Due to its special working conditions, offshore wind turbine will bear large direct and indirect loads under the combined action of air flow and wave flow. In this paper, a variable pitch system composed of variablepitch motorand variable pitch bearing is improved, and the characteristics of system's bending moment, torque, vibration and other physical quantities under the action of multiple physical loads are verified, and the mechanical response characteristics of floating wind turbine under the control of unified variable pitch and independent variable pitch are studied under the running conditions at sea. The results show that mechanical structure of uniform pitch is compared with that of independent pitch, the independent variable pitch structure can effectively reduce the mean oscillation value of wind turbine tower in the parallel direction of air flow by optimizing control strategy, and reduce the thrust at the hub of wind turbine and the bending moment at the root of tower, but increase the vibration frequency and fatigue load of offshore wind turbine tower along parallel direction of air flow. Reduce the fatigue life of equipment. The research results can be used as a reference to reduce the variable pitch control and vibration suppression of offshore wind turbines and improve the reliability of wind turbines.
{"title":"Study on Dynamic Response Characteristics of Offshore Floating Wind Turbine Pitch System","authors":"Xin Guan, Shiwei Wu, Mingyang Li, Yuqi Xie","doi":"10.4108/ew.5800","DOIUrl":"https://doi.org/10.4108/ew.5800","url":null,"abstract":"Due to its special working conditions, offshore wind turbine will bear large direct and indirect loads under the combined action of air flow and wave flow. In this paper, a variable pitch system composed of variablepitch motorand variable pitch bearing is improved, and the characteristics of system's bending moment, torque, vibration and other physical quantities under the action of multiple physical loads are verified, and the mechanical response characteristics of floating wind turbine under the control of unified variable pitch and independent variable pitch are studied under the running conditions at sea. The results show that mechanical structure of uniform pitch is compared with that of independent pitch, the independent variable pitch structure can effectively reduce the mean oscillation value of wind turbine tower in the parallel direction of air flow by optimizing control strategy, and reduce the thrust at the hub of wind turbine and the bending moment at the root of tower, but increase the vibration frequency and fatigue load of offshore wind turbine tower along parallel direction of air flow. Reduce the fatigue life of equipment. The research results can be used as a reference to reduce the variable pitch control and vibration suppression of offshore wind turbines and improve the reliability of wind turbines.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The double salient pole structure of Switched Reluctance Motor (SRM) makes its electromagnetic field exist nonlinear saturation characteristics, resulting in its large torque pulsation in operation, so it is difficult to achieve speed regulation smoothly by traditional control methods. In view of this problem, a sliding mode control strategy which is based on synchronous transmission technology was proposed.Firstly, the basic structure of switched reluctance motor was analyzed, and the mathematical model of mechanical motion of switched reluctance motor was established. Secondly, an improved sliding mode controller which is based on synchronous signal transmission technology was designed by analyzing the reason of large torque ripple of switched reluctance motor, and the stability of the system was proved. Finally, simulation is used to verify the effectiveness of the control strategy.Compared with the traditional PID (Proportional Integral Differential) control algorithm, this control technology not only suppresses the SRM torque ripple effectively , but also makes the sliding mode controller output the precise target electromagnetic torque quickly by increasing the control variables. The results of research indicate that this design can not only restrain the torque ripple effectively, but also adjust the convergence speed and overshoot of the controller by adjusting the design parameters.
