CN118017698A - Power consumption terminal monitoring system, method, electronic equipment and storage medium - Google Patents
Power consumption terminal monitoring system, method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN118017698A CN118017698A CN202410421003.6A CN202410421003A CN118017698A CN 118017698 A CN118017698 A CN 118017698A CN 202410421003 A CN202410421003 A CN 202410421003A CN 118017698 A CN118017698 A CN 118017698A
- Authority
- CN
- China
- Prior art keywords
- phase
- phase sequence
- sequence
- data
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000000819 phase cycle Methods 0.000 claims abstract description 160
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 39
- 230000005856 abnormality Effects 0.000 claims abstract description 36
- 230000002159 abnormal effect Effects 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 16
- 238000013135 deep learning Methods 0.000 claims abstract description 14
- 238000004891 communication Methods 0.000 claims abstract description 11
- 238000005516 engineering process Methods 0.000 claims abstract description 6
- 238000004364 calculation method Methods 0.000 claims description 16
- 238000001514 detection method Methods 0.000 claims description 15
- 230000005611 electricity Effects 0.000 claims description 14
- 238000012423 maintenance Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 11
- 230000003044 adaptive effect Effects 0.000 claims description 10
- 230000007246 mechanism Effects 0.000 claims description 7
- 230000003449 preventive effect Effects 0.000 claims description 7
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 4
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 2
- 230000006872 improvement Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/18—Indicating phase sequence; Indicating synchronism
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/18—Multiprotocol handlers, e.g. single devices capable of handling multiple protocols
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Power Engineering (AREA)
- Economics (AREA)
- Signal Processing (AREA)
- Molecular Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Computer Security & Cryptography (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention relates to the technical field of monitoring systems, in particular to a power consumption terminal monitoring system, a method, electronic equipment and a storage medium, comprising the following steps: and the phase sequence identification module is used for: the sensor array is configured, and the electric signal processing algorithm is utilized to monitor and identify the phase sequence of the power grid in real time; and a dynamic adjustment module: adopting a self-adaptive control algorithm, and dynamically adjusting wiring configuration of terminal equipment or a power access point according to data provided by a phase sequence identification module so as to correct phase sequence abnormality; real-time remote communication module: supporting multi-protocol wireless communications; prediction module of deep learning drive: and analyzing historical phase sequence data and power grid operation parameters by using a deep learning technology, and predicting phase sequence abnormality. According to the invention, the degree of data distribution density is considered, so that the abnormal phase sequence is recognized and predicted in the actual power utilization terminal circuit environment with uneven data distribution, and the accuracy and reliability of prediction are improved.
Description
Technical Field
The present invention relates to the field of monitoring systems, and in particular, to a power consumption terminal monitoring system, a power consumption terminal monitoring method, an electronic device, and a storage medium.
Background
In the current power system management, monitoring and predicting the phase sequence abnormality of the power utilization terminal in real time is important to ensure the stability and reliability of the power grid. Traditional electricity monitoring systems mainly focus on statistics and analysis of overall energy consumption, and tend to ignore finer level monitoring in an electric power system, particularly phase sequence monitoring of terminal equipment. Abnormal phase sequence, such as reverse sequence or open phase, may cause equipment damage, reduced operation efficiency, or even safety accidents, so that effective monitoring and timely early warning are important tasks in the electric network management.
However, the prior art has some limitations in dealing with phase sequence anomaly monitoring and prediction. On one hand, the traditional method often depends on simple threshold judgment or rules, and lacks adaptability to complex power utilization terminal circuit network environment and dynamic change; on the other hand, many methods do not take full advantage of historical data and grid operating parameters, and thus cannot identify and prevent potential phase sequence anomalies in advance. Furthermore, the processing of data distribution non-uniformities and time series characteristics is often neglected, which limits the accuracy and application range of the predictive model.
Under the background, a monitoring system capable of comprehensively utilizing historical phase sequence data and terminal operation parameters and realizing accurate prediction and automatic adjustment through advanced data analysis and machine learning technology is developed, and the monitoring system becomes an important requirement for improving the stability and safety of a power grid.
Disclosure of Invention
Based on the above purpose, the invention provides a power consumption terminal monitoring system, a method, electronic equipment and a storage medium.
An electricity usage terminal monitoring system, comprising:
and the phase sequence identification module is used for: the sensor array is configured, and the electric signal processing algorithm is utilized to monitor and identify the phase sequence of the power grid in real time;
and a dynamic adjustment module: adopting a self-adaptive control algorithm, and dynamically adjusting wiring configuration of terminal equipment or a power access point according to data provided by a phase sequence identification module so as to correct phase sequence abnormality;
Real-time remote communication module: supporting multi-protocol wireless communication, being capable of immediately sending an alarm and a detailed diagnosis report to a remote maintenance center or maintenance personnel when a phase sequence abnormality is detected;
Prediction module of deep learning drive: and analyzing the historical phase sequence data and the power grid operation parameters by using a deep learning technology, and predicting the phase sequence abnormality, so that preventive maintenance and optimization of power grid operation are realized.
