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 Implementation of Hybrid ABC-PSO Algorithm for Directional Overcurrent Relays Coordination Problem

2020 ◽  
Vol 9 (7) ◽  
pp. 721-727
Keyword(s):  
Power System ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Fast Response ◽  
Vital Role ◽  
Coordination Problem ◽  
Bee Colony ◽  
Overcurrent Relays ◽  
Optimal Coordination ◽  
Protective Relays

In modern power system, protective relays are playing a vital role for protection of the whole system. The efficiency and reliability of whole protection system depends upon the combined and coordinated operation of protective devices such as relays, circuit breakers etc. Moreover, both types of relays viz., primary and backup relays have been used for smooth and reliable operation of the power system from years. A primary directional over current relay (DOCR) is setup for the fast response of any faulty condition. If it fails, then backup relay perform the same task after some time gap. Three different setting such as plug-setting multiplier (PSM), pickup current settings and time multiplier setting (TMS) are required of performing the operation. In this paper, three very popular swarm based meta-heuristic such as particle swarm optimization (PSO), artificial bee colony (ABC) and a recent hybridization of both, i.e., hybrid ABC-PSO have been implemented for the calculation of optimal coordination problem. This coordination problem is treated for continuous settings optimization for TMS and pickup current. An IEEE 8 bus system without grid has been opted for validation of the results. It is evident from the study that the hybrid ABC-PSO based proves to generate optimal solution providing better convergence rate as compared to individual PSO and ABC algorithm.

scholarly journals The study of power transformer differential protection’s operation in the internal fault conditions

2021 ◽  
Vol 288 ◽  
pp. 01096
Author(s):  
Ilya Litvinov ◽  
Aleksandra Naumova ◽  
Vasiliy Titov ◽  
Andrey Trofimov ◽  
Elena Gracheva
Keyword(s):  
Power System ◽  
High Speed ◽  
Power Transformer ◽  
Short Circuit ◽  
Differential Protection ◽  
Current Transformers ◽  
Secondary Currents ◽  
A Value ◽  
Internal Fault ◽  
Internal Short Circuit

Special attention is paid to high-speed relay protections’ operation in transient modes due to a number of major failure events that have occurred over the past 10 years in the power system of the Russian Federation. Operation of power transformer’s differential protection in case of internal short circuit is studied in this research. False blocking of protection is possible in such mode due to saturation of current transformers. A value of blocking time may exceed the maximum permissible short-circuit disconnection time under conditions of maintaining the dynamic stability of the power system. Primary and secondary currents in transient modes are obtained by simulation of short circuits. Windings of the modeled current transformers are connected in a star to a null wire. RMS values are calculated using a mathematical model of the Fourier filter. The current transformers were checked according to the methods declared in PNST 283-2018 and GOST R 58669-2019. The analysis carried out in this work allows to estimate possibility of long-term blocking of the differential protection of a power transformer in case of internal short circuit, especially in case of significant value of time constants.

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 New CUK–SEPIC converter based photovoltaic power system with hybrid GSA–PSO algorithm employing MPPT for water pumping applications

2020 ◽  
Vol 13 (13) ◽  
pp. 2824-2830 ◽  
Author(s):  
Neeraj Priyadarshi ◽  
Mahajan Sagar Bhaskar ◽  
Sanjeevikumar Padmanaban ◽  
Frede Blaabjerg ◽  
Farooque Azam
Keyword(s):  
Power System ◽  
Pso Algorithm ◽  
Water Pumping ◽  
Photovoltaic Power

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.

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