708results about "Frequency analysis" patented technology

The invention discloses a distribution line fault intelligent diagnosis method based on multi-source information fusion, and belongs to the technical field of power system fault detection and diagnosis, and the method comprises the steps: obtaining distribution line data and meteorological data; superposing the distribution line data with the meteorological data to generate a space-time fusion feature matrix based on the wire space displacement; dynamically adjusting a preset initial threshold value by using the space-time fusion feature matrix; and performing multi-round verification on the abnormal fluctuation of the electrical parameters of the line according to the fault judgment threshold, generating a fault positioning instruction, performing real-time matching with the topological structure of the line, correcting the priority queue of the fault section based on the switching action feedback data of the feeder terminal, and triggering the fault removal action matched with the priority. According to the method, spatial-temporal feature fusion of current, voltage, weather and spatial displacement data and a dynamic threshold adjustment mechanism are adopted, the fault type can be accurately identified, the fault section can be positioned in real time, and the diagnosis precision and the response speed in a complex environment are remarkably improved.

The invention provides a mining circuit fault self-diagnosis method and system. According to the method, current and voltage waveforms and three-dimensional vibration signals of a cable are collected, and anti-interference data are generated through self-adaptive noise reduction and time sequence synchronization; a waveform interception window is dynamically adjusted based on the correlation between the mechanical vibration intensity and the current transient rate, the wave crest slope variable quantity is extracted from the current transient segment, high-frequency harmonic energy is separated from the voltage transient segment, time-frequency analysis is carried out on the vibration signal to extract an energy sudden increase frequency point, and a mechanical damage spectrum feature set is constructed; inputting the features into a space-time correlation model, and verifying space-time consistency of current distortion and voltage abnormity to generate composite features; dynamically correcting a fault threshold based on the environmental interference factor; and finally, a short-circuit peak or open-circuit oscillation diagnosis result is output according to the association strength of the electrical and mechanical characteristics. According to the invention, accurate fault self-diagnosis of the mining cable under a complex working condition is realized.

The invention relates to the field of electric meter box monitoring, and discloses an anti-electricity-stealing method and system for an intelligent electric meter box, which is used for solving the problem of false alarm caused by harmonic interference when anti-electricity-stealing early warning is carried out, and comprises the following steps: collecting time domain signals of current and voltage in the intelligent electric meter box in a historical detection time period; converting the time domain signal into a frequency domain signal, extracting harmonic information of each harmonic, and establishing a normal harmonic feature model to obtain a target harmonic range; the method comprises the following steps: acquiring time domain signals of current and voltage in an intelligent electric meter box in real time, extracting harmonic information, performing filtering processing according to a target harmonic range, performing first abnormality judgment by using an abnormality detection algorithm, performing multi-parameter joint evaluation if abnormality is judged for the first time, and performing second abnormality judgment according to a multi-parameter joint evaluation result. And electricity larceny early warning is carried out according to the second abnormal judgment result, so that false alarm and missing alarm are effectively reduced, and the adaptability of the system to a complex electricity larceny mode is remarkably enhanced.

A wide-band oscillation wide-area monitoring system and a wide-band oscillation source localization method, which relate to the technical field of power system stability. The wide-band oscillation wide-area monitoring system comprises: a plurality of signal sensing modules, configured to acquire electrical signals from measured points; a plurality of wide-band measurement devices connected to the signal sensing modules, each wide-band measurement device being configured to perform spectrum analysis on the basis of electrical signals acquired by each signal sensing module, and generate oscillation alarm information on the basis of the spectrum analysis result, and the wide-band measurement devices being further configured to: in response to a waveform recording instruction, start to carry out waveform recording to generate waveform recording files for a same time section, and upload the waveform recording files and the spectrum analysis results; and a wide-band measurement master station in communication connection with each wide-band measurement device, the wide-band measurement master station being configured to: on the basis of the oscillation alarm information uploaded by each wide-band measurement device, determine whether a wide-band oscillation risk is present for a power system, and if so, send the waveform recording instruction to all of the wide-band measurement devices, and on the basis of the spectrum analysis result fed back by each wide-band measurement device, perform oscillation source localization.

