CN117031182B - Method and system for detecting abnormal telemetering value of substation terminal - Google Patents
Method and system for detecting abnormal telemetering value of substation terminal Download PDFInfo
- Publication number
- CN117031182B CN117031182B CN202311294480.2A CN202311294480A CN117031182B CN 117031182 B CN117031182 B CN 117031182B CN 202311294480 A CN202311294480 A CN 202311294480A CN 117031182 B CN117031182 B CN 117031182B
- Authority
- CN
- China
- Prior art keywords
- telemetry
- abnormal
- feature data
- data
- state
- 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.)
- Active
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 242
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000009826 distribution Methods 0.000 claims abstract description 78
- 238000012549 training Methods 0.000 claims abstract description 60
- 238000001514 detection method Methods 0.000 claims abstract description 25
- 230000005856 abnormality Effects 0.000 claims description 31
- 239000013598 vector Substances 0.000 claims description 28
- 238000012360 testing method Methods 0.000 claims description 9
- 238000013480 data collection Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 3
- 230000008439 repair process Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000015654 memory Effects 0.000 description 13
- 238000003860 storage Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000003062 neural network model Methods 0.000 description 3
- 230000003190 augmentative effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/28—Testing of electronic circuits, e.g. by signal tracer
- G01R31/2832—Specific tests of electronic circuits not provided for elsewhere
- G01R31/2834—Automated test systems [ATE]; using microprocessors or computers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/28—Testing of electronic circuits, e.g. by signal tracer
- G01R31/2832—Specific tests of electronic circuits not provided for elsewhere
- G01R31/2836—Fault-finding or characterising
- G01R31/2846—Fault-finding or characterising using hard- or software simulation or using knowledge-based systems, e.g. expert systems, artificial intelligence or interactive algorithms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Mathematical Optimization (AREA)
- Probability & Statistics with Applications (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Algebra (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computer Hardware Design (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Medical Informatics (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a method and a system for detecting abnormal telemetry values of terminals of a power distribution station, which relate to the technical field of abnormal telemetry value detection, and are characterized in that a first state judgment is carried out in real time based on a telemetry value range set and telemetry feature data by training an abnormal state prediction model for predicting whether the power distribution station is abnormal or not and an abnormal equipment positioning model for positioning the abnormal equipment, a first state label is generated, a second state judgment is carried out based on the telemetry feature data and the abnormal state prediction model, a second state label is generated, the telemetry feature data is converted into telemetry statistical feature data, and positioning information of the abnormal equipment is output based on the telemetry statistical feature data and the abnormal equipment positioning model; automatic detection and automatic positioning of circuit faults are realized.
Description
Technical Field
The invention relates to the technical field of telemetry value anomaly detection, in particular to a telemetry value anomaly detection method and system for a terminal of a power distribution station.
Background
In the long-term process of the existing remote measuring system of the terminal for operation, due to reasons such as sensor aging, connection failure, external interference and the like, jump, sudden increase or decrease of remote measuring parameters and weaker long-term deviation can be caused;
The existing distribution station terminal system cannot automatically identify abnormal conditions of telemetry parameters, and operation and maintenance personnel are required to find faults through experience judgment, so that a plurality of faults cannot be found in time, the fault response time is prolonged, and the operation and maintenance efficiency is reduced; and experience-based fault analysis also makes it difficult to quickly locate faulty equipment and to provide an accurate sequence of equipment fault detection, thereby presenting challenges to the maintenance efficiency of normal operation of the substation.
The Chinese patent with the application publication number of CN113820561A discloses a substation bus fault detection method based on current analysis, wherein three-phase current signals of the substation bus and three-phase current signals of each branch are collected; calculating the bus characteristic frequency band of the power distribution station; extracting the bus three-phase current signals and the branch three-phase current signals of the characteristic frequency band; calculating the instantaneous zero sequence current; calculating an integral of the zero sequence current signal over a period of nxΔt within the characteristic frequency band; judging whether the bus of the power distribution station is abnormal or not according to the integral; however, this approach fails to achieve localized analysis of the fault;
therefore, the invention provides a method and a system for detecting abnormal telemetry values of terminals of a power distribution station.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides the method and the system for detecting the abnormal telemetry value of the terminal of the power distribution station, and the safety and the stability of the power distribution system are improved.
