CN117074852A - Power distribution network electric energy monitoring and early warning management method and system - Google Patents
Power distribution network electric energy monitoring and early warning management method and system Download PDFInfo
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Abstract
The invention discloses a power monitoring and early warning management method and system for a power distribution network, and relates to the technical field of power monitoring, wherein the method comprises the steps of collecting power data of all equipment and circuits of the power distribution network, preprocessing the collected power data, converting the preprocessed power data into waveform signals and storing the waveform signals; performing feature extraction on the preprocessed electric energy data, establishing a fault diagnosis model according to the feature extracted data, and performing power quality index training on the fault diagnosis model; and inputting the real-time operation data into a trained fault diagnosis model to output a fault diagnosis result, determining the type and position coordinates of the fault, and carrying out real-time monitoring and early warning on various faults in the power distribution network. The invention monitors the electric energy condition of the distribution network in real time, timely sends out an early warning signal, rapidly discovers potential electric energy abnormality or fault condition, and reduces the power failure time and loss caused by the fault; and predicting the fault risk of the electric energy system by monitoring the key indexes and the parameters.
Description
Technical Field
The invention relates to the field of electric energy monitoring, in particular to an electric energy monitoring and early warning management method and system for a power distribution network.
Background
In an electric power system, a distribution network is one of indispensable important components, and distribution network automation specifically refers to taking a primary network frame and related equipment as a basis, taking a distribution automation system as a core, realizing monitoring on the running state of the distribution system by means of various communication modes, and scientifically and normalized managing the distribution system through information integration with other systems, wherein the realization of the aim is realized by virtue of the distribution automation system, and the system has the functions including feeder line automation, distribution SCADA, communication monitoring, fault processing, system interconnection and power grid analysis, and mainly comprises the following parts: the power distribution main station, the terminal, the substation and the communication channel, wherein the main station is a core part, the terminal is generally installed on a distribution network site, and the substation can realize information gathering, fault processing and communication monitoring functions in a controlled range.
At present, the main network operates for many years, the power distribution network is much lower than the main network in the importance degree of the industry, the technical means applied to the power distribution network are not more, the fault searching speed of the power distribution network is low, the power supply recovering time is long, the power distribution network is nearest to a user, the power supply quality is directly related to the economic benefit and the life quality of the user, and therefore, the monitoring of the electric energy of the power distribution network must be enhanced, the early warning and the treatment of faults are carried out in time, and the power supply is recovered as soon as possible. The power distribution network fault has high burst, whether the fault is about to occur is difficult to predict in advance, and the power supply is required to be stopped immediately after the fault occurs, so that the cause of the fault is found as soon as possible, and the power supply is recovered quickly.
Disclosure of Invention
The present invention has been made in view of the above and/or existing problems with power distribution network monitoring.
The problem underlying the present invention is therefore how to provide a method for monitoring alarms in time in the event of a fault burst.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for monitoring and early warning management of electric energy of a power distribution network, which includes collecting electric energy data of each device and line of the power distribution network, preprocessing the collected electric energy data, converting the preprocessed electric energy data into waveform signals, and storing the waveform signals; performing feature extraction on the preprocessed electric energy data, establishing a fault diagnosis model according to the feature extracted data, and performing power quality index training on the fault diagnosis model; and inputting the real-time operation data into a trained fault diagnosis model to output a fault diagnosis result, determining the type and position coordinates of the fault, and carrying out real-time monitoring and early warning on various faults in the power distribution network.
As a preferable scheme of the power distribution network electric energy monitoring and early warning management method, the invention comprises the following steps: the electrical energy data includes real-time monitored voltage, current, frequency, power factor, amplitude and phase of each subharmonic data, and amplitude and phase of the fundamental wave.
As a preferable scheme of the power distribution network electric energy monitoring and early warning management method, the invention comprises the following steps: the preprocessing of the collected electric energy data comprises data cleaning and data integration; the data cleaning comprises removing noise and abnormal values, and cleaning the repeatedly recorded and incomplete data in each period T; the data integration includes defining data requirements, identifying data sources, and extracting corresponding data.