The invention discloses an automatic anchor point searching and processing method for grid-connected test data of a photovoltaic inverter, and belongs to the technical field of automatic test of a power system. According to the method, a three-phase voltage and current signal output by a power grid simulator and an inverter power instruction signal are aligned through a high-precision time synchronization device; wavelet transform multi-scale noise reduction and moving average filtering combined preprocessing is adopted to improve the signal-to-noise ratio; identifying a voltage zero crossing point candidate set, a drop starting point candidate set and a recovery termination point candidate set based on a self-adaptive dynamic threshold value; transient energy characteristic verification is introduced for a voltage drop starting point; effective anchor points are confirmed through time window association of power instruction step changes. According to the method, the problems of low efficiency of manual key event point identification, misjudgment caused by noise interference, grid event and inverter response time sequence correlation missing and the like are solved, and the automation degree of test data analysis, anchor point positioning precision and control response time sequence analysis reliability are remarkably improved.

The invention relates to the technical field of cable partial discharge detection and diagnosis, provides a high-voltage cable multi-parameter intelligent diagnosis method, system and equipment and a storage medium, and aims to solve the problems of low accuracy and poor reliability of high-voltage cable multi-parameter diagnosis caused by multiple factors. The method comprises the following steps: decomposing a partial discharge signal after interference filtering, extracting a time domain amplitude change rate and an energy distribution parameter, and generating a multi-dimensional time-frequency domain feature vector; calibrating a multi-dimensional time-frequency domain feature vector, carrying out nonlinear manifold mapping, generating a low-dimensional feature vector matched with dielectric spectrum characteristics, matching a partial discharge type with the highest similarity with an identification result, judging an insulation degradation level in combination with a final loss threshold interval, and generating a comprehensive diagnosis report. According to the invention, the ultrahigh-frequency sensor array captures the partial discharge signal matched with the dielectric spectrum, precise identification of the discharge type and dynamic evaluation of the insulation degradation level are realized through multi-scale analysis and deep learning fusion, and the reliability of high-voltage cable diagnosis is improved.

The invention belongs to the field of power equipment monitoring, and discloses an electrical signal acquisition system based on intelligent dynamic sampling, which comprises a signal acquisition module, a signal conditioning module, an abrupt change detection module, an impedance calculation module and an upper computer, and is characterized in that the signal acquisition module comprises a first ADC module, a second ADC module, a first MCU module and a second MCU module. Short-circuit impedance real-time calculation and waveform abrupt change detection are adopted for cooperative judgment, and when equipment operates stably, the sampling rate of the system is kept low; when impedance changes or waveform changes suddenly, the sampling rate is improved automatically, important signal changes are recorded accurately, and waveform abrupt change detection is carried out by adopting an edge detection algorithm improved based on an FIR (Finite Impulse Response) filter. The system ensures the time sequence consistency of data under different sampling frequencies through multi-channel data synchronization and timestamp management, and meanwhile, smooth transition is performed by applying an interpolation algorithm during sampling switching, so that the continuity of a data sequence is ensured. The data redundancy is reduced; and the integrity of key information is ensured.

The invention relates to the technical field of meteorological monitoring, in particular to a ring main unit lightning micrometeorological online monitoring system and method, and the system comprises a feature extraction module, a frequency spectrum detection module, a frequency band pool screening module, a path adjustment module and an interference avoidance module. The method comprises the following steps: collecting an electric field intensity extreme value sequence, calculating a change rate difference ratio, identifying thunder and lightning precursor characteristics, combining a frequency spectrum energy density map, analyzing an energy peak frequency band and a drop point of a frequency hopping signal, judging a thunder and lightning frequency signal concentration area, performing frequency spectrum interference filtering, and combining a dynamic time warping algorithm. The time deviation between a lightning stroke time point and frequency hopping scheduling is analyzed; a frequency hopping path is adjusted in real time; the response timeliness is improved; the difference value before and after electric field disturbance is monitored; and support is provided for monitoring stability and response adaptability of the thunder and lightning risk environment.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention relates to the technical field of power frequency resistance real-time monitoring, and provides a traction power supply 27.5 kV cable interval grounding grid power frequency resistance real-time monitoring method and system, and the method comprises the steps: obtaining the temperature distribution data of the surface of a conductor of a grounding grid; acquiring voltage and current response of the grounding grid when the local temperature rise value exceeds a preset reference value to perform frequency domain conversion, and generating a broadband impedance spectroscopy analysis result; aligning earth surface coordinates corresponding to the temperature measurement nodes with underground coordinates corresponding to the impedance measurement points, generating a space correlation grid, generating a temperature rise phase deviation coupling relation matrix, and generating a real-time monitoring result of the power frequency resistance of the grounding grid according to a power frequency impedance correction value in the space correlation grid and the temperature rise phase deviation coupling relation matrix; according to the invention, accurate space matching between the abnormal temperature rise area and the impedance change of the corresponding conductor is realized, the adaptability of a monitoring network to the extension direction and the burial depth change of the conductor is enhanced, and feature signal omission caused by fixed measurement point deployment is avoided.

