Open Fault Research Articles

A Novel Power Processing Unit (PPU) System Architecture Based on HFAC Bus for Electric Propulsion Spacecraft

This article proposes a novel power processing unit (PPU) system architecture based on the high-frequency alternating current (HFAC) bus for electric propulsion spacecraft. The proposed PPU system architecture takes the HFAC output of the inverter network in the main load power supply as the bus while supplying power for other low-power supplies. Compared with the conventional PPU system architecture, the number of transistors is significantly reduced, and the outputs of low-power supplies are regulated with nonisolated controllers, leading to a lower cost, more compact size, and higher reliability. Moreover, the redundancy design and the fault mode analysis are introduced, which greatly improve the flexibility of the fault operation strategies. Finally, the test on the electrical characteristic of the HFAC bus in the experimental prototype proves the feasibility and stability of the proposed system architecture. The obvious performance enhancement is further demonstrated through a systematic comparison with the conventional PPU system architecture.

Review on online operation insulator identification and fault diagnosis based on UAV patrol images and deep learning algorithms

Artificial intelligence has great potential for use in smart grids. Power system image recognition based on artificial intelligence is an important research direction. The insulator is essential equipment for the power grid and is related to operational safety. Online operating insulator location identification and fault diagnosis technologies based on unmanned aerial vehicle (UAV) patrol the images, and deep learning algorithms have been continuously suggested and developed. These technologies have achieved good results in practical application. By compiling the recent literature on insulator detection technology, three common application scenarios and research difficulties are uncovered: The need for increased detection accuracy and real-time speed; faulty image recognition of complex backgrounds and target occlusion; and multiscale object and small object detection improvements. At the same time, the improved algorithms in the literature are comprehensively summarized, and the performance evaluation indices of various algorithms are compared.

Comparative Analysis of a Family of Sliding Mode Observers under Real-Time Conditions for the Monitoring in the Bioethanol Production

Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be difficult and expensive due to the lack of adequate sensors, or simply because some variables cannot be directly measured. From the above, a model-based approach such as state observers may be a viable alternative to solve the estimation problem. This work shows a comparative analysis of the real-time performance of a family of sliding-mode observers for reconstructing key variables in a batch bioreactor for fermentative ethanol production. These observers were selected for their robust performance under model uncertainties and finite-time estimation convergence. The selected sliding-mode observers were the first-order sliding mode observer, the proportional sliding mode observer, and the high-order sliding mode observer. For estimation purposes, a power law kinetic model for ethanol production by Saccharomyces cerevisiae was performed. A hybrid methodology allows the kinetic parameters to be adjusted, and an approach based on inference diagrams allows the observability of the model to be determined. The experimental results reported here show that the observers under analysis were robust to modeling errors and measurement noise. Moreover, the proportional sliding-mode observer was the algorithm that exhibited the best performance.

Transformer Fault Diagnosis Based on an Improved Sine Cosine Algorithm and BP Neural Network

Background: The operation state evaluation and fault location of the transformer is one of the technical bottlenecks restricting the safe power grid operation. Methods: A hybrid intelligent method based on the Improved Sine Cosine Algorithm and BP neural network (ISCA-BP) is developed to improve the accuracy of transformer fault diagnosis. First, the cloud model is introduced into the Sine Cosine Algorithm (SCA) to determine the conversion parameter of each individual to balance the global search and local exploitation capabilities. After that, six popular benchmark functions are used to evaluate the effectiveness of the proposed algorithm. Finally, based on the dissolved gas analysis technology, the improved SCA algorithm is employed to find the optimal weight and threshold parameters of the BP neural network, and the transformer fault classification model is established. Results: Simulation results indicate that the improved SCA algorithm exhibits strong competitiveness. Furthermore, compared with the BP neural network optimized by the Sine Cosine Algorithm (SCA-BP) and BP neural network, the ISCA-BP method can significantly improve the diagnostic accuracy of transformer faults. Conclusion: The proposed intelligent method can provide a valuable reference idea for transformer fault classification.