As a preferable scheme of the power distribution network electric energy monitoring and early warning management method, the invention comprises the following steps: the specific steps of the feature extraction are as follows: calculating and recording the average value, standard deviation, maximum value, minimum value, peak value, effective value, skewness and kurtosis of the harmonic data in each period; the spectrum distribution of the signal is calculated by the following specific calculation formula:
where n is the number of data points; x (i) is the ith data point of the original signal; n is the sampling frequency; j is an imaginary unit; k is the frequency index; the specific calculation formula for extracting harmonic content is as follows:
wherein Q (N) is the amplitude of the nth harmonic; q (1) is the amplitude of the fundamental wave; n is the number of harmonics; for waveform data which slowly changes in the preprocessed signal data, directly adopting approximate coefficients decomposed by discrete wavelet transformation as characteristic value vectors of waveforms, and filling missing values of the data at a certain sampling point before decomposition operation in order to make the lengths of the approximate coefficients of the same layer number consistent in the decomposition process; and extracting the characteristics of each frequency band of the data by adopting detail coefficients of non-sampling wavelet transformation for the waveform data which are subjected to rapid change in the preprocessed signal data, firstly obtaining the maximum value vector of the detail coefficients, equally dividing the maximum value vector into the number of the frequency bands, and then selecting the maximum value of each frequency band to form a new vector serving as the characteristic value vector of the waveform.
As a preferable scheme of the power distribution network electric energy monitoring and early warning management method, the invention comprises the following steps: the specific process for determining the fault type and the position coordinates is as follows: the specific formula of the construction adjusting function is as follows:
wherein m represents the adjustment result, a 1 Representing the distance of fault data, a 2 Representing the distance of the data to be detected, l 1 Representing the number of samples of fault data，l 2 Representing the number of samples of the data to be detected, b 1 Representing the standard error of the fault data, b 2 Representing standard error of data to be detected; setting a current harmonic fluctuation threshold value and a power distribution network voltage flicker threshold value in a harmonic range from 2 times to 50 times; determining the performance of different fault types on the electric energy monitoring parameters according to the characteristics of the electric energy system of the distribution network and the known characteristic range of the fault types; inputting ranges corresponding to different features into an adjusting function to obtain the corresponding feature adjusting result ranges, wherein the feature adjusting result ranges are divided into normal features, a fluctuation first stage and a fluctuation second stage; establishing a mapping relation between the fault type and the output of the characteristic adjustment result range: when the feature adjustment result is normal feature, namely the fault type is in a normal state; when the characteristic adjustment result is a fluctuation primary, the fault type is a secondary early warning; when the characteristic adjustment result is the fluctuation second level, the fault type is the first-level early warning; and mapping the numerical value of the electric energy monitoring parameter to the corresponding fault type according to the electric energy monitoring data and the output range of the regulating function.
As a preferable scheme of the power distribution network electric energy monitoring and early warning management method, the invention comprises the following steps: the specific dividing and judging process of each corresponding characteristic adjusting result range is as follows: bringing the boundary value of the current harmonic fluctuation set value into the adjusting function, and outputting two adjusting results, wherein the two adjusting results are boundary values of normal characteristics; bringing the threshold value into the adjusting function, outputting an adjusting result, and forming a boundary value of a fluctuation stage by the output result of the maximum value of the current harmonic fluctuation set value; and (3) determining the residual range as a fluctuation second level, and dividing the data into different fault types according to the output result of the data in the regulating function.
As a preferable scheme of the power distribution network electric energy monitoring and early warning management method, the invention comprises the following steps: the specific judging process of the early warning is as follows: dividing the fault type into three different early warning grades, namely, the normal state, wherein the fault factors needing to be manually subjected to fault maintenance are primary early warning and the other fault factors are secondary early warning; when the equipment is detected to be in a normal state, the equipment needs to keep a good running state, and routine inspection and maintenance are carried out periodically; when the detection is the primary early warning, immediately notifying related personnel, and carrying out emergency power failure or cutting off power input on the fault equipment; when the detection is the secondary early warning, verifying the authenticity of early warning information through monitoring equipment, a sensor and system data, timely notifying related personnel of the early warning information, sending technical personnel to the site, and carrying out detailed inspection and investigation on equipment or a system indicated by the early warning; monitoring various faults in the power distribution network in real time, when faults are detected in the power distribution network, matching fault codes with fault information in a database, and if the matching is successful, sending out signals by an early warning system and giving out alarms according to early warning levels; and an administrator of the power distribution network monitors the system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and processing.
