A new analytic approach for power system fault diagnosis employing the temporal information of alarm messages

Guo, Wu; Wen, Fushuan; Ledwich, Gerard; Liao, Zhiwei; He, Xiangzhen; Huang, Jiansheng

doi:10.1016/j.ijepes.2012.06.064

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…Table 5 compares the performance of the proposed method with the existing methods in [10,[20][21][22]24] that consider temporal features.…”

Section: Performance Comparisonmentioning

confidence: 99%

Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis

Shao,

Chen,

Xie

et al. 2024

Applied Sciences

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Fuzzy and temporal reasoning can effectively improve the accuracy of fault diagnosis methods. However, there are challenges in practical applications, such as missing alarm messages, temporal reasoning with complex calculations, and complex modeling processes. Therefore, this study proposes an improved temporal fuzzy reasoning spiking neural P (ITFRSNP) system for power system fault diagnosis. First, the ITFRSNP system and its reasoning method are proposed to perform association reasoning between confidence degrees and temporal constraints. Second, a general fault diagnosis model and process are developed based on the ITFRSNP system to diagnose various faulty components and simplify the modeling process. In addition, a search method is provided for identifying suspected faulty components, considering the missing alarm message of the circuit breaker. Simulation results of fault cases demonstrate that the proposed method exhibits high accuracy and fault tolerance. It can precisely identify faulty components despite incorrect operations or inaccurate alarm messages of protective relays and circuit breakers. Moreover, the search method effectively narrows down the diagnostic scope without missing suspected faulty components in scenarios where alarms from boundary circuit breakers are missing, thereby enhancing the fault diagnosis efficiency. The fault diagnosis model features a straightforward structure and reasoning process with minimal computational complexity, making it suitable for real-time diagnosis of complex faults within power systems.

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“…Table 5 compares the performance of the proposed method with the existing methods in [10,[20][21][22]24] that consider temporal features.…”

Section: Performance Comparisonmentioning

confidence: 99%

Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis

Shao,

Chen,

Xie

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…To compare the calculation complexity of the algorithm in terms of temporal reasoning, assume that m is the number of the received alarms, and k is the number of suspected faulty sections. The method proposed in [23] needs to search all possible fault sections for each received alarm, and then diagnosis each candidate section using all related alarms. The calculation complexity for temporal reasoning is O(m 3 k).…”

Section: Performance Analysismentioning

confidence: 99%

“…In [21]- [23], the temporal information of alarms is introduced into the expert system, artificial neural network, and analytic model for fault diagnosis respectively, but the effect is not significant for lack of effective temporal reasoning methods. The temporal constraint network (TCN) is a kind of directed acyclic graph suitable for temporal reasoning in the artificial intelligence field [24], and has been introduced into power system fault diagnosis recently.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Power Systems Based on Temporal Constrained Fuzzy Petri Nets

Yin

et al. 2019

IEEE Access

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Modern power systems are equipped with comprehensive protective devices to remove the fault section, and the fault diagnosis problem is to interpret the alarms of the protective devices and estimate the fault section. To deal with the uncertainty and temporal constraint of the alarms, a novel fault diagnosis model based on the temporal constrained fuzzy Petri nets (TCFPNs) is proposed in this paper. The truth degree and the timing contribute of the alarms are introduced into the graphic model of the TCFPNs, and the matrix algorithm, considering both the fuzzy reasoning and temporal reasoning, is carried out to obtain the fault probability as well as the time point constraint of each candidate section. The developed approach is performed on different test systems for case studies, and the results demonstrate the feasibility, efficiency, and fault tolerance of the method.

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“…In reference [31], different weight coefficients are assigned to the protective devices in the objective function, and the results show that the coefficients can eliminate the problem of multiple solutions. In References [32,33], temporal reasoning is employed to analyze the timing information of the alarms, and the temporal constraints are further introduced into the objective function, which enriches the theoretical connotation of the analytic model. In reference [34], the discrepancy index, temporal conflict index and minimum index are comprehensively considered in the objective function, which can adapt to various protection schemes and wiring modes.…”

Section: Introductionmentioning

confidence: 99%

An Analytic Method for Power System Fault Diagnosis Employing Topology Description

Yin

et al. 2019

Energies

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When a fault occurs in a power system, fault section estimation is the primary premise for troubleshooting and power recovery, and an effective fault diagnosis system will play a big role in decision making. However, the topology information is not well employed in existing fault diagnosis methods, and it is complex and time consuming to analyze the relationship between the protective devices and the sections. In this paper, a novel analytic method which employs topology description is proposed for fault diagnosis. The topology descriptions of the sections and the protective devices are firstly established according to the network structure, and based on which the operating logic and the cooperative relationship of the protective devices can be easily analyzed by matrix operation. Considering the factors of logic error and communication error, the fault diagnosis problem is formulated as an integer programming problem and can be solved by intelligent algorithm. The case studies of different power systems show that the proposed method can quickly identify the fault section, even with the abnormal operation or error alarm of protective devices.

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A new analytic approach for power system fault diagnosis employing the temporal information of alarm messages

Cited by 14 publications

References 22 publications

Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis

Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis

Fault Diagnosis of Power Systems Based on Temporal Constrained Fuzzy Petri Nets

An Analytic Method for Power System Fault Diagnosis Employing Topology Description

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