Further, the phase sequence identification module is configured with three voltage sensors and three current sensors, corresponding to three phase lines of a three-phase power grid respectively: phase a, phase B and phase C, each sensor being mounted on a respective phase line to capture in real time the voltage and current signals of the power grid, the captured analog signals being converted to digital signals by an analog-to-digital converter ADC, the electrical signal processing algorithm comprising:
signal pretreatment: noise in the signals is removed through a filter, and accuracy of phase sequence identification is enhanced;
Feature extraction: extracting key features including peak values, zero crossing points and phase differences from the processed voltage and current signals;
And (3) phase sequence judgment: calculating phase differences among phases by using the extracted features, and identifying and confirming phase sequences of a power grid according to the sequence of the phase differences;
Anomaly detection algorithm: and detecting abnormal phase sequence (reverse sequence or open phase).
Further, in the three-phase power, the voltage waveforms between each phase are different by 120 degrees, the phase sequence of the power grid is determined by measuring the phase difference, and thenAnd/>Representing the voltage waveform functions of phase A, phase B, phase C, respectively, where/>Time is;
The signal preprocessing uses a low-pass filter, and is provided with As a filtering function, the processed signal is represented asSimilar processing/>And/>；
Zero crossing point detection: for the voltage signal of each phase, find its zero crossing, i.eThe zero crossing provides a time stamp for the phase measurement;
The phase difference is calculated as: is provided with And/>The time of a zero crossing point of the voltage waveforms of the phase A, the phase B and the phase C respectively, and the phase difference/>The calculation is as follows:
wherein/> Is the period of the grid frequency (e.g., in a 50Hz grid,/>) Similarly calculate/>And/>；
The phase sequence is determined by comparisonDetermining the phase sequence, normal phase sequence (/ >)Phase sequence) should be positive, i.e./>And/>Taking periodicity into account, the phase difference may need to be suitably adjusted to ensure that it is in the range of 0 to 360);
The anomaly detection algorithm specifically includes:
Phase difference reconfirmation: based on phase difference Confirm whether the phase difference meets the expected range of the normal phase sequence, namely/>(Taking into account small amplitude deviations that may exist under practical conditions);
and (3) reverse sequence detection: for the reverse order case, the order of the phase differences is reversed, and therefore, by checking And/>Is identified as the reverse order if the phase difference is shown as/>The phase sequence is indicated to be the reverse sequence;
and (3) phase failure detection: in the case of phase failure, the voltage of at least one phase is significantly lower than the other two phases, resulting in distortion of phase difference calculation, detecting whether the voltage amplitude of each phase is lower than a preset threshold, if the voltage amplitude of any phase is lower than the threshold, considering that the phase is missing, checking the magnitude of the phase difference, if found Or/>Any one of the deviations/>Also indicating the existence of a phase failure condition.
Further, the dynamic adjustment module specifically includes:
The method comprises the steps of adopting a model reference self-adaptive unit as a self-adaptive control algorithm, wherein the self-adaptive unit dynamically adjusts wiring configuration of terminal equipment or a power supply access point according to real-time phase sequence data provided by a phase sequence identification module and a preset phase sequence reference model, and the method comprises the following specific steps:
setting a reference model: setting an ideal phase sequence reference model to define the expected phase sequence of the circuit in the power utilization terminal And the ideal phase difference between each phase is 120 °;
Phase sequence error calculation: the adaptive unit calculates the phase sequence error by comparing the actual monitored phase sequence data (from the phase sequence identification module) with the ideal phase sequence reference model ；
Self-adaptive adjustment rules: according to phase sequence errorAnd implementing an adaptive adjustment rule, and dynamically adjusting the wiring configuration.
Further, the adaptive adjustment rule is defined as:
If it is Exceeds a predetermined threshold/>And judging that wiring configuration adjustment is needed, and determining an adjustment strategy according to the sign and the magnitude of the phase error:
If it is All are positive or negative, which indicates that the overall phase sequence is reverse, and at the moment, the wiring of two phases needs to be exchanged to try to match the ideal phase sequence, and if the phase error of one phase is larger than that of other two phases, the wiring position of the phase and other phases needs to be adjusted;
According to the self-adaptive regulation rule, the dynamic regulation module changes the wiring configuration by controlling the relay or the switching device, if the reverse order is judged, the wiring of the B phase and the C phase is exchanged, and after the regulation, the phase error is recalculated to verify whether the regulation is successful, if Decreasing and approaching zero indicates that the wire adjustment is effective, and if the phase error does not improve or the error increases after adjustment, other adjustment strategies will be traversed, including swapping wires of the other two phases.
