## 4 Simulation Results and Discussion  
In order to assess the suitability of the proposed optimization framework, it is tested on the feeder 4 connected to bus 6 of the IEEE RBTS, which is taken as an example [27]. The test system is a typical power distribution network in rural areas, which includes residential and agricultural loads. Figure 5 shows this test system. As shown in Figure 5, some areas of this feeder cross mountainous or forest zones with the poor or lack of coverage of communication networks (dashed line). Consequently, installing the traditional fault indicators based on GPRS and 3G/4G in these types of areas is impossible. Therefore, the intelligent fault indicator based on LoRa and NB-IoT can be installed here.  
According to the statistical analysis and historical data, the relevant model parameters are set as follows: The time for fault resection is 5 min, the fault rate of the line is 0.125, the electricity price is 0.15 USD/kWh, and the speed of overhead line inspection is 4 km per hour. The unit price of the LoRa module is 5 USD, and the unit price of the NB-IoT module is 10 USD. The annual communication fee of the NB-IoT module is 6 USD per year. The unit price of other parts of the acquisition unit is 6 USD, and the unit price of other parts of the collection unit is 40 USD. The annual operation and maintenance expenses are calculated to be 8% of the unit price of each part. The service life of LoRa module and NB-IoT module is both 10 years. Inflation rate is assumed to be 6% and interest rate is assumed to be 7%. The outage time of the distribution network is assumed to be 2 h.  
By solving the optimal configuration model, the optimal installation number of the acquisition unit of fault indicator is 14, and the corresponding installation position is 3, 5, 6, 8, 9, 10, 11, 12, 13, 15, 18, 19, 22, and 23, as shown in Figure 6. The optimal installation number of the collection unit of fault indicator is 1, and the corresponding installation position is 7, as shown in Figure 6.  
Figure 5: Feeder of the test system with poor or lack of coverage of communication networks.  
In this optimized configuration, the time required for any fault point to be accurately located is shown in Figure 7. The location number of fault points can be seen in Figure 5. For example, when the fault occurs at point 1, after locating the corresponding fault section through the fault indicator and patrolling the line at a certain speed, the locating time is 1.5 h. Similarly, when the fault occurs at point 10, the fault location can be accurately located within 0.1 h.  
In order to verify the effectiveness of the proposed scheme, the optimal scheme is compared with the random scheme. The random configuration scheme is set as Scheme 1, and the optimal configuration scheme of the model proposed in this paper is set as Scheme 2. It can be seen from Table 1 that when the same number of fault indicator acquisition units and collection units are installed, the average time for fault location of the Scheme 1 is 0.332 h, and the average time for fault location  
Figure 6: The installation position of IoT-based fault indicator.  
Figure 7: The time required for any fault point to be accurately located.  
of the Scheme 2 is 0.303 h, therefore the average time for fault location will be shortened by 0.029 h. In addition, the total cost of Scheme 1 is 237.38 USD, while the total cost of Scheme 2 is 228.48 USD, which is lower than that of Scheme 1. That is to say, under the premise of ensuring the reliability of communication and power supply, the economy of the scheme proposed in this paper is better.  
Influence on Fault Indicator Configuration Scheme by Changing Some Set Parameter Values Related to the Proposed Model  
In the actual distribution system, due to the influence of various factors, some parameters related to the model are often not fixed, so we can observe the impact on the configuration scheme of fault indicator by changing some set parameter values.