Health Status Evaluation of Catenary Based on Normal Fuzzy Matter-Element and Game Theory

Lingzhi Yi,Jian Zhao,Wenxin Yu,Guzong Long,Haoyi Sun,Wang Li 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5

At present, there is no unifi ed standard for the health status evaluation of electrifi ed railway catenary in China. The current catenary evaluation model only considers quantitative detection indicators, without qualitative indicators such as weather, which is one-sided to some extent. Thus, an improved catenary status evaluation model is constructed with both quantitative indicators and qualitative indicators. In this evaluation model, the normal fuzzy matter-element method is used to determine the correlation value of each grade, and the weighted average principle is used to re-determine the status grade of catenary when the maximum correlation principle fails. Meanwhile, entropy weight method and particle swarm optimization algorithm to optimize analytic hierarchy process method are combined to improve the shortcomings of single weight method, and game theory is used to determine the subjective and objective weight coeffi cients, so as to reduce the infl uence of subjective experience. Select a Chinese railway catenary in 2018 as an example for verifi cation analysis, the results show that the model constructed in this paper can eff ectively help professionals to make correct judgments on the health status of catenary, and provide a new idea and method for the comprehensive evaluation of the catenary operation status, which has certain practicability

Fault Detection of Induction Motor Based on ALO Optimized TKSVDD

Yi Lingzhi,Xu Xiu,Zhao Jian,Duan Renzhe,Guo You,Sun Tao 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.1

Failure of asynchronous motor will cause motor short circuit accident, personal electric shock and other hazards, so it is very important to detect its abnormalities during its operation. In order to solve the problems of low detection accuracy and inaccurate detection results in asynchronous motor detection, a fault detection method of asynchronous motor based on ant lion optimizer optimizes three kernel support vector data description (ALO-TKSVDD) is proposed in this paper. Firstly, for the current signal of asynchronous motor, stochastic resonance is used to improve the signal-to-noise ratio; Secondly, ant lion optimizer (ALO) is used to optimize the three kernel support vector data description (TKSVDD) to detect abnormal data of the target signal; Finally, the accuracy and feasibility of ALO-TKSVDD are verifi ed. Comparative experiments show that the asynchronous motor anomaly detection method proposed in this paper has the highest accuracy and the lowest false detection rate.

Dynamic Multi-peak MPPT for Photovoltaic Power Generation Under Local Shadows Based on Improved Mayfly Optimization

Yi Lingzhi,Shi Hao,Liu Jiangyong,Zhou Dongfang,Liu Ximeng,Zhu Jiang 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.1

The P-U curves of PV array panels under partial shading shows multi-peaked characteristics, therefore, conventional Maximum Power Point Tracking (MPPT) methods may fail or not be accurate enough. In this paper, a Chaos Mayfl y Optimization with Lévy Flight and Adaptive Algorithm (CMOFA) is proposed to track the maximum power points of PV arrays under partial shading. Firstly, sin chaos is used to initialize the population, then adaptive adjustment of inertia weights is introduced and the learning factor is changed to enhance the local search ability of the algorithm. Later in the operation of the algorithm, when the search falls into the local optimum, Lévy fl ight with induced variable step length is used to make the algorithm jump out of the local optimum and improve the local optimum avoidance ability of the mayfl y algorithm. The simulation results show that CMOFA is not only able to avoid local shading and dynamic shading changes, but also has a signifi cant improvement in convergence speed and search accuracy compared with conventional intelligent algorithms.

Task Offloading of Intelligent Building Based on CO–HHO Algorithm in Edge Computing

Yi Lingzhi,Gao Xieyi,Li Zongpin,Feng Xiaodong,Huang Jianxiong,Liu Qiankun 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.6

