- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Karachi, Pakistan (2020.3.26-2020.3.27)] 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Terminal Sliding Mode Nonlinear Control Strategy for MPPT Application of Photovoltaic System
摘要： The electricity generation from the photovoltaic (PV) system has been considered as an alternative energy resource to the fossil fuels since last decade. Solar energy is the most abundantly available renewable resource on earth. However, source to load conversion efficiency of PV system is low but installation cost is appreciable. In order to achieve maximum power, the system must be operated at maximum power point (MPP). Maximum power point tracking (MPPT) is very essential in the process of maximum power extraction of the PV system. This research article presents the terminal sliding mode control (TSMC) nonlinear MPPT control paradigm for stand-alone PV system using buck-boost converter. Radial basis function neural network (RBF NN) is generated the reference for the proposed TSMC in controller. The simulations are performed in MATLAB/Simulink. To evaluate the developed controller performance, TSMC is tested under varying conditions of environment and resistive load with fault and uncertainty. Moreover, proposed nonlinear TSMC MPPT control technique is compared with the conventional techniques such as proportional integral derivative (PID) and perturb and observe (P&O). The finite time stability analysis is explained via Lyapunov function.
关键词： TSMC,Finite time stability,Buck-Boost converter,MPPT,RBF NN
更新于2025-09-23 15:21:01
-
[IEEE 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Winterton, South Africa (2019.8.5-2019.8.6)] 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Maximum Rooftop Photovoltaic Hosting Capacity with Harmonics as Limiting Factor a?? Case Study for Mauritius
摘要： This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance the performance of shunt active power ?lter (APF).The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model and the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method con?rm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.
关键词： adaptive fuzzy control,Sliding mode control,radial basis function neural network (RBF NN)
更新于2025-09-23 15:19:57
-
[IEEE 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - HangZhou, China (2018.9.5-2018.9.7)] 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - Graphene-based THz Antenna with A Graphene-metal CPW Feeding Structure
摘要： This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance the performance of shunt active power ?lter (APF).The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model and the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method con?rm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.
关键词： adaptive fuzzy control,Sliding mode control,radial basis function neural network (RBF NN)
更新于2025-09-19 17:13:59