A mobile measurement solution for fault detection and diagnosis in buildings

Buildings are equipped with increasingly complex Heating, Ventilation and Air Conditioning (HVAC) systems whose energy efficiency can degrade over time because of unnoticed faults. Facility managers (FM) often do not have the time and adapted tools to identify and correct issues. Building Automation Systems generate a multitude of measurement data that can be used to identify operational malfunctions at an early stage by applying fault detection and diagnosis (FDD) methods. However, this data itself can be hardly accessible and contain errors for example due to the degradation of the sensor quality over time. These issues limit the application of analytic methods like FDD in existing buildings. In this paper, we present a prototype implementation of a mobile measurement system based on Internet of Things (IoT) technologies that functions together with an automated FDD system to detect operational faults in HVAC systems independently of the existing measurement infrastructure. Additionally, the solution provides a user interface giving a quick overview on found faults and displaying them in spatial relation with the HVAC system through an integrated BIM visualization. Here, we describe the technical details and the required conditions for the use of our prototype and show the results of an exemplary implementation on an air handling unit.

Electromagnetic Field Analysis and Optimization Method of High-Temperature Superconducting Transformer under the Influence of Abnormal Voltage

High-temperature superconducting transformers are an important research topic of superconducting technology in power applications, and electromagnetic field analysis and optimization are the basis for the design and application of high-temperature superconducting transformers. The electromagnetic field analysis of high-temperature superconducting transformers should consider the superconducting properties of the materials, that is, the properties of critical current and magnetic field. This paper aims to study the electromagnetic field analysis and optimization method of high-temperature superconducting transformer under the influence of abnormal voltage. Due to the energy loss of high-temperature superconducting transformers, in order to study the economy and reliability of high-temperature superconducting transformers, in this paper, the core loss, winding AC loss, and coil power consumption of high-temperature superconducting transformers are analyzed under normal operation and short-circuit fault conditions, respectively. The power and stress on the windings are analyzed. In order to take into account the current-carrying capacity, short-circuit loss, and short-circuit electromotive force of superconducting windings in normal operation, a concentrically placed double-cake coil structure is selected in this paper, and according to different optimization objectives, a global optimization method is used to evaluate the structure of the coil. The structural parameters of the high-temperature superconducting transformer are optimized, including the structural parameters of the magnetic conducting ring. It is found that that abnormal voltage will affect the electromagnetic field of high-temperature superconducting transformers, including winding circulating current, leakage magnetic field, and current distribution.

Wave front rising time based traveling wave protection for multi-terminal VSC-HVDC grids

To deal with the problem of high-resistance fault identification in multi-terminal VSC-HVDC grids, a novel wave front rising time based traveling wave protection is proposed in this paper. For such purpose, the traveling wave is firstly analyzed from its generation, propagation to refraction for the sake of discovering its influence factors. The wave front rising time is revealed to be approximately proportional to the fault distance while insensitive to the fault resistance and operation condition during line propagation. Moreover, it significantly increases when refraction occurs at the line terminal. Secondly, based on these characteristics, the novel wave front rising time based traveling wave protection as well as its analytical setting calculation is proposed. The proposed protection can work with higher-fault-resistance identification capability and faster-operating speed, compared with other protections. Besides, the efficiency of the proposed protection setting calculation is greatly improved since tedious electromagnetic transient simulations are avoided. Finally, the feasibility and robustness of the proposed protection are validated by a four-terminal VSC-HVDC grid on PSCAD/EMTDC platform.

Pantograph Catenary Performance Detection of Energy High-Speed Train Based on Machine Vision

With the rapid development of high-speed rail in China, addressing the issue of safety assurance during the operation of the train is very important. A very important part of a train’s power supply system is the pantograph and catenary system, which consists of a pantograph and a catenary. Failure of the pantograph-catenary system can cause significant damage to the normal operation of the train. The dynamic performance of the pantograph-catheter system must be detected in real time during the operation of the train. This paper is based on the study and analysis of pantograph-catheter dynamic performance parameters and developed a system for real-time detection of pantograph-catheter dynamic performance parameters based on a car visual system. The results are as follows: based on this detection method, the visual error is low and the accuracy is high. The machine-based directional height detection module developed in this paper has a good detection effect and high test accuracy; the arcing detection module designed in this paper can effectively detect the arcing and store the arcing pictures and can display the duration of single arcing and the arcing rate of the section in real-time. The practical application effect is good. The results show that the focal length of the camera lens is 16 mm, and the error of the machine vision system is low. The system designed in this paper may make a great contribution to the operation condition monitoring and fault diagnosis of the pantograph-catenary system of a high-speed train in the future.

