802results about "Testing using acoustic measurements" patented technology

A fault monitoring system for an electrical substation includes one or more arrays of acoustic sensors placed around a substation boundary and configured to measure acoustic pressure waves. A processing circuitry in the fault monitoring system localizes and identifies faults in the substation and includes a sound source localization module to identify an area of a sound source based on the acoustic pressure waves. A beamforming module of processing circuitry provides enhanced acoustic pressure waves by eliminating background noise and interfering noise from the acoustic pressure waves and a component signature classification module in the processing circuitry compares enhanced acoustic pressure waves with component signatures to classify the acoustic pressure waves into various events to detect faults in the electrical substation.

Apparatuses, systems and methods for detecting corona using audio data are disclosed. A method of processing audio data to detect corona includes determining an indicator of energy at a substantially fundamental frequency in the audio data, determining an indicator of the energy at a plurality of harmonic frequencies in the audio data, determining an indicator of the noise energy in the audio data, determining a comparison indicator of the noise energy relative to the energy at the harmonic frequencies, determining a masking indicator having thresholds for each of the harmonics relative to the fundamental frequency, and determining a corona detection indicator. An apparatus for detecting corona includes a memory, an audio detector configured to obtain the audio data near an electrical conductor in an AC system, and a processor to process the audio data, fundamental and harmonic frequencies of the audio data and their corresponding thresholds to detect corona.

The invention provides an in-depth learning-based partial discharge ultrasonic audio frequency identification method and system. The method comprises the following steps: partial discharge ultrasonic signals of an electric power device are detected, partial discharge ultrasonic audio frequency data is obtained and then converted into a sound spectrogram, a depth convolution nerve network model is built, samples are used for training a network, partial discharge ultrasonic audio frequency data to be diagnosed is input into a trained network, and a partial discharge defect type is output and obtained. According to the in-depth learning-based partial discharge ultrasonic audio frequency identification method and system, the partial discharge ultrasonic audio frequency data is converted into the sound spectrogram, a depth convolution nerve network is used for identifying the sound spectrogram, ultrasonic signals of all kinds of defects in partial discharge can be accurately and effectively identified, and a convenient and reliable diagnosis method is provided for electric power device insulation state assessment.

The invention belongs to the electric power equipment fault detection and diagnosis technology field and especially relates to an XLPE power-cable partial discharge diagnosis method and a system thereof. The method comprises the following steps of collecting a low-frequency ultrasonic wave signal and an ultrahigh-frequency partial discharge signal generated by power-cable partial discharge, acquiring time delay of the ultrasonic wave signal and acquiring a distance between a partial discharge point and a partial discharge detection point; determining a phase window which has partial discharge; establishing a relation between an ultrasonic wave signal amplitude of the phase window and a phase of an applied power frequency voltage; drawing ultrasonic partial discharge phase information and amplitude information into a phase spectrum graph; and carrying out statistics characteristic calculating and extracting 27 statistics operators representing a partial discharge characteristic. In the invention, the ultrahigh-frequency partial discharge signal generated by the partial discharge is taken as a time reference and is combined with a low-frequency ultrasonic partial discharge signal so as to acquire time delay of the ultrasonic signal, and finally identification of the partial discharge signal is realized.

The invention discloses a partial discharge fault state identification method based on ensemble learning, and relates to the field of power equipment partial discharge detection and identification. The method includes: firstly, collecting a partial discharge signal through a partial discharge acoustic-optical combined detection system; obtaining raw data, carrying out data preprocessing includingabnormal value detection and cleaning, data denoising and data standardization; extracting signal features from multiple analysis domains, inputting the obtained ultrasonic signal features and ultraviolet signal features into a two-stage Stacking-Bagging ensemble learning model designed by the invention, and finally judging the partial discharge fault state of the current input signal through relative majority voting. The method is high in recognition rate of the partial discharge fault state of the power equipment, and is high in recognition stability.

A method of detecting and localizing a source of partial discharge in an electrical apparatus placed in an enclosure containing an acoustically-conductive fluid. The method consists in:

• • positioning (31) acoustic detectors and acquiring the positions of the detectors;
  • defining (35) groups of detectors, each group including at least three detectors;
  • ensuring (37) that each group is free from any triangle having vertices embodied by three detectors in which at least one angle lies outside a range of angles, and otherwise eliminating the group;
  • for each non-eliminated group, calculating (38) an approximate position for the source;
  • ensuring (39) that each non-eliminated group is free from any triangle having vertices embodied by two detectors of the group and by the approximate position, including at least one angle that lies outside the range, and otherwise eliminating the group; and
  • calculating (43) the looked-for position of the source by averaging the approximate positions of all or some of the non-eliminated groups.

