Article
Mathematics, Interdisciplinary Applications
Abdullah Gokyildirim
Summary: Interest in fractional calculus and its applications has been increasing, and fractional-order analysis has the potential to enhance chaotic systems. This study presents the implementation of a lower-order fractional electronic circuit for the Sprott K system, which is pioneering in achieving a fractional-order parameter of approximately 0.8. Various numerical analyses are conducted to examine the dynamic characteristics and complexity of the system, and the voltage outputs from the oscilloscope show good agreement with the numerical analysis and simulations.
FRACTAL AND FRACTIONAL
(2023)
Article
Mathematics, Interdisciplinary Applications
Pongsakorn Sunthrayuth, Hina M. Dutt, Fazal Ghani, Mohammad Asif Arefin
Summary: This article presents a method for solving fractional delay differential equations and combines it with the steps method. The results show that the proposed method is accurate, reliable, and has a fast error convergence rate.
Article
Mathematics
Yunus Yalman, Tayfun Uyanik, Adnan Tan, Kamil Cagatay Bayindir, Yacine Terriche, Chun-Lien Su, Josep M. Guerrero
Summary: This paper proposes a novel algorithm to classify the relative location and fault type of voltage sag, which is a common power quality problem. The algorithm is investigated through numerical simulation and tested in a real distribution system, showing satisfactory performance.
Article
Computer Science, Information Systems
Toqeer Ahmed, Asad Waqar, Rajvikram Madurai Elavarasan, Junaid Imtiaz, Manoharan Premkumar, Umashankar Subramaniam
Summary: This paper proposes a fractional-order sliding mode control for a D-STATCOM to improve power quality issues in the grid. By utilizing appropriate sliding mode design and robust control methods, the stability and performance optimization of the system are achieved.
Article
Automation & Control Systems
Manoj Badoni, Alka Singh, Sandeep Pandey, Bhim Singh
Summary: This article discusses the development of a fractional-order notch filter for a grid-connected solar PV system, which aims to address power quality issues. The effectiveness of this control approach is demonstrated through simulation and experimental results.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Chemical
Lai Peng, Zhichao Fu, Tao Xiao, Yang Qian, Wei Zhao, Cheng Zhang
Summary: This paper proposes a PLL strategy adapted to extremely harsh grid conditions, which achieves real-time regulation of harmonic signals and adaptive tracking of grid phase by introducing repetitive control internal model and eliminating DC bias.
Article
Computer Science, Information Systems
Mohammad Kazem Bakhshizadeh, Sujay Ghosh, Lukasz Kocewiak, Guangya Yang
Summary: There is a rapid increase in offshore wind power plants worldwide, which is essential for providing grid stability and ancillary services. However, the Phase Locked Loop converter control has been identified as a cause of instability in wind turbine systems during severe grid faults. This paper presents an improved reduced-order model that accurately tracks grid-angle disturbances and considers time-varying parameters and initial state changes. The proposed model shows effectiveness for offline studies compared to detailed simulation models.
Article
Energy & Fuels
Ali Darvish Falehi, Hossein Torkaman
Summary: In order to address power quality issues in the DVR, an efficient and reliable power source and controller are required to compensate for uncertainties and non-linearities in HESS, enabling smooth tracking of current references and control of DC-link voltages.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Engineering, Multidisciplinary
Vivek Narayanan, Seema Kewat, Bhim Singh
Summary: This article presents a solar photovoltaic (PV)-battery energy storage (BES) system for a microgrid system, which utilizes a bidirectional dc-dc converter and a voltage source converter to control the dc-link voltage, enhancing system reliability and performing various functions.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2021)
Article
Mathematics, Applied
Michael Donello, Mark H. Carpenter, Hessam Babaee
Summary: We propose a model-driven low-rank approximation method for computing sensitivities in evolutionary systems. This approach allows for accurate and tractable computation of sensitivities with respect to a large number of parameters by extracting correlations between different sensitivities.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2022)
Article
Energy & Fuels
Pavan Babu Bandla, Indragandhi Vairavasundaram, Yuvaraja Teekaraman, Ramya Kuppusamy, Srete Nikolovski
Summary: This paper analyzes the impact of voltage sag on induction motors and considers different types of voltage sags in the experiment. The results aid in the design of more accurate induction motors and provide a proposed modification to further study the performance of induction motors during voltage sag events.
Article
Engineering, Electrical & Electronic
Yanling Lv, Yuchen Zhang, Qi Liu, Shuo Wang, Dalei Shi
Summary: This paper establishes a fourth-order power system dynamic model and a two-parameter fourth-order power system mathematical model with load reactive power and mechanical input power as chaotic parameters, and explores the mechanism and characteristics of chaos in the power system using chaos theory. A sliding mode controller is designed to suppress the chaotic oscillation of the power system.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Computer Science, Information Systems
Hyoung-Kyu Yang, Usman Ali Khan, Zeeshan Aleem, Jung-Wook Park
Summary: This paper proposes a new constant power generation method for photovoltaic systems to mitigate power variations caused by external conditions. The method can rapidly track the constant power point and effectively handle sudden changes in external conditions, making it a valuable solution for increasing the penetration level of photovoltaic systems in power grids.
