https://onlinelibrary.wiley.com/doi/pdf/10.1002/inf2.12028

Machine learning in materials science

A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions

Maximum power point tracking (MPPT) control techniques play a vital role in efﬁciency improvement of photovoltaic (PV) systems. It is mainly used to extract maximum possible power of the PV modules under both variable climatic and partial shaded condition (PSC). From literature, various types of MPPT techniques have been developed to track the maximum power point efficiently, ranging from conventional methods to artificial intelligence and bio-inspired methods. Each technique has its own advantage and drawbacks. This paper focuses mainly on a review on advancements of global maximum power point tracking (GMPPT) techniques of photovoltaic (PV) system subjected to partial shading conditions (PSC) to help the users to make the right choice when designing their system. The most recent and selected MPPT methods are discussed and classified as optimization, hybrid, mathematical model based methods and other GMPPT methods. This study will serve as interesting guide for researchers working on PV system field.

A review of global maximum power point tracking techniques of photovoltaic system under partial shading conditions

A novel gas turbine fault diagnosis method based on transfer learning with CNN

Intelligent welding system technologies: State-of-the-art review and perspectives

In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

https://iopscience.iop.org/article/10.1088/1742-6596/933/1/012006/pdf

Automatic Detection of Welding Defects using Deep Neural Network

Deep features based on a DCNN model for classifying imbalanced weld flaw types

In order to extract the maximum power from PV system, the maximum power point tracking (MPPT) technology has always been applied in PV system. At present, various MPPT control methods have been presented. The perturb and observe (P&O) and conductance increment methods are the most popular and widely used under the constant irradiance. However, these methods exhibit fluctuations among the maximum power point (MPP). In addition, the changes of the environmental parameters, such as cloud cover, plant shelter, and the building block, will lead to the radiation change and then have a direct effect on the location of MPP. In this paper, a feasible MPPT method is proposed to adapt to the variation of the irradiance. This work applies the glowworm swarm optimization (GSO) algorithm to determine the optimal value of a reference voltage in the PV system. The performance of the proposed GSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy by utilizing MATLAB/SIMULINK. The simulation results demonstrate that the tracking capability of the GSO algorithm is superior to that of the traditional P&O algorithm, particularly under low radiance and sudden mutation irradiance conditions.

/pdf/a-novel-maximum-power-point-tracking-algorithm-based-on-2fd9gd5864.pdf

A Novel Maximum Power Point Tracking Algorithm Based on Glowworm Swarm Optimization for Photovoltaic Systems

The output power of a photovoltaic (PV) system depends on the external solar irradiation and its own temperature. In order to obtain more power from the PV system, the maximum power point tracking (MPPT) is necessary. However, when the PV is partially shaded, there will be multiple peaks in the power-current (P-I) curve. The conventional MPPT methods may be invalid due to falling into the local peak. In addition, in a photovoltaic-thermal (PV/T) system, the non-uniform temperature distribution on PV will also occur, which complicates the situation. This paper presents a MPPT method with glowworm swarm optimization (GSO) for PV in a PV/T system under non-uniform solar irradiation and temperature distribution. In order to study the performance of the proposed method, the conventional methods including the perturbation and observe algorithm (P and O), and the fractional open-circuit voltage technique (FOCVT) are compared with it in this paper. Simulation results show that the proposed method can rapidly track the real maximum power point (MPP) under different conditions, such as the gradient temperature distribution, the fast variable solar irradiation and the variable partial shading. The outcome indicates the proposed method has obvious advantages, especially the performance being superior to the conventional methods under the partial shading condition.

A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution

The invention discloses a forging press machine operation condition information acquisition and analysis system based on the internet of things technology. According to the system, the operation condition analog quantity is obtained through a sensor; the forging press machine operation condition switching values are obtained through acquiring the PLC signal points; the information contents are processed by a data acquisition terminal interface circuit and then transmitted to processor chips of the data acquisition terminal; then the information contents are transmitted to a cloud server through a GPRS (General Packet Radio Service) communication module for the convenience of data sharing. By means of the server built-in software, the cloud data is analyzed in real time; on one hand, machine tool users can obtain the machine tool operation reports or pre-warning information through PC so that they can monitor the machine tool condition at any time, and obtain fault diagnosis reports from machine tool seller technical staff; on the other hand, the seller technical staff can inquire the machine tool operational efficiency and working curves through PC at any time to acquire the machine tool physical condition in real time and to estimate faults in time.

Wenhui Hou

Papers

Automatic Detection of Welding Defects using Deep Neural Network

Deep features based on a DCNN model for classifying imbalanced weld flaw types

A Novel Maximum Power Point Tracking Algorithm Based on Glowworm Swarm Optimization for Photovoltaic Systems

A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution

Forging press machine operation condition information acquisition and analysis system based on the internet of things technology