To achieve the above object, embodiment 1 of the present invention provides a method for detecting abnormality of a telemetry value of a terminal of a power distribution station, comprising the steps of:
step one: collecting distribution station layout data, and collecting historical telemetry feature data, historical telemetry state tag data and historical telemetry anomaly locating data based on the distribution station layout data;
step two: taking the historical telemetry feature data as input and the historical telemetry state label data as output, training an abnormal state prediction model for predicting whether the power distribution station is abnormal or not;
step three: converting the historical telemetry feature data into historical telemetry statistical feature data, taking the historical telemetry statistical feature data as input, taking the historical telemetry abnormal positioning data as output, and training an abnormal equipment positioning model for positioning the equipment for the abnormality;
step four: after the abnormal state prediction model and the abnormal equipment positioning model are generated, telemetry feature data of the power distribution station are collected in real time;
Step five: presetting a telemetry value range set, and carrying out first state judgment in real time based on the telemetry value range set and telemetry feature data to generate a first state label; if the first state label is abnormal, turning to the step six, otherwise, continuing to judge the first state;
step six: based on the telemetry feature data and the abnormal state prediction model, performing second state judgment to generate a second state label, if the second state label is abnormal, turning to a step seven, otherwise, turning to a step five;
step seven: converting the telemetry feature data into telemetry statistical feature data, and outputting positioning information of the abnormal equipment based on the telemetry statistical feature data and the abnormal equipment positioning model;
the mode of collecting the layout data of the distribution station is as follows:
the method comprises the steps of collecting the positions and the numbers of all the electric equipment to be positioned in a power distribution station in advance, wherein the layout data of the power distribution station comprise the corresponding relation between the numbers and the positions of each electric equipment to be positioned;
the mode for collecting the historical telemetry feature data, the historical telemetry state label data and the historical telemetry anomaly locating data is as follows:
collecting telemetry values of all electrical characteristics at each moment in a substation, and arranging the telemetry values into telemetry value sequences of corresponding electrical characteristics in time sequence;
Dividing a telemetry value sequence of each electrical characteristic into a normal telemetry characteristic data set and an abnormal telemetry characteristic data set according to the abnormal state of the circuit;
the mode of dividing the normal telemetry feature data set and the abnormal telemetry feature data set is as follows:
in the process of collecting the telemetry value sequence in time sequence, taking the starting time of collecting the telemetry value sequence as the starting time of the 0 th sequence;
if any electrical equipment to be positioned is abnormal at any time, taking the time as the starting time of the nth sequence, and taking the time when the repair of the abnormality of the electrical equipment to be positioned is completed as the ending time of the nth sequence; wherein n is the number set for the event of occurrence of abnormality of the electrical equipment to be positioned in the time sequence in the power distribution station, and n is more than 0;
the telemetry value sequence segments of all electric characteristics form a group of normal telemetry characteristic data in the time period from the beginning time of the 0 th sequence to the beginning time of the 1 st sequence; the telemetry value sequence segment is a telemetry value subsequence in the telemetry value sequence;
for any n >0, the telemetry value sequence segments of all electric features form a group of abnormal telemetry feature data in the time period from the starting time of the nth sequence to the ending time of the nth sequence;
For any n >0, the telemetry value sequence segments of all electric characteristics form a group of normal telemetry characteristic data in the time period from the end time of the nth sequence to the start time of the n+1th sequence;
all the normal telemetry feature data form a normal telemetry feature data set;
all abnormal telemetry feature data form an abnormal telemetry feature data set;
setting a state label for each group of normal telemetry feature data in the normal telemetry feature data set to be normal, and setting a state label for each group of abnormal telemetry feature data in the abnormal telemetry feature data set to be abnormal;
all the state labels of the normal telemetry feature data and the abnormal telemetry feature data form historical telemetry state label data;
in the abnormal telemetry feature data set, a group of positioning tag vectors are set for each group of abnormal telemetry feature data, the length of each positioning tag vector is the number of all electrical devices to be positioned, each element corresponds to one electrical device to be positioned, and each element value is one of 0 or 1;
the positioning tag vectors of all abnormal telemetry feature data form historical telemetry abnormal positioning data;
the mode of training an abnormal state prediction model for predicting whether the power distribution station is abnormal is as follows:
Converting each group of normal telemetry feature data and abnormal telemetry feature data into a positive sample set and a negative sample set respectively by using a preset sliding step length and a sliding window method, setting a predictive label of 0 for each sample in the positive sample set and setting a predictive label of 1 for each sample in the negative sample set;
randomly selecting M% of positive samples and negative samples from the positive sample set and the negative sample set respectively as training samples, and taking the remaining 1-M% of positive samples and negative samples as test samples;
taking each training sample as input of an abnormal state prediction model, taking a prediction label corresponding to the training sample as output of the abnormal state prediction model, performing iterative training on the abnormal state prediction model, and verifying the prediction accuracy of the abnormal state prediction model by using a test sample set until the prediction accuracy of the test sample set reaches a preset accuracy threshold;
the mode of converting the historical telemetry feature data into the historical telemetry statistical feature data is as follows:
for each set of abnormal telemetry feature data in the historical telemetry feature data, collecting each item of mathematical statistics of a telemetry value sequence segment of each electrical feature in the abnormal telemetry feature data;
For each group of abnormal telemetry feature data, each item of mathematical statistics value of each electrical feature of the abnormal telemetry feature data is formed into a telemetry statistics feature vector;
all telemetry statistic feature vectors form historical telemetry statistic feature data;
the method for training the abnormal equipment positioning model for equipment positioning of the abnormality comprises the following steps:
constructing a Bayesian network model structure;
the method for constructing the Bayesian network comprises the following steps:
constructing a Bayesian network model with the layer number of 2 layers;
setting the number of nodes of a first layer in the Bayesian network model as the number of all the mathematical statistics values, wherein each node corresponds to the mathematical statistics value of one electrical characteristic;
setting the number of nodes of a second layer in the Bayesian network model as the number of all electrical equipment to be positioned;
for each node in the first layer, having one directed edge pointing to any node in the second layer;
taking each group of telemetry statistic feature vectors in the historical telemetry statistic feature data as the input of a first layer of the Bayesian network model, taking each node in a second layer as an output node, and taking the range of the output value of each output node as [0,1]; taking the positioning label of each electrical device to be positioned in the positioning label vector corresponding to the set of telemetry statistic feature vectors as a prediction target, and taking the sum of the prediction value of the minimized Bayesian network model and the prediction error between the prediction targets as the prediction target; training the Bayesian network model until the sum of the prediction errors reaches convergence, and stopping training;
The abnormal equipment positioning model is the Bayesian network model with the training completed;
the mode for collecting the telemetry characteristic data of the power distribution station in real time is as follows:
collecting real-time telemetry values of all electrical characteristics in a power distribution station in real time, and combining the real-time telemetry values of all electrical characteristics according to time sequence by taking the length of a sliding window as a time unit to form a unit telemetry value sequence;
the telemetry feature data comprises a unit telemetry value sequence of each acquired electrical feature in the length of each sliding window;
the first state judgment is carried out in real time, and the mode of generating the first state label is as follows:
presetting a telemetry value range of each item of mathematical statistics for each electrical characteristic;
calculating each item of mathematical statistics corresponding to the unit telemetry value sequence of each electrical characteristic in telemetry characteristic data;
the first state judgment mode is as follows:
setting the first status tag to normal;
if any one of all the mathematical statistics values corresponding to the unit telemetry value sequence exceeds the corresponding telemetry value range, setting the first state label as abnormal;
the second state judgment is carried out, and the mode of generating the second state label is as follows:
Inputting all unit telemetry value sequences in telemetry feature data into an abnormal state prediction model to obtain the predicted value of an output state label;
the second state determination is performed in the following manner:
if the predicted value of the state label indicates abnormality, setting the second state label as abnormality; if the predicted value of the state label indicates normal, setting the second state label to be normal;
based on the telemetering statistic feature data and the abnormal equipment positioning model, the positioning mode of the output abnormal equipment is as follows:
inputting vectors formed by the mathematical statistics values corresponding to each unit telemetry value sequence in telemetry feature data into a first layer of an abnormal equipment positioning model to obtain abnormal probability of each electric equipment to be positioned output by a second layer; and sequencing the output abnormal probability from large to small to obtain the positioning information of the abnormal equipment.