The invention relates to the technical field of electric energy measurement, and provides a power grid side electric energy metering method and device considering inter-harmonic components, and the method comprises the following steps: obtaining voltage data and current data of a power grid side, and extracting a voltage waveform signal and a current waveform signal through constructing a steady-state signal model; based on the obtained waveform signals, fundamental component extraction is carried out, based on a unary linear regression method, the amplitudes and phases of harmonic signals and inter-harmonic signals are compensated, and harmonic components and inter-harmonic components are extracted; based on the obtained fundamental component, each harmonic component and inter-harmonic component of each signal, calculating to obtain fundamental electric energy, harmonic electric energy and inter-harmonic electric energy; and based on the fundamental wave electric energy, the harmonic electric energy and the inter-harmonic electric energy, correcting the measured electric energy of the power grid side. And through steady-state model fitting and a unary linear regression compensation mechanism, the response and adaptability to high-frequency disturbance components are improved.

The invention discloses a power adapter parameter dynamic optimization method for prolonging the service life of equipment, and relates to the technical field of adapter performance optimization, and the method comprises the steps: monitoring and recording the output voltage, current and power factor parameter changes of a power adapter in real time under static and dynamic load conditions by using a test instrument; identifying the trend and reason of abnormal parameter change; performing thermal design optimization and temperature management on the power adapter; performing over-power and over-current limitation on the pulses one by one; introducing a power factor correction circuit; optimizing the switching frequency and the design of an output filter; a load line adjusting circuit is introduced, and space is reserved for dynamic ripples when output current jumps; and evaluating whether the optimization effect reaches an expected target or not. By introducing the power factor correction circuit and the load line adjustment circuit, the power factor of the power adapter is improved, the harmonic pollution is reduced, the stability of the output voltage of the power adapter is improved, and the performance of the power adapter under the dynamic load condition is improved.

The invention relates to the technical field of power systems, in particular to a power system broadband oscillation disturbance source grading positioning method and system, and the method comprises the steps: calculating the compression ratio of a measurement signal according to the sampling resolution of an original measurement signal and a real-time transmission interval, and obtaining an initial compression coefficient; performing frequency domain decomposition on the original measurement signal to obtain frequency spectrum characteristic data, and compressing the original measurement signal based on the frequency spectrum characteristic data and the initial compression coefficient to obtain a measurement compression signal; embedding a time sequence position code in the measurement compression signal to generate time sequence input data; analyzing the time sequence input data by using a multi-head attention mechanism to obtain an attention weighted signal; and generating a reconstruction signal according to the attention weighted signal, and obtaining a broadband oscillation disturbance source grading positioning result based on the reconstruction signal and the disturbance propagation path. According to the method, rapid and accurate grading and positioning of the broadband oscillation disturbance source are realized through technologies of self-adaptive compression, broadband oscillation characteristic reservation, topology propagation path analysis and the like.

The invention discloses a high-voltage direct-current cable state online monitoring method and system based on MMC low-frequency injection. The method comprises the steps that an MMC controller injects a low-frequency three-phase alternating-current signal into a direct-current cable based on an MMC low-frequency injection control strategy; the MMC low-frequency injection control strategy comprises the steps that an MMC controller adopts a double-closed-loop control mode composed of an outer voltage loop and an inner current loop; a voltage error is calculated through an outer voltage loop, and a differential-mode voltage Udiffdq * and a common-mode voltage Ucomdq * are calculated through an inner current loop, so that the MMC outputs a target voltage Ugoal signal to a direct-current cable; according to the method, active injection of the low-frequency alternating-current signal with the specific frequency and amplitude is achieved by setting the specific MMC control strategy, a hardware structure of a main loop of an MMC system does not need to be modified, and implementation is safe, easy, convenient and flexible; and meanwhile, online quantitative evaluation of the insulation state of the direct-current cable is realized, and the monitoring process is high in precision, simple, convenient and efficient.

