843 results about "Voltage sensor" patented technology

The invention discloses an electromechanical installation engineering automatic debugging method and system based on intelligent control, and the method comprises the steps: enabling an intelligent control system to monitor the energy consumption of equipment in real time, optimizing an operation mode, adjusting the working state according to data, and enabling the equipment to work cooperatively through the intelligent control system; deploying temperature and humidity, pressure, current and voltage sensors for stable wiring, and configuring parameters by adopting an intelligent debugging tool; based on the preliminary data, optimizing the system by applying an adaptive control algorithm, adjusting a strategy, fusing multi-dimensional data, and identifying performance bottleneck and abnormality; the centralized monitoring platform monitors the equipment state in real time and sends out an early warning signal; after multiple rounds of debugging, the energy-saving effect and performance are evaluated, a control algorithm is optimized, and optimization is continuously carried out according to historical data and environmental changes; according to the electromechanical installation engineering automatic debugging method and system based on intelligent control, the adaptive control algorithm and the machine learning technology are introduced, and the strategy is dynamically optimized according to the equipment operation state, the environment change and the historical data.

The invention relates to the field of machine learning, and discloses a charging pile fault diagnosis method, which comprises the following steps: S1, preparing an environment, building a deep learning development environment, and providing a necessary technical environment for the training and deployment of a subsequent model; and S2, data acquisition and processing: installing a current sensor, a voltage sensor and a temperature sensor on a new energy automobile charging detection vehicle, and acquiring operation data of the charging pile in real time. And S3, model definition: a fault diagnosis model based on deep learning is constructed, a fully connected neural network (DNN) is adopted, and the model comprises an input layer, a hidden layer and an output layer. The key operation data of the charging pile are collected in real time through the sensor, the fault state is intelligently judged in combination with the deep learning model, rapid and accurate fault positioning and diagnosis are achieved, and the maintenance efficiency of the charging pile is remarkably improved. The system can automatically extract data features and analyze fault types, and manual intervention is reduced.

The invention relates to the technical field of power grid monitoring, and discloses a voltage sensor multi-parameter real-time monitoring method and device in an Internet of Things environment. According to the method, real-time voltage fluctuation data of a target area is acquired by deploying a multi-channel voltage sensor array and is transmitted to an edge computing node; dynamic feature extraction is executed in the edge nodes, and a multi-dimensional feature matrix containing voltage fluctuation ratio, harmonic distortion and phase deviation features is generated; identifying voltage sag, overvoltage and harmonic resonance event characteristics based on an anomaly detection model; spatial correlation modeling is carried out in combination with power grid topological structure parameters, the coupling strength of adjacent sensor node parameters is calculated, and a multi-parameter correlation map containing event propagation paths and influence ranges is generated; inputting the voltage into a diagnosis model to generate a power grid state diagnosis report, and dynamically adjusting the sampling frequency and filtering parameters of the voltage sensor according to the power grid state diagnosis report. According to the method, real-time monitoring and analysis of multiple parameters of the power grid can be realized, and comprehensive support is provided for power grid state evaluation.

The invention discloses a power battery state real-time monitoring system, and relates to the technical field of new energy automobiles, and the system comprises the following components: sensor network construction: installing a voltage sensor, a temperature sensor, a current sensor and an internal resistance monitoring sensor on each single battery of a power battery pack to form an omnibearing and high-density monitoring network; according to the integrated fault diagnosis technology, the comprehensive and high-density sensor network is constructed, key parameters of voltage, temperature, current and internal resistance of each single battery in the battery pack are collected in real time, comprehensiveness and accuracy of data are ensured, multiple advanced fault diagnosis algorithms are combined, and the fault diagnosis accuracy of the battery pack is improved. According to the method, characteristics can be rapidly extracted from mass data according to a battery comprehensive fault identification index formula, a battery physical model prediction index and an abnormal value detection index, and pattern identification is carried out, so that accurate judgment of a fault type of a single battery and accurate positioning of a fault position are realized.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable operation state real-time evaluation system based on multi-mode deep learning, and the system comprises the steps: synchronously collecting the load, partial discharge and temperature strain data of a cable through a current and voltage sensor, an ultrahigh frequency sensor and a distributed optical fiber sensor; feature extraction and cross-dimension fusion are carried out on the multi-source data, and a multi-dimensional feature matrix is constructed; a pre-trained deep learning model is utilized to analyze internal association between the features, and cable health degree scores, fault risk levels and defect type identification results are output; automatically generating and executing a load adjustment, loop switching or precise maintenance strategy according to an evaluation result; according to the method, the problems of inaccurate evaluation and early warning lag caused by data isolation analysis of a traditional monitoring method are solved, and real-time accurate evaluation and intelligent closed-loop operation and maintenance of the operation state of the cable are realized.

