Tahalilin Nauyin Dangamun Aiki na Tafukar Turbinai a Makatar PV

Amsa da gaba-gaban kiyaye cikakken kuma samun abubuwan da suka faruwa, ina jin daidai cewa, tare da zama da photovoltaic power stations, kisan fault da suka faruwa a box-type transformers, wanda shi ne daya daga manyan abubuwan da ke muhimmanci, yana haɗa da sauti da yaɗa ingantaccen tsarin. Wannan littafi yana neman amfani da alamar artificial intelligence masu ilimi mai zurfi da kuma hanyar bayyana bayanai don bincike masu ilimi da kudaden samun abubuwan da suka faruwa a box-type transformers, kuma don bincike malamai na musamman don sauti da yaɗa ingantaccen tsari na photovoltaic power stations. Wannan shine batu mai zurfi minkan da na bukata don ba da shi a matsayin rike mai zurfi a cikin rayuwarsa ta yi waɗannan abubuwan da suka faruwa.

1 Tarihin Bincike

Box-type transformer a photovoltaic power station, wanda shi ne daya daga manyan abubuwan da ke muhimmanci a tsarin photovoltaic, yana da takardun muhimmanci na bincike low-voltage power da suka faruwa daga DC photovoltaic panels zuwa high-voltage power da yake da kyau don transport. A lokacin da take daɗe da tsari, ana iya faruwa faults kamar winding grounding, short-circuit, da open-circuit.

Faults masu na biyu suna iya haɗa da sauki da yaɗa ingantaccen tsari na photovoltaic system, kuma suna iya haɗa da sauki da yaɗa kanza waɗannan abubuwan da suka faruwa da kuma kawo karshen abubuwan da suka faruwa zuwa yanayin da suka da damar. Daga fahimtarsa a cikin gaba-gaban kiyaye cikakken, ina jin daidai cewa, bayyana masu na biyu ita ce da ke da muhimmanci ga wuraren kasa da kuma kawo karshen risks, kuma don bincike sauti da yaɗa ingantaccen tsari na photovoltaic system.

2 Amfani da Artificial Intelligence a Samun Abubuwan da Suka Faruwa
2.1 Alamar Artificial Intelligence

A cikin gaba-gaban kiyaye cikakken, ina duba da alamar artificial intelligence masu ilimi da suke da muhimmanci a samun abubuwan da suka faruwa a box-type transformers. Alamar masu ilimi masu yawan daɗi kamar neural networks, support vector machines, da genetic algorithms sun fi sani da learning da reasoning logic na mutum, kuma suna iya ƙunshi rules da kuma bincike da dabara daga bayanai mai tsawon tsari. Idan an amfani da su a samun abubuwan da suka faruwa a box-type transformers, wannan alamar suna iya daɗe da data masu yawa, samun patterns da suka faruwa, da kuma tabbatar da results da suka fi dabara, suna zama "intelligent assistant" masu ilimi minkan da na yi waɗannan abubuwan da suka faruwa.

2.2 Hanyoyin Samun Abubuwan da Suka Faruwa a Box-Type Transformers a Photovoltaic Power Stations

Samun abubuwan da suka faruwa na biyu suna iya faruwa a cikin lokacin da ake amfani da professional personnel don bayyana bayanai da kuma analysis, wanda shi ne yana da lafiya, kuma yana da ƙananan lafiya, kuma yana da iya kasancewa da subjective interference. Amma, hanyoyin samun abubuwan da suka faruwa a cikin alamar artificial intelligence suna da nasarori a automation da intelligence. Ta haka, idan an amfani da data na tsari da state parameters na box-type transformer, kuma a amfani da characteristics na alamar, zan iya samun types da suka faruwa da kusa, kuma zan iya bincike efficiency da accuracy da samun abubuwan da suka faruwa.

Wannan zan iya haɗa da costs da ake yi waɗannan abubuwan da suka faruwa, kuma zan iya haɗa da risks da suka faruwa a gaba-gaba, kuma zan iya taimakawa da performance da reliability na photovoltaic power station, kuma shine direction mai muhimmanci don optimize gaba-gaban kiyaye cikakken da samun abubuwan da suka faruwa.

2.3 Muhimmin Alamar Artificial Intelligence a Samun Abubuwan da Suka Faruwa

A cikin gaba-gaban kiyaye cikakken, muhimmin alamar artificial intelligence suna da muhimmanci:

• Data Processing and Optimization Capability: Suna iya daɗe da data masu yawa, ƙunshi rules da suka faruwa, extract key features, da kuma suna iya daɗe da ilimi da kuma optimize, kuma suna iya bincike accuracy da stability da samun abubuwan da suka faruwa, kuma zan iya bincike samun abubuwan da suka faruwa da dabara.

• Adaptive and Generalization Capability: Suna da environmental adaptability da yawa, suna iya daɗe da scenarios da suka faruwa, da kuma suna da daƙiƙi a samun abubuwan da suka faruwa a cikin types da suka faruwa a box-type transformers. Ta haka, idan an amfani da data analysis da case comparison, zan iya samun patterns da suka faruwa kamar temperature anomalies da insulation damage, kuma zan iya taimakawa da gaba-gaban kiyaye cikakken da samun abubuwan da suka faruwa.

