Electrical Faults Research Articles

Balancing Wildfire Risk and Power Outages Through Optimized Power Shut-Offs

Electric grid faults can ignite catastrophic wildfires, particularly in regions with high winds and low humidity. In real-time operations, electric utilities have few options for wildfire risk mitigation, leading to use of disruptive measures such as proactive de-energization of equipment, frequently referred to as public safety power shut-offs. Such power shut-offs have significant impacts on customers, who experience power cuts in an attempt to protect them from fires. This work proposes the optimal power shut-off problem, an optimization model to support short-term operational decision making in the context of extreme wildfire risk. Specifically, the model optimizes grid operations to maximize the amount of power that can be delivered, while proactively minimizing the risk of wildfire ignitions by selectively de-energizing components in the grid. This is the first optimization model to consider how preventive wildfire risk measures impact both wildfire risk and power systems reliability at a short-term, operational time-frame. The effectiveness of the method is demonstrated on an augmented version of the RTS-GMLC test case, located in Southern California, and compared against two approaches based on simple risk thresholds. The proposed optimization-based model reduces both wildfire risk and lost load shed relative to the benchmarks.

Characterization of 400 Volt High Impedance Fault With Current and Magnetic Field Measurements

Electrical faults, which can occur at all voltage levels in an electricity supply system, are a health and safety risk. Multi-branch distribution networks represent a significant ongoing challenge for fault detection, with the greatest challenge being high impedance fault (HIF) detection. To date, research has focused on higher voltage levels, and fault monitoring sensors have traditionally only been installed in limited locations within the higher voltage networks. The main contributions of this paper are to characterize a high impedance fault (HIF) involving a tree branch and to experimentally verify the feasibility of giant magneto-resistive (GMR) sensors, located distant from the overhead lines, for fault detection. In a purpose-built 400 V physical simulation test facility, we have collected current and magnetic field data during HIF involving a tree branch. We have identified new characteristics in the early stages of this fault type, which persist for a reasonable length of time but are only observable when suitable signal processing techniques are applied. New detection schemes will, therefore, need to be developed to detect such faults. GMR sensors were found to be suitable for observing the characteristics of HIF, validating their potential use for fault detection.

An assessment of fire safety measures in healthcare facilities in Nigeria

PurposeThe occurrence of fire accidents in hospital buildings has become a serious challenge and more serious in developing nations. The purpose of this paper intends to assess fire safety measures in Nigerian hospital facilities. The significance of this study is to ensure that the design and construction of hospital facilities enhance the safety of users and properties.Design/methodology/approachThe data were collected via a case study and questionnaire survey and administered to the facility users. The study survey is to assess the respondents' perception of fire safety measures in hospital facilities and suggest possible policy measures that will be employed to enhance safety.FindingsThis paper found that 91% of the respondents have awareness of fire safety measures in hospital facilities. Electrical faults and combustible materials were identified as the frequent causes of fire occurrences in hospital facilities. This can be averted where flammable materials and electrical appliances are correctly installed, and safety rules enforced. Findings show that safety rules are lax in public than standard private hospitals.Research limitations/implicationsThis paper is limited to fire safety measures in Nigerian healthcare facilities. Future research is needed to evaluate the level of compliance from design, construction and post-construction of precautionary fire safety measures in hospital facilities in Nigeria.Practical implicationsThis paper recommended that designers and hospital administrators should improve on fire safety measures via the development of fire safety management plan and education. Thus, enforcement of fire safety measures in hospital facilities as specified in building codes should be implemented and monitored during and after the design of the hospital buildings. Findings provide valuable lessons on how to improve the fire safety measures in healthcare facilities across the states and other developing countries with similar healthcare situations.Originality/valueThis paper demonstrates that the stakeholders, especially government agencies concern with approval and enforcement of fire safety measures in healthcare facilities need to reawaken to her responsibility because of the lax implementation across the states.

A review on status monitoring techniques of transformer and a case study on loss of life calculation of distribution transformers

Transformer failure is one of the main causes of distribution faults. Factors like an inter-winding failure, over current, oil leakage, ambient temperature, electrical faults, etc. are few of the main reasons for transformer failures. In this paper, various monitoring techniques are reviewed along with their advantages and disadvantages so that a proper monitoring technique for various transformer applications can be chosen. A case study is performed on a 500 KVA substation transformer using IEC 60076-7 specified thermal model of transformer. The parameters like top oil temperature and hot-spot temperature are calculated using differential method and exponential method to estimate the loss of life of the transformer and both the methods are compared with one another to decide on the best suited method for calculation of loss of life of the transformer.