The invention relates to the technical field of power distribution network fault early warning systems and methods, in particular to an intelligent power distribution network fault early warning system and method based on the Internet of Things, and the system comprises a sensing unit, a data processing unit, a data analysis unit, a fault early warning unit, a monitoring management unit, and a personnel inspection subsystem unit. The sensing unit is used for installing a current sensor, a voltage sensor, a temperature sensor and a humidity sensor at key node positions of a transformer, a switch cabinet and a line of the power distribution network, the current sensor is used for monitoring the current in the line in real time, the voltage sensor is used for monitoring the voltage value, and the temperature sensor is used for monitoring the operating temperature of equipment; faults are analyzed and judged from multiple angles, misjudgment and missed judgment are reduced, whether fault hidden dangers exist or not is judged more accurately, the operation state of equipment is evaluated in real time through monitoring of the Internet of Things, the frequency of manual inspection is appropriately reduced, the labor cost is saved, and the resource utilization efficiency is improved.

A control system for an AC-DC converter includes a mixed-signal power controller and first and second gate drivers. The controller has a digital module and an analog module. The analog module has a current sensor generating a current sense voltage based on a sensed switch current. The digital module has a DC voltage sensor generating a DC sense voltage based on a DC bus voltage, and an AC voltage sensor generating an AC sense voltage and a binary polarity signal based on the AC voltage. The digital module induces the first gate driver to output first and second control signals for controlling a high-side switch and a low-side switch of a current shaping half-bridge based on first and second clock signals and the comparator voltage, such that an inductor current flowing through an inductor is proportional to a current command value.

The invention belongs to the technical field of electrical safety, and particularly discloses an insulation resistance intelligent detection system and method for an energy storage converter, and the system is based on a bridge balance structure, integrates a temperature and humidity sensor and a voltage sensor, and collects environment parameters and bridge unbalance signals in real time. A digital potentiometer and a resistor array are driven through a PID algorithm to dynamically adjust bridge balance, and environmental interference is eliminated in combination with a temperature and humidity compensation model; and carrying out intelligent analysis on the preprocessed multi-dimensional data by utilizing a band-pass filtering and detection model, and outputting a high-precision insulation resistance value. The method achieves full-automatic detection, is high in precision, is quick in response, is suitable for complex working conditions, and remarkably improves the reliability of the insulation performance evaluation of the energy storage converter.

The invention discloses a remote real-time monitoring and fault early warning method for a power supply operation state, and relates to the technical field of power electronics and energy management, and the method comprises the steps: collecting the temperature data of a key part of a power supply through an optical fiber Bragg grating sensor array, and analyzing the wavelength offset; electrical parameters are collected through a differential MEMS current / voltage sensor, and a permalloy / ferrite composite shielding layer is arranged in the sensor; collecting a mechanical vibration signal through a piezoelectric ceramic vibration sensor; for device data from different manufacturers, Modbus and CAN heterogeneous protocols are matched and analyzed through a regular expression, and the Modbus and CAN heterogeneous protocols are converted into a unified RDF triple format; inputting temperature, current and vibration data into a space-time alignment module, and constructing a semantic mapping relation by adopting an ontology; calculating a sensor confidence coefficient weight, and performing weighted fusion to generate an equipment health index; operating a lightweight LSTM model at an edge node, and detecting current harmonic and vibration resonance frequency band abnormity in real time; cOMSOL multi-physical field simulation is combined at the cloud end, and the evaporation rate of the capacitor electrolyte is predicted.

