CN115128345A - Power grid safety early warning method and system based on harmonic monitoring - Google Patents
Power grid safety early warning method and system based on harmonic monitoring Download PDFInfo
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Abstract
The invention provides a power grid safety early warning method and system based on harmonic monitoring, which are applied to the technical field of power grid safety early warning, and the method comprises the following steps: and acquiring harmonic signal time sequence information through the power quality monitoring module, and extracting harmonic current time sequence information from the harmonic signal time sequence information. And inputting the harmonic current time sequence information into an early warning characteristic analysis model to obtain the early warning frequency characteristic of the harmonic current. And carrying out region segmentation on the power grid region to be monitored to obtain a region segmentation result. Traversing the region segmentation result, and extracting the harmonic characteristic information of the region equipment, wherein the harmonic characteristic information of the region equipment comprises the harmonic current deviation frequency. And positioning equipment according to the harmonic current early warning frequency characteristic and the harmonic current deviation frequency, and determining harmonic abnormal equipment. And carrying out harmonic abnormality early warning according to the harmonic current early warning frequency characteristic and the harmonic abnormality equipment. The technical problems that in the prior art, false alarm probability is high and early warning accuracy is low in power grid safety early warning based on harmonic monitoring are solved.
Description
Technical Field
The invention relates to the technical field of power grid safety early warning, in particular to a power grid safety early warning method and system based on harmonic monitoring.
Background
Harmonic sources of an electric power system are electrical equipment, the harmonic generation can cause harm to the whole electricity utilization environment, in order to guarantee the stable operation of the electricity utilization environment, the harmonic sources need to be pre-warned and positioned, and the influence caused by the harmonic sources is reduced. However, in the prior art, due to the existence of circuit noise, the power grid safety pre-warning based on harmonic monitoring has a high probability of false alarm, and thus the pre-warning accuracy is poor.
Therefore, the power grid safety early warning based on harmonic monitoring in the prior art has the technical problems of high false alarm probability and low early warning accuracy.
Disclosure of Invention
The application provides a power grid safety early warning method and system based on harmonic monitoring, which are used for solving the technical problems of high false alarm probability and low early warning accuracy of power grid safety early warning based on harmonic monitoring in the prior art.
In view of the above problems, the present application provides a power grid safety early warning method and system based on harmonic monitoring.
In a first aspect of the present application, a power grid safety early warning method based on harmonic monitoring is provided, the method is applied to a power grid safety early warning system based on harmonic monitoring, the system includes distributed power quality monitoring modules, and the method includes: uploading harmonic signal time sequence information of a public connection point of a power grid area to be monitored through a distributed power quality monitoring module; extracting harmonic current timing information from the harmonic signal timing information of any one of the common connection points; inputting the harmonic current time sequence information into an early warning characteristic analysis model to obtain early warning characteristic information, wherein the early warning characteristic information comprises harmonic current early warning frequency characteristics; performing region segmentation on the power grid region to be monitored according to the public connection point to obtain a region segmentation result; traversing the region segmentation result, and extracting harmonic feature information of the region equipment, wherein the harmonic feature information of the region equipment comprises harmonic current deflection frequency; positioning in the region segmentation result according to the harmonic current early warning frequency characteristic and the harmonic current deviation frequency, and determining harmonic abnormal equipment; and carrying out harmonic abnormality early warning according to the harmonic current early warning frequency characteristic and the harmonic abnormality equipment.
