# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 2 Extreme Scenario Feature Selection for Power Systems
### Extreme Scene Feature Extraction for Power Systems  
In the previous section, extreme weather events are defined. In this section, power system extreme scenarios are analyzed on the basis of the defined extreme weather events, and key features affecting the generation of extreme scenarios are extracted. As shown in Section 2.1, a power system's wind-solar-load output is affected by various weather conditions, and in this paper, the five types of extreme weather described above are selected. In addition to weather factors, the load power demand is equally affected by time factors. Therefore, the input features are selected for extreme scenario generation, as shown in Table 2. Among the selected extreme weather events, each extreme scenario affects the wind-solar-load output. Under extremely hot and windless conditions, wind power is associated with long-term low output, solar power long-term high output, and long-term high load demand. During cold waves and typhoon weather, the wind power output rapidly increases, and PV's long-term low output and long-term low load demand increase. When extreme weather is considered, the change in the wind-solar-load output is concentrated on the mathematical features of the mean and slope. Therefore, for the classification of extreme scenarios, the extraction of mathematical features related to the wind-solar-load output is needed. The process of extracting features from the data in this paper is shown in Equations (1)-(4). First, the input features are compressed to the same interval by uniform distribution normalization. Uniform distribution normalization involves mapping input data to a specified uniform distribution. This process operates by calculating the cumulative distribution function (CDF) of the input data and subsequently mapping the data to the corresponding percentiles of the target distribution. The implementation steps are as follows:  
1. Calculate the percentiles: \[F(x_{i})=\frac{rank(x_{i})}{n}\] (1) where \(rank(x_{i})\) represents the position of \(x_{i}\) in the dataset after sorting the data in ascending order, and \(n\) is the total number of data points. 2. Generate a uniform distribution: \begin{table}
\begin{tabular}{c c} \hline
**Hidden Influencing Factors** & **Categorization** \\ \hline \hline \multirow{4}{*}{Seasons} & Spring \\ \cline{2-3}  & Summer \\ \cline{2-3}  & Autumn \\ \cline{2-3}  & Winter \\ \hline \multirow{2}{*}{Date type} & Working days \\ \cline{2-3}  & Nonworking day \\ \hline \multirow{4}{*}{Climatic factor} & Temperature \\ \cline{2-3}  & Humidity level \\ \cline{2-3}  & Pneumatic \\ \cline{2-3}  & Air velocity \\ \cline{2-3}  & Dew point \\ \cline{2-3}  & Solar intensity \\ \cline{2-3}  & Type of weather \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hidden influencing factors for the generation of wind–solar–load scenarios. Define a uniform distribution U over the interval [0, 1], which will be used to generate values corresponding to the percentiles of the input data in the uniform distribution. 3. Map the percentile of each data point to the corresponding position in the target uniform distribution using linear mapping. The means and slopes of these variables are subsequently computed by (3) and (4). Finally, the quantitative data features are weighted. To synthesize the impact of the extreme scenario on the considered wind-solar-load to obtain the scenario's eigenvalues, the extreme scenario is judged to be a power-protection or consumption-protection scenario. \[Ave_{t}=\frac{x_{t}}{\sum_{i=m}^{n}x_{t}} \tag{2}\]  
\[Slo_{t}=\frac{x_{t-m}-x_{t+n}}{m+n} \tag{3}\]  
\[S_{t}=\mu_{ave,t}Ave_{t}+\mu_{slo,t}B_{t} \tag{4}\]  
where, in this context, \(Ave_{t}\) represents the mean index of extreme scenarios, describing the power levels of \(x_{t}\), and \(x_{t}\) refers to wind power, photovoltaic output, and load output. \(Slo_{t}\) denotes the slope index of extreme scenarios, indicating the growth trend or decline of xxx under extreme conditions.