Stability Study of Low Voltage Electrical Distribution Network: Audit and Improvement of DJEGBE Mini Solar Photovoltaic Power Plant in the Commune of OUESSE (Benin)

The supply of quality energy is a major concern for distribution network managers. This is the case for the company ASEMI, whose subscribers on the DJEGBE mini-power station network are faced with problems of current instability, voltage drops, and repetitive outages. This work is part of the search for the stability of the electrical distribution network by focusing on the audit of the DJEGBE mini photovoltaic solar power plant electrical network in the commune of OUESSE (Benin). This aims to highlight malfunctions on the low-voltage network to propose solutions for improving current stability among subscribers. Irregularities were noted, notably the overloading of certain lines of the PV network, implying poor distribution of loads by phase, which is the main cause of voltage drops;repetitive outages linked to overvoltage caused by lightning and overcurrent due to overload;faulty meters, absence of earth connection at subscribers. Peaks in consumption were obtained at night, which shows that consumption is greater in the evening. We examined the existing situation and processed the data collected, then simulated the energy consumption profiles with the network analyzer “LANGLOIS 6830” and “Excel”. The power factor value recorded is an average of 1, and the minimum value is 0.85. The daily output is 131.08 kWh, for a daily demand of 120 kWh and the average daily consumption is 109.92 kWh, or 83.86% of the energy produced per day. These results showed that the dysfunctions are linked to the distribution and the use of produced energy. Finally, we proposed possible solutions for improving the electrical distribution network. Thus, measures without investment and those requiring investment have been proposed.

Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images

Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.

Detection of Subsurface Cavities in a Power Plant Through Artificial Neural Network from Micro-Gravity Data

Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam construction.In order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered for construction of a power plant site in Iran.First we gain the residual anomalies through bouger anomalies and then we design an Artificial Neural Network(ANN)which is trained by a set of training data.The ANN was tested for both synthetic and real data.For real data some suitable features are derivate from residual anomalies and applied to

Dynamic Characteristics Analysis on MHTGR Plant’s Secondary Side Fluid Flow Network

Multipe NSSS (Nuclear Steam Supply System) modules use the common feeding-water system to drive the common turbine power generation set. The SSFFN (secondary side fluid flow network) of MHTGR plant has features i.e. strong-coupling and nonlinearity. A wide range of power switching operation will cause unsteady flow, which may destroy the working elements and will be a threat for normal operation. To overcome those problems, a differential-algebraic model and PI controllers are designed for the SSFFN. In MATLAB\SIMULINK environment, a simulation platform is established and used to make a simulation of SSFFN of a MHTGR plant with two NSSS modules, which uses feedwater valves to control the mass flow rate in each module instead of feedwater pump. Results reflect good robustness of controllers.

Application of Optimized BP Neural Network in Addressing for Garbage Power Plant

Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects. One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.

Simulation of Low TDS and Biological Units of Fajr Industrial Wastewater Treatment Plant Using Artificial Neural Network and Principal Component Analysis Hybrid Method

Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.

Estimation of Copper and Molybdenum Grades and Recoveries in the Industrial Flotation Plant Using the Artificial Neural Network

In this paper, prediction of copper and molybdenum grades and their recoveries of an industrial flotation plant are investigated using the Artificial Neural Networks (ANN) model. Process modeling has done based on 92 datasets collected at different operational conditions and feed characteristics. The prominent parameters investigated in this network were pH, collector, frother and F-Oil concentration, size percentage of feed passing 75 microns, moisture content in feed, solid percentage, and grade of copper, molybdenum, and iron in feed. A multilayer perceptron neural network, with 10:10:10:4 structure (two hidden layers), was used to estimate metallurgical performance. To obtain the optimal hidden layers and nodes in a layer, a trial and error procedure was done. In training and testing phases, it achieved quite correlations of 0.98 and 0.93 for Copper grade, of 0.99 and 0.92 for Copper recovery, of 0.99 and 0.92 for Molybdenum grade and of 0.99 and 0.94 for Molybdenum recovery prediction, respectively. The proposed neural network model can be applied to determine the most beneficial operational conditions for the expected Copper and Molybdenum grades and their recovery in final concentration of the industrial copper flotation process.