According to the embodiment 2 of the invention, a system for detecting the abnormal telemetry value of a terminal of a power distribution station is provided, which comprises a training data collection module, a model training module and an abnormal detection module; wherein, each module is electrically connected with each other;
the training data collection module is used for collecting distribution station layout data, collecting historical telemetry feature data, historical telemetry state label data and historical telemetry abnormal positioning data based on the distribution station layout data, and sending the historical telemetry feature data, the historical telemetry state label data and the historical telemetry abnormal positioning data to the model training module;
The model training module is used for taking the historical telemetry feature data as input, taking the historical telemetry state label data as output, training an abnormal state prediction model for predicting whether the power distribution station is abnormal, converting the historical telemetry feature data into historical telemetry statistical feature data, taking the historical telemetry statistical feature data as input, taking the historical telemetry abnormal positioning data as output, training an abnormal equipment positioning model for positioning the abnormal equipment, and transmitting the abnormal state prediction model and the abnormal equipment positioning model to the abnormal detection module;
the abnormal detection module is used for collecting telemetry feature data of the power distribution station in real time after the abnormal state prediction model and the abnormal equipment positioning model are generated, presetting a telemetry value range set, and carrying out first state judgment in real time based on the telemetry value range set and the telemetry feature data to generate a first state label; and if the first state label is abnormal, performing second state judgment based on the telemetry feature data and an abnormal state prediction model to generate a second state label, and if the second state label is abnormal, converting the telemetry feature data into telemetry statistic feature data, and outputting positioning information of abnormal equipment based on the telemetry statistic feature data and the abnormal equipment positioning model.
An electronic device according to embodiment 3 of the present invention includes: a processor and a memory, wherein the memory stores a computer program for the processor to call;
and the processor executes the remote measurement value abnormality detection method of the power distribution station terminal based on the augmented reality technology by calling the computer program stored in the memory.
A computer-readable storage medium according to embodiment 4 of the present invention has stored thereon a computer program that is erasable;
when the computer program runs on the computer equipment, the computer equipment is caused to execute the distribution substation terminal telemetry value anomaly detection method based on the augmented reality technology.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of collecting distribution station layout data, collecting historical telemetry feature data, historical telemetry state label data and historical telemetry abnormal positioning data based on the distribution station layout data, taking the historical telemetry feature data as input, taking the historical telemetry state label data as output, training an abnormal state prediction model for predicting whether the distribution station is abnormal, converting the historical telemetry feature data into historical telemetry statistical feature data, taking the historical telemetry statistical feature data as input, taking the historical telemetry abnormal positioning data as output, training an abnormal equipment positioning model for positioning an abnormality, collecting telemetry feature data of the distribution station in real time after the abnormal state prediction model and the abnormal equipment positioning model are generated, presetting a telemetry value range set, judging a first state in real time based on the telemetry value range set and the telemetry feature data, and generating a first state label; if the first state label is abnormal, carrying out second state judgment based on the telemetry feature data and an abnormal state prediction model, generating a second state label, if the second state label is abnormal, converting the telemetry feature data into telemetry statistic feature data, and outputting positioning information of abnormal equipment based on the telemetry statistic feature data and the abnormal equipment positioning model; the method and the system realize the representation of each telemetry value based on the circuit in the distribution station, realize the automatic detection and the automatic positioning of the circuit faults and improve the safe and stable operation performance of the distribution system.