The invention belongs to the technical field of power equipment state monitoring, and discloses a cable branch box operation state intelligent evaluation system based on multi-sensor fusion, which comprises a state acquisition module used for acquiring a partial discharge signal, a voltage fluctuation signal, a terminal temperature signal and a terminal contact resistance signal of a cable branch box in an operation process, constructing a multi-source operation state parameter set of the cable branch box through time synchronization and physical quantity normalization processing; the mutual coupling analysis module is used for performing transient correlation deduction on the partial discharge signal and the corresponding voltage fluctuation signal based on the multi-source operation state parameter set to obtain a partial discharge-voltage coupling amount; when the partial discharge-voltage coupling amount exceeds a preset partial discharge-voltage coupling amount threshold value, determining that partial discharge abnormity caused by insulation degradation exists in the cable branch box; comprehensive and accurate evaluation of the operation state of the cable branch box is realized, and reliable support is provided for safe operation and maintenance of equipment.

The invention discloses a permanent magnet synchronous motor fault diagnosis method and system based on improved empirical wavelet transform, and the method comprises the steps: determining a power spectrum of a fault signal through employing an improved Welch method, and segmenting the power spectrum into a plurality of continuous frequency spectrum intervals; adopting a Littlewood-Paley method and a Meyer method to construct a wavelet filter bank of each power spectrum interval, and extracting each modal component of each power spectrum interval; constructing a fault feature vector of the fault signal according to the energy moment of each modal component in each power spectrum interval; constructing a fault feature matrix by using the fault feature vector of each fault signal; establishing a motor fault diagnosis model by using a CatBoost method, and training the motor fault diagnosis model by using the improved training set and the improved gradient enhancement method; and the trained motor fault diagnosis model carries out motor fault diagnosis according to the fault feature matrix, and the modal aliasing problem caused by unclear signal spectrum boundary in the motor diagnosis process is solved.

The present disclosure relates to a partial discharge monitoring system and a partial discharge monitoring method that are capable of monitoring and determining a defect generated in a high-voltage power device in real time by classifying signals generated from the high-voltage power device with a machine learning algorithm being applied, and capable of easily forming feature dot data clusters in both a high-density area and a low-density area of two-dimensional feature dot data by performing a process of clustering feature dot points generated from the signals of the power device on the basis of density and distance in parallel, respectively, thereby generating PRPD data for each cluster without missing a signal to improve partial discharge determination accuracy.

The invention provides a weak current acquisition circuit and electronic equipment, which are applied to the field of control or regulation systems, and the circuit converts an input current signal to be processed into a corresponding first voltage signal through a first analog switch, a second analog switch and sampling resistors corresponding to different gear control signals; the operation integration unit is used for amplifying the first voltage signal and generating an integration voltage signal, and then the analog-to-digital conversion unit is used for converting the integration voltage signal into a digital voltage signal; a control unit collects the digital voltage signal and calculates a target current value corresponding to the current signal to be processed according to the digital voltage signal. The circuit provided by the embodiment of the invention can be used for amplifying the current signal to be processed, carrying out analog-to-digital conversion on the integral voltage signal and acquiring the corresponding digital voltage signal according to the preset digital filtering algorithm, and can effectively reduce the influence of noise on the acquisition precision in the weak current acquisition process.

The invention relates to the technical field of transformer fault detection, in particular to a converter transformer fault diagnosis method and device based on robot return data. The method comprises the following steps: accessing an original partial discharge signal of the converter transformer acquired by an on-site inspection robot into a diagnosis platform in the form of a dynamic return data packet by adopting a target communication mode to obtain a filtered pure partial discharge signal in a time domain; obtaining a dominant mode representing the characteristics of the pure partial discharge signal; inputting the dominant mode into the trained classifier to obtain a fault defect type of the converter transformer corresponding to the pure partial discharge signal; and visualizing the fault defect position and type of the converter transformer and the original partial discharge signal on the three-dimensional model of the converter transformer. According to the embodiment of the invention, the identification precision of the defect type of the converter transformer is improved, and the fault information of the converter transformer is visually displayed.