An apparatus (10) for detecting the occurrence of partial discharge in electrical equipment, such as high voltage transformers. The apparatus comprises a detecting means including an ultrasonic transducer (11) mounted at one end of an electrically insulating elongate rod (13). The ultrasonic transducer (11) is adapted to detect ultrasonic pulses or waves generated by the occurrence of a partial discharge in the electrical equipment. A radio frequency (RF) transducer (17) is also utilized to detect radio frequency (RF) pulses or waves also generated by the partial discharge. The rod (13) can be mounted in the wall of the electrical equipment and so provide a mechanism for adjusting the position of the detecting means within the electrical equipment. The apparatus is also removable from the electrical equipment and re-locatable to other equipment.

The invention relates to a partial discharge detection method based on multi-source image fusion. The partial discharge detection method based on multi-source image fusion is characterized in that S100, an ultrasonic sensor array, a camera, an infrared thermal imager, and an ultraviolet imager are respectively used to acquire ultrasonic wave signals, visible images, infrared images, and ultraviolet images of a detected device synchronously; S 200, the ultrasonic visible images can be acquired based on the acquired ultrasonic wave signals and the visible images; S 300, the ultrasonic visible images, the infrared images, the ultraviolet images can be used as the partial discharge detection result after being fused by adopting the image fusion algorithm, and can be displayed. Compared to the prior art, the partial discharge detection method has advantages of detection precision and high reliability.

The invention discloses a fault location method of acoustic electric joint partial discharge detection based on a BP (Back Propagation) network in a GIS (Gas Insulated Switchgear). The method comprises the following steps that: 1, a sensor converts the information acquired on site into an electrical signal, and comprises an ultrasonic sensor and an ultra high frequency sensor; 2, the ultrasonic sensor conducts data preprocessing on the converted electrical signal and fuses the preprocessed data, and the ultra high frequency sensor conducts data preprocessing on the converted electrical signal and fuses the preprocessed data; 3, fusing the data fused by the ultrasonic sensor and the data fused by the ultra high frequency sensor by utilizing the BP neural network, and determining the fault location; 4, outputting the fault location. The method provided by the invention solves the problem that the accuracy rate and the correct rate of the fault location identification of an on-line monitoring system based on a single type sensor are low, and can avoid the false report, missing report and non-report phenomena of the on-line detection system based on the single type sensor.

A monitoring system includes an acoustic emission monitoring system including acoustic emission sensors, a partial discharge monitoring system including partial discharge sensors and synchronized with the acoustic emission monitoring system, and a computer receiving acoustic emission data from the acoustic emission sensors and electrical data from the partial discharge sensors. The computer is configured to classify a first statistical event as a fatigue cracking event by pattern recognition of the acoustic emission data and determine a first location and a first damage condition resulting from the fatigue cracking event, classify a second statistical event as a partial discharge event by pattern recognition of the acoustic emission data or the electrical data, and fuse the acoustic emission data and the electrical data for the second statistical event and determine a second location and a second damage condition resulting from the partial discharge event. Methods of monitoring are also disclosed.

The invention discloses a feature value extraction method applied to ultrasonic partial discharge detection. According to the invention, a wavelet packet decomposition and mutual information amount-based IMF component screening method is adopted. Firstly, wavelet packet decomposition is performed. Preliminary frequency division is carried out on the signal. The IMF component of the single-frequency component is obtained. The initial bandwidth of the EMD decomposition signal is reduced. The ultrasonic signal is decomposed more accurately, the problems of modal aliasing and false IMF componentsexisting in EMD decomposition are effectively solved by applying a mutual information amount method, the accuracy of time-frequency analysis is improved, waveform characteristics are extracted more effectively, and finally, the ultrasonic partial discharge signal is classified by applying a neural network.

The invention discloses a high voltage cable state monitoring system based on a direct grounding box and a realization method thereof. The monitoring system comprises a high frequency current partial discharge acquisition unit, a cable intermediate joint ultrasonic wave local discharge acquisition unit, a cable intermediate joint optical fiber temperature measuring unit, a gas detection unit, an environment temperature and humidity detection unit, a water level alarm unit, an ARM processor unit, a wireless GPRS module and a remote monitoring main station. A monitoring method comprises steps of reading monitoring data and carrying out determination and alarm on the monitoring data. By using the system and the method of the invention, the monitoring data can be assessed and diagnosed; resources are greatly saved and operation efficiency is increased.