Drawings
Fig. 1 is a flowchart of a method for detecting abnormality of a telemetry value of a terminal of a power distribution station in embodiment 1 of the present invention;
fig. 2 is a block diagram of a system for detecting abnormality in telemetry values of terminals of a power distribution station in embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of an electronic device in embodiment 3 of the present invention;
FIG. 4 is a schematic diagram showing the structure of a computer-readable storage medium in embodiment 4 of the present invention;
the reference numerals in the figures are: 100. an electronic device; 101. a bus; 102. a CPU; 103. a ROM; 104. a RAM; 105. a communication port; 106. an input/output assembly; 107. a hard disk; 108. a user interface; 200. computer readable storage media.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the method for detecting abnormal telemetry values of terminals of a power distribution station comprises the following steps:
Step one: collecting distribution station layout data, and collecting historical telemetry feature data, historical telemetry state tag data and historical telemetry anomaly locating data based on the distribution station layout data;
step two: taking the historical telemetry feature data as input and the historical telemetry state label data as output, training an abnormal state prediction model for predicting whether the power distribution station is abnormal or not;
step three: converting the historical telemetry feature data into historical telemetry statistical feature data, taking the historical telemetry statistical feature data as input, taking the historical telemetry abnormal positioning data as output, and training an abnormal equipment positioning model for positioning the equipment for the abnormality;
step four: after the abnormal state prediction model and the abnormal equipment positioning model are generated, telemetry feature data of the power distribution station are collected in real time;
step five: presetting a telemetry value range set, and carrying out first state judgment in real time based on the telemetry value range set and telemetry feature data to generate a first state label; if the first state label is abnormal, turning to the step six, otherwise, continuing to judge the first state;
step six: based on the telemetry feature data and the abnormal state prediction model, performing second state judgment to generate a second state label, if the second state label is abnormal, turning to a step seven, otherwise, turning to a step five;
Step seven: converting the telemetry feature data into telemetry statistical feature data, and outputting positioning information of the abnormal equipment based on the telemetry statistical feature data and the abnormal equipment positioning model;
the mode of collecting layout data of the distribution station is as follows:
the method comprises the steps of collecting the positions and numbers of all electrical equipment to be positioned in a power distribution station in advance, wherein the electrical equipment to be positioned influences power transmission when abnormality occurs, so that various telemetry signals are influenced, and the specific electrical equipment to be positioned is determined according to the actual architecture of the power distribution station;
the distribution station layout data comprise the corresponding relation between the number and the position of each electrical device to be positioned;
the method for collecting the historical telemetry feature data, the historical telemetry state label data and the historical telemetry abnormality positioning data comprises the following steps of:
collecting telemetry values of all electrical characteristics at each moment in a substation, and arranging the telemetry values into telemetry value sequences of corresponding electrical characteristics in time sequence; in particular, the electrical characteristics include, but are not limited to, voltage, current, power, frequency, harmonic content, etc. in a circuit to which the substation is connected to an external load; it will be appreciated that different electrical characteristics may be obtained in real time using corresponding sensors, respectively, for example voltage sensors collecting voltages in the circuit in real time;
Dividing a telemetry value sequence of each electrical characteristic into a normal telemetry characteristic data set and an abnormal telemetry characteristic data set according to the abnormal state of the circuit;
specifically, the mode of dividing the normal telemetry feature data set and the abnormal telemetry feature data set is as follows:
in the process of collecting the telemetry value sequence in time sequence, taking the starting time of collecting the telemetry value sequence as the starting time of the 0 th sequence;
if any electrical equipment to be positioned is abnormal at any time, taking the time as the starting time of the nth sequence, and taking the time when the repair of the abnormality of the electrical equipment to be positioned is completed as the ending time of the nth sequence; wherein n is the number set for the event of occurrence of abnormality of the electrical equipment to be positioned in the time sequence in the power distribution station, and n is more than 0; it should be noted that, the detection and positioning of the abnormality of the electrical equipment to be positioned may be performed by manual manufacturing or manual positioning after the fact, so as to collect enough training data required;
the telemetry value sequence segments of all electric characteristics form a group of normal telemetry characteristic data in the time period from the beginning time of the 0 th sequence to the beginning time of the 1 st sequence; the telemetry value sequence segment is a telemetry value subsequence in the telemetry value sequence;
For any n >0, the telemetry value sequence segments of all electric features form a group of abnormal telemetry feature data in the time period from the starting time of the nth sequence to the ending time of the nth sequence;
for any n >0, the telemetry value sequence segments of all electric characteristics form a group of normal telemetry characteristic data in the time period from the end time of the nth sequence to the start time of the n+1th sequence;
all the normal telemetry feature data form a normal telemetry feature data set;
all abnormal telemetry feature data form an abnormal telemetry feature data set;
setting a state label for each group of normal telemetry feature data in the normal telemetry feature data set to be normal, and setting a state label for each group of abnormal telemetry feature data in the abnormal telemetry feature data set to be abnormal;
all the state labels of the normal telemetry feature data and the abnormal telemetry feature data form historical telemetry state label data;
in the abnormal telemetry feature data set, a group of positioning tag vectors are set for each group of abnormal telemetry feature data, the length of each positioning tag vector is the number of all electrical devices to be positioned, each element corresponds to one electrical device to be positioned, and each element value is one of 0 or 1; when the group of abnormal telemetry feature data is collected, the element value corresponding to the electrical equipment to be positioned in the normal state is 0, and the element value corresponding to the electrical equipment to be positioned in the abnormal state is 1;
The positioning tag vectors of all abnormal telemetry feature data form historical telemetry abnormal positioning data;
further, the mode of training an abnormal state prediction model for predicting whether the power distribution station is abnormal by taking the historical telemetry feature data as input and the historical telemetry state label data as output is as follows:
converting each group of normal telemetry feature data and abnormal telemetry feature data into a positive sample set and a negative sample set respectively by using a preset sliding step length and a sliding window method, setting a predictive label of 0 for each sample in the positive sample set and setting a predictive label of 1 for each sample in the negative sample set;
it should be noted that, the sliding window method is used as a conventional technical means of a neural network model of a time series type, and the invention is not described in principle here; for a better understanding of the invention, however, the invention provides the following examples regarding sliding window methods:
suppose we want to use telemetry value sequence data for two electrical features: [1,2,1,1,2,1] and [2,3,3,2,1,3] to generate a sample set, the sliding step size is set to 1 and the sliding window length is set to 4; the raw sample data set is: [ [1,2,1,1], [2,3, 2] ], [ (2,1,1,2 ], [3,3,2,1] ], [ (1,1,2,1 ], [3,2,1,3] ]; each sample data in the set is a two-dimensional vector;
Randomly selecting M% of positive samples and negative samples from the positive sample set and the negative sample set respectively as training samples, and taking the remaining 1-M% of positive samples and negative samples as test samples;
