Faulty Power Research Articles

Intelligent distribution network fault monitoring integrating differential evolution and chaos whale optimization algorithm

Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems.

An Open-Circuit Fault Diagnosis System Based on Neural Networks in the Inverter of Three-Phase Permanent Magnet Synchronous Motor (PMSM)

Three-phase motors find extensive applications in various industries. Open-circuit faults are a common occurrence in inverters, and the open-circuit fault diagnosis system plays a crucial role in identifying and addressing these faults to enhance the safety of motor operations. Nevertheless, the current open-circuit fault diagnosis system faces challenges in precisely detecting specific faulty switches. The proposed work presents a neural network-based open-circuit fault diagnosis system for identifying faulty power switches in inverter-driven motor systems. The system leverages trained phase-to-phase voltage data from the motor to recognize the type and location of faults in each phase with high accuracy. Employing separate neural networks for each of the three phases in a three-phase permanent magnet synchronous motor, the system achieves an outstanding overall fault detection accuracy of approximately 99.8%, with CNN and CNN-LSTM architectures demonstrating superior performance. This work makes two key contributions: (1) implementing neural networks to significantly improve the accuracy of locating faulty switches in open-circuit fault scenarios, and (2) identifying the optimal neural network architecture for effective fault diagnosis within the proposed system.

An Open-Circuit Fault Diagnosis Method for LLC Converters

In electrified transportation systems, power system failures can lead to greater disasters. Therefore, the reliability of converters in transportation systems has been a concern. Fault-tolerant techniques are widely applied to ensure that converters can continue to supply loads under fault conditions. Fault diagnosis as a prerequisite for fault tolerance has also become a research hotspot. This paper proposes a fast method for fault diagnosis of high-frequency LLC converters. The proposed fault diagnosis method is based on the observation of the voltage across the resonant capacitor to determine and locate the faulty power switch, providing a basis for fault tolerance. This diagnosis method requires a voltage sensor, which is also necessary for some control methods. When applying these control methods, the proposed fault diagnosis method can be used without additional sensors, beneficial for cost reduction. A full-bridge LLC converter controlled by a digital signal processor was used as an experimental platform to verify the effectiveness and speed of the proposed diagnostic method. The results show that the proposed fault diagnosis method can achieve the fast diagnosis of high-frequency LLC converters in a short time and with only minimal computational resources.

A study on BSR diagnosis of power seat frame based on STRE-VK method

To establish an efficient method for the buzz-squeak-rattle (BSR) noise diagnosis of faulty power seat frame on the noisy production line, the S-transform ridgeline extraction & Vold-Kalman filtering (STRE-VK) method was proposed for the purpose of revealing the time–frequency characteristics of vibration signals of the power seat frame. The extraction of ridgelines from the time–frequency distribution was achieved through the utilization of adaptive generalized S-transform, and the separation of multiple harmonic components was accomplished by the Vold-Kalman filtering algorithm. The simulation analysis results demonstrated that the STRE-VK method performed better in terms of extracting ridgelines and segregating harmonic components. Then, the STRE-VK method was employed to the online BSR diagnosis of faulty power seat frames, and the prominence ratio of critical bands inspired by ECMA-74 was further calculated as a metric to identify different BSR noise types based on the separated vibration components. The results showed that the rapid online BSR diagnosis of power seat frame can be achieved accurately.

An Open-Circuit Fault Diagnosis Method Based on Adjacent Trend Line Relationship of Current Vector Trajectory for Motor Drive Inverter

This paper proposes a power switch open-circuit (OC) fault diagnosis method for a motor drive inverter system. This method utilizes adjacent trend lines to extract fault features from the current vector trajectory, enabling rapid fault diagnosis and localization. Firstly, the current vector trajectory is obtained by applying the Clark transformation to the three-phase currents, and trend line slopes are calculated using equidistant current signal nodes. Subsequently, the method determines fault by calculating changes in adjacent trend line slopes and quickly locates the faulty power switch by considering the average slope of adjacent trend lines and the beta-axis current direction in the alpha–beta coordinate system. Accurate OC fault localization can be achieved with just a few trend line data points calculated in each current cycle, reducing the extra hardware and computational burden. This method is not only suitable for load variations but also applicable to the diagnosis of both single-switch and multiple-switch faults. Finally, simulation and experimental results validate the effectiveness and robustness of the method.