The invention discloses a filling body top plate stability monitoring system for mining. The system comprises a rectangular wooden baffle plate; filling materials; a copper grid mesh; the resistivity tester is used for monitoring the resistivity change condition of the self-sensing filling body material and automatically adjusting the test frequency and voltage according to the data monitored by the sensor module; the sensor module is used for monitoring the change of moisture content, temperature and component proportion in the self-sensing filling material; the central control module comprises a data collection unit, a data processing unit, a stability model unit and an alarm and early warning unit, and the data collection unit is responsible for collecting resistivity data monitored by the resistivity tester in real time and various data of geological exploration, field observation and numerical simulation; the data processing unit is responsible for processing the received resistivity data and various data, and the stability model unit is responsible for constructing a stability model and judging the stability state of the filling body roof according to the change condition of the resistivity.

The invention discloses an electric meter data storage method of a single-phase cost-control electric energy meter, and relates to the technical field of intelligent electric meter data processing, and the storage method comprises the following specific steps: S100, data acquisition and characteristic pro-analysis: acquiring voltage, current and power data of the single-phase cost-control electric energy meter through a voltage sensor and a current sensor, by integrating the voltage sensor and the current sensor, real-time and accurate acquisition of key power utilization data of voltage, current and power of the single-phase charge-control electric energy meter is realized, and by introducing a dynamic time window setting mechanism, the length of a time window is automatically adjusted according to the fluctuation amplitude of the data, so that the accuracy of power utilization is improved. It is ensured that a window is shortened to capture transient changes when data fluctuates violently, the window is prolonged to reduce redundant collection when the data is stable, the flexibility and accuracy of data collection are improved through intelligent adjustment, subsequent data processing can be conducted based on richer and more detailed feature information, and the data collection efficiency is improved. And the intelligent level of the whole data storage system is obviously improved.

The invention discloses a motor shaft voltage testing device, solves the problem of testing difficulty caused by the existence of a coupler at a motor transmission end, is simple and adjustable in structure, can be suitable for a narrow space, and belongs to the technical field of shaft voltage testing devices. The device comprises a data acquisition instrument and an electric brush assembly, a voltage sensor in the data acquisition instrument is connected with the electric brush assembly and a reference ground through electric wires, the electric brush assembly comprises an electric brush box and an electric brush part, the electric brush part is connected with a spring in an insulated mode, and the spring is installed in the electric brush box. The electric brush part slides in the electric brush box through a spring, the electric brush part is connected with an electric wire, and the bottom of the electric brush box is provided with a threading hole for the electric wire to pass through.

A battery cell balancing system contains a switch mode circuit employing voltage sensors across the cells and current sensors on the balancing legs to enable reliable and efficient cell balancing during battery charge.

A battery management system includes a voltage sensor to generate a voltage signal indicating a voltage of a battery, a current sensor to generate a current signal indicating a current flowing through the battery, and a control unit to record a voltage history and a current history of the battery based on the voltage signal and the current signal at a predetermined time interval during constant current charging of the battery. The control unit determines a differential capacity curve indicating a correlation between the voltage of the battery and a differential capacity in a reference voltage range. The control unit performs a first protection operation for the battery by comparing a first characteristic voltage of a main feature point with a reference voltage when the main feature point is detected from the differential capacity curve.

A system includes a circuit protection device (CPD) having an input coupled to an aircraft power generator and an output coupled to an aircraft electrical load. A shorting switch is coupled to the input of the CPD. The shorting switch, when closed, provides a fault current path that returns to the aircraft power generator and avoids passing through the CPD and the aircraft electrical load. A controller, based upon the voltage measurements from one or more voltage sensors and current measurements from at least one current sensor selectively opens and closes one or more of the CPD and the shorting switch.

