Fault Diagnosis Analysis of Pad-Mounted Transformers in Photovoltaic Power Plants

Felix Spark

06/17/2025

Introduction

As the scale of photovoltaic power stations continues to expand, pad - mounted transformers, as one of the key equipment, have a profound impact on the operation of the system when they fail. This paper focuses on using advanced artificial intelligence algorithms and integrating data analysis technology to improve the accuracy and efficiency of fault diagnosis for pad - mounted transformers in photovoltaic power stations, and to build a solid technical foundation for the safe and stable operation of photovoltaic power stations.

1. Research Background

Pad - mounted transformers in photovoltaic power stations, as core components of the photovoltaic system, undertake the crucial task of converting the low - voltage power output by DC photovoltaic panels into high - voltage power suitable for transmission. During long - term operation, typical faults such as winding grounding, short - circuit, and open - circuit frequently occur. These faults not only interfere with the normal operation of the power station but may also lead to equipment damage and even safety accidents. In - depth analysis of these typical faults is of great significance for early diagnosis, problem - solving, and ensuring the safe and stable operation of the photovoltaic system.

2. Application of Artificial Intelligence in Typical Fault Diagnosis
2.1 Artificial Intelligence Algorithms

As emerging technologies, artificial intelligence algorithms have great potential in the field of fault diagnosis for pad - mounted transformers in photovoltaic power stations. Mainstream algorithms such as neural networks, support vector machines, and genetic algorithms [1] simulate the learning and reasoning process of the human brain, and can mine laws from complex data and make accurate predictions. In the scenario of fault diagnosis for pad - mounted transformers in photovoltaic power stations, they can efficiently process large - scale data, identify hidden fault patterns, and output accurate diagnosis results.

2.2 Fault Diagnosis Methods for Pad - mounted Transformers in Photovoltaic Power Stations

Traditional fault diagnosis relies on professional personnel for comprehensive detection and analysis, which is time - consuming, labor - intensive, and easily affected by subjective factors. However, the diagnosis method based on artificial intelligence algorithms can realize automated and intelligent diagnosis. By collecting the operation data and state parameters of pad - mounted transformers and combining the characteristics of algorithms, it can quickly and accurately identify fault types, improve diagnosis efficiency and accuracy, reduce maintenance costs, effectively prevent potential fault risks, and help improve the performance and reliability of photovoltaic power stations.

2.3 Advantages of Artificial Intelligence Algorithms in Technical Fault Diagnosis

Artificial intelligence algorithms have significant advantages in the fault diagnosis of pad - mounted transformers in photovoltaic power stations: Firstly, they can process massive complex data, mine potential laws, extract key features, and can continuously learn and optimize to improve the accuracy and stability of diagnosis; Secondly, they have strong adaptive capabilities and can flexibly adjust with the environment and fault conditions, being efficient, accurate, automated, and having good scalability, suitable for fault diagnosis of pad - mounted transformers in different types of power stations. By analyzing data features and historical cases, they can quickly locate and identify fault patterns such as temperature anomalies and insulation damage [2]; Thirdly, they support real - time monitoring and early warning, can timely detect potential problems, reduce system downtime, and can also fuse multi - source heterogeneous data such as sensor data and operation logs for comprehensive analysis, improving the comprehensiveness and accuracy of diagnosis, and providing reliable support for operation and maintenance decisions. It is of great significance for ensuring the stable and safe operation of equipment and promoting the sustainable development of photovoltaic power stations.

3. Research Methods
3.1 Data Collection and Processing

To carry out the research on typical fault diagnosis of pad - mounted transformers in photovoltaic power stations, sensors are deployed on pad - mounted transformers to monitor key parameters such as temperature, humidity, current, and voltage in real - time. The sensors collect data at fixed time intervals and transmit it to the storage server for recording. The original data undergoes preprocessing procedures such as denoising, outlier handling, and cleaning to ensure data quality and accuracy, and finally, a complete data set is constructed for subsequent feature extraction and model building.

3.2 Feature Extraction and Selection

Multiple - dimensional features such as average temperature, peak current, and frequency distribution are extracted from the original data to characterize the operation state of pad - mounted transformers. Representative feature parameters are mined through statistical analysis and frequency domain analysis. At the same time, methods such as Principal Component Analysis (PCA) are used to screen and optimize features, reduce dimensions, eliminate redundancy, and select key features for model building and training.

3.3 Fault Diagnosis Model Construction

An efficient fault diagnosis model is built based on artificial intelligence algorithms: A Convolutional Neural Network (CNN) in deep learning is adopted. Through multi - layer convolution and pooling operations, advanced abstract learning of feature data is carried out, key features are extracted, and representations are built; a Long Short - Term Memory network (LSTM) is introduced to capture the time dependence of data sequences and enhance the accuracy and generalization ability of the model; by integrating the advantages of both, an end - to - end model is constructed to realize the automatic diagnosis and early warning of typical faults of pad - mounted transformers. After training and verification with a large number of data sets, the model shows effectiveness and reliability in the fault diagnosis task, providing strong support for the safe operation of photovoltaic power stations.

4. Experiment and Result Analysis
4.1 Experiment Design

Representative pad - mounted transformer equipment in multiple photovoltaic power stations is selected, and long - term data collection is carried out, covering data in normal operation and various typical fault modes. The data set is divided into a training set and a test set in a certain proportion to ensure the objectivity and accuracy of model training and evaluation. At the same time, simulation experiments are carried out for different fault types to verify the diagnosis ability of the model.

4.2 Result Display and Analysis

Experiments show that the fault diagnosis model based on artificial intelligence algorithms has excellent performance. When identifying typical faults such as winding grounding, short - circuit, and temperature anomalies, the accuracy and recall rate are quite high. For example, for winding grounding faults, the accuracy rate on the test set exceeds 90%; for short - circuit faults, the accuracy rate exceeds 85%. The model also has a good effect in predicting the occurrence time and location of faults, can alarm in a timely manner and guide operation and maintenance, and effectively reduce fault losses.

4.3 Comparison and Discussion

Compared with traditional methods, the artificial intelligence algorithm model has obvious advantages in accuracy and efficiency. Traditional methods rely on manual manual analysis, which has problems such as subjective errors and time - consuming; while the artificial intelligence model can diagnose faults automatically and quickly, improving the accuracy and reliability of diagnosis. Moreover, it has better adaptability and generalization ability when dealing with large - scale complex data, providing more effective technical support for the safe and stable operation of pad - mounted transformers in photovoltaic power stations, demonstrating the important value and broad application prospects of the research method in this paper.

5. Conclusion

The research on typical fault diagnosis of pad - mounted transformers in photovoltaic power stations based on artificial intelligence algorithms has achieved remarkable results. Through data collection and processing, feature extraction and selection, model building and other links, an efficient and accurate fault diagnosis model has been successfully built. Experiments verify its excellent performance in identifying typical faults, providing reliable guarantee for the operation safety of photovoltaic power stations. In the future, the performance of the model will be continuously optimized to promote the wide - range application of the technology in actual scenarios.

Topics

Pad - Mounted Transformer Failure and maintenance

Felix Spark

Hey there! I'm an electrical engineer specializing in Failure and Maintenance. I've dedicated my career to ensuring the seamless operation of electrical systems. I excel at diagnosing complex electrical failures, from malfunctioning industrial motors to glitchy power distribution networks. Using state - of - the - art diagnostic tools and my in - depth knowledge, I pinpoint issues quickly. On this platform, I'm eager to share my insights, exchange ideas, and collaborate with fellow experts. Let's work together to enhance the reliability of electrical setups.