Further, the deep learning technology adopts an improved K nearest neighbor algorithm, that is SKNN, specifically including:
Feature selection and engineering: the phase difference, the voltage amplitude, the current amplitude and the power factor of the associated phase sequence state are selected as main characteristics, the phase difference change rate of adjacent time points is introduced to capture the dynamic characteristics of the phase sequence change, and the phase difference change is calculated as follows: Wherein/> Is the phase difference of the current time point,/>Is the phase difference at the last time point;
Data preprocessing: the features are normalized using Zscore normalization to ensure that each feature has equivalent weight in the distance calculation, calculated as: wherein/> Is the original data,/>Is an average value/>Is the standard deviation;
Weighted distance calculation: the distances between the data points are calculated using weighted euclidean distances to assign different weights to the different features, the weighted distances being calculated by: Wherein/> Is a feature/>Reflecting the importance of the feature in predicting phase sequence anomalies;
Dynamic state Value selection: dynamic selection/>, based on the size and characteristics of the datasetValues to optimize predictive performance, determination of best/>, by cross-validationA value;
density-based neighbor selection: in addition to finding the nearest Besides the neighbors, consider/>The density of each neighbor in the feature space is calculated, and representative neighbors are screened out for prediction by calculating the local density of the neighbors;
Prediction and anomaly scoring: and carrying out classification prediction by using a weighted voting mechanism, wherein the voting weight of each neighbor is inversely proportional to the distance of each neighbor, and when the prediction power utilization terminal has phase sequence abnormality, assigning an abnormality score to each test point, wherein the score is based on the duty ratio of the abnormality category in the nearest neighbor.
Further, the density-based neighbor selection specifically includes:
defining a local density: for each data point Calculate its Euclidean distance/>, from all other pointsWherein/>Is the other point in the dataset, defining the local density/>For the dot/>At a given radius/>The number of data points within, namely:
wherein/> Is an indication function, takes a value of 1 when the condition in the brackets is true, otherwise takes a value of 0;
determining the neighbor: for each data point Its neighbors are not only based on distance/>To choose, also consider the point/>Local density/>The condition for selecting neighbors is not just/>Is small enough and/>It is also high enough that it is preferable to select points located in the high density region as neighbors;
density-based weight assignment: determining data points The influence of its neighbors is adjusted by the following weight function: /(I)Wherein/>Is a positive adjustment parameter for balancing the influence of distance and density, so that even if a certain neighbor is a little farther away, if it is in a high density area, it can have a larger influence on the prediction result;
predicting phase sequence abnormality: for data points to be predicted Based on it/>The neighbors (selected according to the density and distance based criteria described above) and their weights/>To predict the phase sequence state, prediction of phase sequence anomalies is based on a weighted voting mechanism, namely:
prediction category Wherein/>Is a predicted normal or abnormal category,/>Is neighbor/>Is a category of (2).
The power consumption terminal monitoring method is realized by the power consumption terminal monitoring system and comprises the following steps of:
s1: the method comprises the steps of utilizing a sensor array configured at an electricity utilization terminal to capture current and voltage signals in real time, and analyzing the current and voltage signals through an electric signal processing algorithm so as to identify the phase sequence state of an electricity utilization terminal circuit in real time;
S2: when the phase sequence recognition module detects phase sequence abnormality, starting an adaptive control algorithm, and dynamically adjusting wiring configuration of terminal equipment or a power access point according to the nature and degree of the abnormality so as to attempt to correct the abnormal state;
s3: immediately transmitting diagnostic reports of abnormal information, current phase sequence states and adjustment measures taken to a remote maintenance center or related maintenance personnel through a supported multi-protocol wireless communication network while the phase sequence abnormality is detected;
S4: and periodically analyzing historical phase sequence data and operation parameters of the power utilization terminal, identifying potential modes and abnormal trends in the data by using a deep learning algorithm, predicting future phase sequence anomalies, and realizing preventive maintenance.
An electronic device comprising a memory, a processor and a computer program stored in the memory and running on the processor, the processor implementing a method for monitoring an electrical terminal as described above when executing the computer program.
A storage medium having a computer program stored thereon, which when executed by a processor implements a method for monitoring an electric terminal as described above.
The invention has the beneficial effects that:
according to the invention, by combining historical phase sequence data with power grid operation parameters and utilizing a density-based neighbor selection strategy, the phase sequence abnormality of the power utilization terminal can be predicted more accurately, and the improved KNN algorithm considers the local density of data points, so that the algorithm is not only based on distance, but also considers the density degree of data distribution when selecting neighbors, which is helpful for identifying and predicting the phase sequence abnormality in the actual power utilization terminal circuit environment with uneven data distribution, and the accuracy and reliability of prediction are improved.
According to the invention, the adaptability to dynamic change of the power grid is enhanced by dynamically adjusting the self-adaptive rule of wiring configuration and the density-sensitive neighbor selection mechanism, the self-adaptive control algorithm can automatically adjust according to the phase sequence error monitored in real time, so that the stable operation of the power system is ensured, and meanwhile, the design of the system considers different power grid conditions and data characteristics, so that the system can flexibly cope with various situations, from simple phase sequence reverse sequence to complex power grid unstable events.
The method and the system can learn and identify potential risk modes from historical data, realize early warning of abnormal phase sequence and unstable events of the power utilization terminal circuit, and enable operation and maintenance personnel of the power utilization terminal to take measures in advance to perform preventive maintenance, so that potential faults and accidents are avoided.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a functional module of a monitoring system according to an embodiment of the present invention;
Fig. 2 is a schematic flow chart of a monitoring method according to an embodiment of the invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, an electricity consumption terminal monitoring system includes:
and the phase sequence identification module is used for: the sensor array is configured, and the electric signal processing algorithm is utilized to monitor and identify the phase sequence of the power grid in real time;
and a dynamic adjustment module: adopting a self-adaptive control algorithm, and dynamically adjusting wiring configuration of terminal equipment or a power access point according to data provided by a phase sequence identification module so as to correct phase sequence abnormality;
Real-time remote communication module: supporting multi-protocol wireless communication, being capable of immediately sending an alarm and a detailed diagnosis report to a remote maintenance center or maintenance personnel when a phase sequence abnormality is detected;
Prediction module of deep learning drive: and analyzing the historical phase sequence data and the power grid operation parameters by using a deep learning technology, and predicting the phase sequence abnormality, so that preventive maintenance and optimization of power grid operation are realized.
