1008 results about "Voltage transducers" patented technology

The invention discloses a DC voltage divider parameter anomaly detection method based on harmonic characteristics, and the method comprises the steps: deploying an isolation type voltage sensor at a bus of a DC power transmission system, carrying out the voltage sampling through combining with an ADC, carrying out the data buffering and preprocessing, extracting a fundamental wave component and a target harmonic component through the fast Fourier transform, and calculating the harmonic distortion rate. And constructing a low-voltage arm capacitance and harmonic distortion rate model based on a voltage divider secondary circuit equivalent circuit and an energy distribution principle, and calibrating the low-voltage arm capacitance and harmonic distortion rate model. And designing a multi-objective optimization scheduling algorithm based on energy storage capacity and capacitance characteristics, and realizing dynamic power distribution and harmonic suppression of the system. And after abnormal triggering, the system automatically loads a capacitance compensation coefficient, adaptively adjusts algorithm parameters in combination with a measurement loop PI control link, reduces a harmonic amplification effect, and ensures that a measurement error is within an acceptable range. Harmonic monitoring precision and capacitance compensation optimization capability of the direct-current power transmission system are effectively improved, and stable operation of the system is guaranteed.

The invention provides a design method and system for a high-integration-level low-power-consumption radio frequency front-end chip, and relates to the technical field of chips, the design method comprises the steps that a radio frequency transceiver, a power amplifier, a low-noise amplifier and a switch module are integrated on a single silicon substrate, and a self-adaptive bias control system is realized in the monolithic integrated radio frequency front-end chip; comprising the steps that a plurality of temperature sensors, a plurality of voltage sensors and a plurality of current sensors are arranged, a digital signal processor calculates optimal bias parameters of all modules according to a preset power consumption optimization algorithm, and bias control signals are sent to the corresponding modules through a digital-to-analog converter; based on the self-adaptive bias control system, dynamic power consumption management of the radio frequency front-end chip is achieved, when it is detected that radio frequency signal transmission is finished, the digital signal processor controls all the modules to enter a low-power-consumption standby state, and the overall power consumption of the chip is further reduced.

The invention provides an electric energy load real-time data acquisition and analysis method based on a low-cost scheme, and the method comprises the following steps: constructing a distributed data acquisition network, employing a combined hardware architecture of a current / voltage sensor and a low-power-consumption MCU, and achieving the data acquisition optimization through a dynamic sampling rate adjustment mechanism, when the load fluctuation exceeds a set threshold value, the sampling rate is automatically increased, when the load is stable, the sampling rate is reduced to a calibration sampling rate, and a Kalman filtering algorithm is adopted to carry out real-time data preprocessing; a three-level data transmission system is established, the three-level data transmission system comprises an edge acquisition node layer, a convergence gateway layer and a cloud processing layer, an optimized AODV routing protocol is adopted among nodes to construct a star-shaped and net-shaped hybrid network topology, and an optimal transmission path is dynamically selected according to the signal strength and the network congestion condition; a differentiated QoS transmission strategy is designed, and data is divided into three priorities of fault alarm data, real-time monitoring data and historical data.

The invention provides a voltage fluctuation compensation control method and system for a chip, and relates to the technical field of voltage compensation, and the method comprises the steps: arranging a voltage sensor and an intelligent agent on each chip of a multi-chip system, and obtaining the distributed voltage fluctuation data of the multi-chip system; according to the distributed voltage fluctuation data, generating a voltage fluctuation distribution diagram representing the overall voltage distribution of the multi-chip system, and obtaining chip-level voltage compensation control data by using a model prediction control algorithm; global optimal voltage control compensation data is obtained by using a distributed optimization algorithm based on a consensus mechanism, adaptive optimization is carried out on the global optimal voltage control compensation data by using an online learning algorithm, and the optimal voltage control compensation data is obtained according to the optimized global optimal voltage control compensation data. And controlling a power management circuit in each chip to adjust the power supply voltage of the chip, and minimizing energy loss in the power supply voltage adjusting process through an intelligent power management strategy to realize voltage fluctuation compensation of the multi-chip system.

