77 results about "Fault resistance" patented technology

The invention discloses a new energy automobile fault diagnosis and early warning system based on artificial intelligence, and the system employs an advanced artificial intelligence technology, and is designed according to the fault diagnosis and early warning demands of a new energy automobile. By integrating a high-precision sensor and a real-time data acquisition module, the system can comprehensively monitor key performance parameters of the vehicle; by utilizing machine learning and deep learning algorithms, the system can automatically identify a fault mode, the diagnosis accuracy is improved, and the diagnosis time is shortened; meanwhile, based on a potential fault early warning function of big data analysis, vehicle data can be monitored in real time, potential faults can be found in advance, and measures can be taken in time to prevent the faults. The system also has a strong data processing capability, can process and analyze a large amount of vehicle data in real time, and provides powerful support for fault diagnosis and early warning. Generally speaking, the system can significantly improve the safety, reliability and maintenance efficiency of the new energy vehicle, reduce the maintenance cost of a vehicle owner, and improve the user experience.

The invention discloses a self-adaptive dynamic fusion method and system for heterogeneous data, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous data of power equipment; detecting the state of the equipment and judging whether the equipment is in a fault mode, and recording a fault feature vector if the equipment is in the fault mode; inputting the fault feature vectors into a fault resistance evaluation model, evaluating the fault resistance of each data source, and outputting a plurality of fault resistance indexes; setting a preset fault resistance index, and dividing the data into a first type and a second type of heterogeneous data; and configuring fusion weights for the two types of data, performing data fusion according to the weights, and outputting a fusion result. The technical problem that in the prior art, the reliability difference of multi-source heterogeneous data cannot be effectively recognized in the power equipment fault state, and consequently the fault diagnosis accuracy is poor is solved, and the purposes of dynamically optimizing data fusion, improving the data reliability and improving the fault diagnosis accuracy are achieved through fault resistance evaluation and a self-adaptive sampling strategy. Therefore, the fault diagnosis accuracy is improved.

An integrated circuit (IC) includes a thermal memory device, a fault injection (FI) detector and a security control circuit. The thermal memory device includes (i) a thermal storage zone and (ii) a heating element configured to heat the thermal storage zone in response to write operations. The FI detector is configured to detect attempts to inject faults into the IC. The security control circuit is configured to perform a write operation to the thermal memory device in response to an attempt detected by the FI detector, to identify a sequence of the attempts that meets a density criterion, by reading the thermal memory device, and initiate a responsive action upon identifying the sequence.

The invention relates to the technical field of circuit breaker state monitoring and fault prediction, in particular to a circuit breaker residual life prediction method based on multiple parameters and dynamic correction, which comprises the following steps: S1, constructing a data set, S2, constructing a multi-parameter fusion model, S3, constructing a circuit breaker residual life prediction model, S4, dynamically correcting a prediction result, and S5, referring to a circuit breaker state maintenance guide rule. S6, dividing a sample set for training, verifying and testing the model; and S7, performing model performance evaluation. According to the method, multiple parameters are fused, and the improved convolutional neural network and the dynamic correction mechanism are combined, so that the residual life prediction and the state accurate evaluation of the circuit breaker contact are realized, the operation safety of the circuit breaker is improved, operation and maintenance personnel are assisted to carry out maintenance in advance, and faults are effectively prevented.

The invention provides a power grid cable wiring path optimization method and device, and the method comprises the steps: carrying out the load analysis of geographical distribution data and power utilization data of a power grid system, and generating load region types and weight data corresponding to each region type; performing fault propagation risk assessment on power grid nodes in the power grid system according to the load area type and the weight data, and generating a cable capacity scheme according to a risk assessment result; generating a candidate path set through a multi-target balance mechanism and an adaptive path growth algorithm according to the load area type, the weight data and a cable capacity scheme; and carrying out toughness evaluation and optimization on the candidate path set under various preset simulation scene conditions to generate a power grid cable wiring scheme. According to the method, the power grid load distribution characteristic, the fault propagation risk and the system toughness are comprehensively considered, a cable wiring scheme which adapts to a complex load environment and has fault resistance can be generated, and the reliability and stability of a power grid system are remarkably improved.

