A Novel ANFIS Algorithm Architecture for FPGA Implementation

This paper presents a new architecture for the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm targeting FPGA implementation. This new architecture offers higher efficiency and scalability in comparison to the existing methods. The proposed architecture is modeled and simulated using VHDL and is targeted at a Xilinx FPGA. Existing implementation architectures are also modeled and comparisons are drawn between them in terms of both performance and logic utilization. The results show that the new architecture offers a reduction in calculation cycles of around 50% in comparison to the architecture from which it’s derived. This increase in calculation speed comes with only a modest increase in logic utilization, specifically a 2.5% increase in look-up table (LUT) usage and a 1.5% increase in flip-flop usage. The new architecture also eliminates scalability issues which can arise in the existing architectures when extra input members are required

Control of a photovoltaic source emulator using artificial neural network

The photovoltaic (PV) emulator is a nonlinear power supply that produces a similar current-voltage characteristic of the PV module. However, the PV emulator output is volatile due to the nonlinear characteristic of the PV module. Conventionally, the overdamped PV emulator is required to prevent instability but results in slow dynamic response. On the other hand, the dynamic response of the PV emulator varies with changes in solar irradiance, ambient temperature and output resistance. The researches carried out in recent years for the control techniques include direct calculation method, look-up table method, piecewise linear method, neural network method, and curve segmentation method. Each of the method has advantages and disadvantages in terms of processing burden, memory required, accuracy, adaptability and independency. This research project focuses on the simulation of a combination of interleaved buck converter with two-stage inductor and capacitor filter to improve the dynamic performance of the PV emulator. Artificial neural network is used to overcome the complexity in the adaptive proportional-integral (PI) controller to achieve a stable and fast dynamic response of the PV emulator. The proposed control technique is simulated using MATLAB/Simulink® simulation package with varied output resistance and irradiance. ANFIS Editor toolbox is used for the training and learning process. The PI gains of the conventional method are set to limit output current overshoot under various output resistance. By comparison to conventional method during start-up response, the proposed control technique shows improvement of 40% to 90% faster in dynamic performance of the output current

Control of the photovoltaic emulator using fuzzy logic based resistance feedback and binary search

Photovoltaic (PV) emulator is a power supply that produces similar currentvoltage (I-V) characteristics as the PV module. This device simplifies the testing phase of PV systems under various conditions. The essential part of the PV emulator (PVE) is the control strategy. Its main function is to determine the operating point based on the load of the PVE. The direct referencing method (DRM) is the widely used control strategy due to its simplicity. However, the main drawback of DRM is that the output voltage and current oscillate due to the inconsistent operating point under fixed load. This thesis proposes an improved and robust control strategy named resistance feedback method (RFM) that yields consistent operating point under fixed load, irradiance and temperature. The RFM uses the measured voltage and current to determine the load of the PVE in order to identify the accurate operating point instantaneously. The conventional PV models include the I-V and voltage-current PV model. These PV models are widely used in various control strategies of PVE. Nonetheless, the RFM requires a modified PV model, the current-resistance (I-R) PV model, where the mathematical equation is not available. The implementation of the I-R PV model using the look-up table (LUT) is feasible, but it requires a lot of memory to store the data. A mathematical equation based I-R PV model computed using the binary search method is proposed to overcome the drawback of the LUT. The RFM consists of the I-R PV model and the closed-loop buck converter. In this work, the RFM is investigated with two different controllers, namely the proportional-integral (PI) and fuzzy logic controllers. The RFM using the PI controller (RFMPI) and the RFM using the fuzzy logic controller (RFMF) are tested with resistive load and maximum power point tracking (MPPT) boost converter. The perturb and observe algorithm is selected for the MPPT boost converter. In order to properly design the boost converter for the MPPT application, the sizing of the passive components is proposed, derived and confirmed through simulation. This derivation allows adjustment on the output voltage and current ripple of the PVE when connected to the MPPT boost converter. The simulation results of the proposed control strategies are benchmarked with the conventional DRM. To validate the simulation results, all controllers are implemented using dSPACE ds1104 rapid prototyping hardware platform. The RFM computes an operating point of the PVE at 20% faster than the DRM. The generated output PVE voltage and current using RFMPI and the RFMF are up to 90% more accurate compared to the DRM. The efficiency of the PVE is beyond 90% when tested under locus of maximum power point. In transient analysis, the settling time of RFMF is faster than the RFMPI. In short, the proposed RFMF is robust, accurate, quick respond and compatible with the MPPT boost converter