Single-ended multi-method phasor-based approach for optimized fault location on transmission lines

This paper presents the development of a transmission line multi-method single-ended phasor-based fault location (SEPHFL) approach in a real digital fault recorder (DFR). Unlike classical solutions, it processes the outputs taken from different SEPHFL methods, allowing the calculation of accurate punctual fault distances, such as traditional approaches do, and reliable fault search fields, which is innovative from the point of view of SEPHFL applications. To properly select the algorithms to be used in the proposed solution, a Real-Time Digital Simulator (RTDS) is used to carry out massive hardware-in-the-loop fault simulations on 500 kV and 230 kV test power systems. Firstly, the DFR hardware capabilities are analyzed to identify SEPHFL methods with compatible requirements, and then, eight SEPHFL methods are chosen to be embedded in the DFR. Parametric studies in a fully controllable 500 kV test power network are carried out, varying the system operation conditions and fault features. Based on the obtained results, three techniques are selected to compose the proposed multi-method SEPHFL solution, which is finally implemented in the DFR and validated through tests in a realistic IEEE benchmark 230 kV/60 Hz system. The obtained results show the proposed approach is able to calculate accurate punctual fault distance estimations, with the advantage of providing a reliable search field, which can be used to optimize line inspection procedures, mainly when there is uncertainty on the punctual fault location estimations provided by traditional technologies.

Diagnostics and Control of Pumping Stations in Water Supply Systems: Hybrid Model for Fault Operating Modes

This paper indicates the importance and advantages of the application of hybrid models in the control of water supply systems. A range of possibilities provided by this scientific approach is presented in the practical examples related to the fault diagnostics and fault-tolerant control in the pumping station (PS) control systems. It is presented that continuous monitoring and recording of the data of the pumping stations operation processes (electrical parameters such as electrical power, pressure or flow in the pipelines, water levels in the tanks, changes in various discrete states, etc.) could represent a significant resource that can be used to develop various hybrid models using the appropriate “data-driven” techniques. During this process, data are transformed into information, and thereafter, information into knowledge. Based on this knowledge, the control of PS operation can be significantly improved and a significant increase in the user’s satisfaction can be achieved while the maintenance and operation costs can be reduced.

Experimental dataset for an AHU air-to-air heat exchanger with normal and simulated fault operations

Fault Detection and Diagnosis (FDD) is an important tool in building commissioning. Providing a consolidated dataset for FDD benchmarking is necessary to accurately evaluate the FDD prediction accuracy and detect anomalies. In this study, we provide an experimental dataset for an air handling unit containing two ducts linked by an air-to-air heat exchanger. The dataset is composed of nominal and faulty operations of the system, including the ground truth in order to investigate various faults in 52 cases. The dataset was obtained by measuring a representative system with real climate variations like the ones obtained by Building Automation Systems. The transition between nominal and fault sequences was continuous, as in real operating conditions. An uncertainty evaluation was carried out to provide confidence bounds in the experimental dataset.

Fault Detection and Diagnosis for Liquid Rocket Engines Based on Long Short-Term Memory and Generative Adversarial Networks

The development of health monitoring technology for liquid rocket engines (LREs) can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. Therefore, we propose a fault detection and diagnosis (FDD) method for a large LOX/kerosene rocket engine based on long short-term memory (LSTM) and generative adversarial networks (GANs). Specifically, we first modeled a large LOX/kerosene rocket engine using MATLAB/Simulink and simulated the engine’s normal and fault operation states involving various startup and steady-state stages utilizing fault injection. Second, we created an LSTM-GAN model trained with normal operating data using LSTM as the generator and a multilayer perceptron (MLP) as the discriminator. Third, the test data were input into the discriminator to obtain the discrimination results and realize fault detection. Finally, the test data were input into the generator to obtain the predicted samples and calculate the absolute error between the predicted and the real value of each parameter. Then the fault diagnosis index, standardized absolute error (SAE), was constructed. SAE was analyzed to realize fault diagnosis. The simulated results highlight that the proposed method effectively detects faults in the startup and steady-state processes, and diagnoses the faults in the steady-state process without missing an alarm or being affected by false alarms. Compared with the conventional redline cut-off system (RCS), adaptive threshold algorithm (ATA), and support vector machine (SVM), the fault detection process of LSTM-GAN is more concise and more timely.