The invention relates to the technical field of cable line operation and maintenance, in particular to a cable fault accurate positioning method and system based on electromagnetic time reversal correction. And in combination with a reflection coefficient correction and local energy extraction method in a time inversion process, the problems that the fault traveling wave signal is attenuated and distorted under strong noise and high transition resistance, and the EMTR positioning effect is influenced by fault resistance are solved. Constructing a multi-scale evaluation system fusing time domain, frequency domain and wavelet domain features, and screening an optimal modal component; and constructing an actual fault resistor in a reverse phase of the EMTR to correct a reflection coefficient, and providing a criterion for positioning by using local energy at a first traveling wave to realize accurate positioning of a cable fault. According to the method, the ranging error can be effectively reduced under the conditions of high transition resistance, low signal-to-noise ratio and unknown fault resistance, and the robustness and engineering applicability of cable fault diagnosis are improved.

The invention relates to the technical field of transformers, in particular to a transformer state prediction and adaptive control method based on digital twinning, which realizes real-time monitoring, fault prediction and adaptive control of a transformer by constructing a high-precision digital twinning model and a deep reinforcement learning algorithm. According to the method, finite element analysis software is used for modeling the physical structure of the transformer, a high-precision three-dimensional geometric model is generated, circuit simulation software is used for modeling electrical characteristics, a comprehensive digital twinborn model is formed, and the actual operation state of the transformer is comprehensively reflected. A fault module is added in the digital twin model, potential fault modes such as coil short circuit and iron core oversaturation are simulated, and faults are recognized and prevented in advance.

The invention discloses a multi-protocol supported transformer area intelligent fusion terminal detection system, which is used for detecting a transformer area intelligent fusion terminal and a functional module thereof, and is characterized by comprising front-end processor software connected with a standard source cabinet and a test tool cabinet through a communication service unit, the standard source cabinet is connected with the test tool cabinet through a two-way communication line; the standard source cabinet comprises a program control power source, a three-phase standard meter, a standard clock source and a positioning system; the test tool cabinet comprises a communication interface test unit, a complete machine power consumption test unit, a loop inspection test unit, a station test control unit and a station connection unit. The front-end processor software deploys the standard source cabinet and the test tool cabinet through the communication service unit to realize power supply adaptability detection, grounding fault resistance capability detection, power consumption detection, alternating current analog quantity experiment detection, state quantity experiment detection and data acquisition processing experiment detection of the SCU to be detected; loop state inspection experiment detection and clock and positioning experiment detection are carried out.

The invention discloses a data writing method and electronic equipment, and relates to the technical field of storage, the method comprises the steps that after writing data sent by a host is received, first detection information is sent to a second node, and the writing data is the writing data sent to a first node after the host determines that the first node joins a cluster; receiving a first detection result sent by the second node; when it is determined that the first detection result is that the second node does not write all data in the cache of the second node into the rear-end hard disk, whether the node state of the second node is an online state or not is determined; and when the node state of the second node is determined to be an online state, forwarding the write data to the second node, so that the second node writes the write data into a rear-end hard disk. According to the method and the device, the problem that the redundancy capability and the anti-fault capability of the cluster are poor due to the fact that the node leaving the cluster cannot join the cluster as soon as possible after recovery and cannot perform data processing is solved.

The invention relates to the technical field of sensors, and discloses a circuit breaker contact mechanical property state detection system which comprises a multi-sensor deployment module, an original data acquisition module, an original data preprocessing module, a contact pressure monitoring module, a data error calculation module, a data error analysis and adjustment module and a contact pressure comprehensive judgment module. The early warning information output module is used for performing error analysis and calculation on data acquired by the sensors to obtain error indexes of the sensors, performing parameter adjustment on the sensors which do not accord with a preset threshold value, and outputting early warning information when all the error indexes accord with the preset threshold value. According to the error index of each sensor and the stress deformation index of each pressure sensor calculated through the pressure monitoring mathematical model, a comprehensive judgment model is established to calculate the contact pressure stability index, and comprehensive judgment is performed according to the contact pressure stability index, so that the change of the contact pressure of the contact can be found in time; and faults are prevented.

