# Analysis of AMI Communication Methods in Various Field Environments
## 5 Experimental Results
### Measurement Results of PLC and Regression Analyses  
Measurement results of the signal and noise powers of the PLC method for the 48 MPs of 18 sites are first summarized. For each area type, the measurement and modeling results are given. At the Overhead Residential Area, there are five sites, and each site has 3 to 5 MPs, as shown in Table 1. The received power with respect to the distance from the transmitter is shown in Figure 20a. The signal power depends on the distance and decreases as the distance increases at most of the MPs. It is observed that the slope of the received power with respect to the distance differs depending on the sites. Sites 1-2 and 1-3 have very steep slopes within a short distance range. Site 1-1 has a gentle slope between the first and second MPs. This is due to the different number of paths and/or loads connected to the power line. With the 18 MP results, a linear regression analysis was applied to make a model of the received power with respect to the distance for the Overhead Residential Area. The coefficients for the regression model in (8) were found as \(\alpha=-0.212\) and \(\beta=-28.8\). The model with these coefficients is shown as a black dotted line in Figure 20a. Noise spectra were also measured and stored in the average mode, and then the stored spectrum was converted to the noise power. The noise power measured in the Overhead Residential Area is shown in Figure 20b. The MPs were the same as where the signal powers were measured, and the measurements were taken at a total of 18 points. From the results, it is observed that the noise power is independent of the distance. However, the noise power depends on the environment of MP such as the area types and number of loads connected to the power line. The noise power was in a range of \(-68.6\) dBm to \(-39.5\) dBm. The difference between the minimum and maximum values was as large as 30 dB and thus it was difficult to estimate the noise power at a given point. Thus, two values of the noise power are used; the maximum and the average values. The maximum value is indicated by a dotted red line and the average value is indicated by a dotted blue line in Figure 20b. Here, the average value of the noise power was \(-47.2\) dBm. Figure 19: Example of the NDIS map (Site 1-2, Overhead Residential Area). (**a**) NDIS map with a utility pole and distribution lines. (**b**) Aerial photography with a utility pole and MPs. The Overhead Commercial Area consists of small shops, where many power line branches with short paths are present. Figure 21a shows the powers of received signals. The measurements were taken at 10 MPs. From the results, we can observe that the received power decreases as the distance increases at most of the MPs. We can also notice that even at the same measurement site, the ARs were not the same. At Site 2-4, the third MP had more received power than the second MP case, and even the third MP was a little farther than the second MP case. This is due to different numbers of branches at the MPs. An MP with more branches may suffer more attenuation. With the 10 MP results, a linear regression analysis was applied to model the received power with respect to the distance for the Overhead Commercial Area. The coefficients for the regression model were found as \(\alpha=-0.135\) and \(\beta=-31.6\). The line of the regression model is shown as a black dotted line in Figure 21a. The noise power ranged form \(-53.0\) dBm to \(-33.0\) dBm. The difference between the maximum and minimum was about 20 dB, which is relatively small. However, the average noise power at the Overhead Commercial Area was relatively large. The maximum value is indicated by a red dotted line and the average value (\(-43.2\) dBm) is the blue dotted line in Figure 21b. The Overhead Rural Area was characterized by small numbers of branches and long the path. Figure 22a shows the received powers with respect to the distance. The measurements were taken at 8 MPs. The regression coefficients for the model were found as \(\alpha=-0.074\) and \(\beta=-31.2\).