taking each training sample as input of an abnormal state prediction model, taking a prediction label corresponding to the training sample as output of the abnormal state prediction model, performing iterative training on the abnormal state prediction model, and verifying the prediction accuracy of the abnormal state prediction model by using a test sample set until the prediction accuracy of the test sample set reaches a preset accuracy threshold; preferably, the abnormal state prediction model is a neural network model of a time series type, such as an RNN neural network model or an LSTM network model;
further, the method for converting the historical telemetry feature data into the historical telemetry statistical feature data comprises the following steps:
for each set of abnormal telemetry feature data in the historical telemetry feature data, collecting each item of mathematical statistics of a telemetry value sequence segment of each electrical feature in the abnormal telemetry feature data; specifically, the mathematical statistics may be an average value, a variance value, a maximum fluctuation value, a fluctuation frequency, and the like;
For each group of abnormal telemetry feature data, each item of mathematical statistics value of each electrical feature of the abnormal telemetry feature data is formed into a telemetry statistics feature vector;
all telemetry statistic feature vectors form historical telemetry statistic feature data;
the method for training the abnormal equipment positioning model for equipment positioning of the abnormality is as follows, wherein the historical telemetry statistical characteristic data is taken as input, the historical telemetry abnormal positioning data is taken as output:
constructing a Bayesian network model structure;
the method for constructing the Bayesian network comprises the following steps:
constructing a Bayesian network model with the layer number of 2 layers;
setting the number of nodes of a first layer in the Bayesian network model as the number of all the mathematical statistics values, wherein each node corresponds to the mathematical statistics value of one electrical characteristic;
setting the number of nodes of a second layer in the Bayesian network model as the number of all electrical equipment to be positioned;
for each node in the first layer, having one directed edge pointing to any node in the second layer;
taking each group of telemetry statistic feature vectors in the historical telemetry statistic feature data as the input of a first layer of the Bayesian network model, taking each node in a second layer as an output node, wherein the range of the output value of each output node is [0,1], and each output value is used as the probability expression of the abnormality of the electrical equipment to be positioned corresponding to the node; taking the positioning label of each electrical device to be positioned in the positioning label vector corresponding to the set of telemetry statistic feature vectors as a prediction target, and taking the sum of the prediction value of the minimized Bayesian network model and the prediction error between the prediction targets as the prediction target; training the Bayesian network model until the sum of the prediction errors reaches convergence, and stopping training; the sum of the prediction errors can be any one of a mean square error or a cross entropy;
The abnormal equipment positioning model is the Bayesian network model with the training completed;
the mode for collecting the telemetry characteristic data of the power distribution station in real time is as follows:
collecting real-time telemetry values of all electrical characteristics in a power distribution station in real time, and combining the real-time telemetry values of all electrical characteristics according to time sequence by taking the length of a sliding window as a time unit to form a unit telemetry value sequence;
the telemetry feature data comprises a unit telemetry value sequence of each acquired electrical feature in the length of each sliding window;
the first state judgment is carried out in real time, and the mode of generating the first state label is as follows:
presetting a telemetry value range of each item of mathematical statistics for each electrical characteristic; the telemetry value range is a range of statistical values of each item of mathematical statistics when the circuit is normal, which is set based on actual experience or historical experience, for example, a telemetry value range in which the minimum value and the maximum value of the voltage average value are taken as the voltage average value in the past normal state;
calculating each item of mathematical statistics corresponding to the unit telemetry value sequence of each electrical characteristic in telemetry characteristic data;
the first state judgment mode is as follows:
Setting the first status tag to normal;
if any one of all the mathematical statistics values corresponding to the unit telemetry value sequence exceeds the corresponding telemetry value range, setting the first state label as abnormal;
the second state judgment is carried out, and the mode of generating the second state label is as follows:
inputting all unit telemetry value sequences in telemetry feature data into an abnormal state prediction model to obtain the predicted value of an output state label;
the second state determination is performed in the following manner:
if the predicted value of the state label indicates abnormality, setting the second state label as abnormality; if the predicted value of the state label indicates normal, setting the second state label to be normal;
based on the telemetering statistic feature data and the abnormal equipment positioning model, the positioning mode of the output abnormal equipment is as follows:
inputting vectors formed by the mathematical statistics values corresponding to each unit telemetry value sequence in telemetry feature data into a first layer of an abnormal equipment positioning model to obtain abnormal probability of each electric equipment to be positioned output by a second layer; sequencing the output abnormal probability from large to small, namely, positioning information of abnormal equipment; the method realizes the representation of each telemetry value based on the circuit in the distribution station, and realizes the automatic detection and automatic positioning of the circuit faults.
Example 2
As shown in fig. 2, the remote measurement value anomaly detection system of the substation terminal comprises a training data collection module, a model training module and an anomaly detection module; wherein, each module is electrically connected with each other;
the training data collection module is used for collecting distribution station layout data, collecting historical telemetry feature data, historical telemetry state label data and historical telemetry abnormal positioning data based on the distribution station layout data, and sending the historical telemetry feature data, the historical telemetry state label data and the historical telemetry abnormal positioning data to the model training module;
the model training module is used for taking the historical telemetry feature data as input, taking the historical telemetry state label data as output, training an abnormal state prediction model for predicting whether the power distribution station is abnormal, converting the historical telemetry feature data into historical telemetry statistical feature data, taking the historical telemetry statistical feature data as input, taking the historical telemetry abnormal positioning data as output, training an abnormal equipment positioning model for positioning the abnormal equipment, and transmitting the abnormal state prediction model and the abnormal equipment positioning model to the abnormal detection module;
The abnormal detection module is used for collecting telemetry feature data of the power distribution station in real time after the abnormal state prediction model and the abnormal equipment positioning model are generated, presetting a telemetry value range set, and carrying out first state judgment in real time based on the telemetry value range set and the telemetry feature data to generate a first state label; and if the first state label is abnormal, performing second state judgment based on the telemetry feature data and an abnormal state prediction model to generate a second state label, and if the second state label is abnormal, converting the telemetry feature data into telemetry statistic feature data, and outputting positioning information of abnormal equipment based on the telemetry statistic feature data and the abnormal equipment positioning model.
Example 3
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 3, an electronic device 100 is also provided according to yet another aspect of the present application. The electronic device 100 may include one or more processors and one or more memories. Wherein the memory has stored therein computer readable code which, when executed by the one or more processors, is operable to perform the substation-terminated telemetry value anomaly detection method as described above.