The invention discloses an offshore wind power harmonic transmission quantification method and system considering submarine cable insulation aging, and relates to the field of harmonic transmission characteristic analysis. The method comprises the following steps: dividing the insulation aging severity level of the submarine cable, and determining an insulation capacitance gain coefficient corresponding to each level of submarine cable; determining an actual capacitance gain coefficient of the insulation state of the submarine cable based on cable aging detection results regularly acquired by a power grid company; correcting the capacitance value of the submarine cable based on the actual capacitance gain coefficient; and establishing an equivalent circuit of an offshore wind power grid-connected system through the submarine cable line, estimating a grid-connected system harmonic resonant frequency causing harmonic current amplification according to the corrected submarine cable capacitance value, and estimating a harmonic current amplification coefficient of a grid-connected point under the harmonic resonant frequency of the grid-connected system. According to the method, the evaluation accuracy of the resonant frequency and harmonic amplification severity of the offshore wind power grid-connected system is remarkably improved, so that effective suppression of harmonic resonance amplification can be guided.

The invention provides a direct-current power transmission high-frequency harmonic measuring device and method, and belongs to the technical field of high-frequency harmonic measurement. The measuring device comprises a digital-to-analog conversion module, a photoelectric conversion module and a main control module; the main control module comprises a first processor and a second processor, the first processor is used for acquiring an initial direct-current voltage signal and a rated voltage signal, and the second processor is used for performing data calculation on the initial direct-current voltage signal and the rated voltage signal acquired by the first processor to obtain a high-frequency harmonic signal. The invention provides a miniaturized and portable measuring device which can solve sampling errors and realize accurate measurement of high-frequency harmonic waves.

The invention relates to the technical field of power distribution network fault detection, and discloses a power distribution network high-resistance grounding fault intelligent detection method, system and device and a medium, and the method comprises the steps: collecting a power distribution network line transient signal which comprises a transient zero-sequence voltage signal and a transient zero-sequence current signal; preprocessing the transient signal and extracting a multi-dimensional feature set for representing high-resistance grounding fault characteristics, wherein the multi-dimensional feature set comprises a transient energy feature, a wavelet entropy feature and a harmonic component feature; and inputting the extracted multi-dimensional feature set into a pre-trained machine learning model, and simultaneously outputting a judgment result of the high-resistance grounding fault and an estimated value of the grounding resistance through the machine learning model. According to the method, the transient energy, the multi-scale wavelet entropy, the odd harmonic amplitude and the phase, which are derived from different analysis domains and have complementary physical meanings, are deeply fused, so that the problem that a single feature is weak in characterization capability and easy to fail under the conditions of high noise and high resistance is effectively solved.

The invention discloses a multichannel parallel level conversion circuit test system, which relates to the technical field of circuit test and comprises a test host, a multichannel parallel level conversion circuit, a signal source module, a signal acquisition module, a control module and a display module. The test host performs test logic verification on the whole process of the circuit test, the multichannel parallel level conversion circuit serves as a target circuit to be subjected to circuit test, a test signal of the circuit test is generated according to the signal source module, and a test case of the test signal is constructed. The signal acquisition module acquires an output signal of the multichannel parallel level conversion circuit, and generates a circuit tuning output signal after signal gain, signal noise optimization and frequency band filtering, the control module performs circuit testing on a target circuit according to a test case and the circuit tuning output signal, and finally, the control module performs circuit testing on the target circuit according to the test case and the circuit tuning output signal. And the display module visually displays the test result.

The invention relates to the technical field of power system court management, and particularly discloses a court identification and user binding method based on a special transformer acquisition terminal, which comprises the following steps: separating harmonic interference generated by start and stop of an electric welding machine through a kurtosis index and time-frequency energy entropy fusion detection method, and extracting a fundamental voltage and a pure power signal; constructing a dynamic time bending model containing phase consistency and gradient feature constraint based on the fundamental wave voltage, and calculating voltage track similarity to judge electrical coupling; for the users with electrical coupling, dynamic weight power cross-correlation analysis is fused to generate a comprehensive decision value, and a space-time diagram neural network is utilized to update a transformer area topology structure online in combination with a historical topology change event, so that the dynamic determination of the transformer area to which the users belong is realized; through multi-dimensional feature fusion and anti-interference algorithm design, the problems of phase deviation and waveform distortion caused by electric welding machine harmonic waves are reduced, and the transformer area identification accuracy and real-time performance are improved.