The invention discloses a new energy boost transformer abnormity early warning system and method, and the system comprises a data collection module which employs a sensor technology, a data collection card carries out the collection of the data of a new energy boost transformer, and carries out the early warning of the new energy boost transformer. The sensors comprise a temperature sensor, a voltage sensor, a current sensor, a vibration sensor and an oil level sensor. Feature vectors are selected by adopting a wavelet transform algorithm, a boost variation anomaly positioning model is established by adopting an improved artificial neural network to determine an anomaly position, parameters of an anomaly detection model are determined by adopting a genetic algorithm, and an anomaly type is judged. The MFE-TS model is established to optimize the objective function so as to improve the accuracy of new energy boost variation abnormity identification, the man-machine interaction module is adopted to display the abnormal position and the abnormal type on the user interface, and the new energy boost variation abnormity early warning capability is improved.

A power conversion device includes a power conversion unit and a control unit. The power conversion unit includes a first semiconductor module. The first semiconductor module includes a plurality of power semiconductor elements. Some of the plurality of power semiconductor elements are detection target elements. The power conversion unit further includes a temperature sensor and a voltage sensor. The temperature sensor detects a surface temperature of the detection target element as a sensor position temperature. The voltage sensor detects an inter-terminal voltage of the detection target element. The control unit calculates a loss in the detection target element based on the sensor position temperature and the inter-terminal voltage, and estimates a maximum temperature based on the calculated loss.

The invention discloses a driver controller detection system, which comprises an acquisition module, an acquisition module, a processing module, an identification module, a judgment module, an analysis module and a generation module, the acquisition module captures contact information and electromagnetic interference data of contacts in real time by means of a high-dynamic voltage sensor and a broadband EMC probe; the acquisition module is triggered by a locomotive signal and records dynamic working condition data; the processing module aligns the data, performs denoising and extracts fault parameters; the identification module scans the voltage waveform and judges the transient contact failure; the judgment module monitors EMC data and compares an instruction position, and judges misoperation; the analysis module analyzes fault association, judges a composite type and triggers an alarm; the generation module generates a detection report and a log. According to the system, a multi-dimensional monitoring network is constructed under a dynamic working condition, data reliability is guaranteed, faults are accurately identified, a compound fault source is positioned, and the safety and stability of locomotive operation are improved through risk grading early warning and automatic reporting.

The invention discloses an output voltage detection method and system based on an aviation power supply, and relates to the technical field of aviation power supply voltage detection. A differential voltage sensor is connected to the output end of an aviation power supply, a high-frequency suppression capacitor is connected in parallel, the sampling rate of 20-50 times of the working frequency is set according to the type of the power supply, and the signals are collected and converted into analog voltage signals; the analog signal is subjected to two-stage filtering, is amplified to 0-5V through an instrument amplifier, and is converted into a digital voltage sequence through a 16-bit ADC (Analog to Digital Converter); calculating AC power supply effective value, peak value and other parameters, DC power supply average value, ripple coefficient and other parameters; comparing the characteristic parameters with a preset threshold value, determining that the characteristic parameters exceed the threshold value and recording related information; and a detection result is transmitted to a monitoring terminal through an RS485, data is locally stored, and a switching value is output to an alarm device when the data is abnormal. The method adapts to aviation power supply load fluctuation and environment temperature change, detection precision is high, data transmission and storage are safe, abnormity is accurately identified and early warning is carried out in advance, and aviation power supply operation and maintenance reliability and efficiency are improved.

A fastener driver includes a motor, a trigger, and a lifting assembly operable to be moved by the motor. The fastener driver includes a speed sensor, a voltage sensor, and a current sensor. A controller is configured to provide, in response to actuation of the trigger, power to the motor, receive speed signals from the speed sensor indicative of the speed of the motor, receive voltage signals from the voltage sensor indicative of the voltage of a battery pack, and receive current signals from the current sensor indicative of the current of the motor. The controller determines a torque of the motor based on the signals, determines whether the torque of the motor is less than or equal to a torque threshold, and activate an electronic clutch, in response to the torque of the motor being less than or equal to the torque threshold, to electronically brake the motor.