The method or system according to embodiments of the present application may also be implemented by means of the architecture of the electronic device shown in fig. 3. As shown in fig. 3, the electronic device 100 may include a bus 101, one or more CPUs 102, a Read Only Memory (ROM) 103, a Random Access Memory (RAM) 104, a communication port 105 connected to a network, an input/output component 106, a hard disk 107, and the like. A storage device in the electronic device 100, such as the ROM103 or the hard disk 107, may store the telemetry value abnormality detection method provided by the present application for the substation terminal. The method for detecting abnormal telemetry values of terminals of the power distribution station can comprise the following steps: step one: collecting distribution station layout data, and collecting historical telemetry feature data, historical telemetry state tag data and historical telemetry anomaly locating data based on the distribution station layout data; step two: taking the historical telemetry feature data as input and the historical telemetry state label data as output, training an abnormal state prediction model for predicting whether the power distribution station is abnormal or not; step three: converting the historical telemetry feature data into historical telemetry statistical feature data, taking the historical telemetry statistical feature data as input, taking the historical telemetry abnormal positioning data as output, and training an abnormal equipment positioning model for positioning the equipment for the abnormality; step four: after the abnormal state prediction model and the abnormal equipment positioning model are generated, telemetry feature data of the power distribution station are collected in real time; step five: presetting a telemetry value range set, and carrying out first state judgment in real time based on the telemetry value range set and telemetry feature data to generate a first state label; if the first state label is abnormal, turning to the step six, otherwise, continuing to judge the first state; step six: based on the telemetry feature data and the abnormal state prediction model, performing second state judgment to generate a second state label, if the second state label is abnormal, turning to a step seven, otherwise, turning to a step five; step seven: and converting the telemetry feature data into telemetry statistic feature data, and outputting positioning information of the abnormal equipment based on the telemetry statistic feature data and the abnormal equipment positioning model.
Further, the electronic device 100 may also include a user interface 108. Of course, the architecture shown in fig. 3 is merely exemplary, and one or more components of the electronic device shown in fig. 3 may be omitted as may be practical in implementing different devices.
Example 4
Fig. 4 is a schematic structural diagram of a computer readable storage medium according to an embodiment of the present application. As shown in fig. 4, is a computer-readable storage medium 200 according to one embodiment of the present application. The computer-readable storage medium 200 has stored thereon computer-readable instructions. The substation terminal telemetry value anomaly detection method according to embodiments of the present application described with reference to the above figures may be performed when the computer readable instructions are executed by a processor. Computer-readable storage medium 200 includes, but is not limited to, for example, volatile memory and/or nonvolatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
In addition, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions executable by a processor to perform instructions corresponding to the method steps provided herein, which when executed by a Central Processing Unit (CPU), perform the functions defined above in the methods of the present application.
The methods and apparatus, devices, and apparatus of the present application may be implemented in numerous ways. For example, the methods and apparatus, devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present application. Thus, the present application also covers a recording medium storing a program for executing the method according to the present application.
In addition, in the foregoing technical solutions provided in the embodiments of the present application, parts consistent with implementation principles of corresponding technical solutions in the prior art are not described in detail, so that redundant descriptions are avoided.
The purpose, technical scheme and beneficial effects of the invention are further described in detail in the detailed description. It is to be understood that the above description is only of specific embodiments of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above preset parameters or preset thresholds are set by those skilled in the art according to actual conditions or are obtained by mass data simulation.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (1)
1. A system for detecting abnormal telemetry value of a terminal of a power distribution station is realized based on a method for detecting abnormal telemetry value of the terminal of the power distribution station, and is characterized in that,
the method for detecting the abnormal telemetry value of the terminal of the power distribution station comprises the following steps:
step one: collecting distribution station layout data, and collecting historical telemetry feature data, historical telemetry state tag data and historical telemetry anomaly locating data based on the distribution station layout data;
step two: taking the historical telemetry feature data as input and the historical telemetry state label data as output, training an abnormal state prediction model for predicting whether the power distribution station is abnormal or not;
step three: converting the historical telemetry feature data into historical telemetry statistical feature data, taking the historical telemetry statistical feature data as input, taking the historical telemetry abnormal positioning data as output, and training an abnormal equipment positioning model for positioning the equipment for the abnormality;
Step four: after the abnormal state prediction model and the abnormal equipment positioning model are generated, telemetry feature data of the power distribution station are collected in real time;
step five: presetting a telemetry value range set, and carrying out first state judgment in real time based on the telemetry value range set and telemetry feature data to generate a first state label; if the first state label is abnormal, turning to the step six, otherwise, continuing to judge the first state;
step six: based on the telemetry feature data and the abnormal state prediction model, performing second state judgment to generate a second state label, if the second state label is abnormal, turning to a step seven, otherwise, turning to a step five;
step seven: converting the telemetry feature data into telemetry statistics feature numbers;
the mode of collecting the layout data of the distribution station is as follows: the method comprises the steps of collecting the positions and the numbers of all the electric equipment to be positioned in a power distribution station in advance, wherein the layout data of the power distribution station comprise the corresponding relation between the numbers and the positions of each electric equipment to be positioned;
the mode for collecting the historical telemetry feature data, the historical telemetry state label data and the historical telemetry anomaly locating data is as follows: collecting telemetry values of all electrical characteristics at each moment in a substation, and arranging the telemetry values into telemetry value sequences of corresponding electrical characteristics in time sequence;
Dividing a telemetry value sequence of each electrical characteristic into a normal telemetry characteristic data set and an abnormal telemetry characteristic data set according to the abnormal state of the circuit;