A Reduced-Order Observer-Based Method for Simultaneous Diagnosis of Open-Switch and Current Sensor Faults of a Grid-Tied NPC Inverter

This paper presents a reduced-order observer-based simultaneous diagnosis strategy for grid-tied neutral point clamped (NPC) inverters subjected to open-switch and current sensor faults. First, the augmented descriptor system is constructed for the NPC inverter to transfer the current sensor fault into a generalized state vector. Then, the matrix transformations are applied to decouple the open-switch fault from the inverter system state and the current sensor fault. Subsequently, a reduced-order observer is developed for the transformed augmented descriptor system to achieve simultaneous precise estimation of the phase current and the current sensor fault. Finally, using the estimation results, a diagnosis algorithm with an adaptive threshold is proposed, which can not only distinguish between current sensor faults and open-switch faults, but also locate the faulty power switch and estimate different types of current sensor faults. Experimental results and comparisons are provided to verify the robustness and effectiveness of the proposed fault diagnosis algorithm.

Faulty Power in the Faculty Tower: James Hynes’s The Lecturer’s Tale as a Parody of William Shakespeare’s Macbeth

ABSTRACT The current article takes established theoretical postulates about parody as a point of departure for examining how James Hynes’s postmodern novel, The Lecturer’s Tale (2001), contributes fresh fictional angles to the field of campus literature by building on William Shakespeare’s Macbeth to chart its own protagonist’s transition from victim to victor and to sound a cautionary note about ill-advised and addictive pursuit of power. I maintain that Nelson Humboldt, a visiting adjunct lecturer at the elite University of Midwestern at Hamilton Groves, Minnesota, renders the role of Shakespeare’s ambitious eponymous tragic hero, whose usurpation of the crown wreaks havoc on the Kingdom of Scotland. This accounts for Hynes’s satirical portrayal of academia as a battleground where several argumentative and erudite professors, craving praise and prize and armed with words and swords, tend to wage battles of wits and wills and to get entangled in a web of intrigue. Finally, the paper underlines that the novel’s comic vein veils serious concerns about the defects and difficulties afflicting contemporary American academia.

Reactive Power Circulation Based Fault Tolerance Schemes for Multi-Megawatt 3L-NPC Paralleled Converters in Variable Speed Hydro Applications

Parallel connected multi-MW three-level neutral point clamped power converters (3L-NPC) are widely preferred in large-rated variable speed pumped storage unit as it improves operating efficiency and reliability. During open circuit fault conditions/gate drive malfunction, the entire unit must be shut down to ensure the safety of other healthier components. This article proposes reactive power circulation-based fault tolerance control strategies for grid side converter (GSC) during inner and outer switch fault conditions. In case of an open-circuit fault in outer switches, the normal sinusoidal currents are achieved in GSC by circulating reactive power among the healthy to faulty power converters. In case of an open-circuit fault in inner switches, the reactive power is circulated between healthy converters and a doubly fed induction machine (DFIM). In addition, neutral-point (NP) voltage oscillations are suppressed by injecting a zero-sequence signal into the reference voltage. The feasibility study and performance analysis of the proposed fault tolerance control strategies are carried out in the MATLAB/Simulink 2014a environment for the 250 MW DFIM unit. Furthermore, experimental validation is carried out in 3HP DFIM scale-down laboratory prototype with three parallel connected converters.

DC-DC Buck circuit fault diagnosis with insufficient state data based on deep model and transfer strategy