The invention provides a permanent magnet generator fault monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of motor fault detection, and the method comprises the steps: obtaining the historical operation data of a target permanent magnet generator; according to the historical operation data, establishing a motor model of the target permanent magnet generator in each working condition state; determining time domain features and frequency domain features of the target permanent magnet generator in each working condition state based on the motor model; and performing fault monitoring on the target permanent magnet generator based on the time domain feature and the frequency domain feature. Therefore, compared with the prior art that data acquisition is carried out through installation of a sensor, high-frequency voltage is injected through an inverter, then corresponding high-frequency inductance is calculated, and then identification analysis of specific harmonic waves is completed, the scheme in the invention can realize continuous monitoring and diagnosis, fault occurrence can be effectively prevented, and the fault diagnosis accuracy is improved. The dependence on sensors and monitoring conditions is small, and the operation efficiency and economic benefits of the whole wind power plant can be remarkably improved.

The invention provides a high-resistance grounding fault detection method, device and equipment and a storage medium, and the method comprises the steps: collecting a three-phase current and voltage signal of a small-resistance grounding system, and carrying out the processing of the three-phase current and voltage signal to obtain a zero-sequence current and a zero-sequence voltage; when the zero-sequence voltage energy characteristic meets a preset condition, calculating fault grounding resistance according to the zero-sequence current, the zero-sequence voltage and system electrical parameters; calculating a transient zero-sequence current characteristic value and a steady-state zero-sequence voltage characteristic value according to the fault grounding resistance, and comparing the characteristic values with a threshold value; and when the two characteristic values are both smaller than the corresponding threshold values, determining that the fault is a high-resistance grounding fault. According to the method, the fault grounding resistance is calculated, the transient state and steady state characteristic criteria are deduced based on the resistance, and the detection basis is converted from direct measurement of the fault current to analysis of the fault resistance characteristics, so that accurate identification of the low-current high-resistance grounding fault which is difficult to detect by a traditional method is realized.

The invention relates to the technical field of electric power detection, and provides a low-voltage and low-frequency load shedding function test method and a test device, in the on-site real-model test process, the test method and the test device are hung on a grid along with an FTU, the voltage and frequency change of the power grid and the action of the FTU are recorded in real time, a broken line graph is generated through upper computer software, and the action process of on-site low-voltage and low-frequency detection is displayed. Whether the device correctly executes the low-voltage and low-frequency load shedding action or not is visually observed, and the method has high practical significance (fault prevention), asset configuration optimization (service life prolonging) and final service for efficient, reliable and economical operation of a power grid.

This invention relates to a single-end distributed parameter fault location method for ultra-high voltage (UHV) transmission lines, comprising: acquiring the line parameters of the UHV transmission line under test and determining whether a fault has occurred; acquiring the three-line voltage vector and three-phase current vector of the faulty end of the transmission line, establishing a distributed parameter model of the transmission line, and dividing the distributed parameter model of the transmission line into a fault-side segment model, a fault-opposite-side segment model, and a fault branch model according to the fault region; employing a single-end phasor domain centralized parameter fault location method to solve for the initial values ​​of the fault location and fault resistance; based on the fault-opposite-side segment model, constructing a fault location equation with the fault location and fault resistance as variables, and iteratively solving for the fault location and fault resistance as an optimization problem, as the fault location result. Compared with the prior art, this invention significantly improves the fault location accuracy of UHV long-distance transmission lines.

The invention relates to the technical field of aero-engine fault detection, in particular to a self-detection ignition system for an aero-engine. The invention provides a self-detection ignition system which comprises an ignition exciter, an ignition cable and an ignition electric nozzle. The ignition exciter at least comprises a self-detection module and a fault resistance module. The fault resistor module forms a feedback resistor loop by a plurality of resistors with different resistance values and is used for feeding back a fault state or a normal working state of the ignition exciter; the self-detection module selects a corresponding resistor in the fault resistor module according to a detection result of internal fault information of the ignition exciter to be connected to the feedback resistor loop; and the engine electronic controller identifies and determines a corresponding fault mode according to an acquisition result of the resistance value of the feedback resistance loop. According to the method, the ignition failure reason can be rapidly judged when ignition fails, specific fault components can be accurately positioned when the ignition system breaks down, and the troubleshooting efficiency is improved.