A multi-channel analog-digital converter (ADC) subsystem for test device such as a spectrum analyzer may include multiple multi-channel ADCs to receive down-converted signals and convert the received signals to digital output signals, a field programmable gate array (FPGA) to select one or more ADCs based on a frequency, a bandwidth, and / or a signal type of each received signal and a characteristic of each ADC, and an ADC sample clock to provide a clock signal to the selected ADCs. Characteristics of the ADCs may include a resolution, a signal-to-noise-and-distortion ratio (SINAD), an effective number of bits (ENOB), a signal-to-noise ratio (SNR), a total harmonic distortion (THD), a total harmonic distortion plus noise (THD+N), and / or a spurious free dynamic range (SFDR).

The invention relates to the technical field of line fault detection, in particular to an automatic alarm method and system based on distribution line online fault detection, and the method comprises the steps: detecting a current environment, obtaining current wind speed data, if a current wind speed value greater than a preset standard wind speed threshold exists in the current wind speed data, obtaining a repair line set based on a plurality of distribution lines, if the current wind speed value larger than the preset standard wind speed threshold value does not exist in the current wind speed data, statistical characteristics of the distribution line are obtained, a physical construction model is obtained, the statistical characteristics are input into the physical construction model, a model prediction value is obtained, fault point positioning operation is executed on a fault electric line, the position of a fault point is obtained, and an automatic alarm mechanism is triggered. And performing fault repair on the fault electric line to obtain a normal electric line set, and completing automatic alarm based on distribution line online fault detection based on the normal electric line set and the repair line set. The reliability and the operation efficiency of the power system can be improved.

The application discloses a kind of non-contact track frequency shift signal online monitoring method and system, applied to intelligent monitoring technical field, method includes: receiving first measured voltage and second measured voltage;First measured voltage and second measured voltage are input first conditioning circuit by first transmission line, receive the first output voltage output by first conditioning circuit, and the parallel transmission line formed after first measured voltage and second measured voltage are connected in parallel by second transmission line and third transmission line are input second conditioning circuit, receive the second output voltage output by second conditioning circuit;According to first output voltage and second output voltage, calculate first measured voltage.The application is realized by the way of double-path simultaneous monitoring, reduces the influence of the related capacitance value change caused by environmental conditions on monitoring result, to effectively improve monitoring precision, and then provides solid foundation for non-contact frequency shift signal back-end processing.

The invention relates to the technical field of air door motor control, and discloses an air door motor control method and system based on current detection, and the method comprises the steps: carrying out the single-phase current signal collection of an air door motor system, and obtaining a current change signal in the opening and closing process of an air door; carrying out FFT (Fast Fourier Transform) and wavelet decomposition processing on the current change signal to obtain a current characteristic spectrum component and a time domain characteristic; processing the current characteristic frequency spectrum component and the time domain characteristic to obtain an air door load current characteristic; iterative learning control is executed, and air door rotating speed control parameters are generated; the PWM duty ratio is calculated, and air door motor driving pulses are generated; according to the air door motor control method, a complete air door motor control protection mechanism is established, abnormal conditions such as stalling can be detected and coped with in time, the safety of an air door motor system is improved, and the service life of the air door motor system is prolonged.

A printed circuit board (PCB) test system and method including temperature chamber including at least one slot to accommodate at least one PCB test coupon, input bus connected to inputs of the at least one slot, and output bus connected to outputs of the at least one slot, wherein temperature chamber is configured to apply a temperature to the at least one PCB test coupon; signal generator including output bus connected to the input bus of the at least one slot, frequency meter comprising an input bus connected to the output bus of the at least one slot and an output bus, and a comparator connected to the output bus of the signal generator and the output bus of the frequency meter for comparing corresponding outputs of the signal generator and the frequency meter.

The invention discloses a GIS ultrahigh frequency sensor ultrasonic cooperative detection system based on piezoelectric energization, and relates to the field of electric power detection, and the system comprises an excitation module which is used for transmitting a piezoelectric excitation signal with a specific frequency to a preset detection region in GIS equipment, so as to trigger the potential partial discharge acoustoelectric signal radiation of the region; the sensing module is used for receiving ultrahigh-frequency electromagnetic signals and ultrasonic vibration signals radiated by partial discharge in the GIS equipment; the excitation frequency, the filtering bandwidth and the sampling rate are dynamically optimized according to the detection result, the detection accuracy and efficiency are continuously improved, and reliable and effective guarantee is provided for safe operation and defect early warning of GIS equipment.