An acoustic sensor apparatus includes a housing, a fastener structured to fasten together the housing and an electrical power conductor, an acoustic sensor structured to detect acoustic noise from the electrical power conductor and output a signal, and a circuit structured to detect an electrical conductivity fault from the signal.

The invention discloses an acousto-optic combined detection method and system for partial discharging of a high-voltage cable. On the basis of fusion of ultrasonic and ultraviolet sensing type non-electrical detection technologies, a partial discharge detection mode of non-fixed high-voltage cable inspection in a complex and strong electromagnetic interference environment is performed by using low-cost portable ultrasonic and ultraviolet sensors to realize synchronous and efficient collection of ultraviolet light and ultrasonic signals of partial discharge of the high-voltage cable; and then multi-analytical-domain partial-distribution information mining and fusion processing of ultrasonic and ultraviolet light pulses is performed, so that the partial discharge detection information is enriched substantially. Partial discharge information that can not be provided by the single technology is provided; the accurate identification and determination of the partial discharge type, cable fault diagnosis, and health status assessment are performed; and real-time storage and analysis of two paths of partial discharge information as well as display and alarm interaction and the like on the main control module are realized.

The invention provides a quantum genetic algorithm-based converter transformer partial-discharge ultrasonic location method. Many ultrasonic sensors at different positions of a transformer are adopted to receive ultrasonic signals that are sent by a partial discharge source, and a distance solving model is established by a Cartesian coordinate system. Relevant parameters of the quantum genetic algorithm are initialized. Chromosome is coded, and population Q(t) is initialized. Every individual of the initialized population is measured and a state P(t) is obtained. The fitness to each state is calculated. The optimal individual and its fitness value are recorded. The result is directly output if the termination condition is satisfied. If the termination condition is not satisfied, then t=t+1 is set and every individual of the initialized population is measured to obtain the state P(t). The fitness to each state is calculated, the population individuals are updated by means of quantum rotation gate operation and quantum non-gate to obtain a progeny population Q(t+1), and the optimal individual and its fitness are recorded, until the terminal condition is satisfied. The method of the invention has the characteristics that the iteration frequency is low and the location precision is high under the condition of a small population size, premature convergence is avoided, and rapid convergence is achieved to get the globally optimal solution.

The invention discloses a supersonic wave based partial discharge detection method. The method comprises the steps of supersonic signal collection and pre-processing, abnormal signal interval detection, characteristic vector extraction, partial least squares based dimension reduction optimization, and vector machine based fault and faultless classification. A supersonic wave detection method is used to carry out partial discharge detection on a switchgear, a supersonic signal is analyzed and processed to predict an insulation state of the switchgear; an audio signal processing technology and amode identification classification algorithm are used to carry out down-conversion on the supersonic signal, and a signal within the voice frequency range is obtained; a signal characteristic parameter representing partial discharge is extracted; via partial least squares based dimension reduction optimization, the complex degree of the algorithm is reduced; and further advance voice identification technology and mathematical classification model to realize intelligent identification. Thus, partial discharge condition in the switchgear can be detected online intelligently in a non-intruding way, and the insulation condition of the switchgear is detected and diagnosed.

A nonlinear model transformation solving and optimization method for partial discharge positioning based on multi-ultrasonic sensor includes the following steps: (1) constructing a spatial rectangular coordinate system in a transformer, and setting a position of each ultrasonic sensor; (2) constructing a positioning model on the basis of an arrival time positioning method to obtain a nonlinear positioning equation set for solving a position of a PD source; (3) eliminating second-order terms in the nonlinear positioning equation set to transform the nonlinear positioning equation set into a linear equation set; (4) obtaining multiple sample initial values of a coordinate of the PD source; (5) screening the multiple sample initial values; (6) performing clustering processing on the multiple effective sample initial values by adopting an improved K-means algorithm; and (7) selecting a class with most cluster elements, and calculating a mean of the elements of the class to finally determine an optimal coordinate of the PD source. According to the present invention, the present problems of selection difficulty, iteration non-convergence, long operation time, sensitivity to an arrival time error and the like of an iteration algorithm adopted when a nonlinear model is solved are effectively solved.

