scholarly journals Fault Current Tracing and Identification via Machine Learning Considering Distributed Energy Resources in Distribution Networks

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4333 ◽  
Author(s):  
Wanghao Fei ◽  
Paul Moses
Keyword(s):  
Feature Space ◽  
Distribution Networks ◽  
Power Line ◽  
Support Vector ◽  
Fault Current ◽  
Distributed Energy ◽  
System Protection ◽  
Fault Currents ◽  
First Time ◽  
Current Detection

The growth of intermittent distributed energy sources (DERs) in distribution grids is raising many new operational challenges for utilities. One major problem is the back feed power flows from DERs that complicate state estimation for practical problems, such as detection of lower level fault currents, that cause the poor accuracy of fault current identification for power system protection. Existing artificial intelligence (AI)-based methods, such as support vector machine (SVM), are unable to detect lower level faults especially from inverter-based DERs that offer limited fault currents. To solve this problem, a current tracing method (CTM) has been proposed to model the single distribution feeder as several independent parallel connected virtual lines that traces the detailed contribution of different current sources to the power line current. Moreover, for the first time, the enhanced current information is used as the expanded feature space of SVM to significantly improve fault current detection on the power line. The proposed method is shown to be sensitive to very low level fault currents which is validated through simulations.

scholarly journals Reducing Fault Current by Using FACTS Devices to Improve Electrical Power Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ibrahim M. Mehedi ◽  
Jahin Al Hasan Joy ◽  
Md. Rafiqul Islam ◽  
Nayeema Hasan ◽  
Ubaid M. Al-Saggaf ◽  
...  
Keyword(s):  
Power Flow ◽  
Electrical Power ◽  
Distribution Networks ◽  
Voltage Regulation ◽  
Unified Power Flow Controller ◽  
Fault Current ◽  
Static Synchronous Compensator ◽  
Simulink Model ◽  
Fault Currents ◽  
Ac Transmission

Today, there is a large increase in the demand for electricity. The transmission and distribution networks, however, cannot fulfill unbound demands due to the scarcity of resources. Power lines have losses which make the situation more unfavorable for maximum power transfer. Implementing a flexible AC transmission system (FACTS) is one of the best ways to reduce line losses. This paper proposes a FACTS-based method for minimizing the fault current in the system. Switchgear and protection equipment also perform better when this is done. Moreover, due to the reduced fault current of the switched system, a larger amount of power can be transmitted. Static synchronous series compensator (SSSC), static synchronous compensator (STATCOM), and unified power flow controller (UPFC) are evaluated in this case. With STATCOM and UPFC, fault currents are significantly reduced. Furthermore, STATCOM and UPFC can also reduce the fault currents in the power system in addition to voltage regulation and power flow control. A MATLAB/Simulink model is used to evaluate the model’s feasibility.

scholarly journals Study of Unwanted Emissions Generated by Distributed Energy Resources in Microgrids and their Influence over Narrow Band Power Line Communications

2016 ◽  
Author(s):  
Noelia Uribe-Pérez ◽  
Itziar Angulo ◽  
Luis Hernández ◽  
Txetxu Arzuaga ◽  
David De La Vega ◽  
...  
Keyword(s):  
Low Voltage ◽  
Distribution Networks ◽  
Power Line ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Distributed Energy ◽  
Power Line Communications ◽  
Battery Chargers ◽  
Layer Analysis ◽  
And Control

Distributed Energy Resources might have a severe influence on Power Line Communications, as they can generate interfering signals and high frequency emissions or supraharmonics that may cause loss of metering and control data. In this paper, the influence of various energy resources on Narrowband Power Line Communications is described and analyzed through several test measurements performed in a real microgrid. Accordingly, the paper describes the effects on smart metering communications through MAC layer analysis. Results show that the commutation frequency of inverters and the presence of battery chargers are remarkable sources of disturbance in low voltage distribution networks. In this sense, the results presented can contribute to efforts towards standardization and normative of emissions at higher frequencies higher, such as CENELEC EN 50160 and IEC/TS 62749.