Operational Status Monitoring and Fault Diagnosis System of Transformer Equipment

In order for valuable distribution transformer data to provide a potential solution for monitoring abnormal conditions, the authors propose a data-driven abnormal state monitoring data acquisition algorithm for distribution network transformers. The algorithm can alert operators and maintenance personnel of abnormal conditions in a timely manner. In the proposed algorithm, the Spearman rank correlation coefficient is used to display the correlation between phase currents, and its t statistic is used to determine whether there is an abnormality in the determined data collection based on hypothesis testing. Finally, the effectiveness of the proposed algorithm is verified by using the actual data collected from the power grid, and the characteristics of normal and abnormal conditions are analyzed separately. Sensitivity analyses were performed for different significance levels and sampling rates to consider their impact on monitoring results. The application results show that the power grid recovered a total of 12.98 million yuan from 136 households with a power consumption of 17.57 GWh, proving the practicability of the algorithm. Conclusion. The application in the actual power system is given, and the feasibility of the algorithm is proved.

Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change

The effect of climate change on heating, ventilation and airconditioning (HVAC) system performances has become prominent, and HVAC fault impacts may also vary with climate change. This paper evaluates the impacts of HVAC operational faults on system energy and occupant thermal comfort under the current, 2030s and 2050s climates using a validated model. The energy and thermal comfort impact indicators were proposed to rank single and multiple faults under each climate period. Supply fan stuck at maximum speed, and the combinations associated with this fault were ranked first in energy and thermal comfort rankings, respectively. Based on the investigations of multiple faults interactions, it is found that the synergetic/antagonistic effect of multiple faults combinations can lead to a significantly higher/lower combined impact than any single fault impact among the combination when the single faults present opposite impacts. Moreover, heating coil supply air temperature sensor negative bias, and the combination of thermostat positive offset and outdoor air damper stuck fully open led to the most increase system total electricity by 34.3 GJ and 35.3 GJ from current to 2050s period. The results are useful for researchers to prioritise the faults with significant impacts for developing fault detection and diagnosis framework.

The use of dimensionality reduction techniques for fault detection and diagnosis in a AHU unit: critical assessment of its reliability

Fault detection and diagnosis (FDD) are important tools to perform on-going monitoring of the systems and help in their building commissioning. An innovative method is investigated based on combined data-driven and knowledge-based approaches. This article presents the method. In the first phase, a so-called operating map of the system is built using a dimension reduction method and numerical or experimental dataset. This map is composed of several regions corresponding to nominal operation and to specific faults. The second phase focuses on the FDD. The monitored data are projected on the map. According to the position, a clear and precise FDD can be carried. The method is applied to an air handling unit. The map is built using data generated with a building simulation programme. The reliability of the method is proven using experimental data of nominal and fault operation generated.

Simplified Universal Fault-Tolerant Direct Torque Control of FPFTPM Motor With Steady-Healthy Design Under Open-Circuit Fault

A simplified universal fault-tolerant direct torque control (FTDTC) strategy of five-phase fault-tolerant permanent magnet (FPFTPM) motors under various open-circuit faults is presented in this article. In the proposed FTDTC strategy, the fault-tolerant currents are derived from the fault-tolerant mechanism analysis and steady-healthy design. The problems of existing FTDTC strategies that need to change coordinate transformation and add extra voltage compensation can be overcome. Thus, the minimal control drive system reconfiguration under different faults can be realized in the real sense. Especially, the steady-healthy controller is designed to suppress the torque ripples and achieve the high-quality torque given even under fault operation conditions; then, it is not necessary to set a special voltage vector or add a PI controller. Furthermore, the FTDTC strategy is improved to be realized under the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> coordinate system based on the flux adaption. Hence, the reduced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis current under different operating conditions, including fault conditions, can be ensured. This FTDTC strategy without any control reconfiguration is proposed under the premise of ensuring good performance during both healthy and fault operations. Finally, the simulation and experimental results for an FPFTPM prototype are offered to verify the feasibility of the proposed strategy.