{"title":"Suppression of Torque Ripple in Switched Reluctance Motors Which is Based on Synchronization Technology","authors":"Huixiu Li, Qingtao Wei, Li‐Yi Zhang, Nan Li","doi":"10.4108/ew.5802","DOIUrl":"https://doi.org/10.4108/ew.5802","url":null,"abstract":"The double salient pole structure of Switched Reluctance Motor (SRM) makes its electromagnetic field exist nonlinear saturation characteristics, resulting in its large torque pulsation in operation, so it is difficult to achieve speed regulation smoothly by traditional control methods. In view of this problem, a sliding mode control strategy which is based on synchronous transmission technology was proposed.Firstly, the basic structure of switched reluctance motor was analyzed, and the mathematical model of mechanical motion of switched reluctance motor was established. Secondly, an improved sliding mode controller which is based on synchronous signal transmission technology was designed by analyzing the reason of large torque ripple of switched reluctance motor, and the stability of the system was proved. Finally, simulation is used to verify the effectiveness of the control strategy.Compared with the traditional PID (Proportional Integral Differential) control algorithm, this control technology not only suppresses the SRM torque ripple effectively , but also makes the sliding mode controller output the precise target electromagnetic torque quickly by increasing the control variables. The results of research indicate that this design can not only restrain the torque ripple effectively, but also adjust the convergence speed and overshoot of the controller by adjusting the design parameters.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"13 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140695106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the popularity and development of electric vehicles, the demand for power batteries has increased significantly. Power battery recycling requires a complex and efficient logistics network to ensure that used batteries can be safely and cost-effectively transported to recycling centers and properly processed. This paper constructs a dual-objective mathematical model that minimizes the number of recycling centers and minimizes the logistics cost from the service center to the recycling center, and designs the power battery disassembly and recycling process and the recycling logistics network, and finally uses a genetic algorithm to solve it. Finally, this article takes STZF Company as an example to verify the effectiveness of this method. The verification results show that the logistics intensity of the optimized power battery recycling logistics network has been reduced by 36.2%. The method proposed in this article can provide certain reference for power battery recycling logistics network planning.
{"title":"Research on Optimization of Power Battery Recycling Logistics Network","authors":"Yanlin Zhao, Yuliang Wu","doi":"10.4108/ew.5790","DOIUrl":"https://doi.org/10.4108/ew.5790","url":null,"abstract":"With the popularity and development of electric vehicles, the demand for power batteries has increased significantly. Power battery recycling requires a complex and efficient logistics network to ensure that used batteries can be safely and cost-effectively transported to recycling centers and properly processed. This paper constructs a dual-objective mathematical model that minimizes the number of recycling centers and minimizes the logistics cost from the service center to the recycling center, and designs the power battery disassembly and recycling process and the recycling logistics network, and finally uses a genetic algorithm to solve it. Finally, this article takes STZF Company as an example to verify the effectiveness of this method. The verification results show that the logistics intensity of the optimized power battery recycling logistics network has been reduced by 36.2%. The method proposed in this article can provide certain reference for power battery recycling logistics network planning.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"1 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: In recent years, machine learning and deep learning have emerged as pivotal technologies with transformative potential across various industries. Among these, the automobile industry stands out as a significant arena for the application of these technologies, particularly in the development of smart cars with unmanned driving systems. This article delves into the extensive research conducted on the detection technology employed by autonomous vehicles to navigate road conditions, a critical aspect of driverless car technology. OBJECTIVES: The primary aim of this research is to explore and highlight the intricacies of road condition detection for autonomous vehicles. Emphasizing the importance of this key component in the development of driverless cars, we aim to provide insights into cutting-edge algorithms that enhance the capabilities of these vehicles, ultimately contributing to their widespread adoption. METHODS: In addressing the challenge of road condition detection, we introduce the TidyYOLOv4 algorithm. This algorithm, deemed more advantageous than YOLOv4, particularly excels in pedestrian recognition within urban traffic environments. Its real-time capabilities make it a suitable choice for detecting pedestrians on the road under dynamic conditions. RESULTS: The application of the TidyYOLOv4 algorithm in autonomous vehicles has yielded promising results, especially in enhancing pedestrian recognition in urban traffic settings. The algorithm's real-time functionality proves crucial in ensuring the timely detection of pedestrians on the road, thereby improving the overall safety and efficiency of autonomous vehicles. CONCLUSION: In conclusion, the detection of road conditions is a critical aspect of autonomous vehicle technology, with implications for safety and efficiency. The TidyYOLOv4 algorithm emerges as a noteworthy advancement, outperforming its predecessor YOLOv4 in pedestrian recognition within urban traffic environments. As companies continue to invest in driverless technology, leveraging such advanced algorithms becomes imperative for the successful deployment of autonomous vehicles in real-world scenarios.
