Study on maximum power point tracking of photovoltaic array in irregular shadow

Traditional maximum power point tracking (MPPT) methods can hardly find global maximum power point (MPP) because output characteristics curve of photovoltaic (PV) array may have multi local maximum power points in irregular shadow, and thus easily fall into the local maximum power point. To address this drawback, Considering that sliding mode variable structure (SMVS) control strategy have such advantages as simple structure, fast response and strong robustness, and P&O method have the advantages of simple principle and convenient implementation, so a new algorithm combining SMVS control method and P&O method is proposed, besides, PI controller is applied to reduce system chattering caused by switching sliding surface. It is applied to MPPT control of PV array in irregular shadow to solve the problem of multi-peak optimization in partial shadow. In order to verity the rationality of the proposed algorithm, the experimental circuit is built, which achieves MPPT control by means of the proposed algorithm and P&O method. The experimental results show that compared with the traditional P&O algorithm, the proposed algorithm can fast track the global MPP, tracking speed increases by 60% and the relative error decreased by 20%. Moreover, the system becomes more stable near the MPP, the fluctuations of output power is greatly reduced, and thus make full use of solar energy.     

Evaluation Model of Click Rate of Electronic Commerce Advertising Based on Fuzzy Genetic Algorithm

Mobile Networks and Applications - In order to improve the ability of quantitative evaluation of e-commerce advertising click rate, a model of e-commerce advertising click rate evaluation based on...     

Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification

An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers.     

Investigation of the relationships of process parameters,molten pool geometry and shear strength in laser transmission welding of polyethylene terephthalate and polypropylene

This study concerns the laser transmission welding (LTW) of polyethylene terephthalate (PET) and polypropylene (PP) which are widely used in the automotive, aerospace and medical industries. The relationships of process parameters, molten pool geometry (both width and depth) and shear strength (SS) in LTW process are systematically investigated through finite element method (FEM), response surface methodology (RSM) and experiments. Thereinto, the relationships between the molten pool depths to width (D/W) ratio and SS are firstly investigated. Firstly, a three-dimensional thermal model is developed to simulate the temperature field and molten pool geometry of the LTW process. The simulation results are confirmed by experiments. Then RSM is utilized to design the experiments and establish the mathematical relationships between the process parameters and molten pool geometry based on the simulation results. The interaction effects of the process parameters on the molten pool geometry are analyzed. Finally, the simulation results are further used for searching the relationships between the molten pool D/W ratio and the SS (from tensile experiments). The maximum value of the SS and the corresponding molten pool D/W ratio is found. The result reveals that the molten pool D/W ratio has a significant influence on the SS. Moreover, this finite element model can also play a commendable guiding role in the LTW experiments with acceptable accuracy.     

Improving performance of proton ceramic electrolysis cell perovskite anode by Zn doping

As an important renewable energy, hydrogen energy becomes an important part of the future energy system. Proton ceramic electrolysis cell (PCEC) enables the efficient, clean, large-scale preparation of hydrogen, which is a new type of energy conversion device, attracting the attention of many researchers. Sr₂Fe_1.4Zn_0.1Mo_0.5O_6-δ (SFZM) anode materials were developed to investigate the effect of B-site doping of Zn on the electrochemical properties of the Sr₂Fe_1.5Mo_0.5O_6-δ (SFM) materials. The results reveal that the doping of Zn increases the concentration of oxygen vacancies and improves the electrocatalytic activity, which in turn improves the performance of the material. A current density of 408 mA cm⁻² has been achieved at 1.3 V when the SFZM-based single cell was operated in an electrolysis mode (50% H₂O in air) at 600 °C, higher than SFM-based single cells (286 mA cm⁻² at 1.3 V). In addition, the SFZM-based single cell exhibited good durability in a stability test at an electrolysis current density of 408 mA cm⁻². This work confirms that SFZM is a promising material for proton ceramic electrolysis cell anode.     

