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Research on Upgrading Plan of Transmission Line for Voltage Sag Treatment

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第3期17卷 1509-1516页

作者： Wang, Xue Liu, Changrong North China Elect Power Univ Sch Elect & Elect Engn Baoding Peoples R China North China Elect Power Univ Elect Engn Baoding Hebei Peoples R China

When the overhead lines are replaced with cables, the voltage sag will reduce with the decrease of line failure. For a regional power grid, the whole effect of voltage sag treatment is directly correlated with the upgrading plan of overhead line. In the paper a method to study the transmission line upgrading plan is proposed, in which the treatment cost, treatment stage and order are considered. First, the upgrading benefits and upgrading costs of each overhead line are studied. Then, aimed at the maximum treatment benefit, the overhead line upgrading plan optimization model of is established, and the cuckoo algorithm is used to solve the model to obtain the upgrading plan. If the lines needed to be upgraded are too many or the investment is limited, the upgrading can't be done within one year, the model is solved in two stages to obtain the phased upgrading plan. At last, IEEE 30-node system is adopted to test the proposed method. The result shows that compared with other methods the proposed method can provide upgrading plan with more details, and offer theoretical support for the voltage sag treatment in system side.

关键词： Voltage sag Transmission line Voltage sag treatment cuckoo algorithm Treatment benefit

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Evaluation Method of Classroom Teaching Effect Under Intelligent Teaching Mode

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MOBILE NETWORKS & APPLICATIONS 2022年第3期27卷 1262-1270页

作者： Chen, Jing Lu, Hui SIAS Coll Zhengzhou Acad Affairs & Sci Res Off Xinzheng 451100 Peoples R China Inner Mongolia Univ Coll Comp Sci Hohhot 010012 Peoples R China

In order to improve the accuracy and performance of classroom teaching effect evaluation, an intelligent teaching mode classroom teaching effect evaluation method is proposed. Based on the characteristics of intelligent teaching mode, an intelligent teaching effect evaluation index system including five indexes of basic quality, teaching attitude, teaching method, teaching ability and teaching effect is constructed. After obtaining the scores of each index by expert scoring, it is input into the cuckoo search algorithm extreme learning machine evaluation model and solved by objective function, obtain the final score of teaching effect evaluation. The experimental results show that the proposed method can effectively improve the evaluation accuracy of classroom teaching effect of intelligent teaching mode, and provide a new method for classroom teaching effect evaluation.

关键词： Intelligent course Teaching mode Classroom teaching effect cuckoo algorithm Extreme learning machine

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Modeling, cost optimization and management of grid connected solar powered charging station for electric vehicle

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2022年第4期23卷 587-603页

作者： Khare, Vikas Khare, Cheshta J. Nema, Savita Baredar, Prashant NMIMS Dept Elect STME Indore Madhya Pradesh India SGSITS Indore Indore Madhya Pradesh India NMIMS Indore Dept Elect Indore Madhya Pradesh India

Transportation sector is currently the largest carbon emitter and produces lots of harmful gases in the atmosphere, as a result Electric Vehicle (EV) is an attractive choice for mankind. Increased use of EVs needs the establishment of a large number of EV charging stations. This paper shows comprehensive assessment of solar powered charging station which is used for the charging of electric vehicles. This paper includes the design, cost optimization and management of an EV charging station along with pre-feasibility analysis for location assessment for EV charging stations. A case study for Ahmedabad is presented in this paper. Solar-Pro software is used for design, technical and financial assessment of solar powered based charging stations. Memetic and cuckoo optimization techniques are used for cost optimization. Further Program, Evaluation and Review Technique (PERT) is used to assess the cradle to grave process of charging stations and at the end strengths, weaknesses, opportunities and threats (SWOT) and Political, Economic, Social, Legal, Environmental (PESTLE) analysis is presented, which shows strength and weakness of project from political, economical, social and ecological points of view. Results shows that Ahmedabad is suitable location for the solar powered charging station due to the mean value and maximum value of solar radiation is 5.7516 and 10.8869 kwh/m(2)/day respectively. The available energy of the given system is 30,506 kwh/m(2)/day at the solar fraction of 97.12%. The result of cuckoo algorithm for cost optimization is much better than memtic algorithm. Result also shows the reliability of whole system is 89%.

