115results about "Power factor measurement" patented technology

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

The invention discloses a system for monitoring line loss rate abnormity of a power distribution network station area, and relates to the technical field of power distribution network monitoring. The line loss rate abnormity monitoring system for the power distribution network station area comprises a data acquisition module, a data processing module, a line loss rate calculation module, an abnormity identification and alarm module and an on-site troubleshooting and diagnosis module. Through integrating functions of intelligent electric meter data acquisition, power grid data collection and area division, data processing, line loss rate calculation, abnormity identification and alarm, on-site troubleshooting and diagnosis and the like, comprehensive and real-time monitoring of the line loss rate of the power distribution network station area is realized. And a regional line loss calculation model is established according to the characteristics of the power distribution network of each region, and the line loss rate can be monitored in real time, dynamically calculated and predicted, so that the efficiency and accuracy of line loss management are greatly improved. And the abnormity identification and alarm module can timely discover and give an alarm of line loss abnormity, provides probability analysis of abnormity reasons, and is helpful for operation and maintenance personnel to quickly respond and process.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention provides an electric power and magnetic power dynamic joint detection method and system based on multivariable fusion, and relates to the technical field of power electronic equipment detection. According to the method, voltage, current and magnetic field sensors are arranged in a to-be-tested electromagnetic system, original physical quantity data are collected, and a multivariable time sequence data set is constructed; through preprocessing and feature extraction, a dynamic feature matrix related to electric power and magnetic power is generated; constructing a joint estimation model based on the matrix, and comparing the joint estimation model with a reference value to obtain a detection residual error; generating a state label according to the residual size and trend, and evaluating the estimation credibility in combination with a confidence interval; and a feedback mechanism is further constructed, model parameters and feature structures are optimized by adopting an adaptive algorithm, and online iteration and self-learning are realized. The method is high in detection precision, high in state recognition capability, good in adaptability and robustness, and suitable for energy flow monitoring and intelligent diagnosis of a complex electromagnetic system.

The invention discloses a self-adaptive adjustment method and system for electric energy meter measurement, and relates to the technical field of electric power metering, and the method comprises the steps: building a first measurement mode and a second measurement mode for a target electric energy meter; carrying out load fluctuation monitoring on the target electric energy meter through a load monitoring module, and generating a load fluctuation index; if the load fluctuation index is greater than the preset fluctuation index, activating a first measurement correction module to perform measurement precision adjustment on the target electric energy meter according to a first measurement mode; and if the load fluctuation index is smaller than or equal to the preset fluctuation index, activating a second measurement correction module to perform measurement precision adjustment on the target electric energy meter according to a second measurement mode. The technical problem that the measurement mode cannot be adaptively adjusted according to different loads during measurement of the electric energy meter in the prior art is solved, adaptive adjustment of the measurement of the electric energy meter is realized, and the technical effect of balancing the power consumption and precision of the measurement of the electric energy meter is achieved.

The invention discloses an intelligent operation and maintenance robot real-time state monitoring system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence. An intelligent operation and maintenance robot arrives at each equipment monitoring point of a transformer substation, a communication machine room or a data center to acquire multi-dimensional data; when arriving at a monitoring point, acquiring real-time electrical parameter data, and fusing the real-time electrical parameter data with the multi-dimensional data to obtain a data set; constructing a main analysis model by combining a convolutional neural network with a long short-term memory network, inputting the trained main analysis model, and outputting a current state evaluation result of the equipment; cooling medium parameters are measured, enthalpy difference changes in the cooling process are calculated, and the heat exchange efficiency of the cooling system is quantified; constructing a transformer cooling system performance evaluation model by using a support vector regression algorithm, and outputting a current heat dissipation capability coefficient of the cooling system; and judging whether the cooling system meets the heat dissipation requirement or not by combining the current heat dissipation capability coefficient of the cooling system with the current state evaluation result of the equipment.