{"title":"Pedestrian Perception Tracking in Complex Environment of Unmanned Vehicles Based on Deep Neural Networks","authors":"Ruru Liu, Feng Hong, Zuo Sun","doi":"10.4108/ew.5793","DOIUrl":"https://doi.org/10.4108/ew.5793","url":null,"abstract":"INTRODUCTION: In recent years, machine learning and deep learning have emerged as pivotal technologies with transformative potential across various industries. Among these, the automobile industry stands out as a significant arena for the application of these technologies, particularly in the development of smart cars with unmanned driving systems. This article delves into the extensive research conducted on the detection technology employed by autonomous vehicles to navigate road conditions, a critical aspect of driverless car technology. \u0000OBJECTIVES: The primary aim of this research is to explore and highlight the intricacies of road condition detection for autonomous vehicles. Emphasizing the importance of this key component in the development of driverless cars, we aim to provide insights into cutting-edge algorithms that enhance the capabilities of these vehicles, ultimately contributing to their widespread adoption. \u0000METHODS: In addressing the challenge of road condition detection, we introduce the TidyYOLOv4 algorithm. This algorithm, deemed more advantageous than YOLOv4, particularly excels in pedestrian recognition within urban traffic environments. Its real-time capabilities make it a suitable choice for detecting pedestrians on the road under dynamic conditions. \u0000RESULTS: The application of the TidyYOLOv4 algorithm in autonomous vehicles has yielded promising results, especially in enhancing pedestrian recognition in urban traffic settings. The algorithm's real-time functionality proves crucial in ensuring the timely detection of pedestrians on the road, thereby improving the overall safety and efficiency of autonomous vehicles. \u0000CONCLUSION: In conclusion, the detection of road conditions is a critical aspect of autonomous vehicle technology, with implications for safety and efficiency. The TidyYOLOv4 algorithm emerges as a noteworthy advancement, outperforming its predecessor YOLOv4 in pedestrian recognition within urban traffic environments. As companies continue to invest in driverless technology, leveraging such advanced algorithms becomes imperative for the successful deployment of autonomous vehicles in real-world scenarios.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"82 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: The complexity of the power network, changes in weather conditions, diverse geographical locations, and holiday activities comprehensively affect the normal operation of power loads. Power load changes have characteristics such as non stationarity, randomness, seasonality, and high volatility. Therefore, how to construct accurate short-term power load forecasting models has become the key to the normal operation and maintenance of power.OBJECTIVES: Accurate short-term power load forecasting helps to arrange power consumption planning, optimize power usage and largely reduce power system losses and operating costs.METHODS: A hybrid decomposition-optimization-integration load forecasting method is proposed to address the problems of low accuracy of current short-term power load forecasting methods.RESULTS: The original power load time series is decomposed using the complete ensemble empirical modal decomposition method, while the correlation of power load influencing factors is analysed using Pearson correlation coefficients. The seagull optimisation algorithm is overcome to fall into local optimality by using the random adaptive non-linear adjustment strategy of manipulated variables and the differential variational Levy flight strategy, which improves the search efficiency of the algorithm. Then, the The gated cyclic unit hidden layer parameters are optimised by the improved seagull optimisation algorithm to construct a short-term electricity load forecasting model.The effectiveness of the proposed method is verified by simulation experimental analysis. The results show that the proposed method has improved the accuracy of the forecasting model.CONCLUSION: The CEEMD method is used to decompose the original load time series, which improves the accuracy of the measurement model. The GRU prediction model based on improved SOA optimization not only has better prediction accuracy than other prediction models, but also consumes the least amount of time compared to other prediction models.
