In this paper a new approach to control a gridconnected synchronverter by using a dual heuristic dynamic programing (DHP) design is presented. The disadvantages of conventional synchronverter controller such as the challenges to cope with nonlinearity, uncertainties, and non-inductive grids are discussed. To deal with the aforementioned challenges a neural network-based adaptive critic design is introduced to optimize the associated cost function. The characteristic of the neural networks facilitates the performance under uncertainties and unknown parameters (e.g. different power angles). The proposed DHP design includes three neural networks: system NN, action NN, and critic NN. The simulation results compare the performance of the proposed DHP with a traditional PI-based design and with a neural network predictive controller. It is shown a well-trained DHP design performs in a trajectory, which is more optimal compared to the other two controllers.Index Terms--Dual heuristic dynamic programming, gridconnected inverter, neural network, synchronverter
In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia-based control of grid connected three-phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non-inductive grids. A neural network-based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks: critic network and action network. These networks can be trained during the same training cycle to decrease the training time. The simulation results confirm that the proposed neural network HDP controller performs better than the traditional direct-fed voltage an/or reactive power controllers in virtual inertia control schemes.Index Terms--grid connected inverter, heuristic dynamic programming, neural network, virtual synchronous generator I. X , L X , and L R are the inverter output filter reactance, the inverter to the grid line reactance, and the inverter to grid line resistance respectively.
In this paper, a neural network predictive controller is proposed to regulate the active and the reactive power delivered to the grid generated by a three-phase virtual inertia-based inverter. The concept of the conventional virtual synchronous generator (VSG) is discussed, and it is shown that when the inverter is connected to non-inductive grids, the conventional PIbased VSGs are unable to perform acceptable tracking. The concept of the neural network predictive controller is also discussed to replace the traditional VSGs. This replacement enables inverters to perform in both inductive and non-inductive grids. The simulation results confirm that a well-trained neural network predictive controller illustrates can adapt to any grid impedance angle, compared to the traditional PI-based virtual inertia controllers.
Analog dividers are widely used in analog systems. Analog realization of such circuits suffer from limited dynamic range and non-linearity issues, therefore, extra circuitry should be required to compensate these types of shortcomings. In this paper a gain controllable, analog divider is proposed based on data converters. Our circuit can be implemented both in current and voltage mode by selecting proper architectures. The resolution, power consumption and operation speed can be controlled by proper selecting of components. Another advantage of our circuit is its gain programmability. Moreover, the gain can be adjusted independently based on the relationship between input signals. Our proposed method offers two different gain control abilities, one for situation that the numerator signal is bigger than the denominator, and another gain is applied when the denominator is larger than the numerator. As a result, no extra amplifier is required for signal amplification. Moreover, the input and output signal nature can be chosen arbitrarily in this circuit, i.e. input signal may be a voltage signal while the output signal is current. Simulation results from SPICE confirm the proper operation of the circuit.
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