## 3 Experiments and Analysis of Results
### EO Screening Joint Features  
Feature selection is an essential process for any classification task, which finds the subset of features with the smallest dimensionality by retaining the primary information carried by all features. In feature selection, the metaheuristic algorithm is used as a wrapper-based feature selection method, which has proven superiority in reducing the training time and improving model accuracy. The metaheuristic algorithm EO is used to screen the joint features to obtain a subset of features suitable for identification. Feature selection outcomes are presented in Table 1. According to this table, EO screens nine active power load features and 12 reactive power load features. The dimensionality of the features selected by EO is 29% of the joint feature set (filtering out 21 dimensions from 72). Among them, the entropy value features are more distributed in the subset of screened features, and 57% of the feature proportion is screened in active power and reactive power, respectively. The time-frequency domain exhibits a lower screening ratio. The findings demonstrate that entropy value features make the most significant contribution to the combined feature set. Using EO feature selection in industrial power scenarios can significantly reduce the dimensionality of the joint features.  
Because the distribution of features reflects the inherent structure and patterns of the data, the visualization of feature distribution is an essential component of the feature selection and classification process. In Figure 5, box and whisker plots can identify anomalies among the 21 selected features, provide the data distribution for each feature, and allow for their comparison. The mean of the texture is represented by the horizontal line in the frame at the bottom. The first horizontal line represents the first quartile of the average texture. The second horizontal line indicates the texture median. The horizontal line at the top of the graph represents the utmost value of the texture's mean, and the tiny diamonds are outliers. Due to their feature types and data quality, the distribution of the screened features has  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Feature Type** & \
\begin{tabular}{c} **Feature** \\ **Number** \\ \end{tabular} &
\begin{tabular}{c} **Screened Features** \\ **Number** \\ \end{tabular} & **Proportion** \\ \hline \multirow{3}{*}{Active power} & TD & 1–16 & 3, 11, 13 & 19\% \\  & FD & 17–29 & 19, 28 & 15\% \\  & EV & 30–36 & 30, 31, 33, 34 & 57\% \\ \hline \multirow{3}{*}{Reactive power} & TD & 37–52 & 37, 38, 46 & 19\% \\  & FD & 53–65 & 53, 56, 58, 62, 64 & 38\% \\ \cline{1-1}  & EV & 66–72 & 66, 67, 68, 72 & 57\% \\ \hline \hline \end{tabular}  
* To clarify the feature locations, the features of active power are ordered as time domain (1–16), frequency domain (17–29), and entropy value (30–36). The features of reactive power are ordered as time domain (37–52), frequency domain (53–65), and entropy value (66–72).  
\end{table}
Table 1: Feature locations and ratios for EO screening.  
some variability. There are a few anomalies, among which the feature extraction values of the entropy-valued features are generally lower and more densely distributed.