The phase sequence recognition module is provided with three voltage sensors and three current sensors, which respectively correspond to three phase lines of a three-phase power grid: phase a, phase B and phase C, each sensor being mounted on a respective phase line to capture in real time the voltage and current signals of the power grid, the captured analog signals being converted into digital signals by an analog-to-digital converter ADC, the electrical signal processing algorithm comprising:
signal pretreatment: noise in the signals is removed through a filter, and accuracy of phase sequence identification is enhanced;
Feature extraction: extracting key features including peak values, zero crossing points and phase differences from the processed voltage and current signals;
And (3) phase sequence judgment: calculating phase differences between phases by using the extracted features, and identifying and confirming the phase sequence of the power grid according to the sequence of the phase differences (for example, phase A leads phase B, phase B leads phase C, and ABC phase sequence is formed);
Anomaly detection algorithm: and detecting abnormal phase sequence (reverse sequence or open phase).
In the three-phase power, the voltage waveforms between each phase are different by 120 degrees, the phase sequence of the power grid is determined by measuring the phase difference, and the device is setAnd/>Representing the voltage waveform functions of phase A, phase B, phase C, respectively, where/>Time is;
The signal preprocessing uses a low-pass filter, provided with As a filtering function, the processed signal is represented asSimilar processing/>And/>；
Zero crossing point detection: for the voltage signal of each phase, find its zero crossing, i.eThe zero crossing provides a time stamp for the phase measurement;
the phase difference is calculated as: is provided with And/>The time of a zero crossing point of the voltage waveforms of the phase A, the phase B and the phase C respectively, and the phase difference/>The calculation is as follows:
wherein/> Is the period of the grid frequency (e.g., in a 50Hz grid, t=20 ms), and is similarly calculated/>And/>；
Phase sequence determination by comparisonAnd/>Determining the phase sequence, normal phase sequence (/ >)Phase sequence) should be positive, i.e./>And/>(Taking periodicity into account, the phase difference may need to be suitably adjusted to ensure that it is in the range of 0 to 360);
The anomaly detection algorithm specifically includes:
Phase difference reconfirmation: based on phase difference Confirm whether the phase difference meets the expected range of the normal phase sequence, namely/>(Taking into account small amplitude deviations that may exist under practical conditions);
and (3) reverse sequence detection: for the reverse order case, the order of the phase differences is reversed, and therefore, by checking And/>Is identified as the reverse order if the phase difference is shown as/>The phase sequence is indicated to be the reverse sequence;
and (3) phase failure detection: in the case of phase failure, the voltage of at least one phase is significantly lower than the other two phases, resulting in distortion of phase difference calculation, detecting whether the voltage amplitude of each phase is lower than a preset threshold, if the voltage amplitude of any phase is lower than the threshold, considering that the phase is missing, checking the magnitude of the phase difference, if found Or/>Any one of the deviations/>Also indicating the existence of a phase failure condition.
The dynamic adjustment module specifically comprises:
The method comprises the steps of adopting a model reference self-adaptive unit as a self-adaptive control algorithm, wherein the self-adaptive unit dynamically adjusts wiring configuration of terminal equipment or a power supply access point according to real-time phase sequence data provided by a phase sequence identification module and a preset phase sequence reference model, and the method comprises the following specific steps:
setting a reference model: setting an ideal phase sequence reference model to define the expected phase sequence of the circuit in the power utilization terminal And the ideal phase difference between each phase is 120 °;
Phase sequence error calculation: the adaptive unit calculates the phase sequence error by comparing the actual monitored phase sequence data (from the phase sequence identification module) with the ideal phase sequence reference model ；
Self-adaptive adjustment rules: according to phase sequence errorAnd implementing an adaptive adjustment rule, and dynamically adjusting the wiring configuration.
The adaptation rules are defined as:
If it is Exceeds a predetermined threshold/>And judging that wiring configuration adjustment is needed, and determining an adjustment strategy according to the sign and the magnitude of the phase error:
If it is All are positive or negative, which indicates that the overall phase sequence is reverse, and at the moment, the wiring of two phases needs to be exchanged to try to match the ideal phase sequence, and if the phase error of one phase is larger than that of other two phases, the wiring position of the phase and other phases needs to be adjusted;
According to the self-adaptive regulation rule, the dynamic regulation module changes the wiring configuration by controlling the relay or the switching device, if the reverse order is judged, the wiring of the B phase and the C phase is exchanged, and after the regulation, the phase error is recalculated to verify whether the regulation is successful, if Decreasing and approaching zero indicates that the wire adjustment is effective, and if the phase error does not improve or the error increases after adjustment, other adjustment strategies will be traversed, including swapping wires of the other two phases.
