## 7 Experimental Result  
To demonstrate the impact of false data injection attacks (FDIAs) on power data, we used the IEEE 14-bus model. We injected an FDIA on bus number 2 (the attack increases a generator's active power output at bus no. 2) and conducted a power flow analysis before and after the FDIA. To perform this experiment, we used a Python tool; we carried out the following steps to perform the simulation:  
* We loaded the predefined power network with the help of the raw power dataset. Then, we performed a power flow analysis to determine whether or not it converged successfully and presented the power network data as a result, which contained the following parameters: -vm_pu (voltage magnitude per unit), va_degree (the phase angle of voltage, often measured in degrees, representing the angular displacement of voltage to a reference point or reference phase angle), p_mw (active power in megawatts), and q_mvar (reactive power in MVARs). The results are shown below in Tables 1 and 2. Figures 4 and 5 show the voltage curve before and after the FDIA attack, which clearly shows the attack's impact on bus no. 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bus** & **vm_pu** & **va_degree** & **p_mw** & **q_mvar** \\ \hline
0 & 1.060000 & 0.000000 & \(-\)1845.104233 & \(-\)6684.031922 \\ \hline
1 & 1.045000 & 0.028608 & \(-\)85.000000 & 2046.038644 \\ \hline
2 & 1.010000 & 0.193523 & 200.000000 & 10,699.913750 \\ \hline
3 & 1.035265 & \(-\)0.179392 & 200.000000 & 0.000000 \\ \hline
4 & 1.040079 & \(-\)0.098594 & 200.000000 & 0.000000 \\ \hline
5 & 1.056000 & \(-\)0.561160 & 0.000000 & \(-\)6610.052732 \\ \hline
6 & 1.050408 & \(-\)0.471364 & 200.000000 & 0.000000 \\ \hline
7 & 1.052931 & \(-\)0.578354 & 0.000000 & 0.000000 \\ \hline
8 & 1.052931 & \(-\)0.578354 & 200.000000 & 0.000000 \\ \hline
9 & 1.052885 & \(-\)0.588686 & 0.000000 & 0.000000 \\ \hline
10 & 1.052668 & \(-\)0.638291 & 200.000000 & 0.000000 \\ \hline
11 & 1.052423 & \(-\)0.694122 & 200.000000 & 0.000000 \\ \hline
12 & 1.052282 & \(-\)0.726391 & 200.000000 & 0.000000 \\ \hline
13 & 1.052235 & \(-\)0.737150 & 200.000000 & 0.000000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Power flow results before FDIA. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bus** & **vm_pu** & **va_degree** & **p_mw** & **q_mvar** \\ \hline
0 & 1.060000 & 0.000000 & \(-\)70,218.370276 & 221,716.020580 \\ \hline
1 & 1.945000 & \(-\)13.416744 & \(-\)85.000000 & \(-\)607,688.647079 \\ \hline
2 & 1.010000 & \(-\)4.510858 & 200.000000 & 62,916.456990 \\ \hline
3 & 1.102653 & \(-\)5.666652 & 200.000000 & 0.000000 \\ \hline
4 & 1.242754 & \(-\)6.981959 & 200.000000 & 0.000000 \\ \hline
5 & 1.056000 & \(-\)5.173081 & 0.000000 & 34,449.823189 \\ \hline
6 & 1.117861 & \(-\)5.899939 & 200.000000 & 0.000000 \\ \hline
7 & 1.086644 & \(-\)5.605040 & 0.000000 & 0.000000 \\ \hline
8 & 1.086644 & \(-\)5.605040 & 200.000000 & 0.000000 \\ \hline
9 & 1.086600 & \(-\)5.614741 & 0.000000 & 0.000000 \\ \hline
10 & 1.086389 & \(-\)5.661315 & 200.000 Figure 6 shows the year's grid power three-phase data snapshot under normal conditions and FDI attacks, clearly showing the attack's impact. A detailed comparison is carried out to evaluate the algorithms based on anomaly detection parameters, and a thorough comparison table is given below. The selection of the most suitable anomaly detection method from the options presented in the table is contingent upon the individual requirements and priorities of the user. Every method possesses distinct advantages and disadvantages, and the optimal choice depends upon various aspects, such as the characteristics of the data, the relative significance of precision vs. recall, and computational efficiency. The following algorithms are commonly used in anomaly detection: isolation forest, random forest, SVM, decision tree, logistic regression, and autoencoder. Table 3 below shows the performance metrics of these algorithms.