According to the artificial intelligence ammeter data visualization-based power distribution system fault position identification method provided by the invention, three basic learners, namely a random forest, a K-proximity algorithm and an artificial neural network, are combined, and an integration method of combining a plurality of different machine learning models into a prediction model can be adapted to different scenes, so that the accuracy of the prediction model is improved. For example, noise and data loss, fault resistance change, load or power distribution feeder structure change and the like can be realized. An integrated voting classifier is developed, and a random forest, kNN and an artificial neural network model are utilized to classify fault types and identify a limited number of data points of FL only using voltage measurement. According to the method, the key problem of insufficient multiple features is overcome, the fault position can be quickly identified, the method is not influenced by the initial angle of the fault and the resistance on the line, the anti-interference capability is high, the classification accuracy of multiple fault types is high, the detection precision is high, and the cost is not too high.

This invention discloses a method, system, medium, and product for diagnosing high-resistance faults in low-voltage distribution networks, belonging to the field of distribution network fault diagnosis technology. The method first constructs a comprehensive statistical model integrating amplitude and phase errors, time asynchrony errors, and communication packet loss to obtain measurement data with uncertainty; then, it extracts multi-dimensional node features and incorporates uncertainty to construct feature vectors; next, it constructs and trains a Bayesian graph convolutional neural network based on the power grid topology; finally, it outputs the fault probability and cognitive uncertainty, and combines dual thresholds to complete fault determination. This invention can effectively address the measurement uncertainty problem of smart meters, enhance the ability to identify weak features of high-resistance faults, reduce false alarms and missed alarms, and exhibits excellent robustness and diagnostic accuracy under different fault resistance and noise levels.

The invention relates to the field of power grid data scheduling, and discloses a power grid business data intelligent scheduling method based on multi-source data fusion, which comprises the following steps: fusing different business data of a power grid for risk assessment and fault probability assessment of the power grid and user loads, and combining a fault probability assessment result to obtain a power grid business data intelligent scheduling result; and carrying out load intelligent scheduling strategy construction and fault correction strategy construction on the user load and the power grid in the high-risk state. The system can accurately and actively identify the operation risk of the power grid, achieves the intelligent load transfer of main and distribution cooperation, can prevent faults in advance, achieves the quick response when the faults occur, effectively reduces the power-off time and range, guarantees the normal power demands of all walks of life and residents, and improves the power utilization efficiency. And meanwhile, unnecessary equipment overload and redundant operation can be avoided, the operation cost of the power grid is reduced, the service life of equipment is prolonged, and the operation economic benefit of the power grid is improved on the whole.

The application relates to the technical field of battery fault detection, in particular to a lithium ion battery internal short circuit fault detection method; in the method, first, constant voltage testing is carried out at a high SOC, and the short circuit resistance of the battery is calculated; whether the battery is short-circuited is judged according to the short circuit resistance data; if yes, then constant voltage testing is carried out at a low SOC, and the short circuit resistance is calculated; whether the battery is internally short-circuited or externally short-circuited is judged according to whether the short circuit resistance data is the same as the previous short circuit resistance data; if the battery is internally short-circuited and is a single-layer single-body structure battery, then the specific internal short circuit type is judged according to the two short circuit resistance data; through the fault detection method, the internal short circuit type of the fault resistance can be effectively judged.

