## 4 Operation Results, Verification, and Analysis  
This section presents operation results, verification, and some beneficial analyses based on implementing different case studies under various conditions. A defense mechanism has been established to protect the PV-powered EI structure, which is vulnerable to cyber-attacks from these threats. The generated energy is transferred to the DC bus by optimizing efficiency with appropriate control algorithms. VSCs, commonly used in HVDC and renewable energy applications, are connected to the DC bus, allowing for power flow in both directions. To ensure robust control of these VSCs, measurements of DC bus voltage and load current are required in the control layer. If these sensor data become compromised during a cyber-attack, a secondary ANN structure estimates the DC bus voltage, which is critical for VSC control, using data from normal operations. While this estimation structure does not entirely eliminate the cyber threat, it enhances system reliability by compensating for the loss of sensor data essential for demand-side management. Figure 7 discusses the PV-powered EI operation, focusing on the structure of the PV converter and its controller scheme throughout the training and deployment phases. During training, valuable data is gathered and processed to train ANNs. In the deployment phase, the trained ANNs are put into action. ANN-I serves as a critical primary ANN responsible for detecting cyber-attacks. Once the detection is accurate, the auxiliary ANN-II estimates the precise DC bus voltage, supplying sensor data to the control layer based on the demand side by facilitating accurate detection. The system operates under various input and output conditions to verify the proposed defense mechanism. Solar irradiance and temperature are adjusted as variable inputs, and the load change is initiated as a variable output. In our research, we employ sensor types, e.g., voltage/current sensors, to monitor crucial electrical parameters. The ANN model is designed to detect subtle variations in sensor outputs, differentiating between normal variations and cyber-attacks. This supervised learning model leverages historical data patterns to enhance detection accuracy, reducing the need for multiple sensors. Although using dual-sensor setups provides added redundancy, it increases hardware complexity and costs. Our approach, however, focuses on a data-driven method that guarantees strong classification even with only one sensor. Figure 7: An overview of the PV-powered EI operation highlights the PV converter’s structure and controller scheme during the training and deployment phases. Cyber-attacks can occasionally cause power switch failures in extreme situations, such as excessive duty cycle manipulation or overcurrent conditions. However, our primary focus is on detecting these attacks before any hardware damage can occur. Our attack scenarios predominantly aim at manipulating measurements, such as FDI or hijacking attacks, rather than causing direct physical failures. As observed from the results, Figure 8 illustrates the PV's mission profile, power, capacitor voltage, inductor current, and attack logic state. The attack is identified as a malicious zone. In Figure 9, the PV voltage varies under the first case. As seen in Figures 9-11, it is evident that natural fluctuations occur when no cyber-attack impacts the system (voltage, current, and capacitor voltage, respectively). In the second case, variable input and output conditions are defined to see how the system reacts. As seen in Figure 12, an FDI attack is launched between \(t\) = 0.65 s and \(t\) = 1.85 s. In the third case, different types of cyber-attacks are launched between \(t\) = 0.65 s-1.85 s and \(t\) = 2 s-2.6 s and detected successfully. As can be seen in Figure 13,  
Figure 8: (**a**) The PV mission profile and output power, (**b**) the capacitor voltage and inductor current, and (**c**) the logic state of the attack are displayed. The attack is injected and perceived as a malicious area.