## 1 Introduction
These methods are not subject to factors such as operation mode, fault type, and current sensor saturation, but conditions such as high-speed sampling systems need to be taken into account. In most cases, the effect of fault type and distributed generation (DG) does not need to be considered. The artificial intelligence algorithm, such as that suggested in Reference [18], needs a lot of training data to finish learning the eigenvalues. Nevertheless, there are not enough training data or training samples, so the algorithm will end up in an optimal local situation [19]. Reference [20] introduces a method of using CNN to analyze the bus voltage measurement for fault location in a targeted manner, and it can ensure a certain fault section location ability when some bus voltage data are missing. In the distribution network, intelligent terminals should be logically configured to minimize the negative effects of faults [21], as they can provide high-precision node voltage, current amplitude, and phase angle measurement information in real-time and synchronously for the convenience of distribution network fault location [22; 23]. Reference [24] proposed a fault location algorithm utilizing intelligent terminals without using line parameters, but it does not apply to small current ground faults. Reference [25] modeled the fault node using the equivalent injection current method, but its accuracy is constrained by the equivalent model of distributed power. Reference [26] calculated the voltage index \(V_{VI}^{F}\) and performed the measurement \(V_{sen}^{F}\) at the relay point. By comparing the magnitudes of \(V_{VI}^{F}\) and \(V_{sen}^{F}\), it can know whether the fault is located before or after the intersection point. In general, the massive branches and short lines of a distribution network, along with its own fault characteristics, are constraints on its ability to quickly locate the fault section. In light of the aforementioned issues, this paper analyzes the distribution network state observability and optimizes the configuration of intelligent terminals by performing compatibility processing on the time asynchrony of measurement data brought on by the diversity of equipment. On this foundation, a distribution system short-circuit fault equivalent model was developed. The virtual fault state variables are solved based on the sparse measurement data from meters installed in the feeder and distribution power supply area. The linear least squares residuals are then compared to locate the fault section. Finally, the trend of the three-phase line voltage change in the fault section is used to determine the fault phase. In order to verify the effect of the algorithm in the actual scene, we tested it on the improved IEEE-37 node system and IEEE-123 node system. According to the test results, we draw the following conclusions:  
1. The algorithm used in this paper is relatively simple to use. Additionally, the algorithm has a higher fault location accuracy in high-permeability distribution networks;
2. The algorithm has a high utilization rate of measurement data, reduces the number of intelligent terminals, and has higher economic efficiency;
3. The algorithm can discriminate different types of fault categories with high accuracy. The content of this paper is organized as follows: Section 2 introduces the use of multiple types of intelligent terminals for the observation analysis of distribution network fault states. Section 3 introduces the fault location algorithm for a three-phase unbalanced distribution network. Section 4 verifies the feasibility of the algorithm by using an improved example of IEEE-37 node distribution network. Section 5 concludes this paper. Observation Analysis of Distribution Network Fault States Based on Multi-Type Intelligent Terminals  
In order to fully monitor the faults of the distribution network, intelligent terminals must be configured at the corresponding bus of the distribution system. It is extremely important to obtain higher fault location accuracy through limited intelligent terminals.