The invention relates to the technical field of energy data processing, in particular to an energy-saving and environment-friendly detection method and system for a green building. According to the change trend of all data of a corresponding dimension in a neighborhood range of data of each dimension at a target sampling moment, the abnormal degree of the data of each dimension at the target sampling moment is obtained; obtaining the connectivity between the two dimension data at the target sampling moment by combining the difference of the abnormal degree between the two dimension data and the local distribution association; further obtaining a correction score coefficient of the power efficiency data at the target sampling moment; correcting the initial score value of the corresponding electrical efficiency data to obtain a corrected score value of the electrical efficiency data at the target sampling moment; obtaining a correction score value of the power efficiency data at each sampling moment; and abnormal electric efficiency data are screened out. According to the method, the accurate score value of each electrical efficiency data is obtained, the accuracy of abnormal electrical efficiency data detection is improved, and the energy-saving and environment-friendly detection result is optimized.

The invention provides a power distribution network abnormal state sensing method and system based on data processing, and relates to the technical field of data processing, and the method comprises the steps: obtaining a target function, and setting a differential evolution parameter; the differential evolution parameters comprise population size, iteration times, variation factors and crossover probability; the configuration of differential evolution parameters is iteratively optimized, various operation data are preprocessed, abnormal value and noise removal and normalization processing are included, and processed data are obtained; and extracting characteristic indexes reflecting the running state of the power distribution network from the processed data, wherein the characteristic indexes comprise statistical characteristics, frequency spectrum characteristics and time frequency characteristics of voltage and current. According to the invention, through real-time data acquisition, feature extraction, state evaluation and abnormity identification and positioning, comprehensive monitoring and timely early warning of the operation state of the power distribution network are realized, and the safety and stability of the power distribution network are effectively improved.

The invention relates to an anti-electricity-stealing monitoring method and device based on an intelligent universal meter, equipment and a medium. The method comprises the following steps: acquiring real-time electric sampling data of a universal meter, and preprocessing the data to obtain electric characteristic data; carrying out electricity stealing feature extraction on the electricity feature data by utilizing an edge electricity stealing detection model, and carrying out preliminary judgment on an electricity utilization type to obtain a preliminary judgment result; if the preliminary judgment result is at least one of an electricity larceny event and reverse feed, using a cloud electricity larceny detection model to perform secondary analysis on the electricity characteristic data to obtain an abnormal probability; and if the abnormal probability exceeds a preset threshold, generating a corresponding alarm work order, and pushing the alarm work order to a supervision platform. By adopting the method, the field acquisition capability of the intelligent multimeter can be fully combined, the local and cloud computing characteristics are combined, and the recognition and monitoring of electricity stealing behaviors such as low-current electricity stealing and reverse feed are effectively enhanced through a multi-source feature extraction and judgment mechanism.

The invention provides a power distribution control cabinet fault response method and device, and relates to the technical field of electrical automation control, and the key points of the technical scheme are that a weighted directed graph model representing the relation of internal components of a power distribution control cabinet is constructed in advance; mapping component fault information predicted by the digital twin system to attributes of corresponding nodes in the weighted directed graph model; identifying a fault path or a fault cluster satisfying a predetermined condition; for each identified fault path or fault cluster, calculating a composite risk index according to the prediction probability of the nodes included in the fault path or fault cluster, the weight of the connection edge and the severity of the influence on the system; and generating early warning information according to the composite risk index. According to the power distribution control cabinet fault response method and device provided by the invention, a potential fault propagation path or a fault aggregation area can be identified based on physical, electrical and influence relationships among components, and a composite risk formed by a plurality of potential fault combinations is quantified, so that a preventive maintenance decision is guided.

The invention discloses a film capacitor enabling control method and system, a medium and a program product, and the method comprises the steps: determining current sampling data and voltage sampling data of a film capacitor in a first time period; calculating an equivalent impedance sequence within a first time length, and determining distribution characteristics of impedance components under different frequencies; dividing a plurality of frequency band intervals, and calculating an impedance change rate matrix in each frequency band interval; determining energy transmission efficiency, and determining a target frequency band interval; generating a compensation coefficient, and adjusting the amplitude and the frequency by using the compensation coefficient to obtain a target sine-wave voltage; performing energizing processing on the thin-film capacitor by adopting the target sine-wave voltage within the remaining time of the energizing duration, and calculating an instantaneous power factor in real time; and when the stability of the instantaneous power factor reaches a preset condition and the leakage current is reduced below a preset threshold value, generating an enabling completion signal. According to the invention, the adaptability between the energizing effect and the actual demand of the thin film capacitor is improved, and the energizing effect is further improved.

