Fault Diagnosis Method Of Power System Research Articles

Power system fault diagnosis with quantum computing and efficient gate decomposition

Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from significant time-consuming, memory overhead, and computational complexity issues as the scale of the power system concerned increases. With rapid development of quantum computing technology, the combinatorial optimization method based on quantum computing has shown certain advantages in computational time over existing methods. Given this background, this paper proposes a quantum computing based power system fault diagnosis method with the quantum approximate optimization algorithm. The proposed method reformulates the fault diagnosis problem as a Hamiltonian by using Ising model, which completely preserves the coupling relationship between faulty components and various operations of protective relays and circuit breakers. Additionally, to enhance problem-solving efficiency under current equipment limitations, the symmetric equivalent decomposition method of multi-z-rotation gate is utilized. Furthermore, the small probability characteristics of power system events is utilized to reduce the number of qubits. Simulation results based on the test system show that the proposed methods can achieve the same optimal results with a faster speed compared with the classical higher-order solver provided by D-Wave.

Power System Fault Diagnosis Method Based on Intuitionistic Fuzzy Sets and Incidence Matrices

Power System Fault Diagnosis Method Based on Intuitionistic Fuzzy Sets and Incidence Matrices

Power System Fault Diagnosis Method Based on Deep Reinforcement Learning

Intelligent power grid fault diagnosis is of great significance for speeding up fault processing and improving fault diagnosis efficiency. However, most of the current fault diagnosis methods focus on rule diagnosis, relying on expert experience and logical rules to build a diagnosis model, and lack the ability to automatically extract fault knowledge. For switch refusal events, it is difficult to determine a refusal switch without network topology. In order to realize the non-operating switch identification without network topology, this paper proposes a power grid fault diagnosis method based on deep reinforcement learning for alarm information text. Taking the single alarm information of the non-switch refusal sample as the research object, through the self-learning ability of deep reinforcement learning, it learns the topology connection relationship and action logic relationship between equipment, protection and circuit breakers contained in the alarm information, and realizes the detection of fault events. The correct prediction of the fault removal process after the occurrence, based on this, determines the refusal switch when the switch refuses to operate during the fault removal process. The calculation example results show that the proposed method can effectively diagnose the refusal switch of the switch refusal event, which is feasible and effective.

Fault diagnosis of fracturing truck based on ITD and TET

The power system is the core part of fracturing truck, and its running state will directly determine the operation efficiency of fracturing truck. Fracturing trucks often operate under heavy load and noisy conditions, so the power system is prone to faults and difficult to diagnose. In order to solve the difficult problem of fracturing vehicle power system diagnosis under noise and changing working conditions, one method based on intrinsic time-scale decomposition (ITD) and transient-extracting transform (TET) is proposed. Firstly, using the intrinsic time-scale decomposition to dealing the collected vibration signal of the fracturing vehicle power system, multiple related components have been obtained, and then perform transient-extracting transform to processing the components with larger correlations to the original signal. The local features of the signal are enhanced and the fault features are extracted to complete the fault diagnosis. The test results showing that the proposed fault diagnosis method can accurately identify the fault condition of the power system. the conclusions obtained can provide a certain reference for the research and development of the power system fault diagnosis method of fracturing truck.

An Electric Power System Fault Diagnosis Method Based on PSO and FCM

For effectively analyzing electric power faults, exactly identifying failure type, and highly providing disposal measure, depending on PSO (particle swarm optimization) algorithm, a PSO-FCM (particle swarm optimization-fuzzy c-means) algorithm was constructed by the FCM improvement of fuzzy clustering to avoid get in local optimal state. On this basis, an electric power system fault diagnosis method was established by means of PSO and FCM. Finally, this method was validated by an example. Consequently, this method can intellectively diagnose and identify the fault of electric power system, and can provide a new approach to stably operation in electric power system.