The invention provides an electric energy metering error correction method and system based on vector analysis. Belongs to the technical field of electric energy metering in an electric power system, and comprises the following steps: collecting current and voltage data in a power grid in real time by using a high-precision current and voltage sensor, and transmitting the data to an electric energy metering error correction system for processing; carrying out vector analysis on the collected current and voltage data based on a vector control technology to obtain vector information of the voltage and the current; by collecting current and voltage data in a power grid in real time and utilizing a vector analysis technology, an electric energy metering error can be calculated more accurately, so that the accuracy of electric energy metering is improved.

The invention relates to the technical field of power systems, in particular to a power distribution network voltage sag compensation method, system and device and a medium. The method comprises the following steps: monitoring the voltage level of a power distribution network in real time by using a high-precision voltage sensor to obtain voltage data; identifying a voltage sag event based on the acquired voltage data; performing feature extraction on the determined voltage sag event; according to the extracted characteristic quantity, a compensation strategy is dynamically formulated, and the compensation strategy comprises the step of determining the amplitude and duration of compensation; executing voltage compensation through the grid-connected inverter according to the determined compensation strategy, wherein the output of the grid-connected inverter is adjusted according to the impedance change of the power grid and the influence of voltage disturbance; and monitoring the compensated power grid voltage, evaluating a compensation effect, and feeding back an evaluation result to optimize a subsequent compensation strategy. According to the invention, the voltage sag event can be rapidly and accurately detected, and an effective compensation strategy is formulated for compensation.

In order to diagnose the degree of deterioration of a secondary battery (2), a device (1) comprising a sensor unit (11) including a voltage sensor (15), and a control device (10) having a mathematical model storage unit, a history data storage unit, and a computation function, is electrically connected to the secondary battery (2). Numerical data reflecting a measurement value of internal resistance is stored in the history data storage unit in association with the time axis. A mathematical model, which is defined so as to derive a parameter having a value that varies according to the numerical data at each of a plurality of time points in a predetermined period on the time axis, and the length of said period, is saved in the mathematical model storage unit. The control device (10) derives the parameter by computation in which numerical data accumulated within a period from a specific time point in the past to the most recent time point is applied to the mathematical model, and estimates the degree of deterioration of the secondary battery (2) on the basis of the parameter.

The invention discloses a flexible DC converter valve sub-module fault diagnosis method, which comprises the following steps: acquiring an output sequence of a capacitor voltage sensor of each sub-module, calculating by utilizing capacitor voltage output sequences of adjacent periods to obtain a sub-module self-correlation coefficient, judging the sub-module self-correlation coefficient and a set self-correlation coefficient threshold value, and judging whether the sub-module self-correlation coefficient is smaller than the set self-correlation coefficient threshold value or not. The judgment comprises whether the sub-module has a fault or not; when the sub-module has a fault, calculating an effective value of a capacitor voltage increment, an output average value of a capacitor voltage sensor and a difference between positive zero-crossing times and negative zero-crossing times of the capacitor voltage increment to obtain a calculation result; according to the calculation result, the fault type is identified, joint fault diagnosis is carried out only by utilizing the information of the capacitor voltage of the sub-module, the algorithm is simple, communication with a main controller is not needed, and the hardware cost burden is reduced.

The invention discloses a high-voltage cable outer metal sheath grounding defect on-line monitoring method and monitoring system, which can work under the condition that a cable is not powered off, realizes on-line monitoring, and realizes on-line monitoring through the cooperation of a current sensor, a voltage sensor, a single-chip microcomputer and an upper computer. And the equipment can quickly detect when a fault occurs and is convenient for maintenance workers to timely eliminate obstacles, so that the risk of major electric power accidents is reduced. The algorithm is similar to an improved neural network algorithm and is used for diagnosing the defects of the outer metal sheath of the cable, the weight is obtained through training learning, and finally the defects of the outer metal sheath of the high-voltage cable are determined. The protection content covers each component of the technical scheme, and comprises a monitoring method, a device structure, a sensor technology, a data processing algorithm, a neural network learning algorithm and the like.