The invention provides a water-saving system coupling photovoltaic panel cleaning and vegetation irrigation and a control method of the water-saving system, and belongs to the technical field of photovoltaic power generation. Comprising a water collecting mechanism used for collecting water on the surface of a photovoltaic panel; the water storage mechanism is used for storing the collected water; the pressure pump is connected with the water storage mechanism and used for injecting water into the outer pump; the cleaning mechanism is connected with the pressure pump through a pipeline and used for spraying water to clean the photovoltaic panel, and a first valve is arranged on the pipeline; the irrigation mechanism is connected with the pressure pump through a pipeline and used for spraying water to irrigate vegetation, and a second valve is arranged on the pipeline; the voltage sensor is used for detecting a voltage value output by the photovoltaic panel; the humidity sensor is used for detecting the humidity value of the soil; the light intensity sensor is used for detecting illumination intensity; and the controller controls the booster pump, the first valve and the second valve to work based on the voltage value, the humidity value and the illumination intensity. The overall structure is simple, the photovoltaic panel can be cleaned in time, the generating capacity is improved, water resources are saved, and rapid vegetation restoration is promoted.

The invention relates to the technical field of safety monitoring, in particular to a power transmission line safety early warning system based on artificial intelligence and Beidou positioning. The system comprises a power transmission line data acquisition module, a power transmission line damage evaluation module, a tower safety fault identification module and a safety fault positioning and early warning module, and an image sensor, a current sensor and a voltage sensor are arranged on a power transmission line to acquire multi-source line data and transmit the data to a data processing center in real time; obtaining the distance of the corresponding trees and the number of built bird nests, and carrying out safety damage evaluation calculation and safety fault recognition analysis on the power transmission line to obtain a safety fault recognition result of the power transmission line tower; a Beidou positioning module is integrated and fault positioning early warning is carried out on a corresponding power transmission line tower with a safety fault, so that a fault early warning position of the power transmission line tower is obtained, and corresponding power transmission line fault early warning repair work is executed. Safe and stable operation of the power transmission line can be guaranteed.

The invention relates to the field of unmanned aerial vehicle motor service life prediction, and discloses an unmanned aerial vehicle motor service life test method, which comprises the following steps: collecting motor data in real time through temperature, vibration, current and voltage sensors, carrying out cleaning, denoising and standardization processing, analyzing motor characteristics by adopting physical modeling, extracting parameters such as damage rate, temperature and load, and calculating the service life of the unmanned aerial vehicle motor. According to the method, historical data and physical modeling features are combined, an LSTM deep learning model is constructed for time sequence training, the remaining service life RUL is predicted, feature fusion is carried out, the prediction precision is improved, the health state of the motor is evaluated according to a prediction result, maintenance suggestions are provided, the model is optimized and fed back, and the prediction method is dynamically adjusted. According to the technical scheme of fusing physical modeling and the deep learning LSTM network, high-precision prediction of the service life of the motor is realized, the intelligent degree of data processing is improved, and the adaptability of the model to a complex operation environment is enhanced.

The invention relates to the technical field of frequency converter correction, in particular to a frequency converter direct-current bus correction method and system based on dynamic voltage feedback, and the method specifically comprises the steps: dynamically controlling the activation mode of a voltage sensor array according to a fluctuation state; constructing a direct-current bus voltage deviation model containing dead-zone compensation voltage and a voltage ripple frequency spectrum model containing PWM carrier sideband harmonic waves; respectively obtaining a first adjustment parameter and a second adjustment parameter; and generating a zero-sequence voltage injection instruction according to the second adjustment parameter, and implementing direct-current bus correction control by dynamically adjusting a zero-sequence voltage injection ratio. The problem that in the prior art, the system stability and the device service life are seriously restricted due to the risks of correction mismatch, harmonic amplification and even capacitor overheating of the direct current bus voltage under the dynamic working condition is solved.

The invention belongs to the technical field of battery management systems, and particularly relates to a battery state-of-charge estimation method for resisting a voltage sensor fault, which comprises the following steps: S1, data acquisition and system initialization; s2, state prediction; s3, measurement updating and online compensation are carried out; and S4, recursive circulation is carried out. According to the method, the direct-current bias quantity generated by a voltage sensor due to zero drift is widened into a state variable of a battery model, and the state variable, ohm internal resistance, polarization internal resistance, polarization voltage and charge state of a battery jointly form an expansion state vector; and performing real-time recursive estimation on the extended state vector by adopting an unscented Kalman filtering algorithm, and simultaneously obtaining an estimated value of the offset of the sensor and an estimated value of the SOC of the battery subjected to deviation compensation. Through combination of state extension and unscented Kalman filtering, fault bias of the voltage sensor can be identified and compensated online, non-Gaussian noise interference is effectively suppressed, and accuracy and robustness of battery SOC estimation in a complex industrial environment are remarkably improved.

