• KEPCO Journal on Electric Power and Energy
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• v.8 no.1
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• pp.43-48
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• 2022

Although the distribution system has been structured as complicated as a mesh in the past, the connection points for each line are always kept open, so that it is operated as a radial distribution system (RDS). For RDS, the line utilization rate is determined according to the maximum load on the line, and the utilization rate is usually kept low. In addition, when a fault occurs in the RDS, a power outage of about 3 to 5 minutes occurs until the fault section is separated, and the healthy section is transferred to another line. To improve the disadvantages of the RDS, research on the construction of a networked distribution system (NDS) that linking multiple lines is in progress. Compared to the RDS, the NDS has advantages such as increased facility utilization, load leveling, self-healing, increased capacity connected to distributed generator, and resolution of terminal voltage drop. However, when a fault occurs in the network distribution system, fault current can flow in from all connected lines, and the direction of fault current varies depending on the fault point, so a high-precision fault current direction determination method and high-speed communication are required. Therefore, in this paper, we propose an accurate fault current direction determination method by comparing the peak value polarity of the fault current in the event of a fault, and a communication-based protection coordination method using this method.

• Journal of IKEEE
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• v.18 no.4
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• pp.593-602
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• 2014

One of the purposes of an artificial neural netowrk(ANNet) is to implement the largest number of functions as possible with the smallest number of nodes and layers. This paper presents a CoreNet which has a multi-leveled input value and a multi-leveled output value with a 2-layered ANNet, which is the basic structure of an ANNet. I have suggested an equation for calculating the capacity of the CoreNet, which has a p-leveled input and a q-leveled output, as $a_{p,q}={\frac{1}{2}}p(p-1)q^2-{\frac{1}{2}}(p-2)(3p-1)q+(p-1)(p-2)$. I've applied this CoreNet into the simulation model 1(5)-1(6), which has 5 levels of an input and 6 levels of an output with no hidden layers. The simulation result of this model gives, the maximum 219 convergences for the number of implementable functions using the cot(${\sqrt{x}}$) input leveling method. I have also shown that, the 27 functions are implementable by the calculation of weight values(w, ${\theta}$) with the multi-threshold lines in the weight space, which are diverged in the simulation results. Therefore the 246 functions are implementable in the 1(5)-1(6) model, and this coincides with the value from the above eqution $a_{5,6}(=246)$. I also show the implementable function numbering method in the weight space.