In a second aspect, an embodiment of the present invention provides an electrical energy monitoring and early warning management system for a power distribution network, including an electrical power quality acquisition module, configured to acquire real-time data of voltage, current, frequency and harmonic waves per cycle, and send the real-time data to a data analysis module through a communication network; the data analysis module is used for preprocessing the collected data, including data cleaning and data integration; the model training module is used for extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm; the fault monitoring and early warning module is used for inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result and carrying out real-time monitoring and early warning on various faults in the power distribution network; and the interaction feedback module is used for evaluating and feeding back the predicted data of each period and is used for interacting with a user.
In a third aspect, embodiments of the present invention provide a computer apparatus comprising a memory and a processor, the memory storing a computer program, wherein: and the processor realizes any step of the power distribution network electric energy monitoring and early warning management method when executing the computer program.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: and the computer program is executed by a processor to realize any step of the power distribution network power monitoring and early warning management method.
The invention has the beneficial effects that the invention monitors the electric energy condition of the distribution network in real time, timely sends out an early warning signal, rapidly discovers potential abnormal or fault conditions of the electric energy, and reduces the power failure time and loss caused by faults; predicting the fault risk of the electric energy system by monitoring key indexes and parameters; the system can give maintenance suggestions according to the equipment state and trend analysis, and help maintenance personnel to reasonably arrange maintenance work.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flowchart of a method for monitoring and early warning management of power in a power distribution network in embodiment 1.
Fig. 2 is an early warning schematic diagram of an electric energy monitoring and early warning management method for a power distribution network in embodiment 1.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 and 2, in a first embodiment of the present invention, a method for monitoring and early warning management of electric energy of a power distribution network is provided, including the following steps:
s1: and collecting electric energy data of all equipment and circuits of the power distribution network, preprocessing the collected electric energy data, converting the preprocessed electric energy data into waveform signals, and storing the waveform signals.
Preferably, the electrical energy data includes real-time monitored voltage, current, frequency, power factor, amplitude and phase of each subharmonic data, and amplitude and phase of the fundamental wave.
Further, preprocessing the collected electric energy data comprises data cleaning and data integration; the data cleaning comprises removing noise and abnormal values, and cleaning the repeatedly recorded and incomplete data in each period T; data integration includes defining data requirements, identifying data sources, and extracting corresponding data.
It should be noted that data cleansing includes removing the same data that appears multiple times in the dataset to avoid duplicate calculations and analysis; filling the missing value comprises filling the missing value into the sample with the missing data by adopting an interpolation method or deleting the sample containing the missing value; the abnormal value detection comprises the steps of detecting and processing data with abnormal values by adopting a statistical method or a visual method; converting the processed parameter data from a high-dimensional space to a low-dimensional space through linear transformation, and simultaneously retaining the category information of the data; data integration is built, data from different data sources are combined and stored in the same database, query and association operations are carried out through SQL sentences, repeated data are searched and deleted, and a new data set is generated for subsequent analysis and modeling.
S2: and carrying out feature extraction on the preprocessed electric energy data, establishing a fault diagnosis model according to the feature extracted data, and carrying out power quality index training on the fault diagnosis model.
Preferably, the specific steps of feature extraction are as follows: calculating and recording the average value, standard deviation, maximum value, minimum value, peak value, effective value, skewness and kurtosis of the harmonic data in each period; the spectrum distribution of the signal is calculated by the following specific calculation formula:
where n is the number of data points; x (i) is the ith data point of the original signal; n is the sampling frequency; j is an imaginary unit; k is the frequency index.
Further, the specific calculation formula for extracting the harmonic content is as follows:
wherein Q (N) is the amplitude of the nth harmonic; q (1) is the amplitude of the fundamental wave; n is the number of harmonics.
For waveform data which slowly changes in the preprocessed signal data, directly adopting approximate coefficients decomposed by discrete wavelet transformation as characteristic value vectors of waveforms, and filling missing values of the data at a certain sampling point before decomposition operation in order to make the lengths of the approximate coefficients of the same layer number consistent in the decomposition process; and extracting the characteristics of each frequency band of the data by adopting detail coefficients of non-sampling wavelet transformation for the waveform data which are subjected to rapid change in the preprocessed signal data, firstly obtaining the maximum value vector of the detail coefficients, equally dividing the maximum value vector into the number of the frequency bands, and then selecting the maximum value of each frequency band to form a new vector serving as the characteristic value vector of the waveform.
S3: and inputting the real-time operation data into a trained fault diagnosis model to output a fault diagnosis result, determining the type and position coordinates of the fault, and carrying out real-time monitoring and early warning on various faults in the power distribution network.
