scholarly journals Real-Time Reliability Monitoring of DC-Link Capacitors in Back-to-Back Converters

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2369 ◽  
Author(s):  
Ahmed G. Abo-Khalil ◽  
Saeed Alyami ◽  
Ayman Alhejji ◽  
Ahmed B. Awan
Keyword(s):  
Low Cost ◽  
Pso Algorithm ◽  
Dielectric Materials ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Energy Field ◽  
Electrolytic Capacitors ◽  
Three Phase ◽  
Reliability Monitoring ◽  
Charge Discharge

Electrolytic capacitors have large capacity, low price, and fast charge/discharge characteristics. Therefore, they are widely used in various power conversion devices. These electrolytic capacitors are mainly used for temporary storage and voltage stabilization of DC energy and have recently been used in the renewable energy field for linking AC/DC voltage and buffering charge/discharge energy. However, electrolytic capacitors continue to be disadvantageous in their reliability due to their structural weaknesses due to the use of electrolytes and very thin oxide and dielectric materials. Most capacitors are considered a failure when the capacitance has changed by 25% of its initial value. Accurate and fast monitoring or estimation techniques are essential to be used with low cost and no extra hardware. In order to achieve these objectives, an online, reliable, and high-quality technique that continuously monitors the DC-link capacitor condition in a three-phase back-to-back converter is proposed. In this paper, the particle swarm optimization (PSO)-based support vector regression (PSO-SVR) approach is employed for online capacitance estimation based on sensing or deriving the capacitor current. Because the SVR performance alone severely depends on the tuning of its parameters, the PSO algorithm is used, which enables a fast online-based approach with high-parameter estimation accuracy. Experimental results are provided to verify the validity of the method.

scholarly journals A Hybrid Intelligent Method for Compensation of Current Transformers Saturation Based on PSO-SVR

2021 ◽  
Vol 65 (1) ◽  
pp. 53-61
Author(s):  
Reza Taghipour Gorji ◽  
Seyyed Mehdi Hosseini ◽  
Ali Akbar Abdoos ◽  
Ali Ebadi
Keyword(s):  
Power System ◽  
Rbf Neural Network ◽  
Target Function ◽  
Pso Algorithm ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Current Transformers ◽  
Secondary Current ◽  
Internal Fault ◽  
Protective Relays

The Current Transformers (CT) saturation may cause the protective relays mal-operation either non-recognition of internal fault or undesirable trip under external fault conditions. Therefore, compensation of CT saturation is very important for correct performance of protective schemes. Compensation of CT saturation by combination of signal processing methods and intelligent algorithms is a suitable solution to solve the problem. It decreases the probability of mal-operation and increases the reliability of the power system. In this paper, Support Vector Regression (SVR) method is employed to compensate the distorted secondary current due to CT saturation. In SVR method, despite the other methods such as MLPand ANFIS, instead of minimizing the model error, the operational risk error is considered as target function. In this method, by using Kernel tricks, a smart RBF neural network is obtained, so that all operational procedures will be optimized automatically. In this paper, an intelligent method based on Particle Swarm Optimization (PSO) algorithm is presented to determine the optimal values of SVR parameters. Due to the stability and robustness of this method in presence of noise and sudden changes in current, this method has a high accuracy. In addition, a sample power system is simulated using PSCAD software. Afterwards, current signals are extracted and fed to PSO-SVR algorithm, which is implemented in MATLAB environment. The obtained results show the preference of the proposed method in aspect of estimation accuracy as compared to some presented methods in the field of CT saturation detection and correction.

scholarly journals Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4812 ◽  
Author(s):  
Kamrul H. Foysal ◽  
Sung Eun Seo ◽  
Min Ju Kim ◽  
Oh Seok Kwon ◽  
Jo Woon Chong
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Linear Regression Analysis ◽  
Smartphone Application ◽  
Lateral Flow ◽  
Lateral Flow Assay ◽  
Machine Learning Techniques ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Test Procedures

Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin—10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time.

Advance Protection for Three Phase Induction Motor using Microcontroller Atmega32

2018 ◽  
Vol 8 (1) ◽  
pp. 172
Author(s):  
Karan S Belsare ◽  
Gajanan D Patil
Keyword(s):  
Induction Motor ◽  
Low Cost ◽  
Short Circuit ◽  
Cost Factor ◽  
Protection Scheme ◽  
Three Phase ◽  
The Cost

A low cost and reliable protection scheme has been designed for a three phase induction motor against unbalance voltages, under voltage, over voltage, short circuit and overheating protection. Taking the cost factor into consideration the design has been proposed using microcontroller Atmega32, MOSFETs, relays, small CTs and PTs. However the sensitivity of the protection scheme has been not compromised. The design has been tested online in the laboratory for small motors and the same can be implemented for larger motors by replacing the i-v converters and relays of suitable ratings.

Identificación de polaridad de los devanados en un motor trifásico de 6 terminales

2019 ◽  
pp. 13-19
Author(s):  
José Luis Viramontes-Reyna ◽  
Josafat Moreno-Silva ◽  
José Guadalupe Montelongo-Sierra ◽  
Erasmo Velazquez-Leyva
Keyword(s):  
Low Cost ◽  
Voltage Reference ◽  
Three Phase ◽  
Voltage Phase ◽  
The Law ◽  
And Control

This document presents the results obtained from the application of the law of Lens to correctly identify the polarity of the windings in a three-phase motor with 6 exposed terminals, when the corresponding labeling is not in any situation; Prior to identifying the polarity, it should be considered to have the pairs of the three windings located. For the polarity, it is proposed to feed with a voltage of 12 Vrms to one of the windings, which are identified randomly as W1 and W2, where W1 is connected to the voltage phase of 12 Vrms of the signal and W2 to the voltage reference to 0V; by means of voltage induction and considering the law of Lens, the remaining 4 terminals can be identified and labeled as V1, V2, U1 and U2. For this process a microcontroller and control elements with low cost are used.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

scholarly journals Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 581 ◽  
Author(s):  
Yuanyuan Fu ◽  
Guijun Yang ◽  
Xiaoyu Song ◽  
Zhenhong Li ◽  
Xingang Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Least Squares ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Digital Images ◽  
Predictive Performance ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Biomass Estimation ◽  
High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

Design and Implementation of Wireless Voltage Monitoring System Based on Zigbee

2017 ◽  
Vol 12 (3) ◽  
pp. 83-96 ◽  
Author(s):  
Luo Xiaohui
Keyword(s):  
Monitoring System ◽  
Single Phase ◽  
Low Cost ◽  
Moving Average ◽  
Voltage Sensor ◽  
Wireless Transmission ◽  
Phase Voltage ◽  
Medium Voltage ◽  
Wireless Monitoring ◽  
Three Phase

This paper proposed a low cost wireless monitoring system based on ZigBee wireless transmission, and designed a new floating voltage sensor which is suitable for the monitoring of medium voltage and high voltage(MV/HV) public equipment. The system used TI-CC2530 as the controller, proposed a new moving average voltage sensing(MAVS) algorithm by reasonable assumptions, and adopted algorithms to perform the theoretical analysis for the single phase and three-phase voltage. At last, the author carried out a practical experiment on the wireless floating voltage sensor under the voltage up to 30kV, the experimental results showed that the proposed low cost wireless sensor can achieve a good voltage monitoring function, and the error is less than 3%.

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