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PI controller gain response comparison with ZN, PSO and GA The selected controller parameters are detailed in both Table 5 and Table 6. To initiate the optimization processes, the initial parameters for the PSO and GA are derived from the ZN method, which assists in establishing the upper and lower limits. In Fig. 9, a comparative analysis of the PI controller's settling time (Tss) and maximum peak overshoot (Mp) is conducted across different tuning methods, including ZN, PSO, and GA, and the results are summarized in Table 6. Notably, GA outperforms the other methods by achieving Mp value and Tss that aligns with the desired criteria. Consequently, the GA tuning method is selected for further experimental validation, as it meets all the specified criteria. The exploration into the controller's performance under weak grid conditions is prompted by the intermittent nature of renewable sources and variations in load-side impedance. In this context, the figures, specifically Fig. 10, 11, and 12, illustrate the dynamic response of the PI controller under various conditions during power regulation. These figures underscore the controller's efficacy in the nonlinear time domain, particularly when there are changes in current from 15A to 20A, demonstrating its dynamic response and its capability to control reactive power effectively. The experiment aims to verify the obtained controller parameters from real-coded based GA, see Table 6, with the PI and PR controllers' performance for weak grid operating conditions based on Lg variation and (X/R) variation, fault occurrence at the PCC and also tested for sudden load change in the standalone distributed network.

PI controller gain response comparison with ZN, PSO and GA The selected controller parameters are detailed in both Table 5 and Table 6. To initiate the optimization processes, the initial parameters for the PSO and GA are derived from the ZN method, which assists in establishing the upper and lower limits. In Fig. 9, a comparative analysis of the PI controller's settling time (Tss) and maximum peak overshoot (Mp) is conducted across different tuning methods, including ZN, PSO, and GA, and the results are summarized in Table 6. Notably, GA outperforms the other methods by achieving Mp value and Tss that aligns with the desired criteria. Consequently, the GA tuning method is selected for further experimental validation, as it meets all the specified criteria. The exploration into the controller's performance under weak grid conditions is prompted by the intermittent nature of renewable sources and variations in load-side impedance. In this context, the figures, specifically Fig. 10, 11, and 12, illustrate the dynamic response of the PI controller under various conditions during power regulation. These figures underscore the controller's efficacy in the nonlinear time domain, particularly when there are changes in current from 15A to 20A, demonstrating its dynamic response and its capability to control reactive power effectively. The experiment aims to verify the obtained controller parameters from real-coded based GA, see Table 6, with the PI and PR controllers' performance for weak grid operating conditions based on Lg variation and (X/R) variation, fault occurrence at the PCC and also tested for sudden load change in the standalone distributed network.

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... simulation parameters used for the system design are given in Table 5. Table 5 and Table 6. To initiate the optimization processes, the initial parameters for the PSO and GA are derived from the ZN method, which assists in establishing the upper and lower limits. In Fig. 9, a comparative analysis of the PI controller's settling time (Tss) and maximum peak overshoot (Mp) is conducted across different tuning methods, including ZN, PSO, and GA, and the results are summarized in Table 6. Notably, GA outperforms the other methods by achieving Mp value and Tss that aligns with the desired criteria. ...