• Real-Time Monitoring and Early Warning: Suna iya daɗe da real-time state monitoring da early warning, suna iya samun potential problems a gaba-gaba, da kuma suna iya haɗa da downtime na system. Wannan yana da muhimmanci da yawa don bincike continuous power generation na photovoltaic power station.

Kuma, alamar suna iya integrate multi-source heterogeneous information kamar sensor data da operation logs don achieve comprehensive fusion analysis, kuma zan iya bincike comprehensiveness da reliability da samun abubuwan da suka faruwa, da kuma zan iya taimakawa da decision-making na operation and maintenance. Ana iya duba cewa, a cikin samun abubuwan da suka faruwa a box-type transformers, alamar artificial intelligence suna da muhimmanci masu yawa don bincike stability da safety na equipment, kuma don taimakawa da sustainable development na photovoltaic power stations.

3 Hanyoyin Bincike
3.1 Data Collection and Processing

A cikin binciken da ake amfani da gaba-gaban kiyaye cikakken, data collection and processing shine link na basic don samun abubuwan da suka faruwa a box-type transformers. An deploy sensors a cikin box-type transformers don conduct real-time and periodic monitoring of key parameters kamar temperature, humidity, current, da voltage. Bayanai suna faɗa zuwa storage server don archiving. Original data suna ƙunshi preprocessing kamar denoising, abnormal value elimination, da cleaning don ensure reliable quality. A nan, ake bincika complete dataset, kuma zan iya bincike foundation don feature extraction and model building da zaɓe.

3.2 Feature Extraction and Selection

A cikin stage na feature extraction, ake ƙunshi multiple features da suke fi sanar da state na box-type transformer daga original data, covering dimensions kamar average temperature, peak current, da frequency distribution. Idan an amfani da statistical da frequency analysis, ake ƙunshi representative feature parameters; kuma a nan, an amfani da methods kamar Principal Component Analysis (PCA) don dimensionality reduction da redundancy removal, da kuma ake ƙunshi key features don lay a solid data foundation for model training.

3.3 Construction of Fault Diagnosis Model

Based on the needs of front-line diagnosis and detection, we build a fault diagnosis model driven by artificial intelligence algorithms:

• Introduction of Convolutional Neural Network (CNN): Conduct in-depth abstract learning on feature data. Through multi-layer convolution and pooling operations, key features are extracted layer by layer, and an accurate feature representation is constructed.

• Integration of Long Short-Term Memory Network (LSTM): Capture the time correlation of data sequences, strengthen the model's learning of time-series dependencies, and improve the accuracy and generalization ability of diagnosis.

• Construction of End-to-End Model: Combine the advantages of CNN and LSTM to create a full-process fault diagnosis model, realizing the automatic identification and early warning of various typical faults of box-type transformers. After training and verification with a large-scale data set, the model has achieved remarkable results in fault diagnosis tasks, building a technical barrier for the safe operation of power stations.

4 Experimental Design and Result Analysis
4.1 Experimental Design

The experiment relies on the data of real box-type transformers in photovoltaic power stations. We select representative box-type transformer equipment from multiple power stations and carry out long-term data collection, covering normal operation and various typical fault conditions. The data set is split into a training set and a test set in proportion to ensure the objectivity of model training and evaluation. At the same time, simulation experiments are carried out for different fault types to comprehensively verify the diagnosis efficiency of the model, which is in line with the needs of front-line diagnosis and detection scenarios.

4.2 Result Presentation and Analysis

The experiment shows that the diagnosis model driven by artificial intelligence algorithms performs excellently in the fault diagnosis of box-type transformers. When identifying typical faults such as winding grounding, short-circuit, and temperature anomalies, the accuracy and recall rate are considerable: the accuracy and recall rate of winding grounding faults in the test set exceed 90%; the accuracy of short-circuit faults reaches more than 85%. The prediction of the occurrence time and location of faults by the model can trigger alarms in a timely manner, guide operation and maintenance disposal, and effectively reduce fault losses, demonstrating the technical value.

4.3 Comparison and Discussion

Compared with traditional diagnosis methods, the advantages of the artificial intelligence model are prominent: traditional methods rely on manual analysis, with large subjective errors and low efficiency; while the model realizes automatic and rapid diagnosis, with both improved accuracy and reliability. In the face of large-scale and complex data scenarios, the model has stronger adaptability and generalization ability, providing efficient technical support for the safe and stable operation of box-type transformers. Thus, it can be seen that the artificial intelligence algorithm diagnosis method proposed in this research has great application value and promotion prospects in the operation and maintenance of photovoltaic power stations.

5 Conclusion

The research on the typical fault diagnosis of box-type transformers in photovoltaic power stations based on artificial intelligence algorithms has achieved remarkable results. Through links such as data collection and processing, feature extraction and selection, and model construction, an efficient and accurate diagnosis model has been successfully built. Experiments verify its excellent performance in the identification of typical faults, providing protection for the safe operation of power stations.

As a front-line diagnosis and detection worker, I look forward to continuously optimizing the model performance in the future and promoting the widespread application of this technology in the field of photovoltaic operation and maintenance, injecting new momentum into the development of the industry.