Influence of ageing on oil degradation and gassing tendency under high‐energy electrical discharge faults for mineral oil and synthetic ester

In this work, mineral oil and synthetic esters were selected at different ageing factors (based on acidity values). Fresh and aged oils have been subjected to high-energy discharges (repeated 100 breakdowns) to simulate electric faults of highly vulnerable intensity. The intent of this work is to understand the influence of high-energy electric faults on oil degradation and gassing tendency at different ageing conditions. In this study, the influence of the high-energy discharges on degradation and gassing tendency at different ageing factors is reported for mineral oil and synthetic esters. Oil degradation is reported by adopting ultraviolet spectroscopy, turbidity and particle counter as per american society for testing and materials (ASTM) standard test methods. Gassing tendencies and fault gas analysis are understood by dissolved the gas analysis using Duval's triangle and Duval's pentagon methods for mineral and non-mineral oils. It is found that the influence of high-energy discharges on oil degradation is higher in mineral oils to that of the synthetic esters. The intensity of the gassing tendency is higher for ester fluids; however, as per the Duval methods, the faulty conditions are at lower levels as compared to mineral oils.

Networked Microgrid Operations: Supporting a Resilient Electric Power Infrastructure

The fundamental requirements for electric power-system operations are the same regardless of the system size. While specific operational implementations will change based on the measurements-regardless of whether the power system spans a continent or a single building, it is necessary to regulate the frequency and voltage, adjust generating unit outputs, and to protect equipment from electrical faults. In this way, the operational requirements of electric power systems exhibit fractal behaviors. Because of this operational characteristic, it is technically feasible to separate or segment any portion of an electric power system and operate it independently, if sufficient generating assets and control systems are present. However, the generating assets and controls necessary to implement this type of operation have not traditionally been deployed; centralized generation and control is currently the predominant operational paradigm. For example, the frequency is typically regulated at a regional level by balancing the generation at large central units with the consumption of power by end-use customers and system losses. Small changes in the frequency are used to coordinate the sharing of the load between generators on a regional level.

Detection of negative sequence components in diagnosing and tolerating open‐gate fault for a voltage‐source inverter in an induction motor drive

Power converter faults represent about 12% of electrical system faults. More than 31% of these converter faults are caused by the transistors in the converters. These transistor failures are classified into open-gate and short-gate faults. A fault diagnostic and tolerant control system is proposed in this study for an open-gate fault of a three-phase voltage-source inverter (VSI). The system under study is an induction motor fed by a VSI. The diagnostic technique uses a detection method of positive and negative sequence components, which requires only the measurement of motor currents. The diagnostic signals are derived from the negative sequence components of the motor currents. These signals are then compared using logic functions to trigger the tolerant control. In the case of an open-gate fault, the tolerant system converts the inverter topology from six switches to four switches where the faulty inverter leg is disconnected. The proposed control method was evaluated under steady-state and transient conditions through simulation and experiments. The results confirm an effective and reliable performance of the motor under an open-gate fault.

Analysis of Power Failure with the Phenomenon of an Electric arc in the Electric Power Facility

Abstract This paper describes cause and effect of an electrical arc fault in industrial TS 6/0.4 kV. Results of insulation resistance test, dielectric withstand test, thermographic inspection, testing of LV installation, as well as recorded data analysis are presented in detail and an events explanation is given. The aim of this paper is to show the purpose of complex testing, both during the events of the accident and during the preventive maintenance. It has also been recommended to analyse calculations of short-circuit currents and the effect of an electric arc on the staff in power facilities, the so-called “arc flash”.

On-line fault diagnosis and fault-tolerant operation of modular multilevel converters — A comprehensive review

Modular multilevel converters (MMCs) have been one of the most broadly used multilevel converter topologies in industrial applications, particularly in medium-voltage motor drives and high-voltage dc power conversion systems. However, due to the utilization of large amount of semiconductor devices, the reliability of MMCs becomes one of the severe challenges constraining their further development and applications. In this paper, common electrical faults of the MMC have been summarized and analyzed, including open-circuit switching faults, short-circuit switching faults, dc-bus short-circuit faults, and single line-to-ground faults on the ac side. A thorough and comprehensive review of the existing online fault diagnostic methods has been conducted. In addition, fault-tolerant operation strategies for such various fault scenarios in MMCs have been presented. All the fault diagnosis and fault-tolerant operation strategies are comparatively evaluated, which aims to provide a state-of-the-art reference on the MMC reliability for future research and industrial applications.

Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM).

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%.

Improving the Accuracy of Wound-Rotor Resolvers Under Inter-Turn Short Circuit Faults

The performance of the resolver as a position sensor is significantly influenced by electrical and mechanical faults. Among different faults, short circuit in the stator windings has more destructive effect than others on accuracy of resolver. Furthermore, due to thin wires of the stator it is highly likely to occur. Therefore, in this paper a robust design is proposed for wound-rotor resolvers to not only suppress the influence of short circuit fault in the stator/rotor winding, but also improve the accuracy of the sensor under mechanical faults. The effectiveness of the proposed robust design is demonstrated by an analytical model based on winding function method and then approved by time stepping finite element analysis (TSFEA) and experimental measurements on a prototype sensor.