The invention relates to the technical field of intelligent electric energy meters, in particular to an abnormal event recording method and system of an intelligent electric energy meter, and the method comprises the steps: obtaining historical abnormal event information, according to the historical abnormal event information, determining an initial time period when an electric power abnormal event occurs in the intelligent electric energy meter and an electric power parameter change in the initial time period, and according to the electric power abnormal event, the abnormal degree, and the initial time period and the electric power parameter change corresponding to the electric power abnormal event, generating an abnormal event monitoring standard, and collecting the real-time monitoring electric power parameters within a preset time, determining whether an abnormal event has occurred in the real-time monitoring electric power parameters based on the abnormal event monitoring standard, if the abnormal event has occurred, recording and storing the occurred abnormal event, and controlling to display the recorded and stored occurred abnormal event. The power management efficiency and the response speed of the intelligent electric energy meter are improved.

The invention relates to a switch control method and system for a household stirrer, and relates to the technical field of digital control, and the method comprises the steps: obtaining the food weight and food type of to-be-stirred target food through a weight sensor in the stirrer, carrying out the stirring time prediction, and obtaining the predicted stirring time; in the stirring starting process, monitoring and recording motor power to obtain a motor power sequence; processing and acquiring a time coefficient of the accumulated stirring time and the predicted stirring time, analyzing and acquiring a power change coefficient of a motor power sequence, and configuring a stirring state recognition coefficient; according to the stirring state recognition coefficient, stirring state recognition is conducted based on the motor power sequence and the accumulated stirring time, stirring state information is obtained, and when the preset stirring state is met, the stirrer is closed. The technical problem that in the prior art, a household stirrer is not intelligently controlled, and the use effect of the household stirrer is affected is solved.

The invention relates to a DTU safety monitoring method. The method comprises the following steps: acquiring electrical parameters of a ring main unit through a DTU; verifying the electrical parameters to detect whether the electrical parameters are within a preset electrical parameter safety range; if the electrical parameter is not in the preset electrical parameter safety range, performing multi-scale decomposition on the electrical parameter to obtain a potential hazard characteristic signal; performing frequency domain feature extraction on the potential hazard feature signal to obtain a fault frequency spectrum feature; calling a preset fault mode database, and matching the fault frequency spectrum features with frequency spectrum features in the fault mode database to obtain a fault problem area and a fault type of the ring main unit; giving a weight to the fault problem area, giving a score to the fault type, and obtaining an abnormal risk level of the ring main unit based on the weight of the fault problem area and the score of the fault type; the technical problem that diagnosis accuracy is limited in the face of complex working conditions and weak signal anomalies is solved.

The invention discloses a transformer explosion-proof and fault monitoring device and method, and belongs to the technical field of power equipment. The specific device comprises a transformer oil tank pressure reduction module, an on-load tap-changer pressure reduction module, an oil and gas separation tank, a gas discharge pipeline, a nitrogen injection module, a transformer protection control cubicle, an oil-immersed cable oil tank pressure reduction module and a transformer module. The specific method comprises the following steps: acquiring transformer data; calculating a power factor / loss factor according to the acquired transformer bushing current; setting early warning threshold values of transformer capacitance, a power factor / loss factor and a partial discharge signal, and judging whether the data reach the corresponding early warning threshold values or not; and after any data in the judgment result reaches an early warning threshold value, judging that the transformer has a fault, triggering early warning and performing transformer pressure relief, thereby realizing explosion prevention and fault monitoring of the transformer. According to the invention, pressure relief protection of the transformer cable box, the on-load tap-changer module and the main oil tank is realized, and online monitoring and fault diagnosis of key monitoring signals are provided.

