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The designed controller is ready to be implemented into an FPGA board for real time application. In this chapter, we have presented and discussed in details some case studies of FPGA applications in renewable energy systems, including photovoltaic modules, photovoltaic arrays, and hybrid PV systems (e.g. wind-photovoltaic).
FPGAs have applications mainly in photovoltaic systems and hybrid systems (PV-WT) [as mentioned in Sect. 7.3]. ANNs (Artificial Neural Networks) are popular machine learning techniques that FPGAs can be used for [FPGAs are the main focus of Sect. 7.3 in this context]. ANNs provide successful models and metaphors to improve our understanding of the human brain.
Two ways are presented in the passage to implement algorithms into FPGA boards: using hardware language (e.g. VHDL or Verilog), or using Xilinx System Generator based Matlab-Simulink. The passage recommends readers to use the second method, which is the most suitable for fast prototyping.
It should be noted that a PV module can be integrated into a reconfigurable FPGA. The benefits include: (1) designing a miniature intelligent PV module, (2) real-time performance evaluation, and (3) requiring less computational efforts.