Preferably, the specific process of determining the type and location coordinates of the fault is:
s3.1: the specific formula of the construction adjusting function is as follows:
wherein m represents the adjustment result, a 1 Representing the distance of fault data, a 2 Representing the distance of the data to be detected, l 1 Representing the number of samples of fault data, l 2 Representing the number of samples of the data to be detected, b 1 Representing the standard error of the fault data, b 2 Representing the standard error of the data to be detected.
And the characteristics of different fault types are learned and generalized by adopting a mode identification method, so that the accuracy and the robustness of fault diagnosis are improved.
S3.2: and setting a current harmonic fluctuation threshold value and a power distribution network voltage flicker threshold value in a harmonic range from 2 times to 50 times.
S3.3: and determining the performance of different fault types on the electric energy monitoring parameters according to the characteristics of the electric energy system of the distribution network and the known characteristic range of the fault types.
S3.4: and inputting the ranges corresponding to different features into an adjusting function to obtain the corresponding feature adjusting result ranges, wherein the feature adjusting result ranges are divided into normal features, a fluctuation first stage and a fluctuation second stage.
Further, the specific dividing and judging process of each corresponding characteristic adjusting result range is as follows: bringing the boundary value of the current harmonic fluctuation set value into the adjusting function, and outputting two adjusting results, wherein the two adjusting results are boundary values of normal characteristics; bringing the threshold value into the adjusting function, outputting an adjusting result, and forming a boundary value of a fluctuation stage by the output result of the maximum value of the current harmonic fluctuation set value; the residual range is determined as a fluctuation second level; and dividing the data into different fault types according to the output result of the data in the regulating function.
S3.5: and establishing a mapping relation between the fault type and the output of the characteristic adjustment result range.
Further, when the feature adjustment result is a normal feature, that is, the fault type is in a normal state; when the characteristic adjustment result is a fluctuation primary, the fault type is a secondary early warning; and when the characteristic adjustment result is the fluctuation second level, the fault type is the first-level early warning.
Further, the specific judging process of the early warning is as follows: dividing the fault type into three different early warning grades, namely, the normal state, wherein the fault factors needing to be manually subjected to fault maintenance are primary early warning, and the other fault factors are secondary early warning; when the equipment is detected to be in a normal state, the equipment needs to keep a good running state, and routine inspection and maintenance are carried out periodically; when the detection is the primary early warning, immediately notifying related personnel, and carrying out emergency power failure or cutting off power input on the fault equipment; when the detection is the secondary early warning, the authenticity of the early warning information is verified through monitoring equipment, sensors and system data, related personnel are timely informed of the early warning information, and technical personnel are dispatched to the site to perform detailed inspection and investigation on equipment or systems indicated by the early warning.
Monitoring various faults in the power distribution network in real time, when faults are detected in the power distribution network, matching fault codes with fault information in a database, and if the matching is successful, sending out signals by an early warning system and giving out alarms according to early warning levels; and an administrator of the power distribution network monitors the system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and processing.
S3.6: and mapping the numerical value of the electric energy monitoring parameter to the corresponding fault type according to the electric energy monitoring data and the output range of the regulating function.
The following are to be described: in different fault types, the amplitude, frequency and phase characteristics extracted in the characteristic extraction process show different rules and trends in a normal state and an abnormal state; for short circuit faults, the transient component will exhibit a high amplitude, high frequency characteristic, while for ground faults it will typically exhibit a low amplitude, low frequency characteristic.
The embodiment also provides a power distribution network electric energy monitoring and early warning management system, which comprises: the power quality acquisition module is used for acquiring real-time data of voltage, current, frequency and harmonic waves per cycle and transmitting the real-time data to the data analysis module through a communication network; the data analysis module is used for preprocessing the collected data, including data cleaning and data integration; the model training module is used for extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm; the fault monitoring and early warning module is used for inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result and carrying out real-time monitoring and early warning on various faults in the power distribution network; and the interaction feedback module is used for evaluating and feeding back the predicted data of each period and is used for interacting with a user.
