**1. Introduction**

With the accelerated development of the power system, the number of secondary equipment is also increasing. Therefore, there is an explosive emergence of power big data, which hides massive information. This is related to the safe and stable operation of the power system [1–4]. However, a small proportion of these data can be used to mine important information, and research on these data has become a current hot topic. Among these data, the first category is the time-series structured data represented by output power, temperature and humidity of the equipment and its environment, and the light intensity of the optical module. This type of data mining work is relatively mature; the other is based on semi-structured and unstructured data represented by text, images, audio, etc., which are difficult to express using relational databases. The low value density of these data restricts the mining of unstructured data [5].

During the operation of the secondary equipment, a lot of short text data of faults and defects have been accumulated. These data are often manually recorded by the transportation inspection personnel and rely on the experience of professionals to complete the classification of defects. However, due to the subjective and empirical constraints of transport inspection personnel, the fault data are difficult to classify accurately. At the same time, the high volume of fault data requires a great deal of human participant involvement, and efficiency is difficult to be guaranteed. Moreover, the text information of secondary equipment faults has a short length and sparse semantic features. The improvement of the classification model for short text data is also the focus and hotspot [6].

The earliest text classification can be traced back to an article published by Maron in 1961 on the method of text classification using the Bayesian formula. In the next 20-odd years, a series of classification rules were manually built on the basis of expert knowledge to construct a classifier. This method often requires the experience and knowledge of a large number of expert engineers in related fields, which is difficult to effectively promote [7]. In addition to the development of disciplines such as artificial intelligence, machine learning, pattern recognition, and statistical theory, text classification technology has entered a more intelligent automatic classification era, and text classification methods based on expert knowledge and experience have gradually withdrawn from the historical stage. Using Bayes' [8,9] neural network [10] and support vector machine [11] and other methods to liberate people from heavy tasks, and with high classification efficiency and accuracy, the machine learning methods have developed rapidly in the field of Chinese text classification. Benefits from the development of the machine learning, neural network method are the most prominent [12]. According to some papers, it can be concluded that the long and short-time neural network models used to mine context features has a significant effect on the classification of long document text data [13,14], and the convolutional neural network model has a significant effect on the classification of short text data [15]. In [16], the CNN model was proposed for brain tumor classification. In [17], a feature fusion method based on an ensemble convolutional neural network and deep neural network was used for bearing fault diagnosis. In [18], an enhanced convolutional neural network was designed and analyzed.

The text classification technology is also widely used in professional fields, such as social science information, biomedicine, and so on [19]. There are also endless categories of patents [20], academic papers, academic news, and even the content of WeChat public accounts. In social media, the classification of user emotion recognition is an important part [21]. In e-commerce, user evaluation of products can help companies understand user satisfaction with products [22]. In biomedicine, intelligent triage can save a lot of medical resources and improve the quality and efficiency of services [23].

Text data mining in the power industry is still in the emerging field, and foreign countries have studied the relationship between the historical fault data and the weather to further predict the fault of the substation. However, these text mining methods are mostly based on traditional machine learning methods, seldom adopt deep learning methods, and lack research on the classification of a specific device type or the fault text data itself. Generally speaking, the text mining technology is still in its infancy in the field of electric power, especially the research on text information of secondary equipment faults; most of the research is only based on traditional machine learning methods, and the classification model lacks pertinence [24]. Moreover, due to the short text length and lack of sufficient context for semantic feature analysis, when mining this type of text data, it is easy to cause high-dimensional information features to be sparse, resulting in a serious lack of semantic relations, and ultimately resulting in poor classification results [25]. Considering that some faulty text data are short and the traditional convolutional neural network is insufficient for feature extraction, in this paper, convolution kernels of various sizes are used to extract features from short text data.