Review
Energy & Fuels
Joaquin E. Caicedo, Daniel Agudelo-Martinez, Edwin Rivas-Trujillo, Jan Meyer
Summary: This paper provides a comprehensive literature review on the real-time detection and classification of Power Quality Disturbances (PQDs), with a specific focus on voltage sags and notches. A systematic method based on scientometrics, text similarity, and the analytic hierarchy process is proposed to structure the review and select relevant literature. Bibliometric analysis is performed to reveal patterns such as publication trends, top publishing countries, and distribution of topics. A critical review is conducted on selected articles, covering various aspects of PQD detection and classification.
PROTECTION AND CONTROL OF MODERN POWER SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Huizhong Bai, Na Ni, Yingna Wu, Rui Yang, Guangping Xie
Summary: This paper presents a study on the melting state of keyhole tungsten inert gas (K-TIG) welding, an emerging welding technique. A sensing system is established to collect voltage signals during the welding process of 304 stainless steel, and the analysis reveals that the power spectrum of characteristic signal between 36 kHz and 37 kHz is closely related to the joint penetration of the welding. It is observed that the spectral power density of the characteristic signal varies significantly with the alteration of welding quality, and adjusting welding parameters in real time can ensure the stability of welding quality.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Electrical & Electronic
Wen-Lin Chu, Min-Jia Xie, Qun-Wei Chang, Her-Terng Yau
Summary: This study focuses on real-time identification of machining conditions and chatter conditions in the machining process. Sound and vibration signals are captured and analyzed to identify whether machining is performed and whether chatter is observed. Experimental results show that support vector machine and convolutional neural network can effectively identify machining conditions, and a reduced network architecture can reduce training time while maintaining a high recognition rate.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Ping-Huan Kuo, Meng-Jun Huang, Po-Chien Luan, Her-Terng Yau
Summary: This paper presents an Adaboost algorithm for chatter detection in cutting data analysis. By transforming and analyzing the accelerometer data, learning models are built and compared with different algorithms. The experimental results show that using the transformed bandwidth signals achieves higher accuracy and reliability.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Mechanical
Ping-Huan Kuo, Yung-Ruen Tseng, Po-Chien Luan, Her-Terng Yau
Summary: Chatter not only affects the surface quality of the workpiece and tool wear, but also increases production costs. Accurate detection of chatter signals is therefore necessary. Due to the nonlinear vibration nature of chatter during machining, different chatter characteristics are observed under different conditions. This research uses a machining learning method combined with a database and employs chaotic error mapping to accelerate data processing. With only 60 data points, an accuracy of 94.8% and precision of 99.62% can be achieved. Additionally, this research introduces the fractional-order (FO) convolutional neural network (FOCNN) for chatter detection, reducing trainable parameters by 42.3% compared to approximate training conditions while improving accuracy by 3.8%.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Multidisciplinary
Ping-Huan Kuo, Dian-Ying Cai, Po-Chien Luan, Her-Terng Yau
Summary: Cutter wear has a significant impact on machining quality, especially for high precision machining. This paper proposes a real-time machining status monitoring method using external sensor data. A tool wear forecast model is introduced, and multiple process parameters and sensor data are collected. The model is based on a Branched Neural Network and outperforms other algorithms in terms of prediction accuracy.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Ping-Huan Kuo, Yen-Wen Chen, Tung-Hsien Hsieh, Wen-Yuh Jywe, Her-Terng Yau
Summary: Considering the rapid development of technology, traditional manufacturing methods cannot achieve the required high accuracy in aerospace, national defense, and leading-edge engineering projects. Thermal displacement is a significant source of manufacturing errors, and it is difficult or even impossible to accurately correct such errors using traditional machining methods. This article proposes a machine learning method that can be easily implemented by non-professionals for high-accuracy error prediction. An optimized automatic logistic random generator time-varying acceleration coefficient particle swarm optimization (LRGTVAC-PSO) method is proposed to optimize a branch structured bidirectional gated recurrent unit (GRU) neural network. The proposed method achieves superior accuracy (with a three-axis average of 0.945) compared to other optimized algorithms evaluated in this study. The method not only accurately predicts thermal displacement but also autotunes the hyperparameters of machine learning algorithms.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Chi-Yuan Lin, Shu-Cing Wu, Ping-Huan Kuo, Meng-Jun Huang, Song-Wei Hong, Her-Terng Yau
Summary: In recent years, industries have increased their demand for precision, automatic detection, and visualization interfaces. Machine tool operators install sensors on machine tools to obtain more precise measurements, but this results in complicated wire layouts. Wireless data transmission has emerged as a solution to this problem. However, while machine tool operators focus on optimizing sensing conditions, they often overlook information security. This study proposes a signal transmission encryption and decryption system for sensory toolholders during processing and compares two control methods for synchronization, finding that sliding mode control offers better synchronization and information security.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Ping-Huan Kuo, Chieh-Hsiu Pao, En-Yi Chang, Her-Terng Yau
Summary: In this study, reinforcement learning was used to train humanoid robots to adapt to uneven terrains and automatically adjust their parameters for optimal gait pattern control. The results showed that proximal policy optimization (PPO), combining advantage actor-critic and trust region policy optimization, was the most suitable method. An improved version of PPO, called PPO2, was used in combination with data preprocessing methods such as wavelet transform and fuzzification, which improved the gait pattern control and balance of humanoid robots.
