The acoustic emission (AE) method could be used to detect and locate partial discharges (PD) in cast-resin dry-type transformers.However,due to the high sound attenuation in the filled epoxy,the signal is prone to int...The acoustic emission (AE) method could be used to detect and locate partial discharges (PD) in cast-resin dry-type transformers.However,due to the high sound attenuation in the filled epoxy,the signal is prone to interference from external noises and thus,in practice,there is little possibility of detecting PD.In this study,two techniques were developed to alleviate the shortcomings of the AE method.First,a waveguide is installed on the high-voltage (HV) windings,so that the acoustic signals of PD will propagate to the AE sensors that are installed on both terminals of the waveguide.The location of the winding that has PD can then be detected from the difference in arrival time of the acoustic signals.Test results indicate that the waveguide technique is able to enhance the safety of a measurement system and offers the advantages of easy installation and higher flexibility.Second,a specially designed AE sensor pair is used to distinguish whether acoustic signals are generated by PD inside the HV winding or by the corona outside the transformers.Using these two techniques of waveguide and AE sensor pair not only greatly improves sensitivity but also increases the reliability of the measurement system.Practical test results show that the new techniques can be used to locate precisely the PD in HV windings.展开更多
Dry-Type Cast Resin Distribution Transformers(CRT)is the secondgeneration of air-cooled distribution transformers where oil is replaced by resin for electrical insulation.CRT transformers may installed indoor adjacent...Dry-Type Cast Resin Distribution Transformers(CRT)is the secondgeneration of air-cooled distribution transformers where oil is replaced by resin for electrical insulation.CRT transformers may installed indoor adjacent to or near residential areas since they are clean and safe comparing to the conventional transformers.But,as it is obvious,noise discrepancy is intrinsically accompanied with all types of transformers and is inevitable for CRT transformers too.Minimization of noise level caused by such these transformers has biological and ergonomic importance.As it is known the core of transformers is the main source of the noise generation.In this paper,experimental and numerical investigation is implemented for a large number of fabricated CRT transformers in IT Co(Iran Transfo Company)to evaluate the effective geometrical parameters of the core on the overall sound level of transformers.Noise Level of each sample is measured according to criteria of IEC60651 and is reported in units of Decibel(dB).Numerical simulation is done using noncommercial version of ANSYS Workbench software to extract first six natural frequencies and mode shapes of CRT cores which is reported in units of Hz.Three novel non-dimensional variables for geometry of the transformer core are introduced.Both experimental and numerical results show approximately similar response to these variables.Correlation between natural frequencies and noise level is evaluated statistically.Pearson factor shows that there is a robust conjunction between first two natural frequencies and noise level of CRTs.Results show that noise level decreases as the two first natural frequencies increases and vice versa,noise level increases as the two natural frequencies of the core decreases.Finally the noise level decomposed to two parts.展开更多
Large power transformers have severe vibration and noise problems. Thus, as the source of noise, it is vitally important to have accurate core vibration calculations. The calculation of the vibration is incredibly dif...Large power transformers have severe vibration and noise problems. Thus, as the source of noise, it is vitally important to have accurate core vibration calculations. The calculation of the vibration is incredibly difficult to consider the complex propagation pathway of the vibration in the clamp and the Amorphous Metal Alloy Core Distribution Transformer(AMACDT). To understand the vibration characteristics of AMACDT, this manuscript provides a different approach on investigation of the vibration characteristics of an SCBH15-200/10 type. Based on the vibration characteristics calculation and displacement of an Amorphous Metal Alloy Core Distribution Transformer(AMACDT), they are obtained and solved using Finite Element Analysis software(FEA). Conferring to the outcomes of the vibration analysis, the method by adding bar-reinforcements on the upper clamp of transformer is proposed to lessen the vibration amplitude. To verify the usefulness and application of the suggested approach, the analytical results are obtained. The suggested approach is analyzed with those of experimental.展开更多
The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and...The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.展开更多
基金supported by the National Science Council,Taiwan (No.NSC 92-2622-E-006-142)the Program of Top 100 Universities Advancement,Ministry of Education,Taiwan
文摘The acoustic emission (AE) method could be used to detect and locate partial discharges (PD) in cast-resin dry-type transformers.However,due to the high sound attenuation in the filled epoxy,the signal is prone to interference from external noises and thus,in practice,there is little possibility of detecting PD.In this study,two techniques were developed to alleviate the shortcomings of the AE method.First,a waveguide is installed on the high-voltage (HV) windings,so that the acoustic signals of PD will propagate to the AE sensors that are installed on both terminals of the waveguide.The location of the winding that has PD can then be detected from the difference in arrival time of the acoustic signals.Test results indicate that the waveguide technique is able to enhance the safety of a measurement system and offers the advantages of easy installation and higher flexibility.Second,a specially designed AE sensor pair is used to distinguish whether acoustic signals are generated by PD inside the HV winding or by the corona outside the transformers.Using these two techniques of waveguide and AE sensor pair not only greatly improves sensitivity but also increases the reliability of the measurement system.Practical test results show that the new techniques can be used to locate precisely the PD in HV windings.
文摘Dry-Type Cast Resin Distribution Transformers(CRT)is the secondgeneration of air-cooled distribution transformers where oil is replaced by resin for electrical insulation.CRT transformers may installed indoor adjacent to or near residential areas since they are clean and safe comparing to the conventional transformers.But,as it is obvious,noise discrepancy is intrinsically accompanied with all types of transformers and is inevitable for CRT transformers too.Minimization of noise level caused by such these transformers has biological and ergonomic importance.As it is known the core of transformers is the main source of the noise generation.In this paper,experimental and numerical investigation is implemented for a large number of fabricated CRT transformers in IT Co(Iran Transfo Company)to evaluate the effective geometrical parameters of the core on the overall sound level of transformers.Noise Level of each sample is measured according to criteria of IEC60651 and is reported in units of Decibel(dB).Numerical simulation is done using noncommercial version of ANSYS Workbench software to extract first six natural frequencies and mode shapes of CRT cores which is reported in units of Hz.Three novel non-dimensional variables for geometry of the transformer core are introduced.Both experimental and numerical results show approximately similar response to these variables.Correlation between natural frequencies and noise level is evaluated statistically.Pearson factor shows that there is a robust conjunction between first two natural frequencies and noise level of CRTs.Results show that noise level decreases as the two first natural frequencies increases and vice versa,noise level increases as the two natural frequencies of the core decreases.Finally the noise level decomposed to two parts.
基金supported by the national science foundation of China (51767008)Jiangxi natural science foundation of China (20192ACBL20016)。
文摘Large power transformers have severe vibration and noise problems. Thus, as the source of noise, it is vitally important to have accurate core vibration calculations. The calculation of the vibration is incredibly difficult to consider the complex propagation pathway of the vibration in the clamp and the Amorphous Metal Alloy Core Distribution Transformer(AMACDT). To understand the vibration characteristics of AMACDT, this manuscript provides a different approach on investigation of the vibration characteristics of an SCBH15-200/10 type. Based on the vibration characteristics calculation and displacement of an Amorphous Metal Alloy Core Distribution Transformer(AMACDT), they are obtained and solved using Finite Element Analysis software(FEA). Conferring to the outcomes of the vibration analysis, the method by adding bar-reinforcements on the upper clamp of transformer is proposed to lessen the vibration amplitude. To verify the usefulness and application of the suggested approach, the analytical results are obtained. The suggested approach is analyzed with those of experimental.
文摘The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.