Based on the above discussions, this paper focuses on a mass of short text data produced in the secondary equipment operation production management system and conducts related research on automatic text classification based on convolutional neural networks. In order to solve the problem of poor topic focus and sparse text density in short text data, an improved LDA topic model was proposed based on the Relevance formula for the problem of insignificant characteristics caused by excessive repetition of feature text information [26]. By setting different weighting coefficients to adjust the sampling of words, the problem of repeated vocabulary of different types of defective data was solved. Afterwards, the RLDA model and the word2vec [27] model were combined together, and the document-topic vector was constructed using the RLDA subject word model to obtain the global features. At the same time, the local features attained by using the word2vec word vector technology to mine the latent semantic features were combined. Construct the input matrix of convolutional neural network. Considering the superiority of convolutional neural for feature extraction at the level of short text information word vectors, convolutional neural networks were employed for extracting feature text vectors and classifying text vectors. The traditional convolutional neural network uses a single size

convolution kernel to extract features. When faced with different document lengths, the classification results are not ideal. On the basis of the original convolution model, this paper proposes to use deep convolution kernels of multiple sizes to mine text features in depth to enhance their ability to extract locally sensitive information. Finally, the actual operation data of a northwestern power system company were used to conduct a comparative experiment to test the validity of the presented model and the accuracy of the classification algorithm in this paper. size convolution kernel to extract features. When faced with different document lengths, the classification results are not ideal. On the basis of the original convolution model, this paper proposes to use deep convolution kernels of multiple sizes to mine text features in depth to enhance their ability to extract locally sensitive information. Finally, the actual operation data of a northwestern power system company were used to conduct a comparative experiment to test the validity of the presented model and the accuracy of the classification algorithm in this paper.

and classifying text vectors. The traditional convolutional neural network uses a single

#### **2. Lower-Level Modeling and Optimization 2. Lower-Level Modeling and Optimization**

*Energies* **2022**, *15*, 2400 3 of 16

#### *2.1. Data Analysis 2.1. Data Analysis*

This paper randomly selects 1000 defect text data from a power company in a northwestern province from 2015 to 2019, according to the length of the string statistics as shown in Figure 1. This paper randomly selects 1000 defect text data from a power company in a northwestern province from 2015 to 2019, according to the length of the string statistics as shown in Figure 1.

**Figure 1.** Interval diagram of text information length distribution of secondary equipment faults in a northwestern power system. **Figure 1.** Interval diagram of text information length distribution of secondary equipment faults in a northwestern power system.

The fault data selected were caused by the manufacturing of the same auxiliary device from two different devices recorded by the same person in charge of a northwest power system in January 2017. The two devices belong to the 220 kV plant, and the same batch was delivered by the same company. The secondary equipment protection device of the model PSIU601GC-B-E1 omitted the same data and the content that has less influence on the classification result. The content is shown in Table 1. The fault data selected were caused by the manufacturing of the same auxiliary device from two different devices recorded by the same person in charge of a northwest power system in January 2017. The two devices belong to the 220 kV plant, and the same batch was delivered by the same company. The secondary equipment protection device of the model PSIU601GC-B-E1 omitted the same data and the content that has less influence on the classification result. The content is shown in Table 1.

**Table 1.** Example of the text information record of a second equipment failure in a northwestern company in January. **Table 1.** Example of the text information record of a second equipment failure in a northwestern company in January.


Compared with the general Chinese short text, the text of the secondary equipment fault defect of the power system not only contains the unique attributes of the Chinese

Compared with the general Chinese short text, the text of the secondary equipment fault defect of the power system not only contains the unique attributes of the Chinese language family and the Asian-European language family, but also has the following characteristics:


Through the above feature analysis of short text, it is not ideal to directly apply the topic model to text classification.

#### *2.2. Text Classification Process for Chinese Characters*

For text classification for Chinese characters, the machine learning method is always utilized to find the correspondence between text features and its categories, and relevant technology is used for automatic classification of new text because of its laws.

The steps of the aforementioned text classification model for Chinese characters can be summarized as follows. Firstly, the preprocessing of the text is completed, where the unnecessary information is removed, such as clauses, word segmentation, and stop words. This step is implemented according to the text length and text. The specific content is related. Then, the text can be expressed, namely, the text is transmitted into a computer-recognizable and processed form, which is usually expressed by a matrix or a vector. The text representation affects the effect of later text classification because it is related to the extraction of text features. Then, a suitable classifier is selected to classify the text and output the predicted classification result. Finally, the aforementioned two results of the classifier are compared (practical and predicted results). If the prediction results meet a prior standard, the training is completed, where this standard could be the prediction accuracy rate and iterations. Otherwise, the corresponding parameters need to be adjusted by means of the comparison result, and the classification is re-classified until the classification prediction result reaches the standard.