scholarly journals Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN: a “Conscious-Lab” development

2021 ◽  
Author(s):  
Mehdi Alidokht ◽  
Samaneh Yazdani ◽  
Esmaeil Hadavandi ◽  
Saeed Chehreh Chelgani
Keyword(s):  
Laboratory Experiments ◽  
Scaling Up ◽  
Dense Medium ◽  
Support Vector ◽  
Coal Processing ◽  
Separation Device ◽  
Modeling Methods ◽  
Machine Learning Methods ◽  
Coal Washery ◽  
First Time

AbstractTri-flo cyclone, as a dense-medium separation device, is one of the most typical environmentally friendly industrial techniques in the coal washery plants. Surprisingly, no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses (yield and recovery). To fill this gap, this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method (boosted-neural network “BNN”) which is able to linearly and nonlinearly assess multivariable correlations among all variables, rank them based on their effectiveness and model their produced responses. These assessments and modeling were considered a new concept called “Conscious Laboratory (CL)”. CL can markedly decrease the number of laboratory experiments, reduce cost, save time, remove scaling up risks, expand maintaining processes, and significantly improve our knowledge about the modeled system. In this study, a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo separators. Well-known machine learning methods, random forest, and support vector regression were developed to validate BNN outcomes. The comparisons indicated the accuracy and strength of BNN over the examined traditional modeling methods. In a sentence, generating a novel BNN within the CL concept can apply in various energy and coal processing areas, fill gaps in our knowledge about possible interactions, and open a new window for plants' fully automotive process.

scholarly journals Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 371
Author(s):  
Yu Jin ◽  
Jiawei Guo ◽  
Huichun Ye ◽  
Jinling Zhao ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Feature Space ◽  
Kappa Coefficient ◽  
Classification Model ◽  
Support Vector ◽  
Textural Feature ◽  
Monitoring Accuracy ◽  
Areca Catechu ◽  
High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

scholarly journals Decentralized Voltage Optimization Based on the Auxiliary Problem Principle in Distribution Networks with DERs

2021 ◽  
Vol 11 (10) ◽  
pp. 4509
Author(s):  
Anna Rita Di Fazio ◽  
Chiara Risi ◽  
Mario Russo ◽  
Michele De Santis
Keyword(s):  
Distribution Systems ◽  
Auxiliary Problem ◽  
Optimization Procedure ◽  
Distribution Networks ◽  
Small Dimension ◽  
Quadratic Program ◽  
Voltage Profile ◽  
Distributed Energy ◽  
Auxiliary Problem Principle ◽  
Coordination Process

This paper addresses the problem of optimizing the voltage profile of radially-operated distribution systems by acting on the active and reactive powers provided by distributed energy resources (DERs). A novel voltage optimization procedure is proposed by adopting a decentralized control strategy. To this aim, a centralized voltage optimization problem (VOP), minimizing the distance of all the nodal voltages from their reference values, is firstly formulated as a strictly-convex quadratic program. Then, the centralized VOP is rewritten by partitioning the network into voltage control zones (VCZs) with pilot nodes. To overcome the lack of strictly convexity determined by the reduction to the pilot nodes, the dual centralized VOP working on the augmented Lagrangian function is reformulated and iteratively solved by the method of multipliers. Finally, a fully-distributed VOP solution is obtained by applying a distributed algorithm based on the auxiliary problem principle, which allows for solving in each VCZ a quadratic programming problem of small dimension and to drive the VCZ solutions toward the overall optimum by an iterative coordination process that requires to exchange among the VCZs only scalar values. The effectiveness and feasibility of the proposed method have been demonstrated via numerical tests on the IEEE 123-bus system.

scholarly journals A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

2018 ◽  
Vol 8 (9) ◽  
pp. 1621 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Yong Ren ◽  
Gongbo Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Feature Space ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Acceleration Sensors ◽  
Reconstructed Signal ◽  
Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

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