvious interdecadal variations in the quasi-biennial modes of the Asia-Pacific monsoon, so in the negative phase the biennial modes will not be significant or outstanding. The wind anomalies and SSTA associated with the biennial modes are very different in the SAM, WNPM and EAM regions. Since the WNPM and SCSSM are very similar in the biennial modes, they can be combined into one subsystem, called SCS/WNPM.

Spatial and Annual Variation of Offshore Wind Resource in China

According to the Chinese 'Twelfth Five-Year Plan', two large scale wind farms are planned to be built in the shore of Shandong province and Guangdong province to meet the increasing electricity demand with economic development. Before the construction of wind farm, it is necessary to evaluate the wind potential and its temporal variation along the coast of Shandong province, Guangdong province and Zhejiang province that have been studied in this paper. The data used were obtained from Goddard Earth Observing System (GEOS) Data Assimilation System. The results showed that there is rich wind supply in Zhejiang province with small annual variation. Further away from shore, the wind energy will increase fastest in Guangdong area. The yield of wind energy in Shandong province is not as rich as in the other two provinces as predicted in the study. Furthermore, the layout of wind turbines in wind farm was also investigated to absorb wind energy at the highest efficiency by wind farm. Our results provide a reference for the future construction of wind farms.

The Influence of Radial Area Variation on Wind Turbines to the Axial Induction Factor

Improvements in the aerodynamic design will lead to more efficiency of wind turbines and higher power production. In the present study, a 3D parametric gas turbine blade geometry building code, 3DBGB, has been modified in order to include wind turbine design capabilities. This approach enables greater flexibility of the design along with the ability to design more complex geometries with relative ease. The NREL NASA Phase VI wind turbine was considered as a test case for validation and as a baseline by which modified designs could be compared. The design parameters were translated into 3DBGB input to create a 3D model of the wind turbine which can also be imported into any CAD program. Design modifications included replacing the airfoil section and modifying the thickness to chord ratio as a function of span. These models were imported into a high-fidelity CFD package, Fine/TURBO by NUMECA. Fine/TURBO is a specialized CFD platform for turbo-machinery analysis. A code-geomturbo was used to convert the 3D model of the wind turbine into the native format used to define geometries in the Fine/TURBO meshing tool, AutoGrid. The CFD results were post processed using a 3D force analysis code. The radial force variations were found to play a measurable role in the performance of wind turbine blades. The radial component of the blade surface area as it varies in span is the dominant contributor of the radial forces. Through the radial momentum equation, this radial force variation is responsible for creating the streamline curvature that leads to the expansion of the streamtube (slipstream) that is responsible for slowing the wind velocity ahead of the wind turbine leading edge, which is quantified as the axial induction factor. These same radial forces also play a role in changing the slipstream for propellers. Through the design modifications, simulated with CFD and post-processed appropriately, this connection with the radial component of area to the radial forces to the axial induction factor, and finally the wind turbine power is demonstrated. The results from the CFD analysis and 3D force analysis are presented. For the case presented, the power increases by 5.6% due to changes in airfoil thickness only.

The Background Variation of Natural Source ELF and Its EM Abnormal Phenomena in Yunnan Earthquakes

In order to add earthquake monitoring methods and develop new method research,the ELF Network for Earthquake Monitoring selected 30 stations in the Capital Circle and the Sichuan-Yunnan region. Finding electromagnetic field background variation is the basis of distinguishing the seismic electromagnetic anomalies. This paper introduces the data acquisition and selection of the Shexian,Anqiu,Lijiang and Dali stations which have recorded for longer time with better data and are located on the similar latitude. Then we use the natural source electromagnetic field's auto-power spectrum to express the intensity of the electromagnetic field. By using power spectral data of many frequencies in the observation frequency band,after the data pre-processing and sliding average noising,the background variation of extremely low frequency stations and the range ability were acquired.Taking the Baoshan M5. 1 earthquake on October 30,2015 and Dali M5. 0 earthquake on May 18,2016 as examples,the authors analyzed the earthquake electromagnetic anomaly characteristic of ELF stations around the earthquakes.