As the common power supply of electronic systems, the fault of DC-DC Buck circuit will trigger the faulty power supply which eventually cause serious problems in system tasks. Therefore, it is of great significance to accurately identify the faults of DC-DC Buck circuit. Due to the high cost of collecting circuit fault data set, the lack of effective fault data will lead to the inability to establish an intelligent fault diagnosis model. In this paper, the model of source circuit with sufficient data is used to solve the problem of fault diagnosis of target circuit with lack of data. A deep transfer kernel extreme learning machine auto encoder (DKEA) model is designed. The activation function of Gaussian error linear units (GELU) is used to describe the probability of neuron input, and the kernel extreme learning machine is employed as the classifier to complete the diagnosis task. First, only the output voltage signal of the circuit is collected to extract its primary features in time and frequency domain, and then the primary features are mined and classified by using the deep extreme learning machine auto encoder (ELM-AE) model. Sufficient source domain data is used to train the deep ELM-AE model, and the parameters obtained are used as the initialization parameters of the target domain diagnosis model. With the help of transfer learning, a small number of training samples in the target domain are used to fine-tune the deep ELM-AE model to adapt to the remaining testing samples in the target domain. The experimental results proves that the proposed method can effectively achieve the transfer diagnosis between different DC-DC Buck circuits based on the source domain data.

Fault Diagnosis and Tolerant Control for Three-Level T-Type Inverters

This paper proposes a fault diagnosis system for inverters based on a cerebellar model articulation controller (CMAC). First, a three-level T-type inverter was implemented and used to create a three-level T-type inverter test environment for measuring the output voltage waveforms of faulty power transistors on the main inverter circuit under different output frequencies. The measured waveforms were processed using a fast Fourier transform (FFT) algorithm to create frequency spectrum diagrams and extract the characteristic spectra of corresponding faulty switches. Then, the associations of the spectra were determined and applied as training data for the CMAC to detect the positions of the faulty power transistors. The test results demonstrated that the proposed induction motor fault diagnosis system is capable of fast algorithm, requires less data to train, and has excellent accuracy of identification, with an error margin of ±5%. The detection results were then processed using a fault-tolerant controller (FTC) to enhance the reliability of the proposed system. Finally, some simulations and experimental results were conducted and analyzed to validate the feasibility of the proposed FTC system.

Online Diagnosis and Ride-Through Operation for Cascaded H-Bridge Converter Based STATCOM With a Single Open-Circuit IGBT

To address the most commonly seen single insulated gate bipolar transistor (IGBT) open-circuit fault problems in cascaded H-bridge (CHB) converter based static synchronous compensator, a simple online fault diagnosis method and the corresponding ride-through operation approach are proposed. The method can quickly determine the exact location of the open-circuit IGBT through three steps, via analyzing the relationships between the dc bias in the three-phase output current of CHB converter, the transient dc rail voltage variations with respect to the averaged dc rail voltage, and the response of dc rail voltage and output current when the modulation approach of the faulty power module is adjusted. Afterwards, a fault ride-through approach is adopted to seamlessly transfer the modulation approach of faulty H-bridge module and it behaves as a half-bridge to provide a half of the rated output ac voltage. Meanwhile, the corresponding dc offset voltage from this faulty module is compensated by producing the same dc voltage in the healthy phase legs to avoid the dc bias in the three-phase output current. It has been demonstrated that the proposed fault diagnosis approach and the fault ride-through approach are effective in various adverse situations including grid voltage sudden disturbances and the load power sudden changes. Comprehensive simulated and experimental results have been provided to validate the effectiveness of the proposed approach.

An Intelligent Control Strategy for a Highly Reliable Microgrid in Island Mode

An intelligent control strategy based on a membership cloud model in a high reliable off-grid microgrid with a reconfigurable inverter is proposed in this paper. The operating principle of the off-grid microgrid with the reconfigurable inverter is provided, which contains four operating modes. An open-circuit fault diagnosis for the inverter is presented first. The polarities of the midpoint voltages defined in the paper are used to recognize the faulty power switch. The reconfigurable inverter allows the power switches of different bridges to be reconfigured, when there are power switches faulty, to let the inverter operate in faulty state. The working principle of the reconfigurable inverter is given. The membership cloud model with two output channels is built to obtain the virtual impedance to suppress the circulating currents between inverters when the reconfigurable inverter is in faulty state. A pulse resetting method is presented. The general intelligent control strategy for the reconfigurable inverter is formed as the droop-virtual impedance-voltage-current-pulses resetting control. The validity of the intelligent control strategy of the system is verified by simulation.

