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Novel data-driven open-circuit fault diagnosis method for modular multilevel converter submodules based on optimized deep learning

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JOURNAL OF POWER ELECTRONICS 2025年第1期25卷 58-70页

作者： An, Yang Sun, Xiangdong Ren, Biying Zhang, Xiaobin Xian Univ Technol Coll Elect Engn Xian Peoples R China

As the proportion of clean energy continues to increase, low carbon energy systems will be a significant way to achieve the goal of carbon neutrality. Therefore, the reliability of modular multilevel converters (MMCs) is particularly significant. However, conventional open-circuit fault diagnosis (OCFD) methods usually have a limited localization speed or are difficult to achieve in practical engineering. Therefore, a fast and simpled OCFD approach for MMC SMs based on an optimized deep learning is proposed in this article. In this approach, data on the of submodule capacitance voltages are input into a trained WOA-DKELM model without the manually settings. The problems of randomness in the regularization coefficient C and the kernel parameters K can be solved by DKELM with WOA optimization, which has a strong generalization capability and higher prognostic accuracy. The effectiveness of the proposed approach is verified by experiment results. This approach achieves an average identification probability of 0.96 within 20 ms of the fault.

关键词： Modular multilevel converters Fault diagnosis whale optimization algorithm Deep kernel extreme learning machine

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Assembly line balancing and optimal scheduling for flexible manufacturing workshop

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第6期38卷 2757-2772页

作者： Hou, Wen Zhang, Song Shandong Univ Sch Mech Engn Jinan 250061 Peoples R China Shandong Univ Key Lab High Efficiency & Clean Mech Manufacture Minist Educ Jinan 250061 Peoples R China

In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important position. However, complexity, uncertainty, multi-objective and multi-constraints are the problems faced by production scheduling in flexible manufacturing workshop, which restricts the intelligent transformation of enterprises. This paper takes an assembly shop as the research object, and carries out research on assembly line balancing and multi-automatic guided vehicle (AGV) scheduling problems in shop production. Firstly, for the multi-objective assembly line balancing problem with fluctuating demand, the relevant assembly line model is established by changing only the number of workstations and replanning the boundary, and an improved multi-objective whale optimization algorithm is proposed to reduce the rebalancing cost of the assembly line. Secondly, the multi-AGV dynamic scheduling problem with corresponding scheduling objective weights according to the current number of AGVs is analyzed, and a mathematical model is established to maximize the value of the objective function by considering a variety of factors under the premise of meeting the production requirements of the assembly line. Then, a scheduling rule selection method based on neural network and knowledge base is proposed to determine the optimal combination of scheduling rules for different system states. Finally, a simulation analysis of an assembly workshop example is carried out to verify the feasibility and effectiveness of the proposed method in solving the assembly line balancing problem and the multi-AGV scheduling problem, which improves the manufacturing efficiency and the resilience of the workshop.

关键词： Assembly line balancing Dynamic scheduling Intelligent manufacturing Neural network whale optimization algorithm

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Energy-efficient cluster head using modified fuzzy logic with WOA and path selection using enhanced CSO in IoT-enabled smart agriculture systems

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JOURNAL OF SUPERCOMPUTING 2024年第8期80卷 11149-11190页

作者： Chandrasekaran, Senthil Kumar Rajasekaran, Vijay Anand Vellore Inst Technol Sch Comp Sci Engn & Informat Syst SCORE Vellore India

The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a cluster head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified fuzzy logic, whale optimization algorithm (WOA) and enhanced crow swarm optimization (ECSO). Fuzzy logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that the proposed approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. The proposed system demonstrates substantial advantages in terms of throughput, with a remarkable throughput value of 68 Mbps, outperforming the other methods, including EQSR (12.5 Mbps), E2S-DRL (60 Mbps), and OSEAP. The packet delivery ratio (PDR) is also significantly higher for the proposed system, reaching 90.9%. The proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of the simulation experiments

关键词： Cluster head Internet of things Enhanced crow swarm optimization whale optimization algorithm Fuzzy logic

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Woa-fism planning hexapod robot various gaits

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INTELLIGENT SERVICE ROBOTICS 2024年第4期17卷 963-979页

作者： Hu, Pingzhi Zhang, Mengjian Wang, Deguang Guizhou Commun Polytech Dept Mech & Elect Engn Guiyang 551400 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510000 Guangdong Peoples R China Guizhou Univ Coll Elect Engn Guiyang 550025 Peoples R China

Compared to wheeled and tracked robots, hexapod robots have higher adaptability and higher flexibility in complex terrains. With various gaits, hexapod robots can fulfill different needs better. Existing researches mainly focused on three common gaits, they are single-leg swing gait, wave gait, and tripod gait. Instead of directly planning gaits with swarm intelligence algorithms (SIA), a gait planning method for hexapod robots named finite incremental state machine (FISM) is proposed. FISM focuses on four incremental states between two adjacent gaits of the robot, which greatly reduces the complexity of the gait planning algorithm so that gait planning with SIA is simplified to set the optimal transfer conditions of FISM. In addition, after comparing five optimization algorithms, the whale optimization algorithm (WOA) can set the optimal transfer conditions of FISM. The computer simulation shows WOA-FISM can plan various gaits, finally, a real robot test verifies the effectiveness of various gaits.

关键词： Hexapod robots Finite incremental state machine Incremental states Various gaits optimization algorithm whale optimization algorithm

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Memory-dependent derivative grey Bernoulli model and its application in electricity generation forecast

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GREY SYSTEMS-THEORY AND APPLICATION 2024年第1期14卷 160-184页

作者： Zhang, Yonghong Li, Shouwei Li, Jingwei Tang, Xiaoyu Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Southeast Univ Sch Econ & Management Nanjing Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China

PurposeThis paper aims to develop a novel grey Bernoulli model with memory characteristics, which is designed to dynamically choose the optimal memory kernel function and the length of memory dependence period, ultimately enhancing the model's predictive ***/methodology/approachThis paper enhances the traditional grey Bernoulli model by introducing memory-dependent derivatives, resulting in a novel memory-dependent derivative grey model. Additionally, fractional-order accumulation is employed for preprocessing the original data. The length of the memory dependence period for memory-dependent derivatives is determined through grey correlation analysis. Furthermore, the whale optimization algorithm is utilized to optimize the cumulative order, power index and memory kernel function index of the model, enabling adaptability to diverse *** selection of appropriate memory kernel functions and memory dependency lengths will improve model prediction performance. The model can adaptively select the memory kernel function and memory dependence length, and the performance of the model is better than other comparison *** limitations/implicationsThe model presented in this article has some limitations. The grey model is itself suitable for small sample data, and memory-dependent derivatives mainly consider the memory effect on a fixed length. Therefore, this model is mainly applicable to data prediction with short-term memory effect and has certain limitations on time series of long-term *** implicationsIn practical systems, memory effects typically exhibit a decaying pattern, which is effectively characterized by the memory kernel function. The model in this study skillfully determines the appropriate kernel functions and memory dependency lengths to capture these memory effects, enhancing its alignment with real-world ***/valueBased on the memory-dependent derivative method, a memory-dependent derivative

关键词： Memory-dependent derivative NGBM (1,1) whale optimization algorithm Grey correlation

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Probabilistic load forecasting based on quantile regression parallel CNN and BiGRU networks

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APPLIED INTELLIGENCE 2024年第15-16期54卷 7439-7460页

作者： Lu, Yuting Wang, Gaocai Huang, Xianfei Huang, Shuqiang Wu, Man Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China

In the dynamic smart grid landscape, accurate probabilistic forecasting of electric load is critical. This paper presents a novel 24-hour-ahead probabilistic load forecasting model by integrating quantile regression with a parallel convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) architecture. Carefully tuning hyperparameters can enhance model performance and generalization capability. Consequently, we propose an improved whale optimization algorithm for automatic hyperparameter tuning of the forecasting model. Case studies demonstrate the proposed method's superior performance over benchmark models in terms of average interval score and pinball loss. In addition, it exhibits valid coverage and tight interval bandwidths. The model provides precise short-term load forecasts to support robust smart grid planning and operations.

关键词： Probabilistic load forecasting Quantile regression Deep learning whale optimization algorithm

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The optimization of bellows convolutions in bellows pump for better stress distribution

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第11期236卷 6141-6151页

作者： Gao Zhi-jian Hu Liang Ruan Xiao-dong Su Rui Fu Xin Zhejiang Univ State Key Lab Fluid Power Transmiss & Control Hangzhou Peoples R China

Bellows pump has been widely used for chemical transportation with excellent elasticity and seal ability of the core component bellows. Nevertheless, for repeated alternating compression and expansion, bellows convolutions, that is, the maximum stress points, are at risk of fatigue rupture, which leaves a safety hazard for practical applications. Though a special bellows shape with non-constant wall thickness was proposed as an effective method to strengthen the convolutions, it also affects other characteristics of bellows, such as stiffness, single-stroke displacement, and size, which are equally important for the bellows pump. This article designs a fine-tuned convolution structure on the premise of avoiding its effect on volume properties of bellows. Limiting the value of variables by the stability of bellows stiffness, the structure parameters are optimized with the combination of the finite element method (FEM) and whale optimization algorithm. To ensure the accuracy of the FEM, the stress-strain curve of bellows material and the bellows axial stiffness is measured. Finally, digital image correlation is employed to obtain the strain at convolutions before and after the optimization, and the significant reduction of the strain verifies the effectiveness of the optimization. We believe that this method is valuable for optimizing stress distribution and guiding the design of longer-life pump bellows.

关键词： Pump bellows convolution shape stress optimization whale optimization algorithm digital image correlation

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Intelligent Operation and Maintenance of Wind Turbines Gearboxes via Digital Twin and Multi-Source Data Fusion

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SENSORS 2025年第7期25卷 1972-1972页

作者： Xu, Tiantian Zhang, Xuedong Sun, Wenlei Wang, Binkai Xinjiang Univ Sch Intelligent Mfg Modern Ind Urumqi 830046 Peoples R China

Wind turbine operation and maintenance (O&M) faces significant challenges due to the complexity of equipment, harsh operating environments, and the difficulty of real-time fault prediction. Traditional methods often fail to provide timely and accurate warnings, leading to increased downtime and maintenance costs. To address these issues, this study systematically explores an intelligent operation and maintenance method for wind turbines, utilizing digital twin technology and multi-source data fusion. Specifically, it proposes a remote intelligent operation and maintenance (O&M) framework for wind turbines based on digital twin technology. Furthermore, an algorithm model for multi-source operational data analysis of wind turbines is designed, leveraging a whale optimization algorithm-optimized Temporal Convolutional Network with an Attention mechanism (WOA-TCN-Attention). The WOA is used to optimize the hyperparameters of the TCN-Attention model. Then, the gearbox fault alarm threshold and warning threshold are set using the statistical characteristics of the residual values, and the absolute value of the residuals is used to determine the abnormal operating state of the gearbox. Finally, the proposed method was validated using operational data from a wind farm in Xinjiang. With input data from multiple sources, including seven key parameters such as temperature, pressure, and power, the method was evaluated based on EMAE, ERMSE, and EMAPE. The results demonstrated that the proposed method achieved the smallest prediction error and provided effective early warnings 18 h and 33 min prior to actual failures, enabling real-time and efficient operation and maintenance management for wind turbines.

关键词： wind turbine whale optimization algorithm time convolutional network intelligent O&M fault early warning

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A Flexible Interconnected Distribution Network Power Supply Restoration Method Based on E-SOP

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ENERGIES 2025年第4期18卷 954-954页

作者： Jiang, Lin Wang, Canbin Qiu, Wei Xiao, Hui Hu, Wenshan Guangdong Power Grid Co Ltd Zhuhai Power Supply Bur Zhuhai 519000 Peoples R China Wuhan Univ Sch Elect & Automat Wuhan 430072 Peoples R China

To enhance the self-healing control capability of soft open points with energy storage (E-SOPs) and optimize the fault recovery performance in flexible interconnected distribution networks, this paper proposes a novel power supply restoration method based on E-SOP. The methodology begins with a comprehensive analysis of the E-SOP's fundamental architecture and loss model. Subsequently, a dual-objective optimization function is formulated to maximize the sum of nodal active load restoration while minimizing network losses. The optimization problem is transformed into a second-order cone programming formulation under comprehensive operational constraints. To solve this complex optimization model, an innovative hybrid approach combining the Improved whale optimization algorithm (IWOA) with second-order cone programming is developed. The proposed methodology is extensively validated using the IEEE 33-node test system. The experimental results demonstrate that this approach significantly enhances the power supply restoration capability of distribution networks while maintaining practical feasibility.

关键词： flexible interconnection soft open points with energy storage power supply restoration second-order cone transformation whale optimization algorithm

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A New Hybrid MFO-WO algorithm and Its Application to Design of FOPID/PID Controller for IoT Applications

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IEEE ACCESS 2025年 13卷 14557-14571页

作者： Bhookya, Jailsingh Natl Inst Technol Calicut Kozhikode 673601 Kerala India

The article presents a new optimization algorithm called the hybrid MFO-WOA algorithm, which is designed to tackle global optimization problems and optimize FOPID/PID controller designs. The integration of IoT applications for experimental validation is also discussed. Compared to the existing MFO and WOA algorithms, the proposed algorithm demonstrates faster convergence. To evaluate its efficiency, the algorithm is tested on 23 benchmark functions, and statistical parameters are used to describe its performance qualitatively. The results indicate that the proposed algorithm is competitive and can be considered state-of-the-art. The study also includes a comparison of the efficiency of the hybrid MFO-WOA controller with other controller designs for tuning controller parameters. The results of the FOPID/PID controller design are analyzed and compared. The next step involves implementing the PID controller on the ESP32 microcontroller module using the proposed algorithm. An Android-based application using the Blynk framework is developed for monitoring and controlling plants via IoT. The ESP32 microcontroller serves as the IoT gateway, and the controller and interface software are developed using the Arduino IDE. Based on the comparative findings of various case studies, it is evident that the proposed hybrid MFO-WOA algorithm-based controller design outperforms other approaches in terms of performance.

关键词： Internet of Things optimization microcontroller microcontroller FOPID controller FOPID controller moth-flame optimization moth-flame optimization whale optimization algorithm whale optimization algorithm liquid level control liquid level control PID controller PID controller PID controller

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