Performance Simulation of H-TDS Unit of Fajr Industrial Wastewater Treatment Plant Using a Combination of Neural Network and Principal Component Analysis

Nowadays, with regard to environmental issues, proper operation of wastewater treatment plants is of particular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems and environmental engineers are dealing with them mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant, powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the multilayer perceptron (MLP) feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant located in Mahshahr—Iran that qualitative and quantitative characteristics of its units were used for training, calibration and evaluation of the neural model. Also, Principal Component Analysis technique was applied to modify and improve performance of generated models of neural networks. The results of this model showed good accuracy of the model in estimating qualitative pro- file of wastewater. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of output.

基于数据挖掘模型的智能插秧机作业实践探究

以进一步优化插秧机的整机作业效率为研究目标,选取插秧机智能控制系统为研究对象,基于数据挖掘模型及算法理念展开实践应用探究。结合插秧机的作业特征及网控系统结构原理,从降低网络信号传输延迟及提升抗干扰能力角度搭建数据挖掘算法优化模型,并进行实际的插秧状态算法路径设计及控制指令下发,确保各个环节衔接准确。插秧作业结果表明:基于数据挖掘模型的插秧机智能化作业水平得到显著提升,挖掘算法精度与系统控制准确率分别可相对提升7.90%和5.75%,整机插植均匀度可达90.72%,对于插秧机的智能化开发与优化具有促进作用。

Study of the Impact of Grid Disconnections on the Production of a Photovoltaic Solar Power Plant: Case of Diamniadio Power Plant

Today, renewable energy projects connected to the interconnected network, with powers of the order of tens of megawatts, are more and more numerous in sub-Saharan Africa. And financing these investments requires a reliable amortization schedule. In the context of photovoltaic systems connected to the interconnected electricity grid, the quintessence of damping is the amount of energy injected into the grid. Thus it is fundamental to know the parameters of this network and their variation. This paper presents an evaluation of the impact of power grid disturbances on the performance of a solar PV plant under real conditions. The CICAD photovoltaic solar plant, connected to the Senelec distribution network, with an installed capacity of 2 MWp is the study setting. An energy audit of the plant is carried out. Then the percentage of each loss is determined: voltage drops, module degradation, inverter efficiency. The duration of each disconnection is measured and recorded daily. The corresponding quantity of lost energy is thus calculated from meteorological data (irradiation, temperature, wind speed, illumination) recorded by the measurement unit in one-minute steps. The observation period is three months. The total duration of disconnections related to the instability of the electrical network during the study period is 46.7 hours. The amount of energy lost is estimated at 22.6 MWh. This represents 2.4% of the actual calculated production.

基于元学习的植物虫害识别原型网络VGG-ML

[目的]为解决使用深度学习技术对植物虫害识别时依赖大量训练样本的问题,本文基于元学习的思想设计一个VGG原型网络(VGG-meta learning,VGG-ML),用于在小样本背景下植物虫害种类识别。[方法]采用VGG16作为嵌入单元提取虫害样本特征与类别特征,为提高网络对于新类别的识别能力,采用训练集与测试集异域方式进行模型训练,以解决在小样本情况下植物虫害识别准确率低、新类别虫害无法识别的问题。将测试集划分为支持集(获取类原型)与查询集(样本原型),以欧式距离度量样本原型与类原型之间的相似性,从而判定样本所属类别。[结果]以公开数据集IP102中玉米、甜菜、苜蓿等11种植物的蚜虫、黏虫、跳甲等24类农业虫害图片作为训练数据,以稻纵卷叶螟、稻叶毛虫、亚洲稻螟、稻瘿蚊、稻秆蝇、稻水象甲、稻叶蝉、稻苞虫8类常见的水稻虫害作为测试数据,在5-way、1-shot与5-way、5-shot情况下VGG-ML识别准确率分别为67.98%与81.5%,与原始原型网络相比提高3.53与4.4百分点。5-way、5-shot试验与基于迁移学习的ResNet50与VGG16网络对比,准确率分别提高28.65与25.94百分点。[结论]VGG-ML在进行小样本植物虫害类型识别时有效可靠,可适用于小样本植物识别问题。