1D convolutional neural networks and applications: A survey

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and electrical motor fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publicly shared in a dedicated website. While there has not been a paper on the review of 1D CNNs and its applications in the literature, this paper fulfills this gap.

Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis

The development of electrical control system faults can lead to increased mechanical component degradation, severe reduction of asset performance, and a direct increase in annual maintenance costs. This paper presents a highly accurate data driven classification system for the diagnosis of electrical control system faults, in particular, wind turbine pitch faults. Early diagnosis of these faults can enable operators to move from traditional corrective or time based maintenance policy towards a predictive maintenance strategy, whilst simultaneously mitigating risks and requiring no further capital expenditure. Our approach provides transparent, human-readable rules for maintenance operators which have been validated by an independent domain expert. Data from 8 wind turbines was collected every 10 minutes over a period of 28 months with 10 attributes utilised to diagnose pitch faults. Three fault classes are identified: “no pitch fault”, “potential pitch fault” and “pitch fault established”. Of the turbines, 4 are used to train the system with a further 4 for validation. Repeated random sub-sampling of the majority fault class was used to reduce computational overheads whilst retaining information content and balancing the training and validation sets. A classification accuracy of 85.50% was achieved with 14 human readable rules generated via the RIPPER inductive rule learner. Of these rules, 11 were described as “useful and intuitive” by an independent domain-expert. An expert system was developed utilising the model along with domain knowledge, resulting in a pitch fault diagnostic accuracy of 87.05% along with a 42.12% reduction in pitch fault alarms.

Modeling and fault diagnosis of multi-phase winding inter-turn short circuit for five-phase PMSM based on improved trust region

Multi-phase Permanent Magnet Synchronous Motors (PMSMs) are widely used in fault-tolerant control applications. As an electrical fault with a high probability, inter-turn short circuit (ITSC) fault seriously affects the reliability of five-phase PMSM. However, due to high coupling of fault parameters, most of the fault models and diagnosis methods in fault-tolerant control focus on one-phase winding ITSC fault of three-phase PMSM. In this paper, a novel model and a fault diagnosis method are proposed for multi-phase winding ITSC (MP-ITSC) of five-phase PMSM. First, by analysing the related physical parameters of the motor, a mathematic model of MP-ITSC fault is established. Then, a parameter estimation method based on improved trust region (ITR), which accelerates the convergence speed of trust region algorithm and reformulates the fault diagnosis as the extreme seeking for fault parameters is proposed to detect the ITSC fault level. Experimental results provide verification of the proposed model and fault diagnosis method.

Operational-Condition-Independent Criteria Dedicated to Monitoring Wind Turbine Generators

Condition monitoring is beneficial to the wind industry for both land-based and offshore plants. However, because of the variations in operational conditions, its potential has not been fully explored. There is a need to develop an operational-condition-independent condition monitoring technique, which has motivated the research presented here. In this paper, three operational-condition-independent criteria are developed. The criteria accomplish the condition monitoring by analyzing the wind turbine electrical signals in the time domain. Therefore, they are simple to calculate and ideal for online use. All proposed criteria were tested through both simulated and practical experiments, showing that these criteria not only provide a solution for detecting both mechanical and electrical faults that occur in wind turbine generators, but that they provide a potential tool for diagnosing generator winding faults.

Electrical fault detection in three-phase induction motor using deep network-based features of thermograms

In this paper, an automatic method is proposed for detecting the operating faults in three-phase induction motors based on thermal images. If these faults are not detected or fixed on time, they can lead to permanent motor failure. This is why non-invasive and non-destructive experiments are significantly considered. In this paper, first, the region of interest is detected in the thermograms using SIFT-based key-points matching. Then, these images are transformed into representative feature vectors based on a pre-trained convolutional neural network. Then, the training vector samples are clustered into cold and hot clusters by K-means. For each cluster, an SVM-based classifier is trained. The test feature vector samples are clustered and mapped into classes using the corresponding trained SVM-based classifiers. Evaluating the proposed method on the datasets including real thermal images, shows that this algorithm can detect 100% of the faults of the induction motor.