setting a state label for each group of normal telemetry feature data in the normal telemetry feature data set to be normal, and setting a state label for each group of abnormal telemetry feature data in the abnormal telemetry feature data set to be abnormal;
all the state labels of the normal telemetry feature data and the abnormal telemetry feature data form historical telemetry state label data;
in the abnormal telemetry feature data set, a group of positioning tag vectors are set for each group of abnormal telemetry feature data, the length of each positioning tag vector is the number of all electrical devices to be positioned, each element corresponds to one electrical device to be positioned, and each element value is one of 0 or 1;
the positioning tag vectors of all abnormal telemetry feature data form historical telemetry abnormal positioning data;
the mode of dividing the normal telemetry feature data set and the abnormal telemetry feature data set is as follows: in the process of collecting the telemetry value sequence in time sequence, taking the starting time of collecting the telemetry value sequence as the starting time of the 0 th sequence;
If any electrical equipment to be positioned is abnormal at any time, taking the time as the starting time of the nth sequence, and taking the time when the repair of the abnormality of the electrical equipment to be positioned is completed as the ending time of the nth sequence; wherein n is the number set for the event of occurrence of abnormality of the electrical equipment to be positioned in the time sequence in the power distribution station, and n is more than 0;
the telemetry value sequence segments of all electric characteristics form a group of normal telemetry characteristic data in the time period from the beginning time of the 0 th sequence to the beginning time of the 1 st sequence; the telemetry value sequence segment is a telemetry value subsequence in the telemetry value sequence;
for any n >0, the telemetry value sequence segments of all electric features form a group of abnormal telemetry feature data in the time period from the starting time of the nth sequence to the ending time of the nth sequence;
for any n >0, the telemetry value sequence segments of all electric characteristics form a group of normal telemetry characteristic data in the time period from the end time of the nth sequence to the start time of the n+1th sequence;
all the normal telemetry feature data form a normal telemetry feature data set;
all abnormal telemetry feature data form an abnormal telemetry feature data set;
The mode of training an abnormal state prediction model for predicting whether the power distribution station is abnormal is as follows: converting each group of normal telemetry feature data and abnormal telemetry feature data into a positive sample set and a negative sample set respectively by using a preset sliding step length and a sliding window method, setting a predictive label of 0 for each sample in the positive sample set and setting a predictive label of 1 for each sample in the negative sample set;
randomly selecting M% of positive samples and negative samples from the positive sample set and the negative sample set respectively as training samples, and taking the remaining 1-M% of positive samples and negative samples as test samples;
taking each training sample as input of an abnormal state prediction model, taking a prediction label corresponding to the training sample as output of the abnormal state prediction model, performing iterative training on the abnormal state prediction model, and verifying the prediction accuracy of the abnormal state prediction model by using a test sample set until the prediction accuracy of the test sample set reaches a preset accuracy threshold;
the mode of converting the historical telemetry feature data into the historical telemetry statistical feature data is as follows: for each set of abnormal telemetry feature data in the historical telemetry feature data, collecting each item of mathematical statistics of a telemetry value sequence segment of each electrical feature in the abnormal telemetry feature data;
For each group of abnormal telemetry feature data, each item of mathematical statistics value of each electrical feature of the abnormal telemetry feature data is formed into a telemetry statistics feature vector;
all telemetry statistic feature vectors form historical telemetry statistic feature data;
the mode of training an abnormal equipment positioning model for equipment positioning of the abnormality is as follows: constructing a Bayesian network model structure;
the method for constructing the Bayesian network comprises the following steps:
constructing a Bayesian network model with the layer number of 2 layers;
setting the number of nodes of a first layer in the Bayesian network model as the number of all the mathematical statistics values, wherein each node corresponds to the mathematical statistics value of one electrical characteristic;
setting the number of nodes of a second layer in the Bayesian network model as the number of all electrical equipment to be positioned;
for each node in the first layer, having one directed edge pointing to any node in the second layer;
taking each group of telemetry statistic feature vectors in the historical telemetry statistic feature data as the input of a first layer of the Bayesian network model, taking each node in a second layer as an output node, and taking the range of the output value of each output node as [0,1]; taking the positioning label of each electrical device to be positioned in the positioning label vector corresponding to the set of telemetry statistic feature vectors as a prediction target, and taking the sum of the prediction value of the minimized Bayesian network model and the prediction error between the prediction targets as the prediction target; training the Bayesian network model until the sum of the prediction errors reaches convergence, and stopping training;
The abnormal equipment positioning model is the Bayesian network model with the training completed;
the mode for collecting the telemetry characteristic data of the power distribution station in real time is as follows: collecting real-time telemetry values of all electrical characteristics in a power distribution station in real time, and combining the real-time telemetry values of all electrical characteristics according to time sequence by taking the length of a sliding window as a time unit to form a unit telemetry value sequence; the electrical characteristics include voltage, current, power, frequency, and harmonic content in a circuit to which the substation is connected to an external load;
the telemetry feature data comprises a unit telemetry value sequence of each acquired electrical feature in the length of each sliding window;
the first state judgment is carried out in real time, and the mode of generating the first state label is as follows: presetting a telemetry value range of each item of mathematical statistics for each electrical characteristic;
calculating each item of mathematical statistics corresponding to the unit telemetry value sequence of each electrical characteristic in telemetry characteristic data;
the first state judgment mode is as follows:
setting the first status tag to normal;
if any one of all the mathematical statistics values corresponding to the unit telemetry value sequence exceeds the corresponding telemetry value range, setting the first state label as abnormal;
The second state judgment is carried out, and the mode of generating the second state label is as follows: inputting all unit telemetry value sequences in telemetry feature data into an abnormal state prediction model to obtain the predicted value of an output state label;
the second state determination is performed in the following manner:
if the predicted value of the state label indicates abnormality, setting the second state label as abnormality; if the predicted value of the state label indicates normal, setting the second state label to be normal;
based on the telemetering statistic feature data and the abnormal equipment positioning model, the positioning mode of the output abnormal equipment is as follows: inputting vectors formed by the mathematical statistics values corresponding to each unit telemetry value sequence in telemetry feature data into a first layer of an abnormal equipment positioning model to obtain abnormal probability of each electric equipment to be positioned output by a second layer; sequencing the output abnormal probability from large to small to obtain positioning information of abnormal equipment;
the remote measurement value abnormality detection system of the terminal of the power distribution station comprises a training data collection module, a model training module and an abnormality detection module; wherein, each module is electrically connected with each other;
the training data collection module is used for collecting distribution station layout data, collecting historical telemetry feature data, historical telemetry state label data and historical telemetry abnormal positioning data based on the distribution station layout data, and sending the historical telemetry feature data, the historical telemetry state label data and the historical telemetry abnormal positioning data to the model training module;
The model training module is used for taking the historical telemetry feature data as input, taking the historical telemetry state label data as output, training an abnormal state prediction model for predicting whether the power distribution station is abnormal, converting the historical telemetry feature data into historical telemetry statistical feature data, taking the historical telemetry statistical feature data as input, taking the historical telemetry abnormal positioning data as output, training an abnormal equipment positioning model for positioning the abnormal equipment, and transmitting the abnormal state prediction model and the abnormal equipment positioning model to the abnormal detection module;
the abnormal detection module is used for collecting telemetry feature data of the power distribution station in real time after the abnormal state prediction model and the abnormal equipment positioning model are generated, presetting a telemetry value range set, and carrying out first state judgment in real time based on the telemetry value range set and the telemetry feature data to generate a first state label; and if the first state label is abnormal, performing second state judgment based on the telemetry feature data and an abnormal state prediction model to generate a second state label, and if the second state label is abnormal, converting the telemetry feature data into telemetry statistic feature data, and outputting positioning information of abnormal equipment based on the telemetry statistic feature data and the abnormal equipment positioning model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311294480.2A CN117031182B (en) | 2023-10-09 | 2023-10-09 | Method and system for detecting abnormal telemetering value of substation terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311294480.2A CN117031182B (en) | 2023-10-09 | 2023-10-09 | Method and system for detecting abnormal telemetering value of substation terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117031182A CN117031182A (en) | 2023-11-10 |
| CN117031182B true CN117031182B (en) | 2024-01-09 |
Family
ID=88630392
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311294480.2A Active CN117031182B (en) | 2023-10-09 | 2023-10-09 | Method and system for detecting abnormal telemetering value of substation terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117031182B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117574296B (en) * | 2023-11-23 | 2024-04-23 | 清远市信和实业有限公司 | Plating bath liquid flow distribution detection system and method thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111537831A (en) * | 2020-04-01 | 2020-08-14 | 华中科技大学鄂州工业技术研究院 | Power distribution network line fault positioning method and device |
| CN114265010A (en) * | 2021-11-10 | 2022-04-01 | 国网浙江省电力有限公司绍兴供电公司 | Transformer fault sound source positioning method and system based on microphone array |
| CN114363949A (en) * | 2022-01-21 | 2022-04-15 | 杭州北斗时空研究院 | Abnormal data detection method for UWB positioning system |
| CN116184122A (en) * | 2023-04-13 | 2023-05-30 | 国网重庆市电力公司电力科学研究院 | Fault device positioning method, device, equipment and storage medium |
-
2023
- 2023-10-09 CN CN202311294480.2A patent/CN117031182B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111537831A (en) * | 2020-04-01 | 2020-08-14 | 华中科技大学鄂州工业技术研究院 | Power distribution network line fault positioning method and device |
| CN114265010A (en) * | 2021-11-10 | 2022-04-01 | 国网浙江省电力有限公司绍兴供电公司 | Transformer fault sound source positioning method and system based on microphone array |
| CN114363949A (en) * | 2022-01-21 | 2022-04-15 | 杭州北斗时空研究院 | Abnormal data detection method for UWB positioning system |
| CN116184122A (en) * | 2023-04-13 | 2023-05-30 | 国网重庆市电力公司电力科学研究院 | Fault device positioning method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于贝叶斯估计的配电网智能分布式故障区段定位算法;郑晨玲;朱革兰;;电网技术(第04期);1561~1567页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117031182A (en) | 2023-11-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117031182B (en) | Method and system for detecting abnormal telemetering value of substation terminal | |
| CN117060409B (en) | Automatic detection and analysis method and system for power line running state | |
| CN108512222A (en) | A kind of intelligent substation complex automatic system | |
| CN115238831A (en) | Fault prediction method, apparatus, computer device, storage medium and program product | |
| KR102315580B1 (en) | Fire predictive analysis device and method of building | |
| CN115144765A (en) | Method and device for detecting short-circuit fault in battery | |
| CN117368616B (en) | Intelligent power distribution network ground fault monitoring method and system | |
| CN117375147B (en) | Safety monitoring early warning and operation management method and system for energy storage power station | |
| CN111583594B (en) | Distribution box early warning system and early warning method | |
| CN113570473B (en) | Equipment fault monitoring method, device, computer equipment and storage medium | |
| CN116400165A (en) | Smart power grid fault monitoring method and system | |
| CN116298719A (en) | Equipment insulation aging identification method and device, electronic equipment and storage medium | |
| CN115372752A (en) | Fault detection method, device, electronic equipment and storage medium | |
| CN113311220A (en) | Method, system, medium, and electronic device for diagnosing voltage sag | |
| CN113533891A (en) | Fault diagnosis system and device | |
| CN113964935B (en) | Industrial full-closed loop debugging method and system for power distribution automation terminal | |
| CN117872037B (en) | Fault diagnosis method and device based on intelligent switch and power distribution network system | |
| CN115616437B (en) | Intelligent debugging control method and system for EPC special power supply | |
| CN103389448B (en) | GIS equipment operational condition online test method and system | |
| US20230131038A1 (en) | Distribution grid failure symptom cause estimation system | |
| Tao et al. | Transformer Fault Diagnosis Method via Approximation Relations in Approximation Space | |
| CN116228200A (en) | Operation inspection control method and device for power equipment | |
| CN114899938A (en) | Data processing method and device, electronic equipment and storage medium | |
| US20190326756A1 (en) | Microgrid gateway and control method thereof | |
| CN115425759A (en) | New energy power grid cascading failure prediction method and system capable of achieving autonomous learning and application |
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 | ||
| GR01 | Patent grant |