The invention discloses a radio frequency voltage sensor and a plasma source system. The radio frequency voltage sensor comprises a signal acquisition module and a signal processing circuit. The signal processing circuit comprises a rectangular wave pulse detection module, a signal conversion module, a peak holding module and an operation output module; the input end of the rectangular wave pulse detection module receives a pulse synchronization signal; the signal conversion module is connected with the output end of the signal acquisition module; the first input end of the peak holding module is connected with the output end of the signal conversion module, and the second input end of the peak holding module is connected with the output end of the rectangular wave pulse detection module; and the operation output module is connected with the output end of the peak holding module. According to the radio frequency voltage sensor and the plasma source system provided by the invention, the peak voltage corresponding to the single-stage pulse can be accurately detected, the peak voltage corresponding to the multi-stage pulse can also be stably detected, the output is prevented from jumping to zero during the pulse closing period, and the stability and practicability of measurement are improved.

The invention relates to the technical field of intelligent regulation and control, and particularly discloses a processing system for regulating and controlling the power of a charging pile, which comprises a data acquisition module, a power grid frequency data analysis module, a power grid voltage data analysis module, a comprehensive analysis module and a power dynamic adjustment module, the data acquisition module acquires frequency and voltage data of a power grid in real time through a frequency and voltage sensor, the analysis module processes the acquired data and extracts a power grid frequency fluctuation index and a power grid voltage fluctuation index, and the comprehensive analysis module combines the two fluctuation indexes to calculate a power grid abnormal index; and the power dynamic adjustment module judges the current states of the power grid according to the power grid abnormal index, including a serious abnormal state, a general abnormal state and a normal state, and outputs a corresponding charging power regulation and control command according to the judgment result.

An adaptive IoT power plug system for intelligent load management and remote control, consisting of: a power plug housing configured to be inserted between a power outlet and an electrical appliance; an embedded sensor array comprising at least one voltage sensor, at least one current sensor, and at least one power sensor and configured to continuously monitor the electrical parameters of the connected device; a microcontroller unit (MCU) operatively coupled to the sensor array and configured to execute a load prioritization technique that dynamically determines power allocation based on real-time consumption data and user-defined priority levels for one or more connected loads; a semiconductor switching module electrically connected to the MCU and configured to selectively interrupt or modulate the power supply to the connected device based on commands from the load prioritization technology; a wireless communication module integrated into the MCU for bidirectional data exchange via at least one wireless protocol selected from Wi-Fi, Zigbee or Bluetooth Low Energy; a user interface that can be accessed remotely via a mobile or web application and enables the configuration of load priorities, real-time monitoring of power consumption, scheduling of device operation, and manual override of switching commands; and a power management subsystem including a power harvesting module configured to provide supplemental power to the system's electronic components, thereby reducing dependence on the main power source; The system autonomously adjusts the power distribution between multiple connected devices to load fluctuations and predefined user priorities in order to optimize energy efficiency and prevent overloading of the circuit.

The utility model relates to the technical field of servers, in particular to a server power supply heat dissipation device, which comprises a CPLD (complex programmable logic device) arranged on a power supply backboard and connected with a BMC (baseboard management controller) through an I2C (inter-integrated circuit) link; the CPLD is connected with a first port of the multiplexer, and the BMC is connected with a second port of the multiplexer; a third port of the multiplexer is connected with the power management bus; the CPLD is connected with a control pin of the multiplexer so as to control the multiplexer to communicate the first port to the third port or communicate the second port to the third port; the power management bus is connected with a voltage sensor and a current sensor which are arranged at the downstream of the power converter; and the CPLD is connected with the power supply fan driving chip.

The invention relates to the technical field of power system fault monitoring, and discloses a measurement switch fault identification and early warning system, which comprises a signal acquisition unit, a feature analysis unit, a fault judgment unit and an early warning trigger unit. The signal acquisition unit acquires a potential fluctuation sequence and an exciting current pulse sequence through a contact voltage sensor and a coil current transformer; the feature analysis unit extracts time-frequency domain and statistical domain feature groups by means of wavelet transform and moment function analysis; the fault judgment unit generates a fault judgment vector by adopting kernel principal component analysis fusion features; and the early warning triggering unit triggers four-level early warning by using dynamically updated grading threshold values (including historical reference values and online dynamic values). According to the system, through multi-modal feature fusion, dynamic threshold adjustment and a grading early warning strategy, accurate identification and grading early warning of measurement switch faults are achieved, the fault identification accuracy and early warning timeliness are improved, and the system is suitable for the field of electric power and industrial automation.