Bivariate Zero-Inflated Power Series Distribution

Many researchers have discussed zero-inflated univariate distributions. These univariate models are not suitable, for modeling events that involve different types of counts or defects. To model several types of defects, multivariate Poisson model is one of the appropriate model. This can further be modified to incorporate inflation at zero and we can have multivariate zero-inflated Poisson distribution. In the present article, we introduce a new Bivariate Zero Inflated Power Series Distribution and discuss inference related to the parameters involved in the model. We also discuss the inference related to Bivariate Zero Inflated Poisson Distribution. The model has been applied to a real life data. Extension to k-variate zero inflated power series distribution is also discussed.

基于特征图像组合与改进ResNet-18的电能质量扰动识别方法

针对传统电能质量扰动(power quality disturbance,PQD)识别体系中单一图像特征信息受限与算法识别能力不足等问题,依据特征融合的思想,提出一种基于特征图像组合与改进ResNet-18的PQD识别方法。首先,对PQD信号进行变分模态分解(variational mode decomposition,VMD)得到一系列固有模态函数(intrinsic mode functions,IMFs)与残差分量;其次,将IMFs、残差分量、原始扰动信号与Subtract分量纵向拼接成分量矩阵,利用信号-图像转化方法生成特征分量彩色图;再次,对原始扰动信号进行连续小波变换(continuous wavelet transform,CWT)生成小波时-频图;最后,将特征分量彩色图与小波时-频图组合输入改进的六通道ResNet-18中训练学习并完成扰动识别。通过仿真对PQD识别方法进行分析并将其与目前常用识别体系进行比较。结果表明,所提方法具有较好的抗噪性能并且能够更好地提取PQD特征信息,达到更高的识别准确率。

基于多维气象信息时空融合和MPA-VMD的短期电力负荷组合预测模型

为提高电力负荷预测精度,需考虑区域内不同地区多维气象信息对电力负荷影响的差异性。在空间维度上,提出多维气象信息时空融合的方法,利用Copula理论将多座气象站的风速、降雨量、温度、日照强度等气象信息与电力负荷进行非线性耦合分析并实现时空融合。在时间维度上,采用海洋捕食者算法(MPA)实现变分模态分解(VMD)核心参数的自动寻优,并采用加权排列熵构造MPA-VMD适应度函数,实现负荷序列的自适应分解。通过将时间维度各分量与空间维度各气象信息进行融合构造长短期记忆(LSTM)网络模型与海洋捕食者算法-最小二乘支持向量机(MPA-LSSVM)模型的输入集,得到各分量预测结果,根据评价指标选择各分量对应的预测模型,重构得到整体预测结果。算例分析结果表明,所提预测模型优于传统预测模型,有效提高了电力负荷预测精度。

基于VMD-ISSA-GRU组合模型的短期风电功率预测

为解决风速不确定性和波动性造成风电功率预测精度不高的问题,提出一种基于变分模态分解(VMD)、改进麻雀搜索算法(ISSA)和门控循环神经网络(GRU)的VMD-ISSA-GRU组合模型。首先,利用中心频率法确定采用VMD分解后的模态分量个数,这样有效避免了过分解或者分解不充分。其次引入混沌映射、非线性递减权重以及一个突变策略来改进麻雀搜索算法,用于优化门控循环神经网络,然后对分解得到的各个子序列建立ISSA-GRU预测模型,最后叠加每个子序列的预测值得到最终的预测值。将该模型用于实际风电功率预测,实验结果表明:VMD-ISSA-GRU组合模型的平均绝对误差、平均绝对百分比误差、均方根误差分别为1.2118MW、1.8900及1.5916MW;相较于传统的GRU、长短时记忆(LSTM)神经网络、BiLSTM(Bi-directional LSTM)神经网络模型以及其他组合模型在预测精度上都有明显的提升,能很好地解决风电功率预测精度不高的问题.

基于新型相似日选取和VMD-NGO-BiGRU的短期光伏功率预测

光伏功率预测在现代电力系统调度和运行中起着重要作用.针对光伏发电功率的多变性和复杂性,提出了一种基于新型相似日选取和北方苍鹰算法(Northern Goshawk Optimization,NGO)优化双向门控循环单元(Bidirectional Gated Recurrent Unit,BiGRU)的短期光伏功率预测方法.首先,利用斯皮尔曼相关系数选取主要气象因子,通过变分模态分解(Variational Mode Decomposition,VMD)将原始光伏功率和最大气象因子分解重构为一系列子信号.其次,通过构建新的评价指标筛选出相似日数据集,利用一组BiGRU建立以相似日子信号为网络输入的深度学习模型,并利用NGO对每个BiGRU网络的超参数进行有效优化.最后,对各子信号的预测结果进行综合,得到最终的光伏功率预测值.仿真结果表明,所提混合深度学习方法在预测精度和计算效率方面均优于其他方法.

