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In this article, a robust functional expanded multikernel broad learning system (RFEMBLS) is proposed to compute the complex nonlinear solar photovoltaic (PV) reference voltage more accurately by importing the irradiance and temperature in different uncertainty conditions. The novel droop control mechanism is introduced to obtain reference current to reduce dependence on different connected renewable energy resources. An adaptive integral backstepping sliding mode controller (AIBSMC) is designed to control the dc bus voltage under different abnormal scenarios for the proposed dc microgrid. An asymptotical stability analysis is developed using Lyapunov theory for the PV-battery dc microgrid. A new adoption rule is proposed for the estimation of both references of PV voltage and battery current. Furthermore, the system steady-state error and tracking convergence of the error are improved by adding an integral action. The backstepping method based on the dc bus voltage feedback results in a faster response and negligible chattering. A sliding mode controller is proposed to improve the control precision and robustness. Finally, the proposed RFEMBLS-AIBSMC method is tested using dSPACE platform in the scale-down lab environment to verify the robustness, practicability, feasibility, and efficacy of the proposed system in the real-time scenario.

The improved particle swarm optimization algorithm is integrated with variational mode decomposition (VMD) to extract the efficient band-limited intrinsic mode function (BLIMF) of the single and combined power quality events (PQEs). The selected BLIMF of the robust VMD (RVMD) and the privileged Fourier magnitude spectrum (FMS) information are fed to the proposed reduced deep convolutional neural network (RDCNN) for the extraction of the most discriminative unsupervised features. The RVMD-FMS-RDCNN method shows minimum feature overlapping compared with RDCNN and RVMD-RDCNN methods. The feature vector is imported to the novel supervised online kernel random vector functional link network (OKRVFLN) for quick and accurate categorization of complex PQEs. The proposed RVMD-FMS-RDCNN-OKRVFLN method produces excellent recognition capability over RDCNN, RVMD-RDCNN, and RVMD-RDCNN-OKRVFLN methods in noise-free and noisy environments. The unique BLIMF selection, clear detection, descriptive feature extraction, higher learning speed, superior classification accuracy, and robust antinoise performances are considerable importance of the proposed RVMD-FMS-RDCNN-OKRVFLN method. Finally, the proposed method architecture is developed and implemented in a very-high-speed ML506 Virtex-5 FPGA to text, examine, and validate the feasibility, performances, and practicability for online monitoring of the PQEs. [ABSTRACT FROM AUTHOR]