With the rapid development of intelligent devices, the intelligence of buildings is becoming more and more obvious, which leads to the rapid growth of data generated by building users. The existing network bandwidth is far from enough for the transmission of existing data, which will lead to congestion in the process of data transmission. In this paper, a task offl oading strategy based on edge computing is proposed. The edge server is deployed near the data source, which mainly solves the problems of transmission delay and energy consumption of building users during task offl oading. In this paper, the mathematical model of system delay and energy consumption is established fi rst. In order to better refl ect the quality of the system, the delay and energy consumption are combined into system utility, and then the objective function is established. Since the objective function is a mixed integer nonlinear programming problem, fi nding the optimal solution usually requires exponential time complexity. Therefore, this paper fi rstly uses the Tammer decomposition method to decouple the objective function, and decomposes it into the resource allocation problem of fi xed task offl oading decision and the task offl oad problem of maximizing the objective function. Then the convex optimization (CO) theory is used to greatly reduce the complexity of the objective function and optimize the resource allocation problem. Finally, the task offl oading problem is solved by the improved Harris Hawks Optimization (HHO) . The paper compares various offl oading schemes. The simulation results show that the CO–HHO offl oading strategy based on edge computing proposed in this paper can eff ectively reduce the transmission delay and energy consumption of user tasks in intelligent buildings, and is superior to others in all aspects.

A Fault Diagnosis Method of Oil-Immersed Transformer Based on Improved Harris Hawks Optimized Random Forest

Yi Lingzhi,Jiang Ganlin,Zhang Guoyong,Yu Wenxin,Guo You,Sun Tao 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.4

In order to improve the accuracy and reliability of fault diagnosis for oil-immersed transformers, a fault diagnosis method for oil-immersed transformers based on improved Harris Hawks optimized random forest is proposed in this paper. First, logistic chaotic mapping is used to adjust the key parameters of the algorithm; then a nonlinear energy factor adjustment strategy is used to control the algorithm to transition from global search to local search; fi nally, the method of Gaussian mutation is introduced to strengthen the local search ability, and when the algorithm is stagnant, fi refl y perturbation is performed on the optimal solution to make the algorithm jump out of local optimum. The number of n_trees and n_layers of the random forest are jointly optimized by the improved Harris Hawks optimization algorithm, and the fault diagnosis model of oil-immersed transformer is established. The noncoded ratios of dissolved characteristic gases in oil are used as the characteristic input of the diagnosis model to obtain the fi nal diagnosis results. Compared with other models and verifi ed by examples, the results show that the proposed method has the advantage of high diagnostic accuracy and has certain practical engineering application value.

Optimal Speed Tracking of Freight Trains Combined with Segmented Soft-Switching Control

Yi Lingzhi,Yi Yu,Wang Yahui,Xie Cheng 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1

Smoothness of travel speed and stopping accuracy are important for freight trains. However, due to the large mass of freight trains, their driving speed is easy to jitter at the operating condition switching point. For these purposes, this paper designs a Dual Mode Optimal Control (DMOC) for tracking the target travel speed of freight trains. This controller contains two sub-controllers, Adaptive Model Predictive Control (AMPC) and Preview control (PC). An Elman Neural Network (ENN) is incorporated in AMPC to adjust the control weights of MPC in real time to output the optimal driving speed. The Afnity propagation-Fast-minimum covariance determinant algorithm, combined in ENN identifes the noisy samples in the training samples and improves the ftting efect of the network. PC and AMPC are fused together by a soft-switching control method. The soft switching control method based on Tanh function can achieve smooth switching of controllers and obtain a good control efect. By comparing with active disturbance rejection control and fuzzy proportional-integral-derivative under two speed profles, DMOC can efectively reduce the speed jitter of speed tracking, improve the stopping accuracy and timeliness of freight trains, and reduce energy consumption.

Research of Building Load Optimal Scheduling Based on Multi-objective Estimation of Distributed Algorithm

Liu Jiankang,Lingzhi Yi,Yi Fang,Lin Jiahao,Li Wang,Fan Lǜ 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.2

In the centralized scheduling of multi-residents, the complexity of scheduling time will be greatly increased as the number of resident increases. In order to reduce the time complexity caused by centralized scheduling, a probability model based on the Time-of-use electricity tariff diff erence is proposed and applied to distributed estimation of the algorithm. According to the impact factor mechanism of the probability model of Time-of-use electricity tariff diff erence, not only the time complexity of centralized scheduling is reduced, but also the optimization of the algorithm will not fall into a local optimal situation. In the centralized scheduling model of building residents, the controllable load of residents and new energy are centralized. The consumption rate of new energy was improved by changing the new energy power supply mechanism. Under the conditions of ensuring the comfort of household electricity consumption, three objective functions of the model include: (a) to reduce the total daily electricity consumption, (b) to fl atten the peak-to-valley diff erence of daily electricity, (c) to decrease the discarded rate of new energy. The simulation of the calculation example verifi es the feasibility and eff ectiveness of the proposed method.