The invention relates to an unmanned aerial vehicle zero-value insulator live detection device and method, an execution module comprises an AI image recognition unit and a detection probe, the detection probe integrates a voltage sensor and a resistance sensor and supports two modes of live resistance detection and distributed voltage detection, the AI image recognition unit is adjacent to the root of the detection probe, and the AI image recognition unit is adjacent to the root of the detection probe. The image acquisition module is used for acquiring insulator steel cap images and outputting positioning signals; the controller mainboard is electrically connected with the detection probe, the rotating mechanism and the AI image recognition unit through shielding cables, and the controller mainboard is used for receiving a positioning signal of the AI image recognition unit, receiving voltage detection data and / or current detection data transmitted by the detection probe, processing the detection data and obtaining a detection result of the state of the insulator. According to the invention, a dual-mode cooperation mechanism of'distributed voltage preliminary screening + charged resistance verification 'is adopted, and AI automatic positioning and mode automatic switching functions are combined, so that the detection efficiency is remarkably improved.

The invention discloses a submarine cable interlayer fault detection method and system for a submarine cable grounding system, and a medium. The method comprises the following steps: S1, pre-installing a power frequency current sensor, a power frequency voltage sensor and a traveling wave sensor on a sheath layer and an armor layer of landing sections at two sides of the high-voltage alternating-current submarine cable grounding system; the method comprises the following steps: extracting current and voltage signal data of a fault state through a power frequency current sensor, a power frequency voltage sensor and a traveling wave sensor, and carrying out data preprocessing including detection cleaning, data interpolation and data normalization on the current and voltage signal data; according to the method, fault codes are obtained through data collection, preprocessing, feature fusion, training learning and coding reconstruction and classification, and accurate fault detection is carried out on submarine cable interlayer faults through the fault codes.

A fail-safe automatic mains-electricity cutoff system and method for automatic disconnection from a mains service drop in the event of mains power outage or instability, and automatic reconnection after restoration of stable mains power, with indications of operational status through a unit display, an indicator light, and an RF communication link to a remote unit. A unit enclosure having a unit door and a ground bus houses the fail-safe automatic mains-electricity cutoff system which is installed on the mains service drop, below the electrical meter and above the main breaker panel or main service disconnect switch. Low-voltage DC power for system operation is supplied through a battery unit. Disconnection and connection to mains power is effected through a switching unit providing a mains-voltage sensor, a fail-safe switch and a fail-safe switch actuator. A premises-voltage sensor and current sensor can also be provided in embodiments. A controller controls operation of the fail-safe automatic mains-electricity cutoff system based on real-time measurements and programmed operational parameters, and records and reports system status and measurements.

A vehicle, system and method for monitoring an occurrence of thermal runaway in a battery pack of the vehicle. The system includes a plurality of voltage sensors and a processor. The plurality of voltage sensors obtains a plurality of voltage measurements at each of a plurality of battery cells of the battery pack. The processor is configured to determine a mean value based on the plurality of voltage measurements, compare a voltage measurement obtained from a selected battery cell to the mean value, and generate a notification signal when a difference between the voltage measurement from the selected battery cell and the mean value is greater than or equal to a prognostic threshold.

The invention discloses a differential non-intrusive voltage measurement method based on a charge simulation method, and relates to the technical field of voltage sensor measurement, and the method comprises the steps: obtaining the distance from a plurality of sensors to a simulation line charge; wherein every two sensors form a double-layer differential structure; the simulated line charge is located in the center of the wire; based on each double-layer differential structure, determining a corresponding field-source decoupling relational expression; based on an electromagnetic formula, obtaining a relational expression between the wire surface voltage and the simulated line charge in the single power transmission line model; determining a single line voltage inversion expression based on the field-source decoupling relational expression and the relational expression of the wire surface voltage and the simulated line charge in the single power transmission line model; determining the number of single-line voltage inversion equations based on the single-line voltage inversion expressions and the number of sensors; and based on a plurality of single-line voltage inversion equations, calculating to obtain a voltage solution with the minimum overall error. According to the invention, the overall anti-interference performance of voltage inversion can be improved.