引言： 电网的复杂性、天气条件的变化、地理位置的多样性以及节假日活动等因素全面影响着电力负荷的正常运行。电力负荷变化具有非静止性、随机性、季节性和高波动性等特点。因此，如何构建准确的短期电力负荷预测模型已成为电力正常运行维护的关键：方法：针对目前短期电力负荷预测方法准确性不高的问题，提出了一种分解-优化-积分混合负荷预测方法。结果：采用完全集合经验模态分解法对原始电力负荷时间序列进行分解，同时利用皮尔逊相关系数分析电力负荷影响因素的相关性。利用操纵变量的随机自适应非线性调整策略和差分变异列维飞行策略，克服了海鸥优化算法陷入局部最优的问题，提高了算法的搜索效率。然后，通过改进的海鸥优化算法优化门控循环单元隐层参数，构建短期电力负荷预测模型。结果表明，所提出的方法提高了预测模型的准确性。结论：CEEMD 方法用于分解原始负荷时间序列，提高了测量模型的准确性。与其他预测模型相比，基于改进 SOA 优化的 GRU 预测模型不仅预测精度更高，而且耗时最少。
{"title":"Short-term Electricity Load Forecasting Based on Improved Seagull Algorithm Optimized Gated Recurrent Unit Neural Network","authors":"Mengfan Xu, Junyang Pan","doi":"10.4108/ew.5282","DOIUrl":"https://doi.org/10.4108/ew.5282","url":null,"abstract":"INTRODUCTION: The complexity of the power network, changes in weather conditions, diverse geographical locations, and holiday activities comprehensively affect the normal operation of power loads. Power load changes have characteristics such as non stationarity, randomness, seasonality, and high volatility. Therefore, how to construct accurate short-term power load forecasting models has become the key to the normal operation and maintenance of power.OBJECTIVES: Accurate short-term power load forecasting helps to arrange power consumption planning, optimize power usage and largely reduce power system losses and operating costs.METHODS: A hybrid decomposition-optimization-integration load forecasting method is proposed to address the problems of low accuracy of current short-term power load forecasting methods.RESULTS: The original power load time series is decomposed using the complete ensemble empirical modal decomposition method, while the correlation of power load influencing factors is analysed using Pearson correlation coefficients. The seagull optimisation algorithm is overcome to fall into local optimality by using the random adaptive non-linear adjustment strategy of manipulated variables and the differential variational Levy flight strategy, which improves the search efficiency of the algorithm. Then, the The gated cyclic unit hidden layer parameters are optimised by the improved seagull optimisation algorithm to construct a short-term electricity load forecasting model.The effectiveness of the proposed method is verified by simulation experimental analysis. The results show that the proposed method has improved the accuracy of the forecasting model.CONCLUSION: The CEEMD method is used to decompose the original load time series, which improves the accuracy of the measurement model. The GRU prediction model based on improved SOA optimization not only has better prediction accuracy than other prediction models, but also consumes the least amount of time compared to other prediction models. ","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"50 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A scientific, comprehensive and integrated assessment of urban energy development is of great significance for the establishment of a clean, low-carbon and efficient urban modern energy system. From the perspective of carbon neutrality, this paper sets 25 evaluation indicators in seven dimensions: energy supply, energy consumption, energy efficiency improvement, clean and low-carbon, safety and reliability, low-carbon transport, and scientific and technological innovation, and constructs a secondary indicator system for evaluating the strategic development of urban energy. The system adopts the hierarchical analysis method to determine the weights of the indicators, the double-baseline progression method to standardize the indicator scores, and finally the weighted composite index method to calculate the level of urban energy strategy development. This paper applies the index system to evaluate the current energy development status of Wenzhou city in 2020 and 2022, and to predict the energy strategy development in 2025 and 2030. The scores of Wenzhou city's urban energy strategy development level in the corresponding four periods are 63.56, 70.59, 77.87 and 85.06, indicating that by 2023, Wenzhou city's urban energy development level will go from medium development to high development. Wenzhou City should accelerate the proportion of renewable energy in the future. It is necessary to complement multiple energy sources and improve the integration of heat, electricity, gas and cold. In terms of end consumption, it is necessary to improve the efficiency of energy use, reduce energy intensity, implement electric energy substitution and form an energy consumption pattern centered on electricity.