Open-Circuit Fault Detection and Isolation Method for Five-Level PUC Inverter Based on the Wavelet Packet Transform of the Radiated Magnetic Field

This article proposes a noninvasive open-switch fault detection method based on the analysis of the high-frequency signals. The faulty power switch is detected by metering the emitted near-field above a new topology using the wavelet packet transform and considering a complex control algorithm. The proposed method is tested on a single-phase five-level packed U-cell (PUC5) inverter. The latter is controlled using the finite-control set model predictive control (FCS-MPC) technique that generates the gate signals with a variable switching frequency. Moreover, unlike the conventional pulsewidth modulation strategy, the switching pattern provided by the FCS-MPC technique is not known in advance. Despite these complexities, the proposed fault detection method proves its high performance and effectiveness. Experimental studies are carried out on a single-phase PUC5 inverter using the real-time controller (OP4510) from OPAL-RT and prove the capability of this method to properly identify the faulty switch in this multilevel topology.

Analysis of fault diagnosis of DC motors by power consumption pattern recognition

Early detection of faults in DC motors extends their life and lowers their power usage. There are a variety of traditional and soft computing techniques for detecting faults in DC motors. Many diagnostic techniques have been developed in the past to detect such fault-related patterns. These methods for detecting the aforementioned potential failures of motors can be utilized in a variety of scientific and technological domains. Motor Power Pattern Analysis (MPPA) is a technology that analyzes the current and voltage provided to an electric motor using particular patterns and protocols to assess the operational status of the motors without disrupting production. Engineers and researchers, particularly in industries, face a difficult challenge in monitoring spinning types of equipment. In this work, we are going to explain how to use the motor power pattern/signature analysis (MPPA) of a power signal driving a servo to find mechanical defects in a gear train. A hardware setup is used to simplify the demonstration of obtaining spectral metrics from the power consumption signals. A DC motor, a set of metal or nylon drive gears, and a control circuit are employed. The speed control circuit was eliminated to allow direct monitoring of the DC motor's current profiles. Infrared (IR) photo-interrupters with a 35 mm diameter, eight-holed, standard servo wheel were employed to gather the tachometer signal at the servo's output. The mean value of the measurements was 318 V for the healthy profile, while it was 330 V for the faulty gears power data. The proposed power consumption profile analysis approach succeeds to recognize the mechanical faults in the gear-box of a DC servomotor via examining the mean level of the power consumption pattern as well as the extraction of the Power Spectral Density (PSD) through comparing faulty and healthy profiles

A Generalized Space Vector Modulation for Cascaded H-Bridge Inverters under Faulty Conditions

In this research, a new space vector modulation control algorithm is proposed to increase the reliability and the accuracy of the cascaded H-bridge multilevel inverters in case of faulty situations where one or several power cells do not function. When one or more switches of a cell are opened or shorted, that cell is considered faulty. By giving a detailed analysis on the impact of the faulty power cells on the voltage space vectors, the inapplicable voltage vectors are removed precisely. Consequently, the optimal redundant switching states are chosen such that the highest possible output voltage can be achieved. In addition, the balance of the three phases line-to-line voltage and current are maintained. The proposed algorithm is also generalized so that it can be applied to any level of H-bridge inverters. The validity of the method is verified by numerical simulations using MATLAB Simulink with an 11-level cascaded H-bridge inverter.

Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines

Induction machines have been key components in the industrial sector for decades, owing to different characteristics such as their simplicity, robustness, high energy efficiency and reliability. However, due to the stress and harsh working conditions they are subjected to in many applications, they are prone to suffering different breakdowns. Among the most common failure modes, bearing failures and stator winding failures can be found. To a lesser extent, High Resistance Connections (HRC) have also been investigated. Motor power connection failure mechanisms may be due to human errors while assembling the different parts of the system. Moreover, they are not only limited to HRC, there may also be cases of opposite wiring connections or open-phase faults in motor power terminals. Because of that, companies in industry are interested in diagnosing these failure modes in order to overcome human errors. This article presents a machine learning (ML) based fault diagnosis strategy to help maintenance assistants on identifying faults in the power connections of induction machines. Specifically, a strategy for failure modes such as high resistance connections, single phasing faults and opposite wiring connections has been designed. In this case, as field data under the aforementioned faulty events are scarce in industry, a simulation-driven ML-based fault diagnosis strategy has been implemented. Hence, training data for the ML algorithm has been generated via Software-in-the-Loop simulations, to train the machine learning models.