The invention discloses a charging facility fault automatic diagnosis and repair system and method, and the system comprises a voltage sensor, a current sensor, a temperature sensor, a control module, an alarm module, and an upper computer, and the voltage sensor, the current sensor, and the temperature sensor are connected with the control module through wires. The control module is connected with the alarm module and the upper computer through wires and is in signal connection with the power supply module. Potential fault risks of the charging equipment can be found in time, automatic repairing is rapidly achieved, the reliability and operation efficiency of the charging equipment are effectively improved, and manual intervention requirements are reduced.

The invention discloses a multimodal transport cargo transportation state monitoring system based on a standardized intelligent tray group. The system comprises a modular tray group composed of a main control tray and at least one functional tray; the main control tray is integrated with an edge calculator, an RFID reader-writer, a temperature and humidity sensor and a power supply module; the functional tray comprises a strain sensor and an acceleration sensor; the tray groups are rigidly connected through electromagnetic lock catches, and a voltage sensor is arranged to monitor the connection state; the tray group transmits data through a mixed communication channel; LoRa wireless communication is adopted by default, and magnetic attraction wired connection is switched when the packet loss rate is greater than 1%; after being preprocessed by the edge calculator, the data are encrypted by the Beidou system and uploaded to the background server; the background server executes overload and unbalanced load analysis, vibration grade evaluation, environmental risk early warning and lock catch state monitoring; and triggering a user side to give an alarm when an exception is detected. The safety monitoring problem of goods transportation is effectively solved, the transportation safety is improved, and the accident risk is reduced.

The invention discloses a rapid screening method for battery cells. The method comprises the following steps: 1) constructing an OCV attenuation database under different battery cell SOC and warehouse temperature T; 2) establishing a dynamic compensation model by using the attenuation database; 3) calculating a theoretical attenuation value delta OCVthetical of the battery cell through a dynamic compensation model, and comparing the theoretical attenuation value delta OCVthetical with an actually measured attenuation value delta OCVactual of the battery cell to judge abnormality; the invention further discloses a rapid screening system for the battery cells, the rapid screening system is used for achieving the method, and the rapid screening system comprises a temperature sensor, a voltage sensor, an SOC estimation module, an edge calculation terminal and a database server. According to the invention, the inventory cell detection cost can be significantly reduced, and reliable data support is provided for cell echelon utilization.

The invention provides a digital non-contact alternating-current voltage sensor and a voltage measuring method, the sensor comprises an open-type alternating-current voltage probe, a sensing assembly based on the capacitive voltage dividing principle in the probe converts high voltage on a wire into a first voltage analog signal, and an external grounding shielding layer provides anti-interference protection. Signals output by the probe are sent to the signal acquisition module, a signal conditioning circuit in the signal acquisition module firstly filters, blocks and amplifies the signals, and second voltage analog signals easier to process are generated. And then, the main control unit integrated with an analog-to-digital converter converts the signal into a digital sampling value, and finally, the communication unit outputs a measurement result in a digital signal form. According to the sensor, through non-contact measurement and full-digital processing, safe and convenient alternating current high voltage detection is realized, and stable and anti-interference digital signals can be output.

The present invention discloses an online monitoring system and method for transformer winding deformation, which relates to the technical field of transformers, including a current sensor and a voltage sensor, which are respectively used to sample the three-phase current and voltage waveforms of the transformer, and send the collected data to a data processing device for further processing; the data processing device is used to control the sensor to perform sampling operations, and analyze and process the sampling results, calculate the short-circuit impedance, determine whether the winding is deformed, and give the degree of deformation; the data processing device provides a network interface to the outside, and sends the winding deformation monitoring results to a server; the client is used to connect to the server to display the real-time monitoring results of the winding deformation and the winding deformation alarm information. The present invention can perform long-term online monitoring of transformer winding deformation, discover transformer winding deformation problems early, deal with them as soon as possible, and improve the stability and safety of transformer operation.