In a second aspect of the present application, a power grid safety early warning system based on harmonic monitoring is provided, the system includes a distributed power quality monitoring module, the system includes: the harmonic signal time sequence information acquisition module is used for uploading harmonic signal time sequence information of a public connection point of a power grid area to be monitored through the distributed power quality monitoring module; the harmonic current timing sequence information extraction module is used for extracting harmonic current timing sequence information from the harmonic signal timing sequence information of any one public connection point; the early warning characteristic information acquisition module is used for inputting the harmonic current time sequence information into an early warning characteristic analysis model to acquire early warning characteristic information, wherein the early warning characteristic information comprises harmonic current early warning frequency characteristics; the region segmentation module is used for performing region segmentation on the power grid region to be monitored according to the public connection point to obtain a region segmentation result; the regional equipment harmonic characteristic information acquisition module is used for traversing the regional division result and extracting regional equipment harmonic characteristic information, wherein the regional equipment harmonic characteristic information comprises harmonic current deviation frequency; the harmonic abnormal equipment determining module is used for positioning in the region segmentation result according to the harmonic current early warning frequency characteristic and the harmonic current deviation frequency and determining harmonic abnormal equipment; and the abnormity early warning module is used for carrying out harmonic abnormity early warning according to the harmonic current early warning frequency characteristic and the harmonic abnormity equipment.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method provided by the embodiment of the application, the harmonic signal time sequence information is acquired, the harmonic current time sequence information is extracted, the harmonic current time sequence information is input into the early warning characteristic analysis model, early warning evaluation is carried out on the harmonic current time sequence information, the early warning grade evaluation is carried out on the harmonic abnormal frequency with abnormal rate deviation or abnormal duration deviation, and the harmonic current early warning frequency characteristic is acquired, namely the abnormal harmonic abnormal frequency and the corresponding harmonic abnormal frequency early warning grade are acquired. And then, obtaining the harmonic characteristic information of the equipment in the region according to the region segmentation result to obtain the deviation frequency of the harmonic current. And determining harmonic abnormal equipment according to the harmonic current early warning frequency characteristic and the harmonic current deviation frequency. And carrying out harmonic abnormality early warning through harmonic current early warning frequency characteristics and harmonic abnormality equipment. Due to the fact that the intelligent early warning characteristic analysis model is adopted, harmonic signals are separated, harmonic current characteristic information is obtained accurately, accurate evaluation of harmonic signal time sequence information is achieved, and the technical effect of early warning judgment accuracy is improved. And then the technical problems of high false alarm probability and low early warning accuracy of power grid safety early warning based on harmonic monitoring in the prior art are solved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flow chart of a power grid safety early warning method based on harmonic monitoring provided in the present application;
fig. 2 is a schematic flow chart illustrating the process of acquiring early warning feature information in a power grid safety early warning method based on harmonic monitoring according to the present application;
fig. 3 is a schematic flow chart illustrating a process of acquiring harmonic abnormal devices in a power grid safety early warning method based on harmonic monitoring according to the present application;
fig. 4 provides a schematic structural diagram of a power grid safety early warning system based on harmonic monitoring.
Description of reference numerals: the device comprises a harmonic signal time sequence information acquisition module 11, a harmonic current time sequence information extraction module 12, an early warning characteristic information acquisition module 13, a region segmentation module 14, a region equipment harmonic characteristic information acquisition module 15, a harmonic abnormal equipment determination module 16 and an abnormal early warning module 17.
Detailed Description
The application provides a power grid safety early warning method and system based on harmonic monitoring, which are used for solving the technical problems of high false alarm probability and low early warning accuracy of power grid safety early warning based on harmonic monitoring in the prior art.
The technical solution in the present application will be described clearly and completely with reference to the accompanying drawings. The embodiments described are only a part of the disclosure that can be realized by the present application, and not the entire disclosure of the present application.