The deep learning technique adopts an improved K nearest neighbor algorithm, namely SKNN, and specifically comprises the following steps:
Feature selection and engineering: the phase difference, the voltage amplitude, the current amplitude and the power factor of the associated phase sequence state are selected as main characteristics, the phase difference change rate of adjacent time points is introduced to capture the dynamic characteristics of the phase sequence change, and the phase difference change is calculated as follows: Wherein/> Is the phase difference of the current time point,/>Is the phase difference at the last time point;
Data preprocessing: the features are normalized using Zscore normalization to ensure that each feature has equivalent weight in the distance calculation, calculated as: wherein/> Is the original data,/>Is an average value/>Is the standard deviation;
Weighted distance calculation: the distances between the data points are calculated using weighted euclidean distances to assign different weights to the different features, the weighted distances being calculated by: Wherein/> Is a feature/>Reflecting the importance of the feature in predicting phase sequence anomalies;
dynamic K value selection: dynamically selecting a K value according to the size and characteristics of the data set to optimize the prediction performance, and determining the optimal K value through cross verification;
Density-based neighbor selection: in addition to finding the nearest K neighbors, the density of the K neighbors in the feature space is considered, and the representative neighbors are screened out for prediction by calculating the local density of the neighbors;
Prediction and anomaly scoring: and carrying out classification prediction by using a weighted voting mechanism, wherein the voting weight of each neighbor is inversely proportional to the distance of each neighbor, and when the prediction power utilization terminal has phase sequence abnormality, assigning an abnormality score to each test point, wherein the score is based on the duty ratio of the abnormality category in the nearest neighbor.
Through the improvement, the KNN algorithm can more effectively process the prediction task of the phase sequence abnormality of the power utilization terminal. In particular, improvement measures such as feature engineering, weighted distance calculation and density-based neighbor selection are introduced, so that the capturing capacity of an algorithm on a complex power grid data mode is improved, and the prediction accuracy is improved.
Density-based neighbor selection specifically includes:
defining a local density: for each data point Calculate its Euclidean distance/>, from all other pointsWherein/>Is the other point in the dataset, defining the local density/>For the dot/>At a given radius/>The number of data points within, namely:
wherein/> Is an indication function, takes a value of 1 when the condition in the brackets is true, otherwise takes a value of 0;
determining the neighbor: for each data point Its neighbors are not only based on distance/>To choose, also consider the point/>Local density/>The condition for selecting neighbors is not just/>Is small enough and/>It is also high enough that it is preferable to select points located in the high density region as neighbors;
density-based weight assignment: determining data points The influence of its neighbors is adjusted by the following weight function: /(I)Wherein/>Is a positive adjustment parameter for balancing the influence of distance and density, so that even if a certain neighbor is a little farther away, if it is in a high density area, it can have a larger influence on the prediction result;
predicting phase sequence abnormality: for data points to be predicted Based on it/>The neighbors (selected according to the density and distance based criteria described above) and their weights/>To predict the phase sequence state, prediction of phase sequence anomalies is based on a weighted voting mechanism, namely:
prediction category Wherein/>Is a predicted normal or abnormal category,/>Is neighbor/>Is a category of (2).
Through the improvement, the KNN algorithm not only considers the distance but also considers the local density of the data points when selecting the neighbors, so that the algorithm is more suitable for the prediction task of processing phase sequence data and power grid operation parameters, and particularly under the condition of uneven data distribution. The density-based neighbor selection method is beneficial to improving the accuracy of phase sequence anomaly prediction.
As shown in fig. 2, the method for monitoring the power consumption terminal is implemented by the power consumption terminal monitoring system, and includes the following steps:
s1: the method comprises the steps of utilizing a sensor array configured at an electricity utilization terminal to capture current and voltage signals in real time, and analyzing the current and voltage signals through an electric signal processing algorithm so as to identify the phase sequence state of an electricity utilization terminal circuit in real time;
S2: when the phase sequence recognition module detects phase sequence abnormality, starting an adaptive control algorithm, and dynamically adjusting wiring configuration of terminal equipment or a power access point according to the nature and degree of the abnormality so as to attempt to correct the abnormal state;
s3: immediately transmitting diagnostic reports of abnormal information, current phase sequence states and adjustment measures taken to a remote maintenance center or related maintenance personnel through a supported multi-protocol wireless communication network while the phase sequence abnormality is detected;
S4: and periodically analyzing historical phase sequence data and operation parameters of the power utilization terminal, identifying potential modes and abnormal trends in the data by using a deep learning algorithm, predicting future phase sequence anomalies, and realizing preventive maintenance.
An electronic device comprises a memory, a processor and a computer program, wherein the computer program is stored in the memory and runs on the processor, and the processor realizes the power consumption terminal monitoring method when executing the computer program.
A storage medium having a computer program stored thereon, which when executed by a processor implements a method for monitoring an electrical terminal as described above.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (10)
1. An electricity terminal monitoring system, comprising:
and the phase sequence identification module is used for: the sensor array is configured, and the electric signal processing algorithm is utilized to monitor and identify the phase sequence of the power grid in real time;
and a dynamic adjustment module: adopting a self-adaptive control algorithm, and dynamically adjusting wiring configuration of terminal equipment or a power access point according to data provided by a phase sequence identification module so as to correct phase sequence abnormality;
Real-time remote communication module: supporting multi-protocol wireless communication, being capable of immediately sending an alarm and a detailed diagnosis report to a remote maintenance center or maintenance personnel when a phase sequence abnormality is detected;
Prediction module of deep learning drive: and analyzing the historical phase sequence data and the power grid operation parameters by using a deep learning technology, and predicting the phase sequence abnormality, so that preventive maintenance and optimization of power grid operation are realized.
