Stochastic ordering of extreme value distributions

We investigate the second-order stochastic ordering of the extreme value distributions within each of three families: the Gumbel distribution, the Frechet distribution, and the Weibull distribution. We give conditions for second-order stochastic dominance, conditional second-order stochastic dominance, and order statistics second-order stochastic dominance within the three families     

Modelling of evaporation from the reservoir of Yuvacik dam using adaptive neuro-fuzzy inference systems

Adaptive neuro-fuzzy inference system (ANFIS) models are proposed as an alternative approach of evaporation estimation for Yuvacik Dam. This study has three objectives: (1) to develop ANFIS models to estimate daily pan evaporation from measured meteorological data; (2) to compare the ANFIS model to the multiple linear regression (MLR) model; and (3) to evaluate the potential of ANFIS model. Various combinations of daily meteorological data, namely air temperature, relative humidity, solar radiation and wind speed, are used as inputs to the ANFIS so as to evaluate the degree of effect of each of these variables on daily pan evaporation. The results of the ANFIS model are compared with MLR model. Mean square error, average absolute relative error and coefficient of determination statistics are used as comparison criteria for the evaluation of the model performances. The ANFIS technique whose inputs are solar radiation, air temperature, relative humidity and wind speed, gives mean square errors of 0.181 mm, average absolute relative errors of 9.590% mm, and determination coefficient of 0.958 for Yuvacik Dam station, respectively. Based on the comparisons, it was found that the ANFIS technique could be employed successfully in modelling evaporation process from the available climatic data.     

Missing wind data forecasting with adaptive neuro-fuzzy inference system

In any region, to begin generating electricity from wind energy, it is necessary to determine the 1-year distribution characteristics of wind speed. For this aim, a wind observation station must be constructed and 1-year wind speed and direction data must be collected. For determining the distribution characteristics, the collected data must be statistically analyzed. The continuity and reliability of the data are quite important for such studies on the days when possible faults can occur in any part of the observation unit or on days when, the system is on maintenance, it is not possible to record any data. In this study, it is assumed that the station had not worked at some randomly chosen days and that for these days no data could be recorded. The missing data are predicted using the data that were recorded before and after fault or maintenance by an adaptive neuro-fuzzy inference system (ANFIS). It is seen that ANFIS is successful for such a study.     

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 normal behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months.The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components.In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.     

Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction

In this paper, a new fuzzy adaptive artificial physics optimization (FAAPO) algorithm is used to solve security-constrained optimal power flow (SCOPF) problem with wind and thermal power generators. The stochastic nature of wind speed is modeled as a Weibull probability density function. The production cost is modeled with the overestimation and underestimation of available wind energy and included in the conventional SCOPF. Wind generation cost model comprises two components, viz. reserve capacity cost for wind power surplus and penalty cost for wind power shortage. The selection of optimal gravitational constant (G) is a tedious process in conventional artificial physics optimization (APO) method. To overcome this limitation, the gravitational constant (G) is fuzzified in this work. Therefore, based upon the requirement, the gravitational constant changes adaptively. Hence, production cost is reduced, settles at optimum point and takes less number of iterations. The proposed algorithm is tested on IEEE 30-bus system and Indian 75-bus practical system, including wind power in both the test systems. It is observed that FAAPO can outperform BAT algorithm and APO algorithm. Hence, the proposed algorithm can be used for integration of wind power with thermal power generators.

     

Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting

Accurate and steady wind speed prediction is essential for the efficient management of wind power factories and energy systems. However, it is difficult to obtain satisfactory forecasting performance because of the characteristics of random nonlinear fluctuations inherent in wind speed variation. Considering the drawbacks of statistical models in forecasting nonlinear time series and the problem of artificial intelligence models easily falling into a local optimum, in this study, we successfully integrate the variable weighted combination theory into a new combined forecasting model that simultaneously consists of three disparate hybrid models based on the decomposition technology. Moreover, the extreme learning machine optimized by the multi-objective grasshopper optimization algorithm is adopted to integrate all the forecasting results derived from each hybrid model to further enhance the forecasting accuracy. In this study, we consider a case study that employs several authentic wind speed data aggregates of Shandong wind farms for an evaluation of the forecasting performance of the proposed combined model. The experimental results reveal that this proposed model surpasses the contrasted benchmark models and is satisfactory for intellective grid programs.     

Prediction of aerodynamics performance of continuously variable-speed wind turbine by adaptive neuro-fuzzy methodology

Horizontal-axis wind turbines (HAWT) have the constant rotor speed, while the blade tip speed changes continuously. This could reduce power performance of the wind turbine. In this paper, the accuracy of soft-computing technique was employed for aerodynamics performance prediction based on continuously variable-speed horizontal-axis wind turbine with optimal blades. The process, which simulates the $$\varphi$$ (relative wind angle), BEP (blade element parameter), SP (solidity parameter), CPtot (total power coefficient), CPl (local power coefficient), and CT (local thrust coefficient), with adaptive neuro-fuzzy inference system (ANFIS) was constructed. The inputs were local speed ratios λr and different values of drag-to-lift ratio ε. The performance of proposed system is confirmed by the simulation results. The ANFIS results are compared with the experimental results using root-mean-square error and coefficient of determination and Pearson’s coefficient. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The effectiveness of the proposed strategies is verified based on the simulation results.     

Impact of wind farms on disturbance detection and classification in distributed generation using modified Adaline network and an adaptive neuro-fuzzy information system

A new disturbance detection and classification technique based on modified Adaline and adaptive neuro-fuzzy information system (ANFIS) is proposed for a distributed generation system comprising a wind power generating system (DFIG) and a photovoltaic array. The proposed technique is based on a fast Gauss–Newton parameter updating rule rather than the conventional Widrow–Hoff delta rule for the Adaline network. The voltage and current signals near the target distributed generation (DG), particularly the DFIG, whose speed varies from minimum to the maximum cut-off speed, are processed through the modified Adaline network to yield the features like the negative sequence power, harmonic amplification factor (HAF), total harmonic distortion (THD), etc. These features are then used as training sets for the ANFIS, which employs a gradient descent algorithm to update its parameters. The proposed technique distinguishes the islanding condition of the distributed generation system with some other disturbances, such as switching faults, capacitor bank switching, voltage swell, voltage sag, distorted grid voltage, unbalanced load switching, etc. which are referred to as non-islanding cases in this paper.     

Predicting survival of individual patients with esophageal cancer by adaptive neuro-fuzzy inference system approach

Since esophageal cancer has no symptoms in the early stage, it is usually not detected until advanced stages in which treatment is challenging. Integrated treatment provided by a multidisciplinary team is crucial for maximizing the prognosis and survival of patients with esophageal cancer. Currently, clinicians must rely on the cancer staging system for diagnosis and treatment. An accurate and easily applied system for predicting the prognosis of esophageal cancer would be useful for comparing different treatment strategies and for calculating cancer survival probability. This study presents a hazard modeling and survival prediction system based on adaptive neuro-fuzzy inference system (ANFIS) to assist clinicians in prognostic assessment of patients with esophageal cancer and in predicting the survival of individual patients. Expert knowledge was used to construct the fuzzy rule based prognosis inference system for esophageal cancer. Fuzzy logic was used to process the values of input variables rather than categorizing values as normal or abnormal based on cutoffs. After transformation and expansion, censored survival data could be used by the ANFIS for training to establish the risk model for accurately predicting individual survival for different time intervals or for different treatment modalities. Actual values for serum C-reactive protein, albumin, and time intervals were input into the model for use in predicting the survival of individual patients for different time intervals. The curves obtained by the ANFIS approach were fitted to those obtained using the actual values. The comparison results show that the ANFIS is a practical, effective, and accurate method of predicting the survival of esophageal cancer patients.     

基于FIR神经网络的风电场虚拟风速传感器

风电机组风速传感器易发故障,故障可能导致机组安全风险和发电量损失。针对现行的故障处理方法因与机组控制策略紧密耦合而日益面临挑战,提出了一种基于数据处理的虚拟风速传感器原理与方法：由风电场上风向测量风速计算下风向推算风速,用推算风速取代故障传感器。着重讨论了基于FIR神经网络的推算风速计算方法和计算模型,探讨了系统实现的关键技术。实验证明了虚拟传感器的误差在机组控制系统可接受的程度内。提出的方法独立于机组自身属性,具有普遍适用性,可部署在任意类型的场,在物理传感器故障时向机组提供风速信号,支撑风电机组持续安全运行。     

Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system

Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present paper, we develop an adaptive neuro-fuzzy inference system (ANFIS) model for groundwater electrical conductivity based on the concentration of positively charged ions in water. It is shown that the ANFIS model outperforms more traditional methods of modelling electrical conductivity based on the total solids dissolved in the water, even though ANFIS uses less information. Additionally, the fuzzy rules in the ANFIS model provide a categorization of ground water samples in a manner that is consistent with the current understanding of geophysical processes.     

风能评估系统的研究与实现

风能评估是风电场建设过程中的关键工作,目前采用的评估系统主要以国外的软件为主,价格昂贵,使用复杂,采用VC++和Matlab结合开发实现了风能评估系统,采用weibull分布作为风频计算模型,利用平均风速和最大风速求解A,C参数,推导出计算风机发电量的公式.接着参照国外的风能评估软件,对系统进行功能分析和模块设计,结合VC++界面友好和Matlab计算功能强大的优点,混合编程实现了风能评估系统.最后以Nordex N80和N90为实例进行计算分析,分析结果表明,自主开发的风能评估系统功能全面,跟wasp相比,更接近于实际的发电效果.     

基于威布尔分布的云计算能耗优化模型研究

针对云计算环境中复杂的虚拟机正常运行状态,提出将虚拟机运行看成由硬软件串联的可修复系统,用威布尔分布描述虚拟机硬软件正常运行的方法来建模缓解云计算高能耗问题。运用半马尔科夫过程刻画虚拟机运行过程、Laplace-Stielties变换简化数值计算、Bayesian定理去除限制条件,构建处理器利用率与能耗、性能的关系模型。再结合可修复系统寿命分布理论,调整虚拟机正常运行的威布尔分布函数,得到不同形状参数下处理器运行能耗以及给定任务完成时间,最终分析形状参数、处理器利用率与能耗-性能隐含关系并给出有效降低处理器运行能耗的合理化建议。数值分析表明：增大形状参数比提高利用率更显著降低处理器运行能耗;优化虚拟机配置使得形状参数变大,可以明显降低处理器运行能耗,同时避免云系统性能过度损耗。     

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.     

风廓线雷达风谱的概率分布研究

研究风廓线雷达风谱的概率分布,对估计噪声水平、评价风谱形成算法的性能、提取风廓线回波的信息等等都有重要的作用和意义.目前,接收机噪声的谱的概率分布得到了较好的研究,而风廓线回波的谱的分布却缺少相应的分析,鉴于此,研究了风谱的概率分布.通过对雷达回波做合理的假设,在分析经典风谱形成算法的基础上,提出雷达噪声谱和风廓线回波的谱分别服从X2,分布和非中心X2分布.利用实际的雷达数据和计算机仿真的方法进行的分布的假设检验,说明该理论模型准确有效.     

Nonsingular Terminal Sliding Mode Control With Ultra-Local Model and Single Input Interval Type-2 Fuzzy Logic Control for Pitch Control of Wind Turbines

As wind energy is becoming one of the fastestgrowing renewable energy resources,controlling large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties.The main goal of the current work is to propose an intelligent control of the wind turbine system without the need for model identification.For this purpose,a novel model-independent nonsingular terminal slidingmode control(MINTSMC)using the basic principles of the ultralocal model(ULM)and combined with the single input interval type-2 fuzzy logic control(SIT2-FLC)is developed for non-linear wind turbine pitch angle control.In the suggested control framework,the MINTSMC scheme is designed to regulate the wind turbine speed rotor,and a sliding-mode(SM)observer is adopted to estimate the unknown phenomena of the ULM.The auxiliary SIT2-FLC is added in the model-independent control structure to improve the rotor speed regulation and compensate for the SM observation estimation error.Extensive examinations and comparative analyses were made using a real-time softwarein-the-loop(RT-SiL)based on the dSPACE 1202 board to appraise the efficiency and applicability of the suggested modelindependent scheme in a real-time testbed.     

基于CNN-LSTM的短期风电功率预测

短期风电功率预测对电力系统的安全稳定运行和能源的优化配置具有重要意义。鉴于卷积神经网络(CNN)高效的数据特征提取能力,以及长短期记忆网络(LSTM)描述时间序列长期依赖关系的能力。为了提高短期风电功率预测的精度,设计了一种基于CNN和LSTM的风电功率预测模型。该模型利用卷积神经网络对风电功率、风速、风向数据进行多层卷积和池化堆叠计算,提取风电功率相关数据的特征图谱。为了描述风电功率序列的时序依从关系,将图谱特征信息作为长短期记忆网络的输入信息,计算得到风电功率的预测结果。采用西班牙某风电场的实测数据进行模型预测精度验证。结果表明,该模型较LSTM、Elman模型具有更好的预测性能。     

侧扫声呐图像统计特性研究

在分析侧扫声呐成像的散射模型基础上,给出了海底混响服从瑞利分布的理论依据.分析了其它典型概率分布的合理性并给出了各概率分布参数的估计方法.通过实验来检验比较几种典型的概率分布的最佳拟合.实验结果表明:总体来看,威布尔分布更为精确,但瑞利分布仍保持了较好的拟合特性.特别是当底质粗糙、混响较强时,使用瑞利分布和威布尔分布相当.这一研究结果可为侧扫声呐图像的底质分割提供重要依据.     

A rotor speed estimation algorithm in variable speed permanent magnet synchronous generator wind energy conversion system

A rotor speed estimation algorithm in a direct vector controlled permanent magnet synchronous generator wind energy conversion system is proposed. The proposed method is based on a simple equation obtained from the flux model of the machine and contains only stator flux and current. Constant gain recursive least squares estimator is used for implementing the speed estimation algorithm. Rotor position information used for coordinate transformation is computed using the estimated speed. Stator flux information required by the speed estimator is obtained using the stator voltage equation by implementing a programmable low pass filter. The estimated speed is used as the feedback signal for the speed control loop of the vector controlled machine side converter control system whose command speed is obtained from a wind speed sensorless maximum power point tracking controller, thus, we obtain a complete rotor speed and wind speed sensorless permanent magnet synchronous generator wind energy conversion system. Simulation is carried out to validate the performance of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.