The utility model relates to the technical field of capacitors, and discloses a fault-proof mechanism of a thin-film capacitor taping machine, which comprises a capacitor taping machine body and a box body, the rear side of the top end of the capacitor taping machine body is fixedly connected with a supporting disc, the inside of the left side of the supporting disc is fixedly connected with a motor, the driving end of the motor is fixedly connected with a limiting block, and the limiting block is fixedly connected with the box body. Sliding grooves are formed in the periphery of the left side of the limiting block, a supporting shaft is fixedly connected to the middle of the left side of the limiting block, a threaded strip is arranged at the left end of the exterior of the supporting shaft, the exterior of the threaded strip is in threaded connection with an adjusting ring, and a sleeve is slidably connected to the exterior of the supporting shaft. According to the utility model, mechanical faults caused by loosening or falling of the film winding drum can be prevented, the downtime of equipment is reduced, the production efficiency is improved, the tension of the film is adjusted while the external part of the film is cleaned, the equipment faults caused by impurities are effectively reduced, and the film is prevented from being torn or deformed in the processing process.

The invention discloses a co-excitation Wheatstone full-bridge circuit fault positioning method based on resistance measurement, and belongs to the technical field of flight load spectrum testing. The system comprises a graphical interface module, a measurement data management module, a circuit topology storage module, an admittance matrix calculation module, a fault diagnosis engine module, a result display module and a file management module. The implementation method comprises the following steps: constructing a topological structure of a co-excitation Wheatstone full-bridge circuit, and defining nodes, resistors and a connection relation; inputting a resistance value among the eight fixed measurement points; generating possible fault resistance combination constraints; calculating a theoretical equivalent resistance value through an admittance matrix method; calculating the error between the theoretical value and the measured value; preferentially processing infinite constraints, and searching an optimal fault combination; and outputting a fault resistor positioning position and a diagnosis report. According to the invention, through automatic calculation and an intelligent optimization algorithm, rapid and accurate fault positioning is realized, and the maintenance cost and time of a flight load spectrum test system are substantially reduced.

The present application relates to the technical field of high-voltage direct current transmission, and particularly relates to a non-blocking type direct current fault ride-through control method for a hybrid three-terminal direct current transmission system; comprising the following steps: S1, performing sub-module proportioning design on the hybrid three-terminal direct current transmission system; S2, performing fault detection and triggering, and when the triggering condition is met, performing S3; S3, performing hierarchical fault control strategy, including mode conversion of the LCC converter station and non-blocking control of the FHMMC converter station; S4, when the fault clearing condition is met, resetting the mode conversion of the LCC converter station and stopping the regulation of the DC current size of the FHMMC converter station by the current converter inhibitor. The technical problems of poor fault resistance and operation economy of the LCC-FHMMC hybrid three-terminal direct current transmission system are solved.

A method and apparatus for configuring spinning contingency reserve capacity are disclosed. The method includes: obtaining the difference between the active power deficit shared by the secondary control area and the spinning contingency reserve reserved for the secondary control area in response to a fault, based on the maximum active power deficit caused by a fault in the primary control area and the generation load ratio of the secondary control area to the primary control area; thereby obtaining the active power deficit shared by the secondary control area, the spinning contingency reserve capacity of the secondary control area, and the spinning contingency reserve capacity of the primary control area. The method and apparatus provided by this invention enable the power system's contingency reserve configuration to meet both the requirements of system operational safety and economy.

The application discloses a motor stator winding inter-turn short circuit modeling and simulation system, and relates to the technical field of motor fault simulation.The system comprises a feature extraction module, which is used for calculating and generating a fault excitation index and winding chaos entropy from acquired motor operation data; a fault evolution modeling module, which is used for solving a preset fault evolution model based on the fault excitation index and winding chaos entropy to generate an updated fault resistance; a multi-level parameter dynamic correction module, which is used for correcting parameters of a simulation model according to the winding chaos entropy; and a simulation strategy optimization module, which is used for switching between multiple preset simulation modes according to the winding chaos entropy.The application realizes dynamic simulation of the whole life cycle of motor inter-turn short circuit by constructing a closed-loop adaptive system and combining interdisciplinary theories, and takes into account high fidelity, physical interpretability and computational efficiency through multi-level correction and adaptive strategies.