不同光源条件下与花生株高变化相关基因的转录组分析

【目的】为了探究不同光源条件下与花生株高变化相关的基因。【方法】以冀花5号(直立型)和开选01-6(匍匐型)品种为研究材料,分别在LED光和自然光两种培养条件进行盆栽试验。【结果】冀花5号在强光条件下株高显著变矮,而开选01-6变化幅度不大,表明冀花5号对光照反应相对敏感。转录组的PCA结果显示LED光源下冀花5号和开选01-6距离相对较近,自然光源下冀花5号和开选01-6距离较近,说明光照对基因表达的影响大于基因型间的差异影响。GO和KEGG注释分析发现不论在差异表达数目、基因类型或主要响应的调控网络上,冀花5号和开选01-6对不同光源的响应存在显著差异,尤其差异基因在植物昼夜节律相关的通路上显著富集,暗示着植物昼夜节律调控网络对株型的形态建成至关重要。进一步分析发现分别有16个和19个植物昼夜节律相关基因在开选01-6和冀花5号中特异响应,分别有6个和17个植物激素信号转导相关基因在开选01-6和冀花5号中特异响应,表明这些基因在花生株型形态建成中起着关键调控作用。【结论】昼夜节律调控途径是调控株高变化的主要途径,植物生长激素信号在其中也起了关键作用。以上研究结果对育种家深入理解不同光源条件下花生株型的形态建成具有重要的参考价值。

配电网多馈线负荷协同的直控型虚拟电厂自动功率控制

配电网内具有有功快速调节能力的分布式资源及柔性负荷的增多,为电网运行控制提供了新的方式。研究了配电网多馈线负荷协同的直控型虚拟电厂自动功率控制策略,实现了对自动功率控制指令的主动跟踪。首先,分析了馈线负荷调节特性,建立了基于电能质量等级的调控成本模型,并考虑广泛存在的分布式资源,构建了计及多类型有功资源调节潜力的直控型虚拟电厂。其次,构建了分布式资源聚合与控制指令分解模型,通过聚类算法实现资源分群,并基于经济性原则分解聚合体控制指令;构建了基于解析电压灵敏度的多类型有功调节资源协同优化模型,以确定自动功率控制指令的分解策略。最后,结合MATLAB和实时数字仿真(RTDS)平台搭建中国广东某区域实际配电网仿真模型,验证了所提策略的有效性。

基于先验知识的弓网接触电阻预测模型精度提升方法

高速列车的运行实践表明,随着运行速度提高,受电弓与接触导线分离的可能性越大,越容易产生弓网电弧。导致电弧产生的因素很多,但最终都归结到滑板与接触网的接触电阻上。首先利用滑动电接触实验机,研究了波动载荷、滑动速度和接触电流对接触电阻的影响,并进一步结合表面形貌特征,分析了磨损机制、电弧放电与接触电阻演变规律之间的关系。其次为了预测不同工况下的接触电阻,建立了径向基(RBF)神经网络回归模型,通过在模型训练中融入先验知识和采用改进的食肉植物优化算法(ICPA)优化RBF神经网络超参数,提升弓网接触电阻预测模型的精度。有、无先验知识的ICPA-RBF模型预测性能对比仿真结果表明,两类先验知识分别有助于提高模型的收敛速度和预测精度。最后采用假设检验验证了模型的有效性。

基于microRNA共调控网络的植物多酚调节机体活性研究进展

植物多酚是人类膳食中含量最丰富的抗氧化剂之一,它们通过与细胞信号级联反应相互作用、调节转录因子的活性,从而参与机体活性的调控。此外,多酚已被证明可以影响microRNA(miRNA)的表达。miRNA能参与大多数的细胞分化和稳态过程,在许多病理中发挥着重要作用。近年来,网络生物学的发展促进了人们对miRNA控制的相互交织调控网络的理解。作者综述了共表达miRNA在信号网络中的特征和作用模式,以及miRNA与转录因子之间复杂且有序的关系;总结了单一和多个miRNA介导植物多酚参与调节机体心血管疾病、糖尿病、炎症和癌症的作用机制,有助于更全面、准确地揭示植物多酚等食品营养与健康因子调控生理功能的实际情况。