An energy storage apparatus 1 that is connected to a vehicle 2 includes: a plurality of energy storage cells 30A; a balancer circuit 38 that makes each of the plurality of energy storage cells 30A individually discharge electricity; a voltage sensor 35 that detects a voltage of each of the energy storage cells 30A; and a management unit 37. The management unit 37 performs: request processing (S103) where the management unit 37 requests the vehicle 2 to charge the energy storage apparatus 1 with electricity when a predetermined condition for reducing a difference in electric amount between the energy storage cells 30A is satisfied; and reducing processing (S106) where, after the energy storage apparatus 1 is charged with electricity by the vehicle 2, a voltage of each of the energy storage cells 30A is detected by the voltage sensor 35, and an operation time of the balancer circuit 38 is changed corresponding to a difference between detected voltages so as to reduce a difference in amount of electricity between the energy storage cells 30A.

The invention relates to a power distribution network maintenance method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a voltage value, acquired by a voltage sensor, of each detection node in a power distribution network, and acquiring a potential difference between every two detection nodes based on the voltage values; under the condition that the potential difference is not in the corresponding target potential difference range, inputting a potential difference signal corresponding to the potential difference into the trained K adjacent model to obtain an abnormal state category corresponding to the potential difference signal; and determining a fault category and a fault position of the power distribution network based on the abnormal state category, and maintaining the power distribution network based on the fault category and the fault position. According to the method provided by the invention, by maintaining the power distribution network, the power supply quality of the power distribution network can be improved, and the risk and cost of system operation and maintenance can be reduced.

A charging communication module and a charging method of an electric vehicle are provided. The charging communication module includes a voltage sensor that senses a voltage level of a signal line to generate a sensing result, a controller that generates first control information based on the sensing result and converts information of a first communication format provided from the signal line into information of a second communication format, and a switch device that electrically connects or disconnects the signal line with or from the controller based on the first control information.

The invention provides a power storage device and a current interruption method. A power storage device is provided with a power storage device module, a voltage sensor, a current sensor, an explosion fuse, and a controller. The voltage sensor detects a voltage value of the power storage device module. The current sensor detects a value of a current flowing through the power storage device module. The explosion fuse is connected in series with the power storage device module. In the controller, a voltage condition and a current condition are set. The voltage condition is predetermined according to a voltage value detected by the voltage sensor. The current condition is predetermined according to the current value detected by the current sensor. The controller operates the explosion fuse when both a voltage condition and a current condition are satisfied.

The invention relates to the technical field of power filters, and discloses a fault-tolerant control method, system, equipment, medium and product for an active power filter, and the method comprises the steps: building an observer of a fault sensor through the type of the fault sensor after the type of the fault sensor of the active power filter is determined, and outputting an observation value through the observer; and fault-tolerant control is carried out on the fault sensor through the observed value, so that the fault-tolerant control is carried out on the power grid voltage sensor, the inverter output current sensor and the direct current side voltage sensor without changing a modulation strategy, and the accuracy of fault-tolerant control of the active power filter is improved; and stable operation and reliability of the system of the active power filter are ensured.

The invention provides a DC Buck converter voltage and current sensor fault-tolerant control system, which comprises the following steps: establishing a time-varying state space model of a DC Buck converter, and dynamically reconstructing a parameter matrix by the time-varying state space model according to the real-time change of a duty ratio; the time-varying augmentation fault estimator is used for monitoring faults of a voltage sensor and a current sensor in real time, the fault estimator is implemented through a model reference adaptive filter, a Buck converter is adopted to construct a reference model in a healthy state, and efficient real-time diagnosis and correction can be achieved through the faults of the voltage sensor and the current sensor of the Buck converter. And various types of sensor faults such as a jump fault, an offset fault, a stuck fault, a high-frequency fault and a gap fault can be effectively estimated. For measurement distortion of the voltage sensor and the current sensor, the fault estimator can provide comprehensive evaluation information of fault types and fault severity.