关键词： cuckoo algorithm electric vehicles (EVs) memetic algorithm PERT technique reliability analysis solar pro solar system

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An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm

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Beni-Suef University Journal of Basic and Applied Sciences 2015年第1期4卷 14-25页

作者： Harish Garg Thapar University School of Mathematics and Computer Applications Patiala 147004 India

The main goal of the present paper is to present a penalty based cuckoo search (CS) algorithm to get the optimal solution of reliability – redundancy allocation problems (RRAP) with nonlinear resource constraints. The reliability – redundancy allocation problem involves the selection of components' reliability in each subsystem and the corresponding redundancy levels that produce maximum benefits subject to the system's cost, weight, volume and reliability constraints. Numerical results of five benchmark problems are reported and compared. It has been shown that the solutions by the proposed approach are all superior to the best solutions obtained by the typical approaches in the literature are shown to be statistically significant by means of unpaired pooled t-test.

关键词： Redundancy allocation cuckoo algorithm Reliability Optimization

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Research on photovoltaic multi-peak MPPT based on SCA-INC

Research on photovoltaic multi-peak MPPT based on SCA-INC

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International Conference on Optoelectronic Materials and Devices (ICOMD)

作者： Luo, Yi Xiong, Hong Jun Shanghai Dianji Univ Sch Business Shanghai 201306 Peoples R China

ISBN: (数字)9781510652057

ISBN: (纸本)9781510652057;9781510652040

In order to obtain the maximum output power of the photovoltaic array under partial shadow conditions, a composite algorithm based on the combination of the cuckoo search algorithm and the conductance increment method is proposed to realize the MPPT tracking control. Early use of SCA's efficient global search function to find the vicinity of the global maximum power point, and then use the fast local tracking capability of INC to find the maximum power point. Through Matlab/Simulink simulation, the obtained results confirm that the algorithm has faster convergence speed and higher search accuracy in photovoltaic multi-peak MPPT, and the oscillation amplitude is smaller.

关键词： maximum power tracking cuckoo algorithm conductance increment method photovoltaic multi-peak

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Wearable Sensing Based Virtual Reality Rehabilitation Scheme for Upper Limb Training 15th

Wearable Sensing Based Virtual Reality Rehabilitation Scheme...

引用

15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Zhang, Jialiang Liu, Yaojie Liu, Juan Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China

ISBN: (纸本)9783031138355;9783031138348

Upper limb rehabilitation training is an important method for stroke patients with hemiplegia to restore their upper limb motor ability. The combination of virtual reality (VR) technology and rehabilitation training can increase the effectiveness and interest of the training process. In the process of virtual reality rehabilitation training, dynamic uncertainty factors will affect the effectiveness of rehabilitation training, so it is necessary to adjust the decision of rehabilitation training in real time according to the status of patients. In this paper, a virtual reality upper limb rehabilitation training game with controllable difficulty parameters is designed to collect the electromyographic (EMG) signals and motion signal of patients in the process of movement. Through the fuzzy neural network rehabilitation training decision-making method optimized based on the cuckoo algorithm, the control parameters of virtual reality rehabilitation training scene are adjusted to make the difficulty of rehabilitation training task match the upper limb movement ability of patients adaptively. We recruited 23 stroke patients with different stages of Brunnstrom rehabilitation participated in this experiment. The accuracy of the rehabilitation training decision-making method has an accuracy rate of 96.23%. It can accurately make rehabilitation training decisions, adjust the difficulty of training tasks to a challenging but feasible level, and improve the rehabilitation training effect.

关键词： Wearable sensing Virtual reality cuckoo algorithm Upper limb rehabilitation training

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D-TSVR Recurrence Prediction Driven by Medical Big Data in Cancer

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第5期17卷 3508-3517页

作者： Yang, Ai-Min Han, Yang Liu, Chen-Shuai Wu, Jian-Hui Hua, Dian-Bo North China Univ Sci & Technol Coll Sci Tangshan 063009 Peoples R China North China Univ Sci & Technol Coll Met & Energy Tangshan 063009 Peoples R China North China Univ Sci & Technol Sch Publ Hlth Tangshan 063009 Peoples R China Beijing St Lab Beijing 100000 Peoples R China

Secondary use of medical big data is becoming increasingly popular in healthcare services and clinical research in the medical industry. Cancer recurrence is a common phenomenon of cancer patients after treatment (recovery period). Studying the time and influencing factors of cancer recurrence can provide effective clinical intervention means, which is the gospel of cancer patients. In this article, a sample of 50 000 cases of seven cancer patients, including liver cancer, lung cancer, kidney cancer, breast cancer, uterine cancer, stomach cancer, and bowel cancer, was collected. A twin support regression vector machine (TSVR) algorithm based on dependent nearest neighbor (DNN) weighting is proposed, the eplion-TSVR model is improved by DNN-weighted algorithm with local information mining function, and the solution of the improved model is derived. It is proposed to use the cuckoo algorithm to determine the optimal parameters of DNN to determine the optimal dependency region domain. In this article, the improved TSVR algorithm is used to establish a cancer recurrence prediction model. The prediction accuracy of the model for various cancers can reach more than 91%, which is significantly higher than that of convolutional neural network and e-TSVR models.

