K punitha (12 Ergebnisse)
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Hardcover. Zustand: New. Zustand des Schutzumschlags: New. 1st Edition. Contents: 1. UGC Syllabus for women's studies. 2. Origin and growth of women's studies. 3. Contribution of feminist thinkers. 4. Glossary on women's studies. 5. Database on prominence of women. 6. India census report 2011. 7. Indian constitutional provision…for women. 8. Highlights of Five Year Plans. 9. Women development approaches in Five Year plans. 10. National policy for the empowerment of women (2001). 11. Progress report of UN Millennium development goals. 12. Women development schemes. 13. International and national significant days. 14. Model objective type questions and answers. 15. UGC Previous year questions and answers. 16. Model questions to practice. Women's studies is a relatively new and unique academic discipline. It is a discipline that seeks to asses and challenges the interlinked axes of oppression in society, viz., class, race, caste and gender. It seeks to rediscover the experience and knowledge of the marginalized sections in history and in contemporary societies. It seeks an understanding of the material on ideological structures of women's oppression. This requires an understanding of the social, cultural, historical, political and economic milieu. Women's studies has been one of the most powerful instruments for calling attention of the intellectual and the ideational skills of women across the world. Women's studies is interdisciplinary as a body of learning with a directed concern for women's equality and empowerment. It seeks to find explanations and remedies for inherited conditions of inequality and injustice that women have been subjected while it analyses the origin and basis of discriminatory practices against women. Women's studies in contemporary practice enlarge its scope to promote gender sensitization of men, women and communities. Therefore it is critical instrument for humanities and social science development in the context of social reality. The book entitled "Hand Book on Women's Studies" is an courageous attempt by the authors which will contribute to understand the origin and growth of Women's Studies, Glossary on Women's studies, Database on prominence of women, Indian Constitutional Provisions for Women, Progress Report of UN Millennium Development Goals, Women Development Approaches in Five Year Plans, Women Development Schemes. The book will help the students and scholars for the UGC-NET/JRF examination preparation. (jacket).
Machine Learning Based Fault Detection of CNN+IOT+FAULT PV
K., Punitha (Author)/ G., Sivapriya (Author)/ T., Jayachitra (Author)
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Paperback. Zustand: new. Paperback. This research presents a unique machine learning model based fault diagnosis and detection method for a 33 KW solar PV system at P.S.R. Engineering College, Sivakasi. The real-time data from the PV system for five years, covering 23,000 instances of eight types of faults such as Cell Cracks or… Hot Spots, Partial Shading, sensor fault, Module failure, Ground Faults, Communication Errors, Environmental Factors, Grid Connectivity Issues are collected. CNN is applied to the data and analyzed their performance in terms of accuracy, precision, and standard deviation (SD) score. It is found that CNN achieved the best results, with an accuracy of 98.7% a precision of 95%, a recall of 98%, and an F1 score of 96.5%.Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. The smart data gathering system was achieved utilizing an ESP32 node with several sensors. The obtained data was stored in an authorized Google Sheet and compared to predetermined threshold ranges. When any parameter deviates from its threshold value, the ESP32 node starts a cooling and dust cleaning procedure with a water pump and drip pipe configuration. If the divergence persists, the ESP32 node activates a camera to capture an image of the panel and sends it to the Google Sheet via a connection for further analysis and fault correction. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Zustand: new. Paperback. This research presents a unique machine learning model based fault diagnosis and detection method for a 33 KW solar PV system at P.S.R. Engineering College, Sivakasi. The real-time data from the PV system for five years, covering 23,000 instances of eight types of faults such as Cell Cracks or… Hot Spots, Partial Shading, sensor fault, Module failure, Ground Faults, Communication Errors, Environmental Factors, Grid Connectivity Issues are collected. CNN is applied to the data and analyzed their performance in terms of accuracy, precision, and standard deviation (SD) score. It is found that CNN achieved the best results, with an accuracy of 98.7% a precision of 95%, a recall of 98%, and an F1 score of 96.5%.Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. The smart data gathering system was achieved utilizing an ESP32 node with several sensors. The obtained data was stored in an authorized Google Sheet and compared to predetermined threshold ranges. When any parameter deviates from its threshold value, the ESP32 node starts a cooling and dust cleaning procedure with a water pump and drip pipe configuration. If the divergence persists, the ESP32 node activates a camera to capture an image of the panel and sends it to the Google Sheet via a connection for further analysis and fault correction. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Zustand: new. Paperback. This research presents a unique machine learning model based fault diagnosis and detection method for a 33 KW solar PV system at P.S.R. Engineering College, Sivakasi. The real-time data from the PV system for five years, covering 23,000 instances of eight types of faults such as Cell Cracks or… Hot Spots, Partial Shading, sensor fault, Module failure, Ground Faults, Communication Errors, Environmental Factors, Grid Connectivity Issues are collected. CNN is applied to the data and analyzed their performance in terms of accuracy, precision, and standard deviation (SD) score. It is found that CNN achieved the best results, with an accuracy of 98.7% a precision of 95%, a recall of 98%, and an F1 score of 96.5%.Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. The smart data gathering system was achieved utilizing an ESP32 node with several sensors. The obtained data was stored in an authorized Google Sheet and compared to predetermined threshold ranges. When any parameter deviates from its threshold value, the ESP32 node starts a cooling and dust cleaning procedure with a water pump and drip pipe configuration. If the divergence persists, the ESP32 node activates a camera to capture an image of the panel and sends it to the Google Sheet via a connection for further analysis and fault correction. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This research presents a unique machine learning model based fault diagnosis and detection method for a 33 KW solar PV system at P.S.R. Engineering College, Sivakasi. The real-time data from the PV system for five years, covering 23,000 in…stances of eight types of faults such as Cell Cracks or Hot Spots, Partial Shading, sensor fault, Module failure, Ground Faults, Communication Errors, Environmental Factors, Grid Connectivity Issues are collected. CNN is applied to the data and analyzed their performance in terms of accuracy, precision, and standard deviation (SD) score. It is found that CNN achieved the best results, with an accuracy of 98.7% a precision of 95%, a recall of 98%, and an F1 score of 96.5%.Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. The smart data gathering system was achieved utilizing an ESP32 node with several sensors. The obtained data was stored in an authorized Google Sheet and compared to predetermined threshold ranges. When any parameter deviates from its threshold value, the ESP32 node starts a cooling and dust cleaning procedure with a water pump and drip pipe configuration. If the divergence persists, the ESP32 node activates a camera to capture an image of the panel and sends it to the Google Sheet via a connection for further analysis and fault correction.
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Taschenbuch. Zustand: Neu. Machine Learning Based Fault Detection of CNN+IOT+FAULT PV | Punitha K. | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999326544 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