The invention relates to a real-time fault monitoring method for a circuit breaker. The method comprises the following steps: acquiring a current signal of a zero-sequence current sensor; whether the current signal is abnormal or not is judged, and if yes, the current signal is decomposed into multiple frequency bands through wavelet packet transformation; extracting a high-frequency component and a low-frequency component in the frequency band, and determining whether to trigger a fault early warning condition according to the energy of the high-frequency component and the low-frequency component; if the fault early warning condition is triggered, acquiring a voltage signal of a voltage sensor; and performing FFT harmonic component analysis on the voltage signal, extracting waveform features, and determining the fault type of the circuit breaker by using a trained fault feature model. According to the invention, the current is firstly analyzed to obtain the preliminary analysis of whether the circuit breaker has a fault, and then the voltage is finely analyzed, so that the fault type is determined, the fault judgment time is greatly shortened, the fault type confirmation efficiency is improved, the fault generation trend can be analyzed, and the fault can be effectively handled.

The invention discloses an intelligent cable inspection system and inspection method, and belongs to the technical field of cable inspection, and the system comprises an Internet of Things module, and an automatic inspection module, a data collection module, a data monitoring module, a continuous monitoring module, a data background storage module and a visualization module which are wirelessly connected through the Internet of Things module. The automatic inspection module comprises a robot carrying an infrared temperature sensor and an unmanned aerial vehicle, the data acquisition module comprises a digital voltage sensor, the continuous monitoring module comprises a humidity sensor, a Hall effect sensor and a direct current leakage current sensor, and the data background storage module comprises a data recording module, a data storage module and a data analysis module. The cable inspection efficiency and accuracy are improved through the processes of automatic inspection, data acquisition, data monitoring, data storage and visualization.

The invention relates to a full-electronic interlocking turnout control system and electronic equipment. The full-electronic interlocking turnout control system comprises a processor with a two-out-of-two framework and a point switch, the three-phase power electronic switch, the five-path state switching relay and the five-path circuit connecting nodes are sequentially connected between the processor and the point switch in series; the voltage sensor is connected between the processor and the five-path circuit connection node and is used for monitoring the voltage between the signal input ends of the point switch; and the processor is used for controlling the working state of the point switch by controlling the working states of the three-phase power electronic switch and the five-path state switching relay, acquiring the voltage between the signal input ends of the point switch monitored by the voltage sensor, and determining the position of the turnout based on the voltage between the signal input ends of the point switch. The whole control monitoring circuit is simple and effective, and the working state of the switch machine and the position of the turnout can be monitored in real time.

The invention relates to a power distribution cable operation early warning method and device. The method comprises the steps that current and voltage on a cable are monitored in real time through a current sensor and a voltage sensor; inputting the current and the voltage into a dynamic analysis module based on machine learning, and performing multivariable feature extraction and trend point prediction by adopting a long-short-term memory network model; establishing a baseline model in combination with an adaptive algorithm according to the historical operation data and the environment variables; through model training, the current, voltage and temperature threshold range is adjusted in a self-adaptive mode; when the operation data exceed a confidence interval or exceed a dynamic threshold value, early warning is triggered; performing grading processing on the abnormity through an early warning system based on a multi-stage risk assessment mechanism; and after the early warning is triggered, the accurate positioning of the fault position is realized through the distributed sensor nodes. According to the method and the device, an effective baseline model can be established to monitor and predict the operation state of the power distribution network cable, so that the overload problem can be found and processed in time, and safe and stable operation of a power system is ensured.

The present invention relates to the technical field of new energy vehicles, and discloses a method for monitoring battery fires in energy vehicles based on big data, including: step 1, arranging a multimodal sensor network at the battery unit, module and system level, wherein the sensors include temperature sensors, voltage sensors, current sensors, gas sensors, optical fiber sensors and vibration sensors to collect raw data related to temperature, voltage, current, gas concentration, heat distribution and vibration during battery operation. By arranging a multimodal sensor network, including temperature, voltage, current, gas, optical fiber and vibration sensors, and combining a dynamic sampling frequency adjustment strategy, the granularity and sensitivity of data collection are intelligently adjusted according to the battery operating conditions, and comprehensive feature data is generated to achieve full coverage and accurate monitoring of the battery operating status, and obtain the effect of multi-dimensional and high-quality monitoring data, providing a solid data foundation for subsequent analysis.