{"title":"Research on Establishment and Application of Evaluation System of Urban Energy Strategy Development Indicators under the Perspective of Carbon Neutrality","authors":"Chenyu Chen, Yunlong Song, Xuesong Ke, Yang Ping, Fangze Shang, Chaoyang Xiang, Qiang Chen, Haiwei Yin, Zhenzhou Zhang, Hao Fu, Fan Wu","doi":"10.4108/ew.5791","DOIUrl":"https://doi.org/10.4108/ew.5791","url":null,"abstract":"A scientific, comprehensive and integrated assessment of urban energy development is of great significance for the establishment of a clean, low-carbon and efficient urban modern energy system. From the perspective of carbon neutrality, this paper sets 25 evaluation indicators in seven dimensions: energy supply, energy consumption, energy efficiency improvement, clean and low-carbon, safety and reliability, low-carbon transport, and scientific and technological innovation, and constructs a secondary indicator system for evaluating the strategic development of urban energy. The system adopts the hierarchical analysis method to determine the weights of the indicators, the double-baseline progression method to standardize the indicator scores, and finally the weighted composite index method to calculate the level of urban energy strategy development. This paper applies the index system to evaluate the current energy development status of Wenzhou city in 2020 and 2022, and to predict the energy strategy development in 2025 and 2030. The scores of Wenzhou city's urban energy strategy development level in the corresponding four periods are 63.56, 70.59, 77.87 and 85.06, indicating that by 2023, Wenzhou city's urban energy development level will go from medium development to high development. Wenzhou City should accelerate the proportion of renewable energy in the future. It is necessary to complement multiple energy sources and improve the integration of heat, electricity, gas and cold. In terms of end consumption, it is necessary to improve the efficiency of energy use, reduce energy intensity, implement electric energy substitution and form an energy consumption pattern centered on electricity.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"351 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the increasingly severe climate problems, wind energy has received widespread attention as the most abundant energy on Earth. However, due to the uncertainty of wind energy, a large amount of wind energy is wasted, so accurate wind power prediction can greatly improve the utilization of wind energy. To increase the forecast for wind energy accuracy across a range of time scales, this paper presents a multi-time scale wind power prediction by constructing an ICEEMDAN-CNN-LSTM-LightGBM model. Initially, feature selection is performed using Lasso regression to identify the most significant variables affecting the forecast for wind energy across distinct time intervals. Subsequently, the ICEEMDAN is utilized to break down the wind power data into various scales to capture its nonlinear and non-stationary characteristics. Following this, a deep learning model based on CNN and LSTM networks is developed, with the CNN responsible for extracting spatial features from the time series data, and the LSTM designed to capture the temporal relationships. Finally, the outputs of the deep learning model are fed into the LightGBM model to leverage its superior learning capabilities for the ultimate prediction of wind power. Simulation experiments demonstrate that the proposed ICEEMDAN-CNN-LSTM-LightGBM model achieves higher accuracy in multi-time scale wind power prediction, providing more reliable decision assistance with the management and operation of wind farms.
