Fault Detection Using Fiber-Optic
In electric power supply services, power transmission lines are very important and very indispensable. For that, power transmission lines are equipped with various protection systems that are checked varies times periodically because of the unexpected troubles that may destroy the lines.
For the purpose of protecting these lines, a new system was invented to discover the Fault Location using Composite Fiber Optic Overhead Ground Wire (OPGW). This system deals mainly with most causes of fault situations such as lightning, dew, snow, fog, or gales. This new fault location system was developed to find out where electrical fault happened on overhead power transmission lines by detecting the current induced in the ground wire.
Any fault situation needs the fastest processing in fixing the fault. For that, the fault location system helps engineers to detect the point or the section where an electrical fault happened in very logic time.
Mainly, the fault location method measures the current induced in the OPGW at many points along the line, these points are various sensors mounted on the tower and transmits the information to the central monitoring station through the optical fiber within the OPGW. So the fault information system is mainly given by sensing and data transmission.
Electrical faults occurring on power transmission lines can be classified into two types: grounding and short circuit fault. The transmission that gets to the central monitor station deals with current characteristic features. In order to locate the fault, engineers must use the features of currents that deal with the phase angle and the amplitude and relate these features to Fuzzy Theory.
The idea arose of using Fuzzy Theory as a fault theory algorithm similar to this kind of human thinking.
Fault Detection Using Neural Network
As indicated before, protecting transmission is very important task in safeguard electric power systems. For that, faults on transmission lines need to be detected, classified, and located accurately. All these actions must be taken in very short time to clear the fault.
The new approach of neural network to fault classifications for high speed protective relaying is a good manner in solving any fault classification for high speed protective relaying is a good manner in solving any fault happened to the transmission lines.
Mainly this scheme is based on the use of neural architecture and implementation of digital signal processing concepts. Figure 1 shows functional parts of protective relay.
In general, a knowledge control module controls all other parts of the relay and is responsible for sending trip signals. The fault detection module is signals that a fault location classification module uses samples of normalized currents (i) and voltages (v). A 1 KHz sample rate is use to ensure that the fault type classification can be done in a timely fashion.
This module classifies weather a 1-phase-to-ground, 2-phase-toground, phase-to-phase or a 3-phase fault has occurred.
In the classification process, arcing and non-arcing must be known in order to obtain a successful automatic reclosing. Generally speaking, neuTral network classifies the fault into types. The first type (1-phase, 2-phase, 3-phase faults) is fast 5-7 ms and reliable.
The second type, arcing and non-arcing faults support a successful automatic reclosing.
|Title:||Causes and effects of faults in overhead transmission lines – William Patrick Davis at Department of Electrical and Electronic Engineering, California State University, Sacramento|
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