Fast Fault Detection and Location System for Distribution Network Lines Based on Power Electronic Disturbance Signals

The traditional reclosing device cannot distinguish the fault nature of blind reclosing, which will cause secondary damage to the faulty power system. In addition, the existence of fault resistance makes fault location complicated. This paper proposes an automatic reclosing device which enters the fault line through the single-phase AC voltage signal of the power electronic converter. The device responds to the comprehensive utilization of the system harmonic impedance and locates the fault current through the comprehensive utilization of the system harmonic impedance judgment. So, the device can eliminate the influence of fault resistance for positioning the result. Because the measurement is performed only at the head-end circuit, no communication line is required. If a permanent fault is found in the system, the fault information and the latching circuit breaker should be indicated. If there is no fault notification, the circuit breaker should be switched. The equipment has been proved to have accurate identification to detect the results of various fault types, so it can avoid the secondary damage of the power system caused by blind reclosing.

Modified Space Vector Modulation for Cascaded H-Bridge Multilevel Inverter with Open-Circuit Power Cells

In this research, a new space vector modulation control algorithm is proposed to increase the reliability of the cascaded H-bridge multilevel inverters in case of faulty situations, where one or several power cells do not function. Methods to detect faults ensure finding open-circuit module exactly, which is fast and easy to program. By giving a detailed analysis of the impact of the faulty power cells, optimal redundant level states are chosen such that highest possible output voltage can be achieved, while the balance of the three-phase line-to-line voltage is maintained and common-mode voltage is reduced. The proposed algorithm is generalized so that it can be applied to H-bridge inverters of any level. The validity of the method is verified by numerical simulations and experiment results with an 11-level cascaded H-bridge inverter.

Low voltage high current modular DC power supply for solid state RF amplifiers

A low voltage, high current, modular 50 V, 700 A dc power supply has been developed to bias 35 number of 500 W, 505.8 MHz solid state RF amplifiers. This power supply incorporates 7 number of 50 V, 100 A ac-dc power modules, operating in parallel to share the load current. This power supply has two unique attributes namely modular hot swappability and active redundancy. Hot swappable feature incorporated in power modules enables isolation of faulty power modules and facilitates their online replacement without affecting the operation of RF amplifiers and remaining power modules. Active redundancy increases reliability of the power supply significantly. Both these features reduce the down time and increase the availability of the overall system significantly. This paper presents comparison of system reliability of this modular dc power supply with other modular dc power supplies of same output voltage and current rating and having different number of power modules. Suitable mathematical expressions for system reliability of these modular dc power supplies have been evaluated. This power supply also has a digital controller for monitoring the status of 35 number of RF amplifiers and 7 number of power modules as well as controls load current sharing among the power modules. This power supply achieves current THD ≤ 6.2%, input power factor ≥ 0.98, output voltage stability ≤ 0.4% and peak to peak output ripple ≤ 0.2% with overall system reliability and availability approaching near 100%.

A Comprehensive Review of Auto-Reclosing Schemes in AC, DC, and Hybrid (AC/DC) Transmission Lines

The integration of distributed generators has changed the paradigm of modern power transmission systems. To cope with energy demands, electrical networks emphasize the efficient utilization of power transmission. Thus, high-voltage DC (HVDC) and hybrid (AC/DC) transmission systems are also getting attention owing to their high efficiency in addition to the widely adopted high-voltage AC (HVAC) systems. Most faults in the bulk of transmission lines are temporary or intermittent. Auto-reclosing schemes can be used to prevent these faults. However, conventional auto-reclosing schemes based on constant dead time cannot recognize the fault nature within the assigned duration. Consequently, the accuracy of power grids can be compromised. Therefore, adaptive auto-reclosing schemes are convenient for overcoming the issues caused by the rapid restoration of faulty power lines. This can enhance system reliability and avoid power failures and blackouts. This study is based on a systematic, detailed, and thorough research review of the existing auto-reclosing schemes in all three power transmission lines, i.e., AC, DC, and hybrid (AC/DC). Subsequently, a critical analysis has been performed to assess the pros and cons of each existing adaptive auto-reclosing scheme. Finally, future recommendations are presented to improve adaptive auto-reclosing schemes in each medium.