| Title |
Fault detection and classification in electrical power transmission system using artificial neural network
|
|---|---|
| Published in |
SpringerPlus, July 2015
|
| DOI | 10.1186/s40064-015-1080-x |
| Pubmed ID | |
| Authors |
Majid Jamil, Sanjeev Kumar Sharma, Rajveer Singh |
| Abstract |
This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 239 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 239 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 38 | 16% |
| Student > Master | 33 | 14% |
| Student > Ph. D. Student | 19 | 8% |
| Researcher | 18 | 8% |
| Lecturer | 8 | 3% |
| Other | 24 | 10% |
| Unknown | 99 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 104 | 44% |
| Energy | 9 | 4% |
| Computer Science | 9 | 4% |
| Unspecified | 3 | 1% |
| Linguistics | 1 | <1% |
| Other | 5 | 2% |
| Unknown | 108 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 December 2020.
All research outputs
#12,934,037
of 22,824,164 outputs
Outputs from SpringerPlus
#621
of 1,851 outputs
Outputs of similar age
#115,707
of 262,193 outputs
Outputs of similar age from SpringerPlus
#29
of 99 outputs
Altmetric has tracked 22,824,164 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,851 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 65% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,193 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.