2. An electricity consumption terminal monitoring system according to claim 1, wherein the phase sequence identification module is configured with three voltage sensors and three current sensors, corresponding to three phase lines of a three-phase network, respectively: phase a, phase B and phase C, each sensor being mounted on a respective phase line to capture in real time the voltage and current signals of the power grid, the captured analog signals being converted to digital signals by an analog-to-digital converter ADC, the electrical signal processing algorithm comprising:
signal pretreatment: noise in the signals is removed through a filter, and accuracy of phase sequence identification is enhanced;
Feature extraction: extracting key features including peak values, zero crossing points and phase differences from the processed voltage and current signals;
And (3) phase sequence judgment: calculating phase differences among phases by using the extracted features, and identifying and confirming phase sequences of a power grid according to the sequence of the phase differences;
Anomaly detection algorithm: and detecting the abnormal phase sequence.
3. A power terminal monitoring system according to claim 2, wherein in the three-phase power, the voltage waveforms between each phase differ by 120 degrees, and the phase difference is measured to determine the phase sequence of the power grid, and the phase sequence is setAnd/>Representing the voltage waveform functions of phase A, phase B, phase C, respectively, where/>Time is;
The signal preprocessing uses a low-pass filter, and is provided with As a filtering function, the processed signal is represented asSimilar processing/>And/>；
Zero crossing point detection: for the voltage signal of each phase, find its zero crossing, i.eThe zero crossing provides a time stamp for the phase measurement;
The phase difference is calculated as: is provided with And/>The time of a zero crossing point of the voltage waveforms of the phase A, the phase B and the phase C respectively, and the phase difference/>The calculation is as follows:
wherein/> Is the period of the power grid frequency, and calculates/>, similarlyAnd/>；
The phase sequence is determined by comparisonAnd/>Determining the phase sequence, the phase difference of the normal phase sequence should be positive, i.e，/>And/>；
The anomaly detection algorithm specifically includes:
Phase difference reconfirmation: based on phase difference And/>Confirm whether the phase difference meets the expected range of the normal phase sequence, namely/>；
And (3) reverse sequence detection: for the reverse order case, the order of the phase differences is reversed, and therefore, by checkingAnd/>Is identified as the reverse order if the phase difference is shown as/>The phase sequence is indicated to be the reverse sequence;
and (3) phase failure detection: in the case of phase failure, the voltage of at least one phase is significantly lower than the other two phases, resulting in distortion of phase difference calculation, detecting whether the voltage amplitude of each phase is lower than a preset threshold, if the voltage amplitude of any phase is lower than the threshold, considering that the phase is missing, checking the magnitude of the phase difference, if found Or/>Any one of the deviations/>Also indicating the existence of a phase failure condition.
4. A power consumption terminal monitoring system according to claim 3, wherein the dynamic adjustment module specifically comprises:
The method comprises the steps of adopting a model reference self-adaptive unit as a self-adaptive control algorithm, wherein the self-adaptive unit dynamically adjusts wiring configuration of terminal equipment or a power supply access point according to real-time phase sequence data provided by a phase sequence identification module and a preset phase sequence reference model, and the method comprises the following specific steps:
setting a reference model: setting an ideal phase sequence reference model to define the expected phase sequence of the circuit in the power utilization terminal And the ideal phase difference between each phase is 120 °;
phase sequence error calculation: the self-adaptive unit calculates the phase sequence error by comparing the actual monitoring phase sequence data with an ideal phase sequence reference model ；
Self-adaptive adjustment rules: according to phase sequence errorAnd implementing an adaptive adjustment rule, and dynamically adjusting the wiring configuration.
5. The power usage terminal monitoring system of claim 4, wherein the adaptive adjustment rule is defined as:
If it is Exceeds a predetermined threshold/>And judging that wiring configuration adjustment is needed, and determining an adjustment strategy according to the sign and the magnitude of the phase error:
If it is All are positive or negative, which indicates that the overall phase sequence is reverse, and at the moment, the wiring of two phases needs to be exchanged to try to match the ideal phase sequence, and if the phase error of one phase is larger than that of other two phases, the wiring position of the phase and other phases needs to be adjusted;
According to the self-adaptive regulation rule, the dynamic regulation module changes the wiring configuration by controlling the relay or the switching device, if the reverse order is judged, the wiring of the B phase and the C phase is exchanged, and after the regulation, the phase error is recalculated to verify whether the regulation is successful, if Decreasing and approaching zero indicates that the wire adjustment is effective, and if the phase error does not improve or the error increases after adjustment, other adjustment strategies will be traversed, including swapping wires of the other two phases.
