Recent Advances in Electrical & Electronic Engineering - Volume 14, Issue 8, 2021

Multi Criteria Decision Making (MCDM) helps decision makers (DMs) solve highly complex problems. Accordingly, MCDM has been widely used by DMs from various fields as an effective and reliable tool for solving various problems, such as in site and supplier selection, ranking and assessment. This work presents an in-depth survey of past and recent MCDM techniques cited in the literature. These techniques are mainly categorised into pairwise comparison, outranking and distance-based approaches. Some well-known MCDM methods include the Analytical Hierarchy Process (AHP), Analytical Network Process (ANP), Elimination et Choix Traduisant la Realité (ELECTRE), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Each of these methods is unique and has been used in a vast field of interest to support DMs in solving complex problems. For a complete survey, discussions related to previous issues and challenges and the current implementation of MCDM are also presented.

In order to obtain the precise mathematical model of the position control system of the hydraulic quadruped robot, and to meet the requirements of the system parameters in different stages of motion, this paper studies the position control system of the single-leg joint of the hydraulic quadruped robot: First of all, this paper uses the closed-loop indirect identification method to identify the position of the leg joints of the hydraulic quadruped robot to obtain the mathematical model of the system; And then, the speed PID control algorithm and speed planning algorithm are designed, so that the system can quickly respond to the changes of system input according to the requirements of different speeds; Finally, the joint position control system of the hydraulic quadruped robot is simulated and verified by experiments. Background: The mathematical model of the positioning system of the hydraulic quadruped robot is clear, but the parameters in the model have the characteristics of uncertainty and time-variation. In the joint position control system of a hydraulic quadruped robot, different motion stages have different requirements for system parameters. Objective: The purpose of this study is to obtain the precise mathematical model of the position control system of the hydraulic quadruped robot and to meet the requirements of the system parameters in different stages of motion. Methods: This research takes the hydraulic quadruped robot single-leg system as the research object and uses the closed-loop indirect identification method to identify the position of the leg joints of the hydraulic quadruped robot to obtain the mathematical model of the system. Then, the speed PID control method is designed and compared with the ordinary PID control by taking the positioning control accuracy of the robot before touching the ground as a standard to carry out the controlled trial. Results: In this research, the identification method and control algorithm are designed, and finally, the simulation and experimental research are carried out. The results of the simulation and experiment verify the correctness of the identification method and the effectiveness of the control algorithm. Conclusion: First of all, this paper uses the closed-loop indirect identification method to identify the position of the leg joints of the hydraulic quadruped robot to obtain the mathematical model of the system. Then, the speed PID control algorithm and speed planning algorithm are designed so that the system can quickly respond to the changes of system input according to the requirements of different speeds.

Background: Power line inspection is essential to ensure the safe and stable operation of the power system. Object detection for tower equipment can significantly improve inspection efficiency. However, due to the low resolution of small targets and limited features, the detection accuracy of small targets is not easy to improve. Objective: This study aimed to improve the tiny targets’ resolution while making the small target's texture and detailed features more prominent to be perceived by the detection model. Methods: In this paper, we propose an algorithm that employs generative adversarial networks to improve small objects' detection accuracy. First, the original image is converted into a superresolution one by a super-resolution reconstruction network (SRGAN). Then the object detection framework Faster RCNN is utilized to detect objects on the super-resolution images. Results: The experimental results on two small object recognition datasets show that the model proposed in this paper has good robustness. It can especially detect the targets missed by Faster RCNN, which indicates that SRGAN can effectively enhance the detailed information of small targets by improving the resolution. Conclusion: We found that higher resolution data is conducive to obtaining more detailed information of small targets, which can help the detection algorithm achieve higher accuracy. The small object detection model based on the generative adversarial network proposed in this paper is feasible and more efficient. Compared with Faster RCNN, this model has better performance on small object detection.

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN comprises the frustum sequence module, 3D instance segmentation module SNET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module ENET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved Convolutional Neural Network (CNN) based on image-driven point clouds.

Background: Winding deformation is one of the most common faults an operating power transformer experiences over its operational life. Thus, it is essential to detect and rectify such faults at early stages to avoid potential catastrophic consequences to the transformer. At present, methods published in the literature for transformer winding fault diagnosis are mainly focused on identifying fault type and quantifying its extent without giving much attention to the identification of fault location. Methods: This paper presents a method based on a genetic algorithm and support vector machine (GA-SVM) to improve the faults’ classification of power transformers in terms of type and location. In this regard, a sinusoidal sweep signal in the frequency range of 600 kHz to 1MHz is applied to one terminal of the transformer winding. A mathematical index of the induced current at the head and end of the transformer winding under various fault conditions is used to extract unique features that are fed to a Support Vector Machine (SVM) model for training. Parameters of the SVM model are optimized using a Genetic Algorithm (GA). Results: The effectiveness of mathematical indicators to extract fault type characteristics and the proposed fault classification model for fault diagnosis is demonstrated through extensive simulation analysis for various transformer winding faults at different locations. Conclusion: The proposed model can effectively identify different fault types and determine their location within the transformer winding, and the diagnostic rates of the fault type and fault location are 100% and 90%, respectively.

Background: High-voltage direct current (HVDC) is suitable for high capacity and longdistance power transmission, thus becoming ideal for connecting renewable energies such as solar power and wind power to grids. Objective: Overhead lines in HVDC are vulnerable to short-circuit faults. Non-permanent DC short circuit faults are the most common in HVDC transmission, which can lead to pause in power transmission and interruption in large grids. Thereby, it is crucial to deploy techniques to suppress fault current. Methods: To lower the fault current economically, a coordinated fault current limiting strategy based on a hybrid multilevel modular converter (MMC) is proposed in this paper. Results: Combining hybrid MMC and small-capacity DC circuit breaker reduces total IGBTs required and avoids MMC blocking during pole-to-ground short-circuit fault. This approach is verified using a two-terminal MMC-based system in PSCAD/EMTDC simulation environment. Conclusion: By implementing the introduced scheme, the peak fault current can be lowered by 33.0% using hybrid-MMC with 80% of FBSMs. Economic efficiency can be improved by adopting proposed scheme.