Early detection of photovoltaic system inverter faults

Photovoltaic (PV) systems are increasingly adopted as a source of green energy. The reliability of PV systems mainly depends on the reliability of the power MOSFETs of their inverter(s). It has been shown that short and open faults are the most frequent power MOSFET faults. They may compromise the inverter reliability, with consequent significant impact on the PV system energy efficiency. It has been proven that the likelihood of such faults is related to the value of the MOSFET ON-state resistance, which may increase over time due to aging mechanisms. When the value of such a resistance reaches a critical value, the likelihood of the MOSFETs subsequently failing as open or short becomes very high. In this paper, we first evaluate the effects of the MOSFETs' ON-state resistance increase on the harmonic components of the inverter input and output currents. Then, based on the obtained results, we propose an innovative strategy for the early detection (also referred to as condition monitoring [1]) of inverter faults, which enables to generate an alarm message when the ON-state resistance of any inverter MOSFET reaches the critical value. Upon the generation of such an alarm message, proper recovery strategies can be activated, to enable the online replacement of the affected transistors before they actually become faulty. Our detection strategy does not require to interrupt the inverter normal operation in the field and can be implemented using the microcontroller typically embedded within the control circuitry of PV systems.

A d-axis based current differential protection scheme for an active distribution network

The emergence of distributed generators has changed the operational mode and fault characteristics of the distribution network, in a way which can severely influence protection. This paper proposes a d-axis-based current differential protection scheme. The d-axis current characteristics of inverter-interfaced distributed generators and synchronous generators are analyzed. The differential protection criterion using sampling values of the d-axis current component is then constructed. Compared to conventional phase-based current differential protection, the proposed protection reduces the number of required communication channels, and is suitable for distribution networks with inverter-interfaced distributed generators with complex fault characteristics. Finally, a 10 kV active distribution network model is built in the PSCAD platform and protection prototypes are developed in RTDS. Superior sensitivity and fast speed are verified by simulation and RTDS-based tests.

A robust VRF fault diagnosis method based on ensemble BiLSTM with attention mechanism: Considering uncertainties and generalization

Developing effective fault detection and diagnosis (FDD) model is of great significance to the energy efficiency and comfort of variable refrigerant flow (VRF) system. In recent FDD studies, the consideration of incomplete information and uncertainty, as well as the generalization of model and the interpretability of fault action mechanism have become a great concern. This paper accordingly proposes a fault diagnosis strategy based on attention-BiLSTM with ensemble feature sets. Three methods including Correlation Analysis, Information Entropy, and Gini Impurity are used for feature selection and ensemble feature sets combination. The proposed model can achieve interpretability of fault diagnosis and quantification of diagnosis uncertainties through attention mechanism and ensemble feature sets. A total of four typical faults under different working conditions are used to verify the accuracy and generalization of the proposed method. The results show that the ensemble attention-BiLSTM model has a fault diagnosis accuracy rate of 98.3% if the first two most likely outputs are considered, and when some sensors fail, the accuracy rate can still remain above 90%. In addition, a generalization verification idea is proposed, that is, the model is trained under specific operating conditions and tested on datasets with different operating conditions or fault levels. The results show that the average generalization of the optimized model reaches 86%.

Research on Fault Diagnosis of Switchgear Based on Temperature Cloud Image Technology

In the process of power system operation, contact aging, partial discharge and overcurrent faults are the main causes of switch cabinet faults. At the same time, it is difficult to install sensors inside the switch cabinet due to its closed operation. In this paper, in order to solve the over current problem of switchgear, according to the principle of high current heating, the thermal flow model of power switchgear is established, and the characteristics of heat flow inside the switchgear are studied. The infrared temperature detector is designed to collect the surface temperature cloud map of the switchgear. Combined with the internal heat flow law of the switchgear, the over current phenomenon in the switchgear is judged, so as to judge the operation state of the switchgear. In order to verify the correctness of the theory proposed in this paper, the operation state of the fault switch cabinet is designed for detection and comparative analysis. The test results show that the heat change caused by the over-current fault can be accurately fed back to the surface of the switch cabinet, which can be used as an effective means to diagnose the fault of the switch cabinet.