1240 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 multi-oil-vane-tile detection, and discloses a multi-oil-vane-tile inner hole shape line detection method and a detection system, the detection system comprises a placing cylinder, a multi-oil-vane-tile is arranged on the outer side of the placing cylinder in a sleeving manner, the detection system further comprises a detection cylinder, a telescopic arm, a rotating motor and a control unit, and the detection cylinder is arranged on the upper portion in the placing cylinder in a transverse translation manner; the telescopic arm translates in the radial direction of the detection cylinder, the rotating motor drives the detection cylinder to rotate, the pressurization cavity and the detection cavity are each provided with a pressure sensor, and the telescopic arm comprises an arm body, a detection head and a piezoelectric sensor. The detection method comprises the following steps: S1, centering and positioning; S2, measuring the radius of a circular section of the inner hole; S3, measuring the radius of a circle of the inner hole; and S4, obtaining the shape line of the inner hole. The objective of the invention is to solve or at least alleviate the problems of complex operation and need of recalibration and positioning when a three-coordinate measuring instrument is adopted to carry out multi-oil-vane-tile inner hole shape line detection, and at the same time, the measurement accuracy can be guaranteed.

The invention relates to the field of power distribution network cable main insulation fault detection and management, and discloses a power distribution network cable main insulation fault detection and management integrated test platform construction method comprising the following steps: a) data acquisition: installing a plurality of sensors including a partial discharge sensor, a current and voltage sensor, a temperature sensor and a vibration sensor; acquiring real-time operation data of the power distribution network cable; b) data transmission: transmitting the acquired cable monitoring data to a data processing system in a wired or wireless communication mode; and c) data preprocessing: denoising, missing value interpolation, standardization or normalization processing is carried out on the transmitted original data so as to improve the quality of the data. The real-time operation state of the power distribution network cable is monitored through various sensors, and various advanced data processing methods are combined, so that the accuracy of fault detection is effectively improved, and identification deviation caused by data noise or sensor errors in a traditional fault detection method is avoided.

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 relates to the technical field of photovoltaic energy storage methods, and discloses a method for optimizing hybrid photovoltaic energy storage based on a Perturbobserve algorithm, which comprises a load, a photovoltaic power generation subsystem, an energy storage subsystem, a control and management system and a grid-connected system. The specific control method comprises the following steps: S1, obtaining an illumination intensity value within one day through a meteorological station, and obtaining a power grid net load value within one day through a voltage sensor and a current sensor; s2, taking economy optimization as a target, considering photovoltaic energy storage capacity constraints, and establishing an energy storage SOC control strategy target function; and S3, solving is carried out by using a PerturBobserve algorithm, and an optimal control pulse sequence is obtained. By optimizing the charging and discharging time and intensity of the energy storage battery, the consumption rate of photovoltaic power generation is maximized, the light abandoning phenomenon is reduced, and the energy loss in the charging and discharging process is reduced. Meanwhile, the PertuBobserve algorithm can avoid the excessive charging and discharging of the energy storage battery while considering the system load, prolongs the service life of the battery, and solves the problem of insufficient management of the service life of the battery in a traditional method.

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 offshore photovoltaic platform electric control system and a floating type offshore photovoltaic platform. The offshore photovoltaic platform electric control system comprises a controller, energy consumption equipment, a voltage sensor, a current sensor and a data monitoring system. The controller is electrically connected with a photovoltaic module of the offshore photovoltaic platform, the energy consumption equipment is electrically connected with the controller to form a closed circuit, the voltage sensor is connected with the energy consumption equipment in parallel, the current sensor is connected with the energy consumption equipment in series, and the data monitoring system is electrically connected with each of the voltage sensor and the current sensor. The offshore photovoltaic platform electric control system provided by the invention has the advantage of high efficiency and reliability in data monitoring of the photovoltaic module on the floating type offshore photovoltaic platform.

Intelligent lead-acid battery system capable of monitoring a parameter (e.g., voltage, temperature) of one or more battery cells of a lead-acid battery. In one implementation, the battery system includes a housing having a cells compartment, a battery monitoring system (BMS) compartment, and a wall disposed between the cells compartment and the BMS compartment. A first post and a second post are associated with a battery cell. The first post and the second post protrude through the wall between the cells compartment to the BMS compartment. A voltage sensor electrically couples to the first post and the second post to monitor the voltage of the battery cell. A temperature sensor can couple to the first or second or both posts. The intelligent AGM battery system can predict the battery module's state of health, state of charge, module status, power capability, life expectancy, etc.