Improved dielectric energy storage performance of Na0.5Bi0.5TiO3-based lead-free relaxation ferroelectric ceramics achieved by domain structural regulation and enhanced densification

There is still a problem of low energy storage density in dielectric capacitors which is a core component of power systems. For the improvement of the energy storage density, the linear dielectric material CaTiO₃ (CT) was introduced in Na_0.5Bi_0.5TiO₃ (NBT) ceramics in this paper. By modifying the A site, a new relaxor ferroelectric ceramic was successfully synthesized and attained a recoverable density (W_rec) of 2.34 J/cm³ at x = 0.18. Moreover, the preparation process was optimized in this paper. Through the viscous polymer process (VPP) route, the energy density (W_A) of 82NBT-18CT_VPP ceramic further reaches 6.45 J/cm³ at 340 kV/cm, with efficiency (η) up to 75% and a W_rec of 4.82 J/cm³. At the same time, the change of W_rec is small at temperature (30–150 °C) and frequency (1 Hz–300 Hz), which demonstrates its excellent stability. The discharge power density reaches about 180 MW/cm³ and the discharge time is 0.117 μs, which indicates its excellent pulse discharge performance. The results show that 82NBT-18CT lead-free relaxation ferroelectric material is expected to become ideal for high-energy storage applications.     

Reducing energy usage in drive storage clusters through intelligent allocation of incoming commands

Although significant research has been undertaken to reduce high level energy consumption in a data centre, there has been very little focus on reducing storage drive energy consumption via the intelligent allocation of workload commands at the file system level. This paper presents a method for optimising drive energy consumption within a custom built storage cluster containing multiple drives, using multi-objective goal attainment optimization. Significantly, the model developed was based on actual power consumption values (from current/voltage sensors on the drives themselves), which is rare in this field.The results showed that command energy savings of up to 87% (17% overall energy) could be made by optimising the allocation of incoming commands for execution to drives within a storage cluster for different workloads. More significantly, the transparency of the method meant that it showed exactly how such savings could be made and on which drives. It also highlighted that whilst it is well known that solid state drives use less energy than traditional hard disk drives, the difference is not consistent for different sizes of data transfers. It is far larger for small data transfers (less than or equal to 4 kB) and our algorithm utilised this.Significantly, it highlights how much larger energy savings can be made through using the optimisation results to show which drives can be safely put into a low power state without affecting storage cluster performance.     

Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization

Economic load dispatch (ELD) problems have been an important issue in optimal operation and planning of power system. Characterized by non-convex/non-smooth properties and various practical constraints, the ELD problems are difficult to solve using conventional optimization techniques. In this paper, an improved orthogonal design particle swarm optimization (IODPSO) algorithm is presented for solving the single-area and multi-area ELD problems with nonlinear characteristics of the generators, such as valve-point effects, prohibited operating zones, ramp rate limits and multiple fuels. In the IODPSO algorithm, an orthogonal designed method is used to construct a promising exemplar. Multiple auxiliary vector generating strategies are proposed to enhance the efficiency and effectiveness of orthogonal design operations. A tent chaotic map is employed for the adaptation of the acceleration coefficients, thus improving the proposed algorithm's robustness and global search capabilities. In addition, we designed a repair method to handle the practical constraints. Six cases of ELD problems with different characteristics are utilized to benchmark the proposed algorithm. Experimental results demonstrate that IODPSO algorithm is a promising approach for solving the non-convex/non-smooth ELD problems.     

Data-driven generative design for mass customization: A case study

Generative design provides a promising algorithmic solution for mass customization of products, improving both product variety and design efficiency. However, the current designer-driven generative design formulates the automated program in a manual manner and has insufficient ability to satisfy the diverse needs of individuals. In this work, we propose a data-driven generative design framework by integrating multiple types of data to improve the automation level and performance of detail design to boost design efficiency and improve user satisfaction. A computational workflow including automated shape synthesis and structure design methods is established. More specifically, existing designs selected based on user preferences are utilized in the shape synthesis for creating generative models. For structural design, user-product interaction data gathered by sensors are used as inputs for controlling the spatial distributions of heterogeneous lattice structures. Finally, the proposed concept and workflow are demonstrated with a bike saddle design with a personalized shape and inner structures to be manufactured with additive manufacturing.     

基于深度图正则化矩阵分解的多视图聚类算法

针对现实社会中由多种表示或视图组成的多视图数据广泛存在的问题,深度矩阵分解模型因其能够挖掘数据的层次信息而备受关注,但该模型忽略了数据的几何结构信息.为解决以上问题,本文提出基于深度图正则化矩阵分解的多视图聚类算法,通过获取每个视图的局部结构信息和全局结构信息在逐层分解中加入两个图正则化限制,保护多视图数据的几何结构信...