The embodiment also provides a computer device, which is suitable for the situation of the power distribution network electric energy monitoring and early warning management method, and comprises the following steps: a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the power distribution network power monitoring and early warning management method according to the embodiment.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The embodiment also provides a storage medium, on which a computer program is stored, which when executed by a processor, implements the method for implementing the monitoring and early warning management of the electric energy of the distribution network according to the embodiment; the storage medium may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In summary, the invention monitors the power condition of the power distribution network in real time, sends out early warning signals in time, rapidly discovers potential abnormal or fault conditions of the power, and reduces the power failure time and loss caused by faults; predicting the fault risk of the electric energy system by monitoring key indexes and parameters; the system can give maintenance suggestions according to the equipment state and trend analysis, and help maintenance personnel to reasonably arrange maintenance work.
Example 2
Referring to table 1, for the second embodiment of the present invention, on the basis of the first embodiment, in order to verify the advantageous effects thereof, a verification test of the present invention is provided, and a verification explanation is made for the technical effects employed in the present method.
In the embodiment, the data of each device and each line of the power distribution network are collected in real time through the signal collector, the collected data are preprocessed, and the collected data are shown in table 1;
table 1: data acquisition meter
| Time (seconds) | Current (A) | Voltage (V) | Cause of failure | Fault type |
| 19.08 | 0 | 220 | Normal state | Normal state |
| 19.09 | 50 | 220 | Short circuit | Second-level early warning |
| 19.10 | 100 | 215 | Short circuit | Primary early warning |
| 19.11 | 200 | 210 | Short circuit | Primary early warning |
| 19.12 | 300 | 205 | Short circuit | Primary early warning |
| 19.13 | 400 | 200 | Short circuit | Primary early warning |
As can be seen from the above table, a fault occurred at 19.09 seconds; analyzing the electric energy data collected every second, judging the cause of the fault, inputting the data collected in real time into an adjusting function, mapping the numerical value of the electric energy monitoring parameter to corresponding fault types according to the electric energy monitoring data and the output range of the adjusting function, determining the fault types generated at different times, taking different measures according to the different fault types, immediately notifying related personnel when the first-level early warning is detected, and carrying out emergency power failure or power supply input cutting-off on the fault equipment; when the detection is the secondary early warning, the authenticity of the early warning information is verified through monitoring equipment, sensors and system data, related personnel are timely informed of the early warning information, technical personnel are dispatched to the scene, the equipment or the system indicated by the early warning is subjected to detailed inspection and investigation, potential electric energy abnormality or fault conditions are rapidly found, and the power failure time and loss caused by faults are reduced.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The utility model provides a distribution network electric energy monitoring early warning management method which characterized in that: comprising the steps of (a) a step of,
collecting electric energy data of all equipment and lines of the power distribution network, preprocessing the collected electric energy data, converting the preprocessed electric energy data into waveform signals, and storing the waveform signals;
performing feature extraction on the preprocessed electric energy data, establishing a fault diagnosis model according to the feature extracted data, and performing power quality index training on the fault diagnosis model;
and inputting the real-time operation data into a trained fault diagnosis model to output a fault diagnosis result, determining the type and position coordinates of the fault, and carrying out real-time monitoring and early warning on various faults in the power distribution network.
2. The power distribution network power monitoring and early warning management method according to claim 1, wherein: the electrical energy data includes real-time monitored voltage, current, frequency, power factor, amplitude and phase of each subharmonic data, and amplitude and phase of the fundamental wave.
3. The power distribution network power monitoring and early warning management method according to claim 2, wherein: the preprocessing of the collected electric energy data comprises data cleaning and data integration;
the data cleaning comprises removing noise and abnormal values, and cleaning the repeatedly recorded and incomplete data in each period T;
the data integration includes defining data requirements, identifying data sources, and extracting corresponding data.
4. The power distribution network power monitoring and early warning management method according to claim 3, wherein: the specific steps of the feature extraction are as follows:
calculating and recording the average value, standard deviation, maximum value, minimum value, peak value, effective value, skewness and kurtosis of the harmonic data in each period;
the spectrum distribution of the signal is calculated by the following specific calculation formula:
where n is the number of data points; x (i) is the ith data point of the original signal; n is the sampling frequency; j is an imaginary unit; k is the frequency index;
the specific calculation formula for extracting harmonic content is as follows:
wherein Q (N) is the amplitude of the nth harmonic; q (1) is the amplitude of the fundamental wave; n is the number of harmonics.