The invention discloses a power grid electric energy quality monitoring system, which relates to the field of electric energy quality monitoring, and is characterized in that a data acquisition module is arranged in the power grid electric energy quality monitoring system, a data transmission module is mounted on the outer side of the data acquisition module, and a data storage module is arranged behind the data transmission module; a data processing and analysis module is arranged on the outer side of the data storage module, a fault diagnosis and early warning module is arranged below the data processing and analysis module, and a display and visualization module is installed on the outer side of the fault diagnosis and early warning module. According to the power grid electric energy quality monitoring system, through widely distributed monitoring instruments and sensors, key parameters of a power grid are collected in real time, all nodes of the power grid are covered, data comprehensiveness and no monitoring blind area are ensured, and the real-time performance of data transmission is ensured by adopting multiple communication technologies and an automatic retry mechanism; in combination with a real-time analysis algorithm, power grid abnormity is rapidly identified, and fault response time is shortened.

The invention discloses a transformer area topology identification-based fault positioning method, system and device, and a medium, and the method comprises the steps: inputting an obtained first pulse number of a general meter and second pulse numbers of a plurality of sub-meters into a topology analysis model for solving, so as to determine a first transformer area topology structure; calculating a target correlation coefficient between the users based on each electrical quantity data, removing first user pairs of which the target correlation coefficients do not meet a preset correlation threshold to obtain a plurality of second user pairs, calculating a target DTW distance of each second user pair, taking the second user pair of which the target DTW distance is maximum as a third user pair, and taking the second user pair of which the target DTW distance is maximum as a second user pair of which the target DTW distance is maximum as a second user pair of which the target DTW distance is maximum as a third user pair; performing iterative optimization by taking the third user pair as an initial clustering center until a clustering termination condition is met, and determining a second transformer area topological structure according to an output target clustering result; and comparing the first transformer area topological structure with the second transformer area topological structure, if the first transformer area topological structure and the second transformer area topological structure are the same, determining a target transformer area topological structure, and performing fault positioning based on the target transformer area topological structure. According to the invention, the transformer area fault point can be accurately positioned.

The invention relates to the technical field of power supply and distribution supervision, and particularly discloses a power supply and distribution supervision system based on data processing, which comprises a distributed node module, an operation data analysis module, a fault diagnosis analysis module and a performance optimization supervision module. A power supply and distribution system is divided into a plurality of supervision nodes through a distributed node module, operation parameters of each node in each supervision period can be monitored and counted in real time, and a basic operation state coefficient is comprehensively calculated by calculating voltage, current, power factor and power volatility and a normalization value and combining a set adjustment coefficient. Therefore, the operation state of each supervision node in each supervision period can be accurately mastered, factors such as the parameter average value, the volatility and the rated value are comprehensively considered, the operation condition of the system is evaluated more accurately, abnormity can be found in time, and a powerful basis is provided for optimizing operation of a power supply and distribution system, improving the power supply quality and reducing faults and energy waste.

The invention relates to a test analysis method and device for a radio frequency power supply sensor and a storage medium, and the method comprises the steps: carrying out the power linearity data collection of the radio frequency power supply sensor according to different output powers under the condition of radio frequency open-loop output; obtaining power test acquisition data of the sensor channel and the ADC signal acquisition link channel at a plurality of power test indexes under different output powers; performing frequency linearity data acquisition on the radio frequency power supply sensor according to a plurality of test frequency points set for the sensor to obtain frequency test acquisition data of a sensor channel and an ADC signal acquisition link channel corresponding to the plurality of test frequency points; determining power test results corresponding to different output powers according to the power test acquisition data and the power meter calibration data of the two channels in the plurality of power test indexes under different output powers; and according to the frequency test acquisition data of the two channels corresponding to the plurality of test frequency points, obtaining frequency test results corresponding to different frequency indexes.

The invention is suitable for the technical field of circuit breaker detection, and provides a real-time detection method and system based on residual-current circuit breakers, and the method comprises the steps: firstly obtaining the weight ratio data and real-time operation parameter data of each residual-current circuit breaker of a low-voltage power distribution system, and then carrying out the real-time detection of the residual-current circuit breakers according to the weight ratio data and the real-time operation parameter data. Determining aging parameter data of the residual-current circuit breaker, and finally generating life estimation data according to the aging parameter data. The operation parameters of the low-voltage power distribution system can be monitored and analyzed in real time, adjustment can be conveniently carried out according to needs, abnormal conditions can be detected more accurately, the labor cost is remarkably reduced, the residual-current circuit breaker can keep the optimal performance under various operation conditions, and risks caused by equipment faults or misoperation are greatly reduced.

