This paper proposes a fault diagnosis method for ship electrical power systems on the basis of an improved convolutional neural network (CNN) to support normal ship operation. First, according to the mathematical model of the ship electrical power sy...
With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficiently in ca...
Accurate fault diagnosis in nuclear power systems is essential for ensuring reactor stability, reducing the risk of potential faults, enhancing system reliability, and maintaining operational safety. Traditional diagnostic methods, especially those b...
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 w...
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...
Transformers and circuit breakers are essential equipment in traction power supply systems. Once a fault occurs, it will affect the train’s regular operation and even threaten passengers’ personal safety. Therefore, it is essential to dia...
Artificial intelligence (AI)-based fault diagnosis methods have been widely studied for power grids, with most research focusing on fault interval localization rather than precise fault point identification. In cases involving long-distance transmiss...
Due to the increasing complexity of the entire satellite system and the deteriorating orbital environment, multiple independent single faults may occur simultaneously in the satellite power system. However, two stumbling blocks hinder the effective d...
With the introduction of numerous technologies and equipment, the volume of data in smart substations has undergone exponential growth. In order to enhance the intelligent management level of substations and promote their efficient and sustainable de...
When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necess...
The subway power supply system, as a critical component of urban rail transit infrastructure, plays a pivotal role in ensuring operational efficiency and safety. However, current systems remain heavily dependent on manual interventions for fault diag...
Fault diagnosis in the power systems of Hybrid Electric Vehicles (HEVs) is crucial for ensuring vehicle safety and energy efficiency. This study proposes an innovative CNN-LSTM fusion model for diagnosing common faults in HEV power systems, such as b...
Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss...
The important parts of a transformer, such as the core, windings, and insulation materials, are in the oil-filled tank. It is difficult to detect faults in these materials in a closed area. Dissolved Gas Analysis (DGA)-based fault diagnosis methods p...
In order to complete the function of power grid fault diagnosis accurately, rapidly and comprehensively, the power grid fault diagnosis system based on multi-data sources is proposed. The integrated system uses accident-level information, warning-lev...
Fault prediction and diagnosis are critical for enhancing the maintenance and reliability of power system equipment, reducing operational costs, and preventing potential failures. In power transformers, periodic oil sampling and gas ratio analysis pr...
The development of clean and environmentally friendly energy is necessary to address significant energy challenges, and abundant sea current energy, which plays a key role in the decarbonization of our energy systems and has attracted increasing atte...
This study addresses the need for improved fault diagnosis methods for GIS disconnector mechanisms, specifically targeting jamming faults, which are difficult to detect using conventional approaches. Existing methods often fail to accurately diagnose...
The supply of electric vehicles (EVs), charging infrastructure, and the demand for chargers are rapidly increasing owing to global low-carbon and eco-friendly policies. As the maintenance of charging infrastructure varies depending on the manufacture...
In order to utilize wave energy, various wave power systems are being actively researched and developed and interest in them is increasing. To maximize the operational efficiency, it is very important to monitor and maintain the fault of components o...
Based on the four sets of faults data measured in the practical LCC-HVDC transmission project of China Southern Power Grid Tianshengqiao (Guangxi Province, China)–Guangzhou (Guangdong Province, China) HVDC transmission project, a fault diagnosi...
Power generators are one of the critical assets of power grids. The early detection of faults in power generators is essential to prevent cutoffs of the electrical supply in the power grid. This work presents a comparative analysis of machine learnin...
Synchronous generators with three phases are crucial components of modern integrated power systems in ships. These generators provide power for the entire operation of the vessel. Therefore, it is of paramount importance to diagnose short-circuit fau...
Ship diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness,...
The burgeoning prognostic and health management (PHM) engineering technology with superior performance has lately received extensive attention in the academic circle. Nevertheless, the various types of faults of the power transformer often lead to le...
A transformer is an important part of the power system. Existing transformer fault diagnosis methods are still limited by the accuracy and efficiency of the solution and excessively rely on manpower. In this paper, a novel neural network is designed...
Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context,...
The Electrical Power System (EPS) in an aircraft is designed to interact extensively with other systems. With a growing trend towards more electric aircraft, the complexity of interactions between the EPS and other systems has grown. This has resulte...
This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements...
Fault detection and diagnosis (FDD) in power plant systems is a rapidly evolving field driven by the increasing complexity of industrial infrastructure and the demand for reliability, safety, and predictive maintenance. This review presents a structu...
Emerging technologies such as artificial intelligence (AI), big data analytics, and deep learning have gained widespread attention in recent years and have demonstrated great potential for application in many industrial fields [...]
As the scale and complexity of electrical grids continue to expand, the necessity for robust fault detection techniques becomes increasingly urgent. This paper seeks to address the limitations in traditional fault detection approaches, such as the de...
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...
With the rapid development of more-electric and all-electric aircraft, the role of power supply systems in aircraft is becoming increasingly prominent. However, due to the complex coupling within the power supply system, a fault in one component ofte...
Wind turbines have become a significant part of the global power production and are still increasing in capacity. Pitch systems are an important part of modern wind turbines where they are used to apply aerodynamic braking for power regulation and em...
Three-phase induction motors (IMs) are considered an essential part of electromechanical systems. Despite the fact that IMs operate efficiently under harsh environments, there are many cases where they indicate deterioration. A crucial type of fault...
A systematic experiment verification of Chaos Embedded Sliding Mode Extremum Seeking Control for maximum power point tracking and a method for detecting possible faults in small wind turbine systems in advance are proposed in this paper. The chaotic...
To enhance the reliability of unmanned hydro-mechanical transmission tractors, a fault diagnosis method for their power-shift system was developed. First, fault types were identified, and sample data was collected via a test bench. Next, a feature ex...
As artificial intelligence technology has progressed, numerous businesses have used intelligent diagnostic technology. This study developed a deep LSTM neural network for a nuclear power plant to defect diagnostics. PCTRAN is used to accomplish data...
As the core equipment of wind turbines, rolling bearings affect the normal operation of wind power generators, resulting in huge economic losses and significant social impacts in the case of faults. Most faults are not easily found because of the sma...
Recurrent catastrophic inverter failures significantly undermine the reliability and economic viability of utility-scale photovoltaic (PV) power plants. This paper presents a comprehensive investigation of severe inverter destruction incidents at the...
This paper reviews recent progress in fault detection, reliability analysis, and predictive maintenance methods for grid-connected solar photovoltaic (PV) systems. With the rising adoption of solar power globally, maintaining system reliability and p...
Photovoltaic arrays are exposed to outdoor conditions year-round, leading to degradation, cracks, open circuits, and other faults. Hence, the establishment of an effective fault diagnosis system for photovoltaic arrays is of paramount importance. How...
Strict regulations issued by international administrative bodies limit the CO2 equivalent emissions for new aircraft, while increasing efficiency requirements. To reach this goal, next generations of aircraft will use more electrical power than their...
With advances in machine learning, the fault diagnosis of aircraft systems is becoming more efficient and accurate, which makes condition-based maintenance possible. However, current fault diagnosis algorithms require abundant and balanced data to be...
The specific equipment, installation and machinery infrastructure of an electric power system have always required specially designed data acquisition systems and devices to ensure their safe operation and monitoring. Besides maintenance, periodical...
With the increasing installed capacity of photovoltaic (PV) power generation, it has become a significant challenge to detect abnormalities and faults of PV modules in a timely manner. Considering that all the fault information of the PV module is co...
A key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing...
The electrification of automotive powertrains has accelerated research efforts in the modeling, control, and monitoring of electric drive systems, where reliability, safety, and efficiency are key enablers for mass adoption. Despite a large corpus of...
This study proposes an intelligent method for identifying wiring errors in three-phase three-wire electricity meters using a gradient boosting machine (LightGBM) under complex load conditions, including light load and overcompensation. The work addre...
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