基于概率密度拟合的风电功率波动特性研究

本文分别使用了正态分布、t location scale分布和logistic分布对风功率波动的概率密度进行拟合,并且通过t location scale分布分析了风电机组及风电场的概率数值特征。建立了基于欧式空间的波动信息丢失度量模型,分析了功率波动的平稳性和不同时间尺度下风功率波动信息的丢失率。通过数据分析,得出了随着时间尺度的增大,风电功率波动的相对可变性呈现上升的趋势,风电功率波动的时空分布具有一定的趋势性的结论。

基于敏感荷载效应概率统计特征的桥梁地震荷载辨识方法

为精准辨识实际服役环境下的桥梁地震荷载,采用实桥监测方法获取某桥挠度和加速度在地震发生前后的监测数据,筛选出了对地震荷载敏感的6组结构效应,分析了6组敏感地震效应在日常荷载和地震作用下的时变规律,研究了6种敏感地震效应的概率密度统计特性,选用了正态分布函数、威布尔分布函数和广义极值分布函数对概率密度进行拟合并得到了最佳概率密度函数,利用分布函数求解出6种敏感地震效应阈值并利用阈值进行地震荷载辨识。结果表明:加速度时变趋势具有平稳随机特性,受温度场影响很小,挠度时变趋势具有良好的日变化特征,受温度场影响较大;筛选出的6种效应对地震荷载敏感,能够明显区分出日常荷载效应与地震荷载效应,可用来辨识地震荷载;采用广义极值分布函数可以较好地描述加速度和挠度最大绝对值的概率密度统计特性;在地震发生时刻,6种敏感荷载效应的实测最大绝对值均大于阈值,表明基于敏感荷载效应概率统计特征的桥梁结构地震荷载辨识方法是有效的。

数据提取方法对风电功率波动特性的影响分析

基于实测风电数据的风电功率波动特性分析能提高电网调度等部门对风力发电电源特性的认识,缓解风电并网给系统带来的不利影响。由于风电数据常常涉及到不同数据采集方法,因此有必要分析风电数据采集方法对风电功率波动特性分析的影响。本文根据东北某风电场的实测功率数据,通过间隔取点法和间隔平均值法获得不同功率数据序列进行风电功率波动特性分析。算例分析表明,不同的数据采集方法会对风电功率波动特性分析产生影响,且间隔取点法下的风电功率数据适用于风电功率预测,间隔平均值法下的风电功率数据适用于风电功率波动的概率密度特性分析。

Quantitative Study on Characteristics of the Weibull Distribution

Aim To quantitatively study three characteristics of the Weibull distribution. Methods Theoritical analysis of the three characteristics of parameters of the Weibull distribution was done and mathematics software was used to make some chart analysis. Results 17 equations and 7 figures were made. Conclusion Under the standard form, the class of the Weibull probable density founction(pdf) curves appear double peak shape. Under the standard form, the maximum value point curve of the Weibull pdf takes line t =0 and t=1 as asymptotes. When β = 3 30-3 40, the Weibull distribution is the most similar to the normal distribution.

Cooperative Spectrum Sensing over Generalized Fading Channels Based on Energy Detection

This paper analyzes the unified performance of energy detection(ED) of spectrum sensing(SS) over generalized fading channels in cognitive radios(CRs). The detective performance of SS will be obviously affected by fading channels among communication nodes, and ED has the advantages of fast implementation, no requirement of priori received information and low complexity, so it is meaningful to investigate ED over various fading channels. The probability density function(p.d.f.) of α-κ-μ distribution is derived to evaluate energy efficiency for sensing systems.The detection probability with Marcum-Q function has been derived and the close-form expressions with moment generating function(MGF) method are deduced to achieve SS.Furthermore, exact closed-form analytic expressions for average area under the receiver operating characteristics curve( AUC) also have been deduced to analyze the performance characteristics of ED over α-κ-μ fading channels.Besides, cooperative spectrum sensing(CSS) with diversity reception has been applied to improve the detection accuracy and mitigate the shadowed fading features with OR-rule. At last, the results show that the detection capacity of ED will be evidently affected by α-κ-μ fading channels, but appropriate channel parameters can improve sensing performance. In addition, the establishedED-fading pattern is approved by simulations,and it can significantly enhance the detection performance of proposed algorithms.

Prediction Method for Network Traffic Based on Maximum Correntropy Criterion

This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This method utilizes the MCC as a new error evaluation criterion or named the cost function(CF)to train neural networks(NN).MCC is based on a new similarity function(Generalized correlation entropy function,Correntropy),which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density function.At the same time,by combining the MCC with the Mean Square Error(MSE),a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN training.According to the traffic network characteristics including the nonlinear,non-Gaussian,and mutation,the Elman neural network is trained by MCC and MCC-MSE,and then the trained neural network is used as the model for predicting network traffic.The simulation results based on the evaluation by Mean Absolute Error(MAE),MSE,and Sum Squared Error(SSE)show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with MSE.The overall performance is improved by about 0.0131.

Spectrum Allocation for Cognitive Radio Networks with Non-Deterministic Bandwidth of Spectrum Hole

The spectrum allocation for cognitive radio networks(CRNs) has received considerable studies under the assumption that the bandwidth of spectrum holes is static. However, in practice, the bandwidth of spectrum holes is time-varied due to primary user/secondary user(PU/SU) activity and mobility, which result in non-determinacy. This paper studies the spectrum allocation for CRNs with non-deterministic bandwidth of spectrum holes. We present a novel probability density function(PDF) through order statistics as well as its simplified form to describe the statistical properties of spectrum holes, with which a statistical spectrum allocation model based on stochastic multiple knapsack problem(MKP) is formulated for spectrum allocation with non-deterministic bandwidth of spectrum holes. To reduce the computational complexity, we transform this stochastic programming problem into a constant MKP through exploiting the properties of cumulative distribution function(CDF), which can be solved via MTHG algorithm by using auxiliary variables. Simulation results illustrate that the proposed statistical spectrum allocation algorithm can achieve better performance compared with the existing algorithms when the bandwidth of spectrum holes is time-varied.

Characteristics of strong-coupling bipolaron qubit in two-dimensional quantum dot in electric field

Based on Lee-Low-Pines(LLP) unitary transformation, this article adopts the variational method of the Pekar type and gets the energy and wave functions of the ground state and the first excited state of strong-coupling bipolaron in two-dimensional quantum dot in electric field, thus constructs a bipolaron qubit. The numerical results represent that the time oscillation period T0 of probability density of the two electrons in qubit decreases with the increasing electric field intensity F and dielectric constant ratio of the medium η; the probability density Q of the two electrons in qubit oscillates periodically with the increasing time t; the probability of electron appearing near the center of the quantum dot is larger, while that appearing away from the center of the quantum dot is much smaller.