The invention discloses an abnormal state monitoring method and device of power equipment and electronic equipment. The abnormal state monitoring method of the power equipment in the embodiment of the invention comprises the following steps: acquiring operation data of to-be-monitored power equipment from at least one data source; performing collaborative extraction on the operation data of each data source to obtain fusion features of the operation data of each data source; the fusion features are input into an abnormal state detection model for abnormal state detection, a detection result of the abnormal state of the power equipment is obtained, the abnormal state detection model comprises a clustering model and a classification model, and the detection result of the abnormal state of the power equipment is a fusion value of an output result of the clustering model and an output result of the classification model. According to the invention, the safety and stability of the power equipment are improved.

The invention discloses a comprehensive test system for an alternating-current mobile power station. A power supply unit is electrically connected with a mains supply power supply and used for supplying power to the comprehensive test system. The load distribution unit is connected with a to-be-tested alternating current mobile power station, and an acquisition unit is arranged between the load distribution unit and the to-be-tested alternating current mobile power station; the load distribution unit is used for providing various simulation loads for the alternating current mobile power station to be tested according to the loading instruction of the control calculation unit; the acquisition unit is used for acquiring a simulation test signal under a corresponding simulation load and outputting the simulation test signal to the control calculation unit; the control calculation unit performs test calculation according to the simulation test signal to obtain a performance test result of the to-be-tested AC mobile power station under the corresponding simulation load, and the AC mobile power station comprehensive test system solves the problems that the existing AC mobile power station load test equipment is large in size and low in test efficiency through integrated modular design. The problems of multiple types of equipment required for completing steady-state test and transient test, low load reuse rate, single load simulation characteristic and poor test universality in the prior art are solved.

The present application relates to the field of data processing technology, and specifically to a real-time monitoring method and system for campus energy consumption data based on data analysis, the method comprising: obtaining various electrical parameter data of each classroom in the campus; comparing the change difference between the current power consumption data and the historical power consumption data, obtaining the power consumption ratio, and extracting suspected abnormal classrooms; obtaining all high current continuous segmented sequences and low current continuous segmented sequences of each suspected abnormal classroom; obtaining the current fluctuation of each suspected abnormal classroom; combining voltage data and effective power, obtaining the invalid power ratio and correcting it to obtain the invalid power ratio degree; based on the current fluctuation and the invalid power ratio degree, obtaining the classroom with abnormal energy consumption. The present application aims to analyze whether the energy consumption of each classroom in the campus is a normal load fluctuation, and to improve the sensitivity and accuracy of abnormal energy consumption data identification.

This application relates to an online monitoring and positioning method for electricity theft problems based on Beidou, including the following specific steps: triggering a sampling signal by the Beidou synchronous clock, and simultaneously performing ADC sampling at measuring points of different circuits; accumulating the instantaneous values collected at the same moment on the branch line to form the instantaneous value of the current of the virtual measuring point on the virtual upper-level line; calculating the effective current value respectively according to the measured instantaneous current value and the instantaneous current value of the virtual measuring point; comparing the effective current values of the measured point and the virtual measuring point, calculating the difference of the effective current values, and determining whether there is current abnormality according to the difference; when there is current abnormality, performing subtraction calculation on the instantaneous values of the measured point and the virtual measuring point to analyze whether it is leakage or electricity theft; when there is current abnormality, providing more judgment basis for the qualitative determination of the leakage current. This application can identify whether it is leakage current or electricity theft by performing harmonic analysis on the leakage current and combining the characteristics of electrical equipment.