植物功能性状网络:概念体系发展与应用进展

自然界中,任何植物都具有多种多样的功能性状,这些功能性状协同或互补地帮助植物适应外界复杂多变的环境。因此,多种功能性状间的复杂关系变化规律,很大程度上可以体现植物生存、生长和繁殖等方面的适应策略及其对环境的响应机制。目前,大多数研究局限于单一或特定几个性状间的简单关系,多维度思维及其量化方法在植物功能性状研究中还非常罕见,制约了人们对植物多维度适应机制的深入认知。传统的分析方法,如相关分析、主成分分析、通径分析、结构方程模型等,都难以阐明多种功能性状间的复杂关系。针对该科学难题,科研人员创新性地引入多维度网络分析理念、发展了“植物功能性状网络”的理论体系(Plant trait networks,PTNs),拓展了从功能性状网络的角度揭示植物适应策略的新方法。植物功能性状网络被定义为由多种功能性状间相互关系构成的多维度网络,它采用其自身的整体特征或节点特征来表征植物性状之间的复杂关系、以及植物个体、功能群、群落对环境变化或干扰等的响应与适应途径。在此基础上,科研人员发展了PTNs的5个网络整体参数和4个核心节点参数,并定义了其生理生态意义。植物功能性状网络分析具有多维度捕获和可视化植物多性状间关系的潜力,为揭示植物对环境或资源变化响应与适应策略提供了全新的视角。本文在介绍PTNs的概念、理论、参数和方法的基础上,结合中国东部南北森林样带数据和全球叶片功能性状数据等,从多个角度阐述了PTNs的科学性与适应性。在深入解读先前植物功能性状网络概念体系、理论意义和潜在挑战的基础上,结合最新应用进展进行了补充,希望通过广泛讨论,完善植物功能性状网络概念体系,为探究植物不同尺度的多维度适应机制、及其对全球变化的响应等问题提供新的解决方案,切实推动植物功能性状领域的发展。

基于CNN的作物分类识别图像获取平台研究进展

基于机器视觉的作物精准分类识别是农业自动化、智能化作业的前提。在作物图像分类识别任务中,卷积神经网络(CNN)是当前应用最广泛的算法之一。作物表型特征及生长环境的复杂性,决定作物图像获取平台的多样性。通过分析2020—2022年国内外基于CNN的作物分类识别研究,图像获取平台可划分为通用平台和自建平台两大类:通用平台硬件产品成熟、部署方便,但要做好设备选型和环境搭建;自建平台分为固定式和移动式,能高效获取试验数据,但硬件集成较为复杂。详细对比分析各类平台的优缺点及适用范围。作物图像获取平台的未来趋势包括:高通量、高效率、自动化的通用图像获取装置,集成多种传感器的多模态数据采集与融合应用,自带运算处理的智能摄像头等,更精细化的图像获取平台将有效支撑作物表型的深入研究。

考虑电力网络约束的工业园区虚拟电厂调控边界求解方法

构建以新能源为主体的新型电力系统,亟须挖掘负荷侧灵活调节资源参与电网调控。含电解铝、矿热炉等高耗能负荷的工业园区具备良好的调控潜力,但是考虑园区内部电力网络约束,其精确调控边界求解面临着变量维数高、约束非线性的难题,现有方法不能较好地兼顾计算效率和精度。对此,文中将上述问题抽象为高维非线性状态空间在P-Q耦合平面的投影问题:分别建立考虑工业园区安全运行线性化和非线性约束的调控边界投影求解模型,采用一种新颖的高维状态空间投影算法,通过顶点“搜索-映射”的两步式求解过程,得到工业园区型虚拟电厂调控边界的精确投影。算例结果表明,采用所提方法求解的调控边界可由线性不等式组完全表征,与现有调度系统完全兼容,结合与叠加柔性资源调控能力和传统采样方法的对比,验证了该方法的可行性以及高求解精度和效率。

中试运行中的知识问题缓解:行动者网络管理的作用

中试在科技成果转化中发挥重要作用,但不确定性、复杂性、模糊性3类知识问题制约着中试的有效运行。现有研究未明晰中试运行情境下知识问题破解机制,如何缓解中试运行知识问题有待深入探究。选取264份企业层面调研数据进行实证分析,识别3类知识问题对中试运行有效性的影响,利用行动者激活/解除策略和网络目标实现策略缓解中试运行过程中遇到的知识问题。研究发现:(1)不确定性、模糊性对中试运行有效性具有负向影响,且模糊性的负向影响更显著,复杂性对中试运行有效性的影响作用不显著;(2)行动者激活/解除策略与网络目标实现策略均能够有效缓解不确定性和模糊性对中试运行的负向影响。研究结论不仅能够弥补中试运行管理研究的不足,还可为应对和解决中试运行中的知识问题提供实践启示,促进中试有效运行进一步明朗化。