Impact of distributed power electronics on the lifetime and reliability of PV systems

This paper quantifies the impact of distributed power electronics in photovoltaic (PV) systems in terms of end‐of‐life energy‐capture performance and reliability. The analysis is based on simulations of PV installations over system lifetime at various degradation rates. It is shown how module‐level or submodule‐level power converters can mitigate variations in cell degradation over time, effectively increasing the system lifespan by 5–10 years compared with the nominal 25‐year lifetime. An important aspect typically overlooked when characterizing such improvements is the reliability of distributed power electronics, as power converter failures may not only diminish energy yield improvements but also adversely affect the overall system operation. Failure models are developed, and power electronics reliability is taken into account in this work, in order to provide a more comprehensive view of the opportunities and limitations offered by distributed power electronics in PV systems. It is shown how a differential power‐processing approach achieves the best mismatch mitigation performance and the least susceptibility to converter faults. Copyright © 2017 John Wiley & Sons, Ltd.     

Efficient Deep CNN Model for COVID-19 Classification

Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10⁻⁵ and a minibatch size of 16.     

结合遥感图像处理技术的企业选址研究

利用遥感图像处理技术和改进的粒子群优化算法,提出了一种结合遥感图像处理技术的企业选址算法,可以充分利用GIS的空间和非空间信息,考虑的因素更为全面,极大地避免了人为因素的影响,是对选址决策支持系统的改进。     

数据库物理自调优研究技术综述

数据库物理自调优是自管理自调优数据库中一项重要的内容,为厘清这一领域的重要研究工作,需要对关系数据库物理自调优的背景、问题界定、经典技术和研究的新问题等方面进行综述。总结了数据库物理自调优的经典技术,并从优化程度、可扩展性、可用性可管理性及测试基准四个角度重点阐述了现今数据库物理自调优的关键技术和作为研究开发热点的一些问题,指出不同的物理自调优工具在技术上的共同基础,分析评价了这些关键技术及其方法的各自特点。最后还总结了该领域近几年出现的新的研究问题,并展望了未来的研究方向。     

Stochastic optimization of a biologically plausible spino-neuromuscular system model

Simulations and modeling techniques are becoming increasingly important in understanding the behavior of biological systems. Detailed models help researchers answer questions in diverse areas such as the behavior of bacteria and viruses and aiding in the diagnosis and treatment of injuries and diseases. However, to yield meaningful biological behavior, biological simulations often include hundreds of parameters that correspond to biological components and characteristics. This paper demonstrates the effectiveness of genetic algorithms (GA) and particle swarm optimizer (PSO) based techniques in training biologically plausible behavior in a neuromuscular simulation of a biceps/triceps pair. The results are compared to human subjects during flexion/extension movements to show that these algorithms are effective in training biologically plausible behaviors on both neural and gross anatomical levels. Specific behaviors of interest that emerge include tonic tensions in both muscles during resting periods, biceps/triceps coactivation patterns, and recruitment-like behaviors. These are all fundamental characteristics of biological motor control and emerge without direct selection for these behaviors. This is the first time that all of these characteristic behaviors emerge in a model of this detail without direct selective pressure.

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

This article proposes an efficient metaheuristic based on hybridization of teaching–learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching–learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.     

A model for the simulation of energy gains when using distributed maximum power point tracking (DMPPT) in photovoltaic arrays

Over the past years, the photovoltaic (PV) market has been invaded with numerous power optimizers and micro‐inverters that claim large energy gains when used in PV generators with shading or module mismatch. These products provide distributed maximum power point tracking (DMPPT), normally at module level, allowing the maximum power to be extracted from each PV module. This topology can be beneficial in situations where the PV generator is shaded or when there is large module mismatch. However, it is not clear that this power gain will result in energy improvements over a whole year or the lifetime of the system. This paper presents a very detailed and precise model for simulating energy gains with DMPPT as well as its verification and simulation results with different shading profiles, showing the possible energy gain over a whole year. Simulation results show that the yearly energy gain is much lower than the maximum power gain. However, interesting yearly gains of up to 12% are obtained in one of the simulations. Copyright © 2013 John Wiley & Sons, Ltd.     

Analytical study and evaluation results of power optimizers for distributed power conditioning in photovoltaic arrays

The use of modular or ‘micro’ maximum power point tracking (MPPT) converters at module level in series association, commercially known as “power optimizers”, allows the individual adaptation of each panel to the load, solving part of the problems related to partial shadows and different tilt and/or orientation angles of the photovoltaic (PV) modules. This is particularly relevant in building integrated PV systems. This paper presents useful behavioural analytical studies of cascade MPPT converters and evaluation test results of a prototype developed under a Spanish national research project. On the one hand, this work focuses on the development of new useful expressions which can be used to identify the behaviour of individual MPPT converters applied to each module and connected in series, in a typical grid‐connected PV system. On the other hand, a novel characterization method of MPPT converters is developed, and experimental results of the prototype are obtained: when individual partial shading is applied, and they are connected in a typical grid connected PV array. Copyright © 2011 John Wiley & Sons, Ltd.     

虚拟量规检测孔位置度技术

通过分析孔位置度的综合量规检测法和三坐标测量法的优缺点,提出虚拟量规检测孔位置度方法。利用微粒群算法为虚拟量规寻找最恰当的对准位置,并通过可视化显示出测量点与虚拟量规的配合状态。此方法集成了综合量规检测法和三坐标测量法二者的优点,既能够达到综合量规对孔位置度的最佳评定准确率,又能够准确形象地反映不合格产品的缺陷点。     

基于微粒群算法的同轴度误差评定

通过分析国家标准中同轴度的定义,提出更符合最小区域要求的评定方法,并利用微粒群算法寻找最小误差值。首先提出一种完全根据同轴度定义建立的评定模型,随后针对具体应用场所提出一种改进的评定模型。针对改进的模型分别采用了微粒群算法和最小二乘法评定同轴度误差,对比结果表明此模型下用微粒群算法有很好的处理速度、更准确的评定结果以及较低的计算成本。