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 disclosure relates to an apparatus for diagnosing a battery of a vehicle, the apparatus being capable of performing reliable diagnosis with respect to low voltage of a battery of a vehicle, a battery diagnostic method thereof, and a vehicle system including the same. The apparatus may include a voltage sensor measuring voltage of a plurality of battery cells in the battery, and a processor performing low voltage diagnosis with respect to the battery on the basis of the cell voltage measured by the voltage sensor. The processor may determine whether or not the cell voltage satisfies a preset diagnostic condition when the cell voltage satisfies a diagnosis start condition, and when the processor determines that the cell voltage satisfies the diagnostic condition, the processor may diagnose that the battery is at a low voltage state.

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.

Intelligent lead-acid battery system capable of monitoring a parameter (e.g., voltage, temperature) of one or more battery cells of a lead-acid battery. In one implementation, the battery system includes a housing having a cells compartment, a battery monitoring system (BMS) compartment, and a wall disposed between the cells compartment and the BMS compartment. A first post and a second post are associated with a battery cell. The first post and the second post protrude through the wall between the cells compartment to the BMS compartment. A voltage sensor electrically couples to the first post and the second post to monitor the voltage of the battery cell. A temperature sensor can couple to the first or second or both posts. The intelligent AGM battery system can predict the battery module's state of health, state of charge, module status, power capability, life expectancy, etc.

The present invention describes a system comprised of sensors, microcontrollers, wireless communication modules, and methods for utilizing said sensor system to determine, in real-time, a threat and / or theft of a catalytic converter. An alarm signal is then generated by connecting with the security system of a premises, if available, and relevant parties and law enforcement are notified of the threat and / or theft event.

The utility model discloses an intelligent vacuum circuit breaker, which comprises an operating main shaft, an isolating switch shaft, a solid-sealed polar pole and a grounding switch, the operating main shaft and the isolating switch shaft are respectively driven to rotate by a circuit breaker electric operating mechanism and an isolating switch electric operating mechanism, and an insulating shell of the solid-sealed polar pole is provided with a current sensor and a voltage sensor. The operating main shaft drives a breaker moving contact and a breaker static contact to be in contact or separated through an insulating pull rod, the insulating pull rod is provided with a pressure sensor, the breaker static contact is connected with one end of an isolation switch disconnecting link, and an isolation switch shaft drives the other end of the isolation switch disconnecting link to be in contact or separated from an isolation static contact. The circuit breaker electric operating mechanism is provided with a remote communication antenna and a control module. The purpose of the utility model is to provide an intelligent vacuum circuit breaker, which enables a 35kV prefabricated substation to completely realize remote control, state measurement and health monitoring, improves the operation efficiency, and realizes remote control of the health state of equipment.

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.

A system comprising a power appliance coupler and a rack power distribution unit (rPDU) are provided. The power appliance coupler includes a plug connector configured to connect to a power outlet of the rPDU, a power outlet port; a microprocessor and a memory; an infrared (IR) receiver port configured to receive data via an IR signal and to communicate the received data to the microprocessor; an IR transmitter port configured to transmit output data from the microprocessor as an IR signal; a voltage sensor operatively coupled to the microprocessor; and a current sensor operatively coupled to the microprocessor. The rPDU includes an input power supply cord, an interchangeable monitoring device (IMD), a non-volatile memory, and a plurality of power outlets and is operatively coupled to the power appliance coupler via a wired connector.

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.

Intelligent lead-acid battery system capable of monitoring a parameter (e.g., voltage, temperature) of one or more battery cells of a lead-acid battery. In one implementation, the battery system includes a housing having a cells compartment, a battery monitoring system (BMS) compartment, and a wall disposed between the cells compartment and the BMS compartment. A first post and a second post are associated with a battery cell. The first post and the second post protrude through the wall between the cells compartment to the BMS compartment. A voltage sensor electrically couples to the first post and the second post to monitor the voltage of the battery cell. A temperature sensor can couple to the first or second or both posts. The intelligent AGM battery system can predict the battery module's state of health, state of charge, module status, power capability, life expectancy, etc.

The invention discloses a series battery pack voltage detection device and detection method for dynamic equalization, and relates to the field of battery detection, the series battery pack voltage detection device comprises an equalization control module used for accessing each battery unit of a to-be-detected battery pack, receiving a preset equalization strategy, and outputting the equalization strategy to the to-be-detected battery pack; the charging and discharging state of each battery unit is adjusted in real time according to the battery voltage and temperature data; the voltage detection module is used for performing voltage data sampling on each battery unit in real time through a voltage sensor, and converting an analog signal into a digital signal through an analog-to-digital converter; the model construction unit is used for constructing a machine learning model for fault analysis, identifying and marking an anomaly set, outputting a fault cause and adjusting a strategy reference; the voltage and temperature data of each battery monomer are collected in real time, the abnormal state of the battery monomer is monitored, a dynamic adjustment strategy based on the influence coefficient is generated in combination with analysis of historical data by a machine learning model, the adaptability is higher, and the charging and discharging efficiency is effectively improved.

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.