Example one
As shown in fig. 1, the present application provides a power grid safety early warning method based on harmonic monitoring, which is applied to a power grid safety early warning system based on harmonic monitoring, where the system includes a distributed power quality monitoring module, and the method includes:
step 100: harmonic signal time sequence information of a public connection point of a power grid area to be monitored is uploaded through a distributed power quality monitoring module;
step 200: extracting harmonic current timing information from the harmonic signal timing information of any one of the common connection points;
specifically, distributed power quality monitoring module is a monitoring devices who distributes in the regional public connection point of electric wire netting that awaits measuring, it is used for monitoring can real-time supervision display frequency that produces in the distribution network, voltage fluctuation and flicker, voltage deviation, voltage fundamental wave effective value and true effective value, electric current fundamental wave effective value and true effective value, fundamental wave active power, fundamental wave apparent power, electric energy quality parameters such as true power factor, it contains power supply network region and user network region in the electric wire netting region to await measuring. Wherein the point of common connection is the point of connection of the subscriber network area and the supply network area. For example, a plurality of users are connected to a power supply line, and the point of connection to the home of each user is a point of common connection. And acquiring harmonic signal time sequence information of a public connection point, which is waveform information of harmonic waves generated by monitoring acquired parameters along with time variation, and uploading the harmonic signal time sequence information of a user network area and the harmonic signal time sequence information of a power supply network area through a distributed power quality monitoring module. Harmonic current time sequence information in the harmonic signal time sequence information of any one public connection point is extracted from the harmonic signal time sequence information of the any one public connection point, wherein the harmonic current time sequence information is harmonic waveform information generated by the time variation of an actually monitored current signal, namely the harmonic current waveform information.
Step 300: inputting the harmonic current time sequence information into an early warning characteristic analysis model to obtain early warning characteristic information, wherein the early warning characteristic information comprises harmonic current early warning frequency characteristics;
specifically, the obtained harmonic current time sequence information, namely current harmonic waveform information, is input into an early warning characteristic analysis model, wherein the early warning characteristic analysis model is used for analyzing the harmonic current time sequence information, and performing harmonic current frequency separation, harmonic current abnormity evaluation and harmonic current early warning grade evaluation on the harmonic current time sequence information to obtain early warning characteristic information. The early warning characteristic information comprises a harmonic current early warning frequency characteristic, the harmonic current early warning frequency characteristic is obtained after the harmonic abnormal frequency characteristic is calibrated according to a harmonic abnormal frequency early warning grade, and the harmonic current early warning frequency characteristic comprises the early warning harmonic current frequency, namely the frequency of the harmonic current and the corresponding early warning grade.
As shown in fig. 2, the method steps 300 provided in the embodiment of the present application further include:
step 310: acquiring a harmonic current frequency separation module, a harmonic current abnormity evaluation module and a harmonic current early warning grade evaluation module according to the early warning characteristic analysis model;
step 320: inputting the harmonic current time sequence information into the harmonic current frequency separation module to obtain harmonic current frequency distribution information, wherein the harmonic current frequency distribution information comprises a harmonic current frequency value, a frequency value content and a frequency value duration;
step 330: inputting the harmonic current frequency value, the frequency value content and the frequency value duration into the harmonic current abnormity evaluation module to obtain harmonic abnormal frequency, content deviation or duration deviation;
step 340: inputting the content deviation degree, the duration deviation degree and the harmonic abnormal frequency into the harmonic current early warning grade evaluation module to obtain a harmonic abnormal frequency early warning grade;
step 350: and calibrating the harmonic abnormal frequency characteristic according to the harmonic abnormal frequency early warning grade, generating the harmonic current early warning frequency characteristic, and adding the early warning characteristic information.
Specifically, a harmonic current frequency separation module, a harmonic current abnormity evaluation module and a harmonic current early warning level evaluation module are obtained according to the early warning characteristic analysis model. The harmonic current frequency separation module is used for carrying out frequency separation on the obtained harmonic current time sequence information to obtain harmonic current frequency distribution information, and the harmonic current frequency distribution information comprises a harmonic current frequency value, a frequency value content rate and a frequency value duration time. And then, inputting the frequency value, the frequency value content and the frequency value duration of the harmonic current into the harmonic current abnormity evaluation module, acquiring the harmonic abnormity frequency, the content deviation or the duration deviation, inputting the content deviation, the duration deviation and the harmonic abnormity frequency into the harmonic current early warning grade