The present application discloses a method, device, equipment and medium for predicting early leakage current of tree-line touch faults, belonging to the technical field of distribution line grounding fault detection. The method includes: establishing a multi-factor coupled tree body temperature rise time-domain model, where the tree body temperature rise time-domain model is a mathematical model describing the tree body temperature rise changing with time, and the multi-factors considered include tree size, moisture content and ambient temperature; establishing a variety of equivalent fault resistance-tree temperature models, where the equivalent fault resistance-tree temperature model is a mathematical model describing the change of the equivalent fault resistance value with the tree temperature; calculating the prediction errors of each equivalent fault resistance-tree temperature model respectively, and selecting the equivalent fault resistance-tree temperature model with the smallest prediction error as the optimal equivalent fault resistance-tree temperature model; using the optimal equivalent fault resistance-tree temperature model to calculate the equivalent fault resistance, and the effective value of the leakage current can be predicted according to the calculated equivalent fault resistance and the fault phase voltage.

The present invention belongs to the technical field of motor fault optimization, and discloses a method for optimizing the efficiency of a three-phase four-wire servo motor with high-impedance connection. The method comprises the following steps: measuring the phase sequence current under a high-impedance connection fault and converting it into a dq0 axis current; calculating the output power of the motor in combination with the motor phase voltage and phase current, analyzing the power to obtain the second harmonic component and combining the phase current amplitude to obtain the fault resistance value; calculating the motor copper loss after the high-impedance connection fault and the intervention of fault tolerance based on the fault resistance value and in combination with the stator resistance value and the dq0 axis current; derivatizing the motor copper loss to obtain the zero-sequence current after optimization of the fourth bridge arm under the high-impedance connection fault of the motor; and calculating the minimum copper loss under the high-impedance connection fault of the motor based on the zero-sequence current injected into the fourth bridge arm. Efficiency optimization control under high-impedance connection fault operating conditions effectively reduces the system copper loss during high-impedance connection operation and improves system efficiency.

A system (200) and method (400) for automatic calibration of stator ground fault protection for a generator is disclosed. The system (200) includes an intelligent electronic device, IED, (202) configured to operate in a first calibration mode to connect with a ground terminal (204) and in a second calibration mode to connect with a digital potentiometer (206). The system (200) further comprises a processing unit (104) to: obtain a phase angle value and a fault resistance value from the IED (202) by operating the IED (202) in its first calibration mode; determining one or more of a correct phase angle value and a correct fault resistance value of the IED (202) based on deviations from the respective desired values using linear regression algorithm (s); evaluating the determined values at different resistance values by operating the IED (202) in its second calibration mode; and configuring the IED with the correction value (202) if the evaluation for stator ground fault protection is successful.

The present invention proposes a commutation failure suppression method applicable to a UHV DC hierarchical access system, where the high and low end converter valves of the access system are connected in series on the DC side. The suppression method uses the trigger angle advance amount output by the commutation failure prevention control system CFPREV of the faulty layer as the electrical detection quantity, and by setting a coordination coefficient K, coordinates and cooperates it with the control systems of non-faulty layers to reduce the probability of commutation failure occurring in non-faulty layers; and reduces the DC current of the high and low end converters by triggering the VDCOL instruction in advance. Compared with the traditional commutation failure suppression method for hierarchical access systems, the present invention has stronger fault resistance and recovery effects.

The present invention provides a modular electrical cabinet system with multiple protections and high efficiency energy saving, which relates to the field of system control technology, including an electrical module, a protection module, an energy management module, a diagnosis and prediction module, and an adaptive scheduling module. The fault prediction module based on deep learning analyzes these data to accurately predict potential faults and generate maintenance alarms to prevent faults from occurring in advance. The AI ​​fault self-diagnosis module further automatically diagnoses the fault type and provides specific repair suggestions, thereby improving the accuracy and timeliness of fault handling. The predictive maintenance module combines historical data and prediction algorithms to formulate maintenance plans in advance, reducing the incidence of sudden failures and maintenance costs. Through these intelligent means, the system can not only achieve self-monitoring and self-repair, improve maintenance efficiency, but also extend the service life of the electrical cabinet.