Abstract
A single-phase Cascaded H-Bridge (CHB) grid-tied multilevel inverter is introduced with a detailed discussion of the proposed novel neural controller for better efficiency and power quality in the integration of renewable sources. An LCL (inductor-capacitor-inductor) filter is used in the multilevel inverter system to achieve better harmonic attenuation. The proposed Neural Network (NN) controller performs the inner current control and tracks the references generated from the outer loop to satisfy the requirements of voltage or power control. Two multicarrier-based Pulse Width Modulation (PWM) techniques (phase-shifted modulation and level-shifted modulation) are adopted in the development of the simulation model to drive the multilevel inverter system for the evaluation of the neural control technique. Simulations are carried out to demonstrate the effectiveness and efficient outcomes of the proposed neural network controller for grid-tied multilevel inverters. The advantages of the proposed neural control include a faster response speed and fewer oscillations compared with the conventional Proportional Integral (PI) controller based vector control strategy. In particular, the neural network control technique provides better harmonics reduction ability.
Highlights
The distributed power generation system [1,2] has proven to be a very useful way of using renewable sources in small and large-scale energy production
The specific contributions of the paper are listed as follows: (1) an approach to utilize the neural network controller for the single-phase, grid-tied, Cascaded H-Bridge, multilevel converters, (2) a method to train the NN controller directly in a closed-loop control process by the Levenberg–Marquardt (LM) algorithm combining with a novel Forward Accumulation Through
To calculate the Jacobian matrix, every trajectory needs to be expanded forward through time, with the Forward Accumulation Through Time (FATT) algorithm illustrated in Figure 9, and the general BPPT algorithm for Recurrent Neural Network (RNN) training does not apply in this case [32]
Summary
The distributed power generation system [1,2] has proven to be a very useful way of using renewable sources in small and large-scale energy production. In [14,15], a neural network (NN) was trained based on the Approximate Dynamic Programming principle to control a three-phase L filter-based Grid-Connected. This research work intends to propose a novel neural network control strategy for a single phase five-level Cascaded H-Bridge (CHB), grid-tied inverter to allow better quality grid integration of renewable sources. The specific contributions of the paper are listed as follows: (1) an approach to utilize the neural network controller for the single-phase, grid-tied, Cascaded H-Bridge, multilevel converters, (2) a method to train the NN controller directly in a closed-loop control process by the Levenberg–Marquardt (LM) algorithm combining with a novel Forward Accumulation Through.
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