INTERNATIONAL JOURNAL OF OPTOMECHATRONICS
(2023)
Article
Engineering, Electrical & Electronic
Ping-Huan Kuo, Chia-Ho Lee, Her-Terng Yau
Summary: In the precision machining industry, thermal error is a common and difficult to control factor for machine tools. This study uses temperature sensors and an eddy current displacement meter to collect data for training models, which are then organized and normalized. Different learning models are used to predict the nonlinear factors that affect the errors, and the best two models with better predictive performance are identified for the pre-trained model of transfer learning. Retraining with Multilayer Perceptron (MLP) on these two models improves the predicted results, with an MAE value of 0.40, RMSE of 0.52625, and R-2 score of 0.99696.
INTERNATIONAL JOURNAL OF OPTOMECHATRONICS
(2023)
Article
Engineering, Multidisciplinary
Ping-Huan Kuo, Po-Chien Luan, Yung-Ruen Tseng, Her-Terng Yau
Summary: In this study, an effective procedure for chatter data preprocessing is proposed to improve neural network learning results from extremely low quantity data. By utilizing the characteristics of a chaotic attractor, the variability of chatter data can be minimized. A modified convolutional neural network and a deep convolutional generative adversarial net are used for improved chatter detection and classification. The proposed training strategy generates enough data to compensate for the lack of training data, providing a high-quality deep learning chatter detection model.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Chieh-Li Chen, Li-Hsuan Chen, Her-Terng Yau
Summary: This article analyzes the winding pattern and characteristics of filament winding cylinders for lightweight gas cylinder productions. A range of winding angles is adopted to prevent sliding between the filament material and the cylinder surface. The least composite filament can be determined by calculating the winding pattern using different winding angles. Motion planning for a four-axis filament winding machine can be carried out based on sequential contact points during the winding process.
Article
Engineering, Electrical & Electronic
Her-Terng Yau, Ping-Huan Kuo, Dian-Ying Cai, Chia-Yu Lin
Summary: Tool status testing is crucial for improving processing efficiency and quality. This study develops a tool wear monitoring model using sensor signals and machine learning. By preprocessing the signals and applying a neural network model, higher wear forecast accuracy and reduced training time cost can be achieved.
IEEE SENSORS JOURNAL
(2023)
Article
Energy & Fuels
Ping-Huan Kuo, Yung-Ruen Tseng, Po-Chien Luan, Her-Terng Yau
Summary: The Broad Transfer Learning Network (BTLN) model achieves similar prediction performance as a Multi-Layer Perceptron (MLP) model using only one-third of the parameters. It combines broad learning and transfer learning techniques, and improves performance by enhancing feature extraction and increasing training efficiency. The BTLN model shows a significant improvement of 18.5% in performance compared to common neural network models.
Article
Thermodynamics
Ping-Huan Kuo, Tzung-Lin Tu, Yen-Wen Chen, Wen-Yuh Jywe, Her-Terng Yau
Summary: Using AI algorithms, this study predicted the displacement of a cutting tool caused by thermal deformation based on data collected from machine tool experiments. Multiple machine learning models were constructed and evaluated for accuracy. The incorporation of transfer learning and model optimization was found to improve prediction accuracy and mitigate the negative effects of data collected at different times.
CASE STUDIES IN THERMAL ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Ping-Huan Kuo, Ssu-Chi Chen, Chia -Ho Lee, Po -Chien Luan, Her-Terng Yau
Summary: Many factors such as prolonged and high-intensity usage, tool-workpiece interaction, mechanical friction, and ambient temperatures can increase the temperature of a machine tool. This leads to spindle thermal displacement and reduced machining precision. To address this, an intelligent algorithm is used to predict the thermal displacement of the machine tool. The ensemble model of LSTM-SVM shows higher prediction performance compared to other machine learning algorithms.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Engineering, Multidisciplinary
Ping-Huan Kuo, Ting-Chung Tseng, Po-Chien Luan, Her-Terng Yau
Summary: This study focuses on developing an effective method for predicting the remaining useful life of bearings to prevent machine damage and human accidents. By exploring neural network models and analyzing data, the study successfully predicts the remaining useful life with high accuracy. The proposed method is proven to be superior through a comparison with traditional models.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2022)