scholarly journals Enhanced Dual-Spectrum Line Interpolated FFT with Four-Term Minimal Sidelobe Cosine Window for Real-Time Harmonic Estimation in Synchrophasor Smart-Grid Technology

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 191 ◽  
Author(s):  
Venkata Subrahmanya Raghavendra Varaprasad Oruganti ◽  
Venkata Sesha Samba Siva Sarma Dhanikonda ◽  
Helmo Kelis Morales Paredes ◽  
Marcelo Godoy Simões
Keyword(s):  
Fourier Transform ◽  
Renewable Energy ◽  
Embedded System ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Renewable Energy Sources ◽  
Distribution Grid ◽  
Spectral Leakage ◽  
Current Harmonics ◽  
Spectrum Line

The proliferation of nonlinear loads and integration of renewable energy sources require attention for accurate harmonic estimation along with estimation of fundamental amplitude, phase, and frequency for protection, improving power quality, and managing power effectively in a smart distribution grid. There are currently different Windowed Interpolated Fast Fourier Transform (WIFFT) algorithms for harmonic voltage estimation, but estimation of current harmonics using WIFFT is not explored sufficiently. The existing WIFFT algorithms, when used for current harmonic estimation result in low accuracy due to spectral leakage and picket fence effect. On the other hand, Interpolated Discrete Fourier Transform (DFT) is used for synchrophasor quality metrics, but it is effective only when there are no harmonics and the fundamental frequency is constant. This paper proposes a unified solution, comprising of peak location index search (PLIS)-based Dual-Spectrum Line Interpolated Fast Fourier Transform (DSLIFFT) algorithm with 4-Term Minimal Sidelobe Cosine Window (4MSCW) for estimating both low-amplitude voltage or current harmonics and synchrophasor under variable frequency conditions for high-penetration renewable energy utility grids. The effectiveness of the proposed algorithm is validated by simulation studies and real-time experimentation using the National Instruments reconfigurable embedded system under nonlinear loading conditions.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals Progresses in Analytical Design of Distribution Grids and Energy Storage

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4270
Author(s):  
Gianpiero Colangelo ◽  
Gianluigi Spirto ◽  
Marco Milanese ◽  
Arturo de Risi
Keyword(s):  
Renewable Energy ◽  
Power Flow ◽  
State Of The Art ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Distribution Grid ◽  
Mathematical Methods ◽  
Power Generators ◽  
Bidirectional Power Flow ◽  
Distribution Grids

In the last years, a change in the power generation paradigm has been promoted by the increasing use of renewable energy sources combined with the need to reduce CO2 emissions. Small and distributed power generators are preferred to the classical centralized and sizeable ones. Accordingly, this fact led to a new way to think and design distributions grids. One of the challenges is to handle bidirectional power flow at the distribution substations transformer from and to the national transportation grid. The aim of this paper is to review and analyze the different mathematical methods to design the architecture of a distribution grid and the state of the art of the technologies used to produce and eventually store or convert, in different energy carriers, electricity produced by renewable energy sources, coping with the aleatory of these sources.

scholarly journals Experimental Analysis of Modified DC-P&O Technique with Arm Controller for a Stand-Alone 40 W PV System

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6169
Author(s):  
Ashish Kumar Singhal ◽  
Narendra Singh Beniwal ◽  
Khalid Almutairi ◽  
Joshuva Arockia Dhanraj ◽  
Ali Mostafaeipour ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Renewable Energy Sources ◽  
Duty Ratio ◽  
Resistive Load ◽  
Renewable Energy Resources ◽  
Pv System ◽  
The Real ◽  
Fast Tracking ◽  
Pv Panel

The world is moving towards the generation of electricity with renewable energy sources (RES) due to the deterioration of the green environment and trying to replace non-renewable energy resources. The real-time results are achieved with the help of an arm controller, having good controller efficiency with the Waijung toolbox, compatible with MATLAB using STM32ST-link utility. In this paper, the authors are focused on areas such as easy to implement controller efficiency, and real-time solutions for modified direct-control perturbation & observation (DC-P&O) technique based on 32- bit ARM Cortex microcontroller (STM32F407VGT6) with embedded programming using Waijung blocksets, which offers very expected outcomes of the problem to make the stand-alone system efficient with fast-tracking. The observation setup is tested with a 40-watt photovoltaic (PV) panel with resistive load for achieving its stability. The designed algorithm enhances the efficiency of the controller by 84.48% for the real-time parameters of the PV panel at maximum power point (MPP) for a 57% duty ratio.

scholarly journals Ancillary Services Offered by Distributed Renewable Energy Sources at the Distribution Grid Level: An Attempt at Proper Definition and Quantification

2020 ◽  
Vol 10 (20) ◽  
pp. 7106
Author(s):  
Charis S. Demoulias ◽  
Kyriaki-Nefeli D. Malamaki ◽  
Spyros Gkavanoudis ◽  
Juan Manuel Mauricio ◽  
Georgios C. Kryonidis ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Plants ◽  
Business Models ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Voltage Regulation ◽  
Ancillary Services ◽  
Energy Sources ◽  
Distribution Grid ◽  
Support Functions

The gradual displacement of synchronous generators driven by conventional power plants, due to the increasing penetration of distributed renewable energy sources (DRES) in distribution grids, is creating a shortage of crucial ancillary services (AS) which are vital for the frequency and voltage stability of the grid. These AS, and some new ones, could now be offered by the DRES, particularly those that are converter interfaced, in a coordinated way in order to preserve the grid stability and resilience. Although recent standards and grid codes specify that the DRES exhibit some system support functions, there are no specifications on how to measure and quantify (M & Q) them both at DRES level and in aggregated form. The M & Q of AS is crucial, since it would allow the AS to be treated as tradable AS in the current and future AS markets. This paper attempts to define a number of AS that can be offered by converter-interfaced DRES and suggests methods for their M & Q. The new AS addressed are: (1) inertial response; (2) primary frequency response; (3) active power smoothing (ramp-rate limitation); (4) exchange of reactive power for voltage regulation; (5) fault-ride-through (FRT) and contribution to fault clearing; (6) voltage harmonic mitigation. Additionally, a rough estimation of the additional investment and operational cost, as well as the financial benefits associated with each AS is provided in order to form the basis for the development of business models around each AS in the near future.

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