The invention belongs to the technical field of rail traffic vehicle and high-voltage electrical equipment on-line insulation monitoring and especially relates to a multiple-unit train high-voltage equipment insulation state on-line monitoring system, which is used for realizing on-line monitoring of the insulation state of 25 kV high-voltage equipment of a multiple-unit train. Through classification of multiple-unit train high-voltage equipment, characteristic quantity screening, wireless transmission, high-voltage induction power obtaining, expert database and fuzzy function operation, the difficulty that a conventional power system on-line insulation monitoring mode cannot be applied to the multiple-unit train, which is characterized by high-speed movement, single-phase power supply, non-single-point grounding, harsh environment and strict requirements and the like, is solved; and the system comprises a data acquisition unit and an analysis and diagnosis unit. The data acquisition unit is used for extracting characteristic quantity of a monitoring object, and transmitting the data in a wireless manner to the data analysis and diagnosis unit for analysis and diagnosis and signal output. The system is reliable in principle, high in precision, high in accuracy, high in safety and good in reliability, is of great significance to promotion of continuous development of the high-speed rail cause, and has good economic benefits and a broad market prospect.

The invention discloses a mechanical arm for local discharge detection of a switch cabinet and a control method. A lifting joint, a slide joint, a shoulder rotating joint, an elbow rotating joint and a wrist rotating joint connected in sequence are used; the wrist rotating joint is connected with a local discharge detecting sensor through a flexible connecting mechanism; the slide joint and the lifting joint are arranged in an axis perpendicularity manner, and respectively drive the local discharge detecting sensor to relatively move in the vertical and horizontal directions of the switch cabinet; and through rotating cooperation of the shoulder rotating joint, the elbow rotating joint and the wrist rotating joint, further adjustment of the position of a detecting point and the gesture of the local discharge detecting sensor is realized when relative positions of the local discharge detecting sensor and the switch cabinet are invariable, and precise detection is realized.

The invention discloses a switchgear local discharge detection system based on wireless transmission technology, comprising a terminal and a main unit. The terminal includes a sensor module, a conditioning module, V/f (voltage/frequency) conversion module 1 and a wireless transmitting module; the main unit includes a wireless receiving module, V/f conversion module 2, a wave detector, a smooth filter, an envelope line generator, a peak detector, an A/D (analog/digital) converter and a central processing unit, as well as a storage, an alarm, a display unit and a human-machine interaction unit which are connected respectively with the central processing unit. Simultaneous detection is provided for very high frequency, ultrasonic wave, high frequency current, TEV (trillion electron-volt), power frequency signal, vibration and other various signals; internal insulation state of a switchgear is subjected to comprehensive analysis of various sensing signals to allow mutual verification, andlocal discharge occurrence can be judged more accurately. Particularly, detection signals are transmitted to the main unit in wireless manner, test personnel is freed, dragging of field lines is avoided, and testing safety and reliability are improved.

The invention relates to a partial discharge ultrahigh frequency, ultrasonic wave and light pulse combined detection system and method for a GIS. The partial discharge ultrahigh frequency, ultrasonicwave and light pulse combined detection system for the GIS comprises a monitored object GIS, an ultrahigh frequency, ultrasonic wave and light pulse integrated sensor, a transmission optical fiber, acoaxial cable, a 3dB coupler, a light source, a multi-channel photoelectric conversion and amplifier device, a filtering and amplifier device, a multi-channel data acquisition and storage device, a control and display device and a printing device; and the ultrahigh frequency, ultrasonic wave and light pulse integrated sensor is an integrated combined sensor capable of measuring partial discharge of an ultrahigh frequency, an ultrasonic wave and a light pulse signal simultaneously and is installed and arranged on a shell of the monitored object GIS in a built-in mode. Compared with the prior art, the partial discharge ultrahigh frequency, ultrasonic wave and light pulse combined detection system has the advantages of being simple, practical, reliable, capable of reducing electromagnetic interference and mutually proving signal sources and the like.

The invention provides a switch cabinet big data state evaluation system based on intelligent partial discharge live-line detection. The system comprises an intelligent inspection unit, a partial discharge diagnosis and positioning unit, an equipment state evaluation unit, an intelligent terminal and a control unit, wherein the equipment state evaluation unit comprises a communication unit, a signal storage unit, a characteristic signal extraction unit, a defect type identification unit and an equipment defect severity analysis unit. The system is advantaged in that the system has properties of high intelligentization level, good partial discharge detection sensitivity and high risk evaluation accuracy, and powerful technical support is provided for developing power-on maintenance of the switch cabinet.

A method of detecting and locating a micro-interface abnormality within an electrical system having a plurality of conductors and a plurality of electrical connections includes identifying a subset of the plurality of electrical connections by detecting an acoustic signal within the electrical system and analyzing the detected acoustic signal and determining that the detected acoustic signal is indicative of an electrical fault, measuring a contact resistance of each of the subset of the plurality of electrical connections, and identifying at least one of the subset of the plurality of electrical connection points as having a micro-interface abnormality based on the measured contact resistances.