Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods

Jiang, Huaiguang; Zhang, Jun J.; Gao, Wenzhong; Wu, Ziping

doi:10.1109/tsg.2014.2330624

Cited by 137 publications

(60 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fault impedances are considered as purely resistance and they are arranged in low and high groups, 0.5 and 10 Ω , respectively. Moreover, it is assumed that the fault type has been already detected based on the proposed approach in [8].…”

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Fault location in distribution systems with DG based on similarity of fault impedance

Moravej¹,

Hajhossani²,

Pazoki³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

This paper presents a new method for locating a fault in distribution systems using the similarity of fault impedance. A four-step approach is proposed to locate the fault in the networks. First, pre-and during-fault voltages from smart feeders along the primary feeders are measured. Second, three-phase impedance to calculate fault current corresponding to voltage deviation from each smart meter is achieved. Third, a relation between fault current and fault impedance for each type of fault is extracted. Finally, based on the similarity of the estimated fault impedance for each bus, an error index is calculated. Moreover, an auxiliary process is utilized to analyze the estimated fault impedance. The proposed method uses both smart meters and phasor measurement units to obtain voltage sag. Distributed generation and loads are modeled as constant impedances and then they are considered in the three-phase impedance matrix. The suggested approach is evaluated in the IEEE 34-bus test distribution network, which is simulated in the PSCAD/EMTDC environment. The obtained numerical results confirm the acceptable accuracy of the proposed methodology for all types of faults with various fault resistances.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

“…Moreover, the DT reduced the computational complexity. In [8], matching pursuit decomposition was utilized for feature extraction from voltage signals. Then a hybrid-clustering algorithm was used to cluster some specific vectors for fault location purposes.…”

Section: Introductionmentioning

confidence: 99%

Fault location in distribution systems with DG based on similarity of fault impedance

Moravej¹,

Hajhossani²,

Pazoki³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Problem Specific Methodology Used [3] 2015 Sea wave Ordinal classification SVM, ANN, LR [4] 2015 Solar Classification SVM [5] 2009 Power disturbance Classification SVM, wavelets [10] 2015 Wind Optimization Bio-inspired, meta-heuristics [14] 2015 Wind Classification Fuzzy SVM [15] 2011 Wind Classification DT, SOM [16] 2015 Wind Classification SVM, k-NN, fuzzy, ANN [17] 2010 Solar Classification Semi-supervised SVM [20] 2013 Wind Ordinal classification SVM, DT, LR, HMM [30] 2014 Wind Classification SVM, LR, RF, rotation forest [31] 2011 Wind Classification ANN, LR, DT, RF [32] 2013 Wind Classification k-NN, RBF, DT [33] 2011 Wind Classification, regression BN [34] 2014 Wind Classification, regression Heuristic methodology: WPPT [35] 2011 Wind Classification Bagging, ripper, rotation forest, RF, k-NN [36] 2013 Wind Classification ANFIS, ANN [37] 2012 Wind Classification SVM [38] 2015 Wind Classification ANN, SVM [39] 2015 Wind Classification PNN [40] 2015 Wind Classification DT, BN, RF [41] 2015 Wind Classification, clustering AuDyC [42] 2016 Wind Classification, clustering AuDyC [43] 2010 Power disturbance Classification HMM, SVM, ANN [44] 2015 Power disturbance Classification SVM, NN, fuzzy, neuro-fuzzy, wavelets, GA [45] 2015 Power disturbance Classification SVM, k-NN, ANN, fuzzy, wavelets [46] 2002 Power disturbance Classification Rule-based classifiers, wavelets, HMM [47] 2004 Power disturbance Classification PNN [48] 2006 Power disturbance Classification ANN, RBF, SVM [49] 2007 Power disturbance Classification ANN, wavelets [50] 2012 Power disturbance Classification PNN [51] 2014 Power disturbance Classification ANN Table 3. Summary of the main references analyzed, grouped by application field, problem type and methodologies considered (II)...…”

Section: Reference Year Application Fieldmentioning

confidence: 99%

“…In the field of smart grids, [43] explores the use of Hidden Markov Models (HMMs) and matching pursuit decomposition for the detection, identification and location of power system faults. The proposed system uses voltage and frequency signals measured by a frequency disturbance recorder.…”

Section: Classification Problems and Algorithms In Fault Diagnosis Inmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

View full text Add to dashboard Cite

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

show abstract

“…In [19], a whole function and architecture of an early warning system, and its specific design scheme are put forward, which effectively improved the scientific approach and predictability of operation decision-making for power systems. In [20], a data-driven computational method has been proposed to address fault detection, identification, and location in smart grids. Firstly, two detection hidden Markov models (HMMs) are trained for fault detection to distinguish between normal and abnormal smart grid (SG) operation conditions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Early Warning Model Based on Fault Gene Table for Smart Distribution Grids

et al. 2017

View full text Add to dashboard Cite

Since a smart distribution grid has a diversity of components and complicated topology; it is very hard to achieve fault early warning for each part. A fault early warning model for smart distribution grid combining a back propagation (BP) neural network with a gene sequence alignment algorithm is proposed. Firstly; the operational state of smart distribution grid is divided into four states; and a BP neural network is adopted to explore the operational state from the historical fault data of the smart distribution grid. This obtains the relationship between each state transition time sequence and corresponding fault, and is used to construct the fault gene table. Then; a state transition time sequence is obtained online periodically, which is matched with each gene in fault gene table by an improved Smith-Waterman algorithm. If the maximum match score exceeds the given threshold, the relevant fault will be detected early. Finally, plenty of time domain simulation is performed on the proposed fault early warning model to IEEE-14 bus. The simulation results show that the proposed model can achieve efficient early fault warning of smart distribution grids.

show abstract

Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods

Cited by 137 publications

References 31 publications

Fault location in distribution systems with DG based on similarity of fault impedance

Fault location in distribution systems with DG based on similarity of fault impedance

A Review of Classification Problems and Algorithms in Renewable Energy Applications

A Novel Fault Early Warning Model Based on Fault Gene Table for Smart Distribution Grids

Contact Info

Product

Resources

About