# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 1 Introduction  
The transmission network is the main component of the power system and is responsible for large-capacity power transmission tasks. Therefore, the safety of the transmission network is of vital importance to the entire power system. When a transmission network fails, quickly identifying faulty elements is the primary task of fault diagnosis, and it is also a prerequisite for the smart grid to be self-healing. In order to carry out follow-up maintenance and record fault history information, it is necessary to store fault details such as the fault locations, types, phases, times of the faulty elements. In addition, it is also indispensable to find the circuit breakers and relay protection devices that did not operate correctly at the time of the fault and to track the reasons for their incorrect operations. Therefore, proposing a complete fault diagnosis system to organically combine the above tasks has an urgent need in practical engineering applications. When it comes to transmission network operation, power flow [1], optimal power flow [2], optimal power flow [3], security analysis [4] and state estimation [5] are indispensable and important computational tools. Reference [1] proposed a robust and efficient LF solver based on the Bulirsch-Stoer algorithm, which solves the load-flow (LF) problem of super-large-scale systems and improves computational performance. Reference [2] used marine predator algorithm (MPA) to solve the multi-region optimal power flow (OPF) problem considering renewable energy sources and load variability. Reference [3] proposed a methodology to quickly obtain the saddle-node bifurcation points of power systems, which significantly reduces the computational cost. Reference [4] proposed an online lineswitching methodology to alleviate overloads with look-ahead capability, which provides a set of high-quality line switching solutions and improves speed (online application) and accuracy. Reference [5] proposed an improved probabilistic load and distributed energy resources (DERs) modeling as pseudo-measurements, which has the ability to estimate the states of the power grid with high accuracy and short computational time. These improved new technologies (especially the method proposed in reference [5]) enable the dispatching center to monitor and estimate the operating status of the entire power grid more accurately and quickly, providing feasibility for fault diagnosis. Traditional power grid fault diagnosis methods, including expert system [6], Petri net [7] and other diagnostic algorithms, are based on protection operation information and circuit breaker tripping information. These methods could accurately diagnose faulty elements under the premise of complete information. Reference [8] proposed a fault diagnosis method under the condition of partial circuit breaker information loss, which supplemented practical application. As the data sources available for fault diagnosis become more abundant, multi-source information fusion [9] has also been applied to the field of fault diagnosis. However, these methods can only be used to determine faulty elements and cannot provide fault details. In order to obtain the fault details, the recording wave data were also applied to the field of fault diagnosis by scholars as an information source [10]. However, due to the large storage space required by the fault recording documents, the recorded wave data cannot be uploaded to the dispatching end in real-time. Therefore, the fault diagnosis method based on the recorded wave data as the data source has the problem of time efficiency. Fault tracking [11] refers to finding reasons for the incorrect actions of relay protection devices or circuit breakers by investigating the relevant alarm data in substations. Reference [12] used the inference chain and Bayesian network to track the faults of relay protection devices and achieved favorable results. However, it did not fully consider the problem of partial alarm data information loss.