关键词： Cancer Prediction algorithms Predictive models Support vector machines Computational modeling Tumors Industries Cancer recurrence cancer cuckoo algorithm data mining support vector regression (SVR)

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A low carbon management model for regional energy economies based on blockchain technology

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HELIYON 2023年第9期9卷 e19966页

作者： Tan, Siyue Liu, Guangmin Univ Glasgow Adam Smith Sch Business Glasgow City G12 8QQ Scotland Harbin Univ Sch Econ & Management Dept Accounting Harbin 150086 Peoples R China

As the issue of sustainable energy development becomes more and more important in national economic construction, the potential dangers of climate change are gradually attracting wide-spread attention from countries around the world. In order to better carry out the low-carbon management of the regional energy economy, based on the analysis of the characteristics of blockchain technology, the present study utilized this technology to achieve intelligent and digital management of carbon emissions, and established a carbon emission prediction system. The cuckoo algorithm is used to improve the long-term memory network, and the improved al-gorithm is used in carbon emission prediction and management. The experimental results show that the improved Long Short Term Memory networks are close to the target precision in 240 iterations, and the convergence speed is fast. In the short-term regional carbon emission pre-diction, the average absolute error of the method is only 2%, which is highly consistent with the actual carbon emission. In the long-term carbon emission prediction, the average prediction ac-curacy of the upgraded long-term short-term memory networks can reach 97.26%, and the running time is only 19.46s. With high precision and running efficiency, the upgraded Long Short Term Memory networks can efficiently monitor regional carbon emission and provide a technical reference for the low-carbon management of the regional power industry.

关键词： Blockchain Energy economy Carbon emissions Long short term memory Management mode cuckoo algorithm

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A New Fault Diagnosis Classifier for Rolling Bearing United Multi-Scale Permutation Entropy Optimize VMD and cuckoo Search SVM

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IEEE ACCESS 2020年 8卷 153610-153629页

作者： Guo, Zijian Liu, Mingliang Wang, Yunxia Qin, Huabin Heilongjiang Univ HLJ Prov Key Lab Senior Educ Elect Engn Harbin 150080 Peoples R China Key Lab Informat Fusion Estimat & Detect Harbin 150080 Peoples R China

Aiming at the influence of mixed noise of bearing vibration signal on the extraction of useful information, a fault diagnosis optimize classifier based on multi-scale permutation entropy (MPE) and cuckoo search algorithm (CS) is proposed. Firstly, the MPE threshold method is adopted to select the appropriate variational mode decomposition algorithm (VMD) parameters, and then the signal is reconstructed by adding neutral white noise, and the reconstructed signal is decomposed by MPE-OVMD algorithm to obtain the optimal IMF component. Finally, the cuckoo search algorithm is used to optimize the global optimal solution of the support vector machine, thereby achieving the classification model of support vector machine with the best parameters. The analysis results of motor signals show that the method can eliminate the phenomena of mode aliasing and signal over-decomposition. An analytical comparison of the CSSVM classifier is carried out with the performance of the learners such as recall rate, ROC curve, AUC. The contrast experiment shows that the classification model can avoid misrecognition of the fault sample as the normal condition and maximum the optimal maintenance time of the equipment under the premise of ensuring the accuracy. The classifier model of the cuckoo optimization algorithm has better fitting accuracy than others such as the Grid Search algorithm (GS), Particle Swarm Optimization (PSO), Genetic algorithm search (GA), and the ensemble fault recognition rate is as high as 90%.

关键词： Variational mode decomposition multi-scale permutation entropy signal reconstruction cuckoo algorithm support vector machine

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Short-term Power Load Forecasting based on Machine Learning

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第6期7卷 333-341页

作者： Jinwei Shi Yan Zhang

In order to improve the accuracy of short-term power load prediction, a short-term power load prediction model (VMD-CS-LSTM) based on variational mode decomposition and cuckoo algorithm was proposed to optimize the long and short term memory (LSTM) neural network. In view of the problem that the parameters of LSTM model are difficult to select, CS is used to optimize the parameters of LSTM model. For the simulation of the prediction model, this paper chooses the Jupyter Notebook programming environment and uses Python language to program under the framework of TensorFlow. The model obtained can be used to predict the future power load value. The simulation results show that the method of variational mode decomposition and cuckoo algorithm to optimize LSTM can effectively improve the accuracy and stability of short-term power load prediction.

关键词： Short-term Power Load Forecasting Variational Mode Decomposition cuckoo algorithm Long and Short Term Memory algorithm Error Analysis

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