{"title":"Multi-temporal Scale Wind Power Forecasting Based on Lasso-CNN-LSTM-LightGBM","authors":"Qingzhong Gao","doi":"10.4108/ew.5792","DOIUrl":"https://doi.org/10.4108/ew.5792","url":null,"abstract":"Due to the increasingly severe climate problems, wind energy has received widespread attention as the most abundant energy on Earth. However, due to the uncertainty of wind energy, a large amount of wind energy is wasted, so accurate wind power prediction can greatly improve the utilization of wind energy. To increase the forecast for wind energy accuracy across a range of time scales, this paper presents a multi-time scale wind power prediction by constructing an ICEEMDAN-CNN-LSTM-LightGBM model. Initially, feature selection is performed using Lasso regression to identify the most significant variables affecting the forecast for wind energy across distinct time intervals. Subsequently, the ICEEMDAN is utilized to break down the wind power data into various scales to capture its nonlinear and non-stationary characteristics. Following this, a deep learning model based on CNN and LSTM networks is developed, with the CNN responsible for extracting spatial features from the time series data, and the LSTM designed to capture the temporal relationships. Finally, the outputs of the deep learning model are fed into the LightGBM model to leverage its superior learning capabilities for the ultimate prediction of wind power. Simulation experiments demonstrate that the proposed ICEEMDAN-CNN-LSTM-LightGBM model achieves higher accuracy in multi-time scale wind power prediction, providing more reliable decision assistance with the management and operation of wind farms.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"13 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To augment the accuracy, stability, and qualification rate of wind power prediction, thereby fostering the secure and economical operation of wind farms, a method predicated on quadratic decomposition and multi-objective optimization for ultra-short-term wind power prediction is proposed. Initially, the original wind power signal is decomposed using a quadratic decomposition method constituted by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Fuzzy Entropy (FE), and Symplectic Geometry Mode Decomposition (SGMD), thereby mitigating the randomness and volatility of the original signal. Subsequently, the decomposed signal components are introduced into the Deep Bidirectional Long Short-Term Memory (DBiLSTM) neural network for time series modeling, and the Sand Cat Swarm Optimization Algorithm (SCSO) is employed to optimize the network hyperparameters, thereby enhancing the network’s predictive performance. Ultimately, a multi-objective optimization loss that accommodates accuracy, stability, and grid compliance is proposed to guide network training. Experimental results reveal that the employed quadratic decomposition method and the proposed multi-objective optimization loss can effectively bolster the model’s predictive performance. Compared to other classical methods, the proposed method achieves optimal results across different seasons, thereby demonstrating robust practicality.
{"title":"An Ultra-Short-Term Wind Power Prediction Method Based on Quadratic Decomposition and Multi-Objective Optimization","authors":"Hayou Chen, Zhenglong Zhang, Shaokai Tong, Peiyuan Chen, Zhiguo Wang, Hai Huang","doi":"10.4108/ew.5787","DOIUrl":"https://doi.org/10.4108/ew.5787","url":null,"abstract":"To augment the accuracy, stability, and qualification rate of wind power prediction, thereby fostering the secure and economical operation of wind farms, a method predicated on quadratic decomposition and multi-objective optimization for ultra-short-term wind power prediction is proposed. Initially, the original wind power signal is decomposed using a quadratic decomposition method constituted by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Fuzzy Entropy (FE), and Symplectic Geometry Mode Decomposition (SGMD), thereby mitigating the randomness and volatility of the original signal. Subsequently, the decomposed signal components are introduced into the Deep Bidirectional Long Short-Term Memory (DBiLSTM) neural network for time series modeling, and the Sand Cat Swarm Optimization Algorithm (SCSO) is employed to optimize the network hyperparameters, thereby enhancing the network’s predictive performance. Ultimately, a multi-objective optimization loss that accommodates accuracy, stability, and grid compliance is proposed to guide network training. Experimental results reveal that the employed quadratic decomposition method and the proposed multi-objective optimization loss can effectively bolster the model’s predictive performance. Compared to other classical methods, the proposed method achieves optimal results across different seasons, thereby demonstrating robust practicality.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: As a renewable and clean use of energy, wind power generation has a very important role in the new energy generation industry. For the many parts of various wind turbines, the safety and reliability of wind turbine blades are very important. OBJECTIVES: The energy spectrum simulation algorithm included in the wavelet analysis method is used to simulate and analyzewind turbine blade damage, to verify the correctness and validity of wind turbine blade damage analysis. METHODS: Matlab simulation is used to introduce the experiments related to the static and dynamic detection of fiber grating sensors, analyze the signal characteristics of the wind turbine blade when it is damaged by the impact, and provide a basis for the analysis of the external damage of large wind turbine blade. RESULTS: The main results obtained in this paper are the following. By analyzing the decomposition of wavelet packets, the gradient change of wavelet impact energy spectrum before and after the wavelet damage was obtained and compared with the histogram, and the impact energy spectrum of each three-dimensional wavelet energy packet in the image was compared and analyzed, which can well realize the recognition of wavelet damage gradient for solid composite materials. CONCLUSION: With the help of Matlab simulation to collect the impact response signal, using the wavelet packet energy spectrum method to analyze the signal, can derive the characteristics of wind turbine blade damage.