6. The power consumption terminal monitoring system according to claim 1, wherein the deep learning technique adopts a modified K nearest neighbor algorithm, that is SKNN, specifically comprising:
Feature selection and engineering: the phase difference, the voltage amplitude, the current amplitude and the power factor of the associated phase sequence state are selected as main characteristics, the phase difference change rate of adjacent time points is introduced to capture the dynamic characteristics of the phase sequence change, and the phase difference change is calculated as follows: Wherein/> Is the phase difference of the current time point,/>Is the phase difference at the last time point;
Data preprocessing: using The normalization method performs normalization processing on the features to ensure that each feature has equivalent weight in the distance calculation, calculated as: /(I)Wherein/>Is the original data,/>Is an average value/>Is the standard deviation;
Weighted distance calculation: the distances between the data points are calculated using weighted euclidean distances to assign different weights to the different features, the weighted distances being calculated by: Wherein/> Is a feature/>Reflecting the importance of the feature in predicting phase sequence anomalies;
dynamic K value selection: dynamically selecting a K value according to the size and characteristics of the data set to optimize the prediction performance, and determining the optimal K value through cross verification;
Density-based neighbor selection: in addition to finding the nearest K neighbors, the density of the K neighbors in the feature space is considered, and the representative neighbors are screened out for prediction by calculating the local density of the neighbors;
Prediction and anomaly scoring: and carrying out classification prediction by using a weighted voting mechanism, wherein the voting weight of each neighbor is inversely proportional to the distance of each neighbor, and when the prediction power utilization terminal has phase sequence abnormality, assigning an abnormality score to each test point, wherein the score is based on the duty ratio of the abnormality category in the nearest neighbor.
7. The electricity usage terminal monitoring system of claim 6, wherein the density-based neighbor selection specifically comprises:
defining a local density: for each data point Calculate its Euclidean distance/>, from all other pointsWhereinIs the other point in the dataset, defining the local density/>For the dot/>At a given radius/>The number of data points within, namely:
wherein/> Is an indication function, takes a value of 1 when the condition in the brackets is true, otherwise takes a value of 0;
determining the neighbor: for each data point Its neighbors are not only based on distance/>To choose, also consider the point/>Local density/>The condition for selecting neighbors is not just/>Is small enough and/>It is also high enough that it is preferable to select points located in the high density region as neighbors;
density-based weight assignment: determining data points The influence of its neighbors is adjusted by the following weight function: /(I)Wherein/>Is a positive adjustment parameter for balancing the influence of distance and density;
predicting phase sequence abnormality: for data points to be predicted Based on it/>Individual neighbors and their weights/>To predict the phase sequence state, prediction of phase sequence anomalies is based on a weighted voting mechanism, namely:
prediction category Wherein/>Is a predicted normal or abnormal category,/>Is neighbor/>Is a category of (2).
8. An electricity terminal monitoring method implemented by the electricity terminal monitoring system according to any one of claims 1 to 7, characterized by comprising the steps of:
s1: the method comprises the steps of utilizing a sensor array configured at an electricity utilization terminal to capture current and voltage signals in real time, and analyzing the current and voltage signals through an electric signal processing algorithm so as to identify the phase sequence state of an electricity utilization terminal circuit in real time;
S2: when the phase sequence recognition module detects phase sequence abnormality, starting an adaptive control algorithm, and dynamically adjusting wiring configuration of terminal equipment or a power access point according to the nature and degree of the abnormality so as to attempt to correct the abnormal state;
s3: immediately transmitting diagnostic reports of abnormal information, current phase sequence states and adjustment measures taken to a remote maintenance center or related maintenance personnel through a supported multi-protocol wireless communication network while the phase sequence abnormality is detected;
S4: and periodically analyzing historical phase sequence data and operation parameters of the power utilization terminal, identifying potential modes and abnormal trends in the data by using a deep learning algorithm, predicting future phase sequence anomalies, and realizing preventive maintenance.
9. An electronic device comprising a memory, a processor and a computer program, characterized in that the computer program is stored in the memory and runs on the processor, which processor implements a method for monitoring an electric terminal according to claim 8 when executing the computer program.
10. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for monitoring an electrical terminal according to claim 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410421003.6A CN118017698B (en) | 2024-04-09 | 2024-04-09 | Power consumption terminal monitoring system, method, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410421003.6A CN118017698B (en) | 2024-04-09 | 2024-04-09 | Power consumption terminal monitoring system, method, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118017698A true CN118017698A (en) | 2024-05-10 |
| CN118017698B CN118017698B (en) | 2024-07-02 |
Family
ID=90944723
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410421003.6A Active CN118017698B (en) | 2024-04-09 | 2024-04-09 | Power consumption terminal monitoring system, method, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118017698B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108198408A (en) * | 2017-12-08 | 2018-06-22 | 囯网河北省电力有限公司电力科学研究院 | A kind of adaptive oppose electricity-stealing monitoring method and system based on power information acquisition system |
| CN110687361A (en) * | 2019-09-20 | 2020-01-14 | 中国电力科学研究院有限公司 | Method and system for identifying phase sequence of low-voltage distribution network area |
| US20200293627A1 (en) * | 2019-03-13 | 2020-09-17 | General Electric Company | Method and apparatus for composite load calibration for a power system |
| CN112488367A (en) * | 2020-11-18 | 2021-03-12 | 国网山西省电力公司晋城供电公司 | User phase sequence loss reduction method and system based on quantum inheritance |
| US20220050130A1 (en) * | 2018-12-14 | 2022-02-17 | University Of Georgia Research Foundation, Inc. | Condition Monitoring Via Energy Consumption Audit in Electrical Devices and Electrical Waveform Audit in Power Networks |
| US20220170975A1 (en) * | 2020-11-27 | 2022-06-02 | Mitsubishi Electric Corporation | System and method for sensing fault symptom before occurrence of fault utilizing neural network model |
| CN116819191A (en) * | 2023-07-03 | 2023-09-29 | 连云港海连送变电工程有限公司 | Remote phase sequence checking method and system for fixed-point detection function of power grid |
| CN117613908A (en) * | 2024-01-24 | 2024-02-27 | 北京智芯微电子科技有限公司 | Intelligent operation and maintenance method and system based on power distribution network |
-
2024
- 2024-04-09 CN CN202410421003.6A patent/CN118017698B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108198408A (en) * | 2017-12-08 | 2018-06-22 | 囯网河北省电力有限公司电力科学研究院 | A kind of adaptive oppose electricity-stealing monitoring method and system based on power information acquisition system |
| US20220050130A1 (en) * | 2018-12-14 | 2022-02-17 | University Of Georgia Research Foundation, Inc. | Condition Monitoring Via Energy Consumption Audit in Electrical Devices and Electrical Waveform Audit in Power Networks |
| US20200293627A1 (en) * | 2019-03-13 | 2020-09-17 | General Electric Company | Method and apparatus for composite load calibration for a power system |
| CN110687361A (en) * | 2019-09-20 | 2020-01-14 | 中国电力科学研究院有限公司 | Method and system for identifying phase sequence of low-voltage distribution network area |
| CN112488367A (en) * | 2020-11-18 | 2021-03-12 | 国网山西省电力公司晋城供电公司 | User phase sequence loss reduction method and system based on quantum inheritance |
| US20220170975A1 (en) * | 2020-11-27 | 2022-06-02 | Mitsubishi Electric Corporation | System and method for sensing fault symptom before occurrence of fault utilizing neural network model |
| CN116819191A (en) * | 2023-07-03 | 2023-09-29 | 连云港海连送变电工程有限公司 | Remote phase sequence checking method and system for fixed-point detection function of power grid |
| CN117613908A (en) * | 2024-01-24 | 2024-02-27 | 北京智芯微电子科技有限公司 | Intelligent operation and maintenance method and system based on power distribution network |
Non-Patent Citations (2)
| Title |
|---|
| LINH VU: "Multi-Agent Deep Reinforcement Learning for Distributed Load Restoration", IEEE TRANSACTIONS ON SMART GRID, vol. 15, no. 2, 31 August 2023 (2023-08-31) * |
| 张晶;刘晓巍;张松涛;: "基于营销大数据的用电异常事件统计及窃电特征分析", 供用电, no. 06, 5 June 2018 (2018-06-05) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118017698B (en) | 2024-07-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117082105B (en) | Environment-friendly intelligent hospital facility monitoring system and method | |
| CN115685050B (en) | Electric energy meter fault detection method and system | |
| CN118152969B (en) | Temperature monitoring method and system based on multiple sensors | |
| CN116880402A (en) | Intelligent factory cooperative control system and method thereof | |
| CN117791876A (en) | Substation equipment operation state monitoring method and abnormal control system | |
| CN103389427B (en) | GIS equipment operational condition online test method and system | |
| CN116308300B (en) | Power equipment state monitoring evaluation and command method and system | |
| CN113777434A (en) | Fault monitoring method and device and power supply and distribution system | |
| CN117331790A (en) | Machine room fault detection method and device for data center | |
| KR20190082715A (en) | Data classification method based on correlation, and a computer-readable storege medium having program to perform the same | |
| CN103400308A (en) | Online detection method and online detection system for running state of GIS (gas insulated switchgear) equipment | |
| CN118017698B (en) | Power consumption terminal monitoring system, method, electronic equipment and storage medium | |
| CN118395358B (en) | Intelligent anti-misoperation topology analysis method for transformer substation | |
| CN117728421B (en) | Micro-grid cluster coordinated scheduling method, system, computer equipment and storage medium | |
| CN117767570A (en) | Lightning protection screen and intelligent monitoring system and method thereof | |
| CN116192038A (en) | Photovoltaic system fault diagnosis method and device and computer equipment | |
| CN109828146B (en) | Method for judging equipment working condition through equipment electrical parameter AD sampling | |
| CN114429240A (en) | Method and device for monitoring running state of highway equipment | |
| CN117493129B (en) | Operating power monitoring system of computer control equipment | |
| KR102664055B1 (en) | Industrial power control device using power data decomposition | |
| Zhang et al. | Application of Chaotic Particle Swarm Optimization Algorithm in Remote Fault Detection of Electrical System | |
| CN111767659B (en) | Distribution transformer winding material nondestructive test evaluation system | |
| KR102689674B1 (en) | Industrial power control method for managing power peaks in the upper branch | |
| CN118708944B (en) | Handheld intelligent patrol instrument integrating partial discharge detection and infrared imaging and method | |
| LU505201B1 (en) | Intelligent safety early warning method and system of electric power plant based monitoring data analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |