Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

This article presents a hybrid model involving artificial neural networks and biogeography-based optimization for long-term forecasting of India's sector-wise electrical energy demand. It involves socio-economic indicators, such as population and per capita gross domestic product, and uses two artificial neural networks, which are trained through a biogeography-based optimization algorithm with a goal of perfect mapping of the input–output data in the non-linear space through obtaining the global best weight parameters. The biogeography-based optimization based training of the artificial neural network improves the forecasting accuracy and avoids trapping in local optima besides enhancing the convergence to the lowest mean squared error at the minimum number of iterations than existing approaches. The model requires an input and the year of the forecast and predicts the sector-wise energy demand. Forecasts up to the year 2025 are compared with those of the regression model, the artificial neural network model trained by back-propagation, and the artificial neural network model trained by harmony search algorithm to exhibit its effectiveness.     

Online Droop Tuning of a Multi-DG Microgrid Using Cuckoo Search Algorithm

This article presents an intelligent strategy to achieve appropriate real power sharing among distributed generators in a microgrid. The presented strategy employs two droop-based control methods and automatically adjusts their parameters. The first method is unit power control, which has specifications similar to the conventional droop method, and the second is feeder flow control, showing significant characteristics in both grid-connected and islanded modes operation of a microgrid. A combination of unit power control and feeder flow control methods is used for a multi-distributed generator microgrid. The microgrid operation mode passes from the grid-connected to the islanded through a transition. A new evolutionary algorithm called cuckoo search is employed to coordinate the power management of distributed generators within an on-line droop tuning. In comparison to the predecessor evolutionary algorithms, the cuckoo search algorithm represents more effective random processes with fewer parameters. Using the proposed control strategy, while the distributed generators contribute to load demand provision based on their rated powers, their powers are optimized in terms of overshoot and settling time. Digital time-domain simulation studies are carried out in the MATLAB/SIMULINK (The MathWorks, Natick, Massachusetts, USA) environment to verify the performance of the proposed control system.     

A Gravitational Search Algorithm Based Static VAR Compensator Switching Function Optimization Technique for Minimum Harmonic Injection

Switching function optimization for minimum source harmonic injection for a static VAR compensator is presented. The static VAR compensator is configured with a fixed capacitor and insulated-gate bipolar transistor controlled reactor. The switching function is optimized for minimum source harmonic injection considering the desired fundamental voltage across load terminals. A gravitational search algorithm is employed for this purpose. It is observed that the proposed switching scheme with two different switching angles per half-cycle provides lower source harmonic injection compared to conventional switching, hence improving the source current harmonics. The switching angles are computed off-line using the gravitational search algorithm for varying modulation indices considering the minimum total harmonic distortion of the reactor voltage. The switching angles are stored in a processor as a function of the modulation index for on-line application using a piecewise mixed model approximation technique for low memory usage. It is observed that the proposed switching improves the source current total harmonic distortion on an average of 4 to 5% over most of the operating range compared to conventional switching without optimization. Various simulation and experimental results are presented on different loads to validate the proposed concept.     

考虑用户定量需求的优质电力园区DVR优化配置

优质电力园区是针对区域性高品质供电问题的一种有效解决方案,其中定制电力设备配置方案的确定是园区规划建设的重要内容。针对园区中动态电压恢复器(dynamic voltage restorer,DVR)的配置问题,提出一种考虑用户短时电压质量定量需求的DVR优化配置策略。基于过程抗扰时间和物理参数变化曲线,将用户对过程运行状态需求映射为短时电压质量需求,实现用户需求量化;以DVR投资最少为目标,满足用户需求为约束,建立DVR优化配置模型,用Tabu搜索算法求解,得到优化配置方案。对典型园区的仿真结果表明,该方法所得配置方案经济、可行,能在满足用户短时电压质量定量需求的同时,保证DVR投资最少。     

采用叶尖速比法和爬山搜索法相结合的风力发电系统最大功率点跟踪研究

在风力发电机控制中采用叶尖速比法,可以实现快速功率跟踪,但常由于风速测量值与实际风速值不一致,导致无法达到最大功率点。由于爬山搜索法不需要测量风速,所以采用爬山搜索法和叶尖速比控制法相结合来实现快速最大功率跟踪。当风速变化超过±1 m/s时,采用叶尖速比控制法,按测量风速计算出参考转速,迅速跟随,达到指定转速后,切换到爬山搜索法,实现精确的最大功率跟踪。在Matlab环境下,建立了永磁直驱式风力发电机模型,仿真分析了风速变化时,采用叶尖速比法和爬山搜索法相结合的方法能够实现最大功率快速而精确的跟踪,且具有很好的动态特性和稳态特性。     

基于布谷鸟算法和支持向量机的变压器故障诊断

电力变压器是电力系统运行中的重要设备之一,对故障和缺陷进行正确的诊断,关系到整个电网的运行安全。支持向量机(SVM)能够较好地解决小样本、非线性特征的多分类问题,适用于变压器故障类型判断。利用布谷鸟搜索算法,对支持向量机进行寻优得到全局最优解,从而得到具有最佳参数的支持向量机分类模型。该分类模型将变压器油色谱数据(DGA)中各气体相对含量作为评估指标,将变压器的故障分为低能放电、高能放电、中低温过热、高温过热等4个故障类型。通过已有的数据实例分析得出,利用布谷鸟搜索算法得到的分类模型比常用的网格搜索算法(GS)、粒子群搜索算法(PSO)、遗传算法搜索(GA)等算法得到的模型拟合准确率更好。     

Lactoperoxidase-mediated degradation of single-walled carbon nanotubes in the presence of pulmonary surfactant

Carbon nanotubes (CNTs) may elicit inflammatory responses following pulmonary exposure. Conversely, enzymatic biodegradation of CNTs by inflammatory cells has also been reported. The aim of this study was to study the degradation of oxidized single-walled CNTs (ox-SWCNTs) by lactoperoxidase (LPO), a secreted peroxidase present in the airways, and whether pulmonary surfactant affects this biodegradation. To this end, ox-SWCNTs were incubated in vitro with recombinant bovine LPO + H₂O₂ + NaSCN in the presence and absence of porcine lung surfactant (Curosurf®) and biodegradation was monitored using UV–Vis–NIR spectroscopy, Raman spectroscopy, and scanning electron microscopy. The interaction of recombinant LPO with bundles of ox-SWCNTs was confirmed by atomic force microscopy. Cell-free biodegradation of ox-SWCNTs was also observed ex vivo in murine bronchoalveolar lavage fluid in the presence of H₂O₂ + NaSCN. Our study provides evidence for biodegradation of ox-SWCNTs with a lung surfactant ‘bio-corona’ and expands the repertoire of mammalian peroxidases capable of biodegradation of ox-SWCNTs. These findings are relevant to inhalation exposure to these materials, as LPO serves as an important component of the airway defense system.     

Preparation and studies of transparent conductive monolayers of multiwall carbon nanotubes on quartz and flexible polymer with the use of modified Langmuir technique

An improved Langmuir method is described and applied to produce transparent, conductive and flexible single layer of multiwall carbon nanotubes. This paper presents properties of multiwall carbon nanotubes spread on water subphase and in a form of Langmuir–Schaefer layers on a quartz plate and on flexible polymeric foil. The results show very high homogeneity of the monolayers on a very large area obtained with the use of the proposed modified Langmuir method and indicate their relatively high radiation transmission and electrical conductivity. Laser scanning confocal and scanning electron microscopic images of the layers are presented. The microscopic visualization of the nanolayers is supported by spectroscopic studies (transmittance, photoacoustics) in the range from ultraviolet to mid-infrared. Moreover, electric measurements (current versus voltage characteristics) of the carbon material on the polymeric foil are presented.The obtained results of the investigated multiwall carbon nanotubes are discussed in a view of potential application in optoelectronics.     

Isolation of recombinant antibody fragments (scFv) by phage display technology for detection of almond allergens in food products

Tree nut allergies are considered an important health issue in developed countries. To comply with the regulations on food labeling, reliable allergen detection methods are required. In this work we isolated almond-specific recombinant antibody fragments (scFv) from a commercial phage display library bypassing the use of live animals, hence being consistent with the latest policies on animal welfare. To this end an iterative selection procedure employing the Tomlinson I phage display library and a crude almond protein extract was carried out. Two different almond-specific scFv (named PD1F6 and PD2C9) were isolated after two rounds of biopanning, and an indirect phage ELISA was implemented to detect the presence of almond protein in foodstuffs. The isolated scFvs demonstrated to be highly specific and allowed detection of 40 ng mL⁻¹ and 100 ng mL⁻¹ of raw and roasted almond protein, respectively. The practical detection limit of the assay in almond spiked food products was 0.1 mg g⁻¹ (110–120 ppm). The developed indirect phage ELISA was validated by analysis of 92 commercial food products, showing good correlation with the results obtained by a previously developed real-time PCR method for the detection of almond in foodstuffs. The selected phage clones can be affinity maturated to improve their sensitivity and genetically engineered to be employed in different assay formats.     

改进的树型结构RFID防碰撞算法

将整棵大查询树分为若干分支,在每一个分支内进行标签识别,减少碰撞发生的概率。算法分为前缀确定和分支查询2个阶段,在前缀确定阶段读取所有标签前缀,每个前缀代表一个分支,依次遍历每一分支;查询过程采用后退式搜索策略和动态搜索方式。理论分析和仿真实验表明,算法能够从查询次数、时延、吞吐率多方面改善RFID系统的整体性能。