{"title":"Characterization and Prediction of Wind Turbine Blade Damage Based on Fiber Grating Sensor","authors":"Xin Guan, Qizheng Mu, Xiaoju Yin, Yuxin Wang","doi":"10.4108/ew.5752","DOIUrl":"https://doi.org/10.4108/ew.5752","url":null,"abstract":"INTRODUCTION: As a renewable and clean use of energy, wind power generation has a very important role in the new energy generation industry. For the many parts of various wind turbines, the safety and reliability of wind turbine blades are very important. \u0000OBJECTIVES: The energy spectrum simulation algorithm included in the wavelet analysis method is used to simulate and analyzewind turbine blade damage, to verify the correctness and validity of wind turbine blade damage analysis. \u0000METHODS: Matlab simulation is used to introduce the experiments related to the static and dynamic detection of fiber grating sensors, analyze the signal characteristics of the wind turbine blade when it is damaged by the impact, and provide a basis for the analysis of the external damage of large wind turbine blade. \u0000RESULTS: The main results obtained in this paper are the following. By analyzing the decomposition of wavelet packets, the gradient change of wavelet impact energy spectrum before and after the wavelet damage was obtained and compared with the histogram, and the impact energy spectrum of each three-dimensional wavelet energy packet in the image was compared and analyzed, which can well realize the recognition of wavelet damage gradient for solid composite materials. \u0000CONCLUSION: With the help of Matlab simulation to collect the impact response signal, using the wavelet packet energy spectrum method to analyze the signal, can derive the characteristics of wind turbine blade damage.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"78 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid growth of the number and scale of smart grid users, traditional data encryption transmission methods can no longer meet the performance requirements of data aggregation. In response, a power consumption information collection and management system based on SGX software protection extension is proposed. The system mainly consists of three parts: user electricity data acquisition terminal, SGX data security processing and distributed storage module on the chain, and data monitoring management display platform. The user electricity data collection terminal collects electricity data from various buildings, residences, rooms, and other smart meters, analyzes and uploads it. After calling the trusted function of SGX technology, it enters the security zone provided by SGX for data processing. Finally, the data security processing results and data are uploaded to the blockchain for storage. In order to visually display user electricity usage data, an intelligent monitoring platform for user electricity collection and management has been established. This system can reduce the workload of user electricity data collection, ensure the accuracy of data collection, and provide an efficient and highly reliable system platform for user electricity data management.
{"title":"Design and Implementation of an SGX Based Electricity Information Collection and Management System","authors":"Yao Song, Kun Zhu","doi":"10.4108/ew.5756","DOIUrl":"https://doi.org/10.4108/ew.5756","url":null,"abstract":"With the rapid growth of the number and scale of smart grid users, traditional data encryption transmission methods can no longer meet the performance requirements of data aggregation. In response, a power consumption information collection and management system based on SGX software protection extension is proposed. The system mainly consists of three parts: user electricity data acquisition terminal, SGX data security processing and distributed storage module on the chain, and data monitoring management display platform. The user electricity data collection terminal collects electricity data from various buildings, residences, rooms, and other smart meters, analyzes and uploads it. After calling the trusted function of SGX technology, it enters the security zone provided by SGX for data processing. Finally, the data security processing results and data are uploaded to the blockchain for storage. In order to visually display user electricity usage data, an intelligent monitoring platform for user electricity collection and management has been established. This system can reduce the workload of user electricity data collection, ensure the accuracy of data collection, and provide an efficient and highly reliable system platform for user electricity data management.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"10 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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