5. The method for monitoring and early-warning management of power in a power distribution network according to claim 4, wherein the method comprises the following steps: the specific process for determining the fault type and the position coordinates is as follows:
the specific formula of the construction adjusting function is as follows:
wherein m represents the adjustment result, a 1 Representing the distance of fault data, a 2 Representing the distance of the data to be detected, l 1 Representing the number of samples of fault data, l 2 Representing the number of samples of the data to be detected, b 1 Representing the standard error of the fault data, b 2 Representing standard error of data to be detected;
setting a current harmonic fluctuation threshold value and a power distribution network voltage flicker threshold value in a harmonic range from 2 times to 50 times;
determining the performance of different fault types on the electric energy monitoring parameters according to the characteristics of the electric energy system of the distribution network and the known characteristic range of the fault types;
inputting ranges corresponding to different features into an adjusting function to obtain the corresponding feature adjusting result ranges, wherein the feature adjusting result ranges are divided into normal features, a fluctuation first stage and a fluctuation second stage;
establishing a mapping relation between the fault type and the output of the characteristic adjustment result range:
when the feature adjustment result is normal feature, namely the fault type is in a normal state;
when the characteristic adjustment result is a fluctuation primary, the fault type is a secondary early warning;
when the characteristic adjustment result is the fluctuation second level, the fault type is the first-level early warning;
and mapping the numerical value of the electric energy monitoring parameter to the corresponding fault type according to the electric energy monitoring data and the output range of the regulating function.
6. The method for monitoring and early-warning management of power in a power distribution network according to claim 5, wherein the method comprises the following steps: the specific dividing and judging process of each corresponding characteristic adjusting result range is as follows:
bringing the boundary value of the current harmonic fluctuation set value into the adjusting function, and outputting two adjusting results, wherein the two adjusting results are boundary values of normal characteristics;
bringing the threshold value into the adjusting function, outputting an adjusting result, and forming a boundary value of a fluctuation stage by the output result of the maximum value of the current harmonic fluctuation set value;
the residual range is determined as a fluctuation second level;
and dividing the data into different fault types according to the output result of the data in the regulating function.
7. The method for monitoring and early-warning management of power in a power distribution network according to claim 6, wherein the method comprises the following steps: the specific judging process of the early warning is as follows:
dividing the fault type into three different early warning grades, namely, the normal state, wherein the fault factors needing to be manually subjected to fault maintenance are primary early warning and the other fault factors are secondary early warning;
when the equipment is detected to be in a normal state, the equipment needs to keep a good running state, and routine inspection and maintenance are carried out periodically;
when the detection is the primary early warning, immediately notifying related personnel, and carrying out emergency power failure or cutting off power input on the fault equipment;
when the detection is the secondary early warning, verifying the authenticity of early warning information through monitoring equipment, a sensor and system data, timely notifying related personnel of the early warning information, sending technical personnel to the site, and carrying out detailed inspection and investigation on equipment or a system indicated by the early warning;
monitoring various faults in the power distribution network in real time, when faults are detected in the power distribution network, matching fault codes with fault information in a database, and if the matching is successful, sending out signals by an early warning system and giving out alarms according to early warning levels; and an administrator of the power distribution network monitors the system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and processing.
8. An electric energy monitoring and managing system adopting the electric energy monitoring and early warning managing method of the power distribution network according to any one of claims 1 to 7, characterized in that: also included is a method of manufacturing a semiconductor device,
the power quality acquisition module is used for acquiring real-time data of voltage, current, frequency and harmonic waves per cycle and transmitting the real-time data to the data analysis module through a communication network;
the data analysis module is used for preprocessing the collected data, including data cleaning and data integration;
the model training module is used for extracting features of the preprocessed data, establishing a fault diagnosis model according to the feature extracted data, and training the fault diagnosis model based on a support vector machine algorithm;
the fault monitoring and early warning module is used for inputting the real-time operation data into the trained fault diagnosis model to output a fault diagnosis result and carrying out real-time monitoring and early warning on various faults in the power distribution network;
and the interaction feedback module is used for evaluating and feeding back the predicted data of each period and is used for interacting with a user.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: the steps of the power distribution network power monitoring and early warning management method according to any one of claims 1 to 7 are realized when the processor executes the computer program.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program when executed by a processor realizes the steps of the power distribution network power monitoring and early warning management method according to any one of claims 1 to 7.
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| CN117368651B (en) * | 2023-12-07 | 2024-03-08 | 江苏索杰智能科技有限公司 | Comprehensive analysis system and method for faults of power distribution network |
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