The invention discloses an energy consumption monitoring method and system for moderate refining of torreya grandis oil, and relates to the technical field of energy consumption monitoring, and the method comprises the steps: collecting multi-modal energy consumption data from a refining workshop, carrying out the preprocessing of the data, generating a time sequence feature matrix, constructing a feature variable graph based on feature dimensions, introducing a graph attention mechanism for modeling, and generating a graph embedded representation. The graph embedding features are input into a double-layer GRU structure, dynamic evolution features in a process time sequence are learned, and a semantic behavior vector F is obtained; according to the method, three-variable graph embedding is constructed, semantic behavior vectors are generated, an LSTM prediction mechanism is fused to recognize energy consumption deviation, a dynamic threshold value is accurately set through a POT algorithm, and the adaptability of deviation judgment is improved. And in combination with a random forest classifier and a semantic rule, the energy consumption deviation behavior is accurately classified as a process response mismatch type, so that efficient, automatic and feedback energy consumption abnormity monitoring and intervention control are realized.

An electric meter (100) comprising a non-invasive voltage measurement sensor (12), one or more non-invasive current measurement sensors (14A-14C) for measuring one or more phase currents, and a digital processing unit (10). The voltage measurement sensor (12) and the current measurement sensors (14A-14C) are connected or connectable to the processing unit (10). The processing unit (10) is configured, in a calibration mode, to determine phase shift values between a first voltage sensor signal and first current sensor signals, and to store the phase shift values into a memory. The processing unit (10) is configured, in an operation mode, to determine a second voltage sensor signal, determine voltage estimates based on the second voltage sensor signal and the stored phase shifts, and determine, based on the voltage estimates and second current sensor signals determined by the current measurement sensors (14A-14C), respectively, one or more electric power values.

An electric meter (100) comprising a non-invasive voltage measurement sensor (12), one or more non-invasive current measurement sensors (14A-14C) for measuring one or more phase currents, and a digital processing unit (10). The voltage measurement sensor (12) and the current measurement sensors (14A-14C) are connected or connectable to the processing unit (10). The processing unit (10) is configured, in a calibration mode, to determine phase shift values between a first voltage sensor signal and first current sensor signals, and to store the phase shift values into a memory. The processing unit (10) is configured, in an operation mode, to determine a second voltage sensor signal, determine voltage estimates based on the second voltage sensor signal and the stored phase shifts, and determine, based on the voltage estimates and second current sensor signals determined by the current measurement sensors (14A-14C), respectively, one or more electric power values.

The application discloses a kind of photovoltaic inverter MPPT efficiency evaluation method, system and storage medium, respectively to measured voltage and measured current are evaluated, then using the result of respective independent evaluation to determine final MPPT efficiency result, compared with the way of traditional direct calculation power then evaluation, effectively reduce the degree of being influenced by output error.And, in the process of processing respectively, by respectively to measured voltage and maximum power point corresponding voltage, to measured current and maximum power point corresponding current are calculated deviation, then using deviation calculation result to carry out tracking efficiency calculation mode, can directly fundamentally guarantee that final calculated result will be less than 100%, i.e., it is in reasonable range, and evaluation result also has real meaning, can effectively reflect the work of photovoltaic inverter.

The invention discloses a dry-type air-core reactor turn-to-turn short circuit fault detection method and system. The method comprises the following steps: acquiring a first phase voltage signal and a first phase current signal of a to-be-detected dry-type air-core reactor in real time; the first phase voltage signal and the first phase current signal are preprocessed, the preprocessed first phase voltage signal and the preprocessed first phase current signal in a first specified number of windows are obtained through a sliding window, and a corresponding power factor real-time value is calculated according to the preprocessed first phase voltage signal and the preprocessed first phase current signal in each window; respectively calculating the change rate of each power factor real-time value compared with the power factor reference value; and if the real-time value of the power factor with the change rate greater than the preset threshold value reaches a second specified number, determining that the to-be-detected dry-type air-core reactor has the turn-to-turn short circuit fault. According to the turn-to-turn short-circuit fault detection method, the condition that the number of power factor change rates exceeds a normal range is adopted as a protection criterion, so that the sensitivity and reliability of turn-to-turn short-circuit fault detection are ensured.

The present invention relates to the technical field of aircraft generators, and provides an adaptive loading control method and system for an aircraft generator load loading system. In an aircraft generator power component extraction test, a hardware system is constructed and software adopts a closed-loop control and segmented calibration method to achieve load adaptive voltage and frequency loading, thereby improving control accuracy and achieving rapid regulation.