Condition Monitoring and Fault Diagnosis of Induction Motor

An induction motor is at the heart of every rotating machine and hence it is a very vital component. Almost in every industry, around 90% of the machines apply an induction motor as a prime mover. It is a very important driving unit of the machine. Hence, it is necessary to monitor its condition to avoid any catastrophic failure and stoppage of production. The breakdown of the induction motor would not be affordable due to remarkable financial loss, unpredicted shutdown, and the associated repair cost. Vibration is a manifestation of induction motor due to the issues in alignment, balancing, and clearances. Bearing, the most vulnerable to failure due to continuous working under fatigue loading leads to defects. These defects cause changes in the vibration signature over time. The vibration monitoring techniques helps to effectively diagnose mechanical faults such as bearing defect and stator rotor rub. The purpose of this review paper is to summarize the major faults in induction motor, recent diagnostics methods augmented with advanced signal processing techniques, and real-life applications in electric vehicles. It also discusses possible research gaps and opportunities to contribute based on the review findings in the field of condition monitoring. This article presents a detailed review of recent trends in the research of condition monitoring and fault diagnosis of the induction motor. The emphasis is given on the major faults in the induction motor covering time-domain, frequency-domain, and time–frequency domain methods along with an application of artificial intelligence techniques for fault detection. This article presents a comprehensive review of literature which highlights the development and new propositions by researchers in the field of diagnostic techniques for the different faults of induction motor in the last decade. Researchers documented applications of the different conventional methods, advanced signal processing techniques, and soft computing techniques for fault identification of induction motor. This review is carried out for fault identification of induction motor used in machines in general and in particular for identifying the faults in an induction motor of an electric vehicle. A dedicated discussion on the review findings, research gaps, future trends in the field of condition monitoring of induction motor is presented. Condition monitoring of the induction motor in an electric vehicle is also discussed in this paper. It is observed that the vibration-based techniques are reported to be effective for the identification of mechanical faults while motor current signature analysis is effective for electrical fault in an induction motor. The review presented to analyze the suitability of various condition monitoring techniques for the induction motor fault identification in general and particularly its application in an electric vehicle. It is observed that the diagnosis of faults at the incipient level without using the signal processing technique is challenging. Fault diagnosis of induction motor has witnessed the changes from traditional diagnosis techniques to advanced techniques with a hybrid application of signal processing and artificial intelligence techniques. Still, there is a potential of improvement in reliability, efficiency, robustness, computational time, and real-time diagnostics of faults in IM.

Rotor Electrical Fault Detection in DFIGs Using Wide-Band Controller Signals

This paper presents a novel study of the wide-band spectral signatures in the controller signals of doubly fed induction generators (DFIGs) for the identification of rotor electrical faults. The aim is to advance the understanding of diagnostic information obtainable from the readily available DFIG controller signals. Analytical equations defining the controller signals possible spectral contents are derived to enable characterization of spectral signatures and their correlation to operating conditions and rotor faults. The equations are verified in a DFIG harmonic model study and also validated by undertaking a range of experiments on a laboratory DFIG test-rig. It is shown that the calculated, simulated and experimental results are in good agreement with regards to representing fault induced signatures in the examined DFIG controller signals spectra. Furthermore, it is shown that wide-band rotor electrical fault related spectral signatures in the controller signals carry considerable diagnostic potential for recognition of rotor electrical faults.

Method for fault location in a low-resistance grounded distribution network based on multi-source information fusion

Several uncertain factors, such as the protection and circuit breaker refusing operation and misoperation, make it difficult to obtain accurate results based on a single fault location using the switching quantity. So this study proposes a power grid fault location method via multi-data source information fusion. The compressed sensing algorithm is used to reconstruct the electrical signal twice, and the rough fault range and electrical quantity fault degree are obtained respectively. Subsequently, the Bayesian network is used to obtain the switching fault degree of each element in the rough fault range. Finally, DS evidence theory fuses these two fault degrees to obtain the location result. PSCAD software builds an IEEE 14 node system, and the effectiveness of the proposed method is verified by simulation. Furthermore, it can quickly and accurately locate the fault after its occurrence and has good application prospects with respect to the fault location of a low-resistance grounded distribution network.

An Improved maintenance method for marine electrical equipment based on the Internet of Things

Due to the influence of high temperature and high humidity, the failure rate of marine electrical equipment is higher than that in other situations. The stability of the electrical equipment of ships is of vital importance to the safety of navigation. Traditional electrical maintenance methods have the problem of lagging in response to equipment fault detection and feedback, which is caused by the signal transmission mode. This article applies the Internet of Things technology to the maintenance of marine electrical equipment, which can improve the performance of the maintenance method. Firstly, the fault detect program architecture is adjusted and optimized. The fault sensing modules are placed in the electrical equipment. The traditional electrical signal transmission mode is replaced by the network transmission mode. Then, the fault signal anti-jamming algorithm is used to optimize the compensation calculation of the alarm signal of the electrical equipment fault point to achieve the effect of enhancing the signal sensitivity; finally, the fault detection sensitivity of the proposed method is tested through experiments, and the data shows that the design method is better than the traditional maintenance method.