Linear Active Disturbance Rejection Control based on Finite Impulse Response filter for harmonic control in microgrid

Island for micro grid is vulnerable at the run time, which is caused by the problem of load fluctuation, voltage frequency deviation, and distributed power supply power. These issues are generated by the uncertainty of three-phase voltage waveform output at the same time. All of these problems are urgently needed to solve, this paper designs a linear active disturbance rejection control to do robust estimation. This controller is based on optimal finite impulse response (FIR) filter, which is also combined with the immunity of the micro grid system harmonic method. The proposed controller is simulated. And it is compared with the classical PI controller and the linear active disturbance rejection controller respectively too. The simulation results show that the performance and robustness of the proposed model are better than those of traditional algorithm in microgrids.

An Algorithm for Calculation and Extraction of the Grid Voltage Component

Calculating the values of the parameters of distorted periodic signals in real-time is important for the control of many processes. In particular, this information is necessary for the proper operation of power electronics devices that cooperate with the power grid. In such cases, it is necessary to determine the phase, frequency, and amplitude of the fundamental component of the voltage in the power grid node. Also, in many cases, the control process needs a signal which is synchronised with the power grid voltage. Both processes should be realised in real-time. A number of solutions to the problem of calculating the values of the voltage parameters have been described in the literature. However, these methods generally introduce significant time delays and have several restrictions regarding the variability in the values of these parameters. They also often require the significant computational power of a unit that performs the task of identification. The algorithm presented in this work is based on the properties of a pair of orthogonal signals, generated by a two-dimensional finite impulse response filter, which has a certain transfer function resulting from the needs of the algorithm, what is the innovation of the algorithm. These signals are then used in the program module, which both, calculates, in the time domain, the instantaneous values of the frequency and the amplitude of the fundamental component of the power grid voltage, and generates a signal, being in-phase with this component. The presented algorithm is fast, accurate, and relatively simple; therefore, it does not require a high computational power processor. This algorithm was experimentally verified by implementation in microcomputer-based units, which were then applied in the control systems of the power electronic devices, as well as in analysers of the energy quality.

Wheelchair Movement Based on Convolution Neural Network

This paper intends to develop a methodology for helping amputees and crippled people old, by ongoing voice direction and association between patient and personal computer (PC) where these blends offer a promising response for helping the debilitated people. The major objective of this work is accurately detected audio orders via a microphone of an English language (go, stop, right and left) in a noisy environment by the proposed system. Thus, a patient that utilizes the proposed system can be controlling a wheelchair movement. The venture depends on preparing an off-line dataset of audio files are included 10000 orders and background noise. The proposed system has two important steps of preprocessing to get accurate of specific audio orders, accordingly, the accurate direction of wheelchair movement. Firstly, a dataset was preprocessed to reduce ambient noise by using Butterworth (cutoff 500-5000 Hz) and Wiener filter. Secondly, in the input (a microphone) of the proposed discriminative model put a procedure of infinite impulse response filter (Butterworth), passband filter for cutoff input microphone from 150-7000 Hz for back-off the loud and environment noise and local polynomial approximation (Savitzky-Golay) smoothing filter that plays out a polynomial regression on the signal values. Thus, a better for filtering from ambient noise and keeping on a waveform from distortion that makes the discriminative model accurate when voice orders were recognized. The proposed system can work with various situations and speeds for steering; forward, stop, left and right. All datasets are trained by using deep learning with specific parameters of...