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DVR Using Randomized Self-Structuring Fuzzy and Recurrent Probabilistic Fuzzy Neural-Based Controller
https://orcid.org/http://orcid.org/0000-0003-4745-2998
This article proposes a machine learning-based dynamic voltage restorer (DVR) control strategy for addressing the conventional design procedure of fuzzy logic that needs human expertise to decide membership functions. A randomized evolving Takagi–Sugeno (ReTSK) machine learning approach is proposed for the estimation of fundamental weight components from the polluted grid for enhanced DVR compensating capability. A recurrent probabilistic fuzzy neural network (RPFNN) control is employed for encountering the manual parameters tuning approach of a proportional-integral controller that depends on the optimized coefficients during severe voltage disturbances. The outlined approaches demonstrate robust performance by improving the DC- and AC-link voltage regulation with parametric variations and disturbances. The recommended RPFNN control provides a better response during the transitory state in terms of performance indicators like rise time (0.15 s), settle time (0.08 s), overshoot (3.3%), undershoot (3.3%) and recovery time (0.42 s). Advanced meta-algorithms like Seagull and Rat Swarm Optimization are employed for the self-tuning of the controller’s parameters and compared with competitive algorithms like Moth-Flame Optimization, Spotted Hyena Optimizer and Harris Hawks Optimization. The results of best-fitted forecasting models ReTSK are evaluated by using statistical performance indices (MSE, RMSE, ME, SD and R), while RPFNN are assessed by (MSE, RMSE MAE, MAPE, SI and R). The performance results confirm the validation of the developed control strategies which emphasize their relevance.
DVR Using Randomized Self-Structuring Fuzzy and Recurrent Probabilistic Fuzzy Neural-Based Controller
https://orcid.org/http://orcid.org/0000-0003-4745-2998
This article proposes a machine learning-based dynamic voltage restorer (DVR) control strategy for addressing the conventional design procedure of fuzzy logic that needs human expertise to decide membership functions. A randomized evolving Takagi–Sugeno (ReTSK) machine learning approach is proposed for the estimation of fundamental weight components from the polluted grid for enhanced DVR compensating capability. A recurrent probabilistic fuzzy neural network (RPFNN) control is employed for encountering the manual parameters tuning approach of a proportional-integral controller that depends on the optimized coefficients during severe voltage disturbances. The outlined approaches demonstrate robust performance by improving the DC- and AC-link voltage regulation with parametric variations and disturbances. The recommended RPFNN control provides a better response during the transitory state in terms of performance indicators like rise time (0.15 s), settle time (0.08 s), overshoot (3.3%), undershoot (3.3%) and recovery time (0.42 s). Advanced meta-algorithms like Seagull and Rat Swarm Optimization are employed for the self-tuning of the controller’s parameters and compared with competitive algorithms like Moth-Flame Optimization, Spotted Hyena Optimizer and Harris Hawks Optimization. The results of best-fitted forecasting models ReTSK are evaluated by using statistical performance indices (MSE, RMSE, ME, SD and R), while RPFNN are assessed by (MSE, RMSE MAE, MAPE, SI and R). The performance results confirm the validation of the developed control strategies which emphasize their relevance.
DVR Using Randomized Self-Structuring Fuzzy and Recurrent Probabilistic Fuzzy Neural-Based Controller
https://orcid.org/http://orcid.org/0000-0003-4745-2998
This article proposes a machine learning-based dynamic voltage restorer (DVR) control strategy for addressing the conventional design procedure of fuzzy logic that needs human expertise to decide membership functions. A randomized evolving Takagi–Sugeno (ReTSK) machine learning approach is proposed for the estimation of fundamental weight components from the polluted grid for enhanced DVR compensating capability. A recurrent probabilistic fuzzy neural network (RPFNN) control is employed for encountering the manual parameters tuning approach of a proportional-integral controller that depends on the optimized coefficients during severe voltage disturbances. The outlined approaches demonstrate robust performance by improving the DC- and AC-link voltage regulation with parametric variations and disturbances. The recommended RPFNN control provides a better response during the transitory state in terms of performance indicators like rise time (0.15 s), settle time (0.08 s), overshoot (3.3%), undershoot (3.3%) and recovery time (0.42 s). Advanced meta-algorithms like Seagull and Rat Swarm Optimization are employed for the self-tuning of the controller’s parameters and compared with competitive algorithms like Moth-Flame Optimization, Spotted Hyena Optimizer and Harris Hawks Optimization. The results of best-fitted forecasting models ReTSK are evaluated by using statistical performance indices (MSE, RMSE, ME, SD and R), while RPFNN are assessed by (MSE, RMSE MAE, MAPE, SI and R). The performance results confirm the validation of the developed control strategies which emphasize their relevance.
DVR Using Randomized Self-Structuring Fuzzy and Recurrent Probabilistic Fuzzy Neural-Based Controller
J. Inst. Eng. India Ser. B
Arya, Sabha Raj (Autor:in) / Mistry, Khyati D (Autor:in) / Kumar, Prashant (Autor:in)
Journal of The Institution of Engineers (India): Series B ; 105 ; 483-502
01.06.2024
20 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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