运行工况自适应的风储系统双层功率平滑策略

针对风电并网带来的功率波动问题,提出一种运行工况自适应的风电-混合储能系统双层功率平滑策略。在上层,考虑风储系统并网功率波动和储能运行状态,利用无模型自适应控制动态跟踪经滑动平均算法得到的并网参考功率,生成混合储能总功率任务;在下层,考虑储能介质的技术特性,利用模糊控制动态调节经变分模态分解确定的混合储能总功率任务的频率分界点,完成功率的自适应分配。算例分析表明:所提策略可以自适应平抑多工况下的风电功率波动,实现混合储能各介质间功率的合理分配。

基于特征选择及ISSA-CNN-BiGRU的短期风功率预测

针对风电功率随机性大、平稳性低,以及直接输入预测模型往往难以取得较高精度等问题,提出了一种基于特征选择及改进麻雀搜索算法(ISSA)优化卷积神经网络-双向门控循环单元(CNN-BiGRU)的短期风电功率预测方法。首先,利用变分模态分解(VMD)将原始功率分解为一组包含不同信息的子分量,以降低原始功率序列的非平稳性,提升可预测性,同时通过观察中心频率方式确定模态分解数。其次,对每一分量采用随机森林(RF)特征重要度的方法进行特征选择,从风速、风向、温度、空气密度等气象特征因素中,选取对各个分量预测贡献度较高的影响因素组成输入特征向量。然后,建立各分量的CNN-BiGRU预测模型,针对神经网络算法参数难调、手动配置参数随机性大的问题,利用ISSA对模型超参数寻优,自适应搜寻最优参数组合。最后,叠加各分量的预测值,得到最终的预测结果。以中国内蒙古某风电场实际数据进行仿真实验,与多种单一及组合预测方法进行对比,结果表明,本文所提方法相比于其他方法具有更高的预测精度,其平均绝对百分比误差值达到2.644 0%;在其他4个数据集上进行的模型准确性及泛化性验证结果显示,模型平均绝对百分比误差值分别为4.385 3%、3.174 9%、1.576 1%和1.358 8%,均保持在5.000 0%以内,证明本文所提方法具有较好的预测精度及泛化能力。

H桥级联型静止同步补偿器容值减小产生的相间电压不平衡分析与抑制机理

采用小容值薄膜电容的H桥级联型静止同步补偿器(cascaded H-bridge static synchronous compensator,CHB-STATCOM)直流侧电压控制以直流侧电压平方值为被控量,在补偿功率发生跃变时相间电压不平衡加剧,传统相间电压平衡控制动态效果不佳。基于容值减小的CHB-STATCOM控制原理,推导各相直流侧电压平方值与跃变功率、电容容值的数学关系,揭示相间电压不平衡的原因,讨论直流侧电压峰值与电容电压纹波的关系。建立指令功率渐变的给定方法,分析指令功率渐变时间与相间电压不平衡的关系,揭示指令功率渐变时间对CHB-STATCOM补偿功率变化时相间电压不平衡的抑制机理。仿真和实验验证了理论分析与相间电压不平衡抑制机理的有效性。研究结论可为该类型的相间电压不平衡抑制提供理论基础。

基于VMD-BiLSTM-WOA的短期风电功率预测

风力发电对于解决全球能源短缺问题有重要意义,准确预测风电功率有助于风电并网的合理调度和可靠的电网运行.文章提出了一种基于变分模态分解(Variational Mode Decomposition, VMD)、双向长短期记忆网络(Bidirectional Long Short-term Memory Network, BiLSTM)以及鲸鱼优化算法(Whale Optimization Algorithm, WOA)的混合深度学习模型,以用于短期风电功率预测.首先,VMD将原始风电功率分解为多个子模态,有效减少了序列的波动性;然后对每个子模态分别建立BiLSTM模型,使用WOA对BiLSTM中的参数进行优化,以提高混合模型的效率和预测性能;最后将各个子模型的结果叠加得到最终预测结果.在实验中通过建立不同的比较模型来说明改进策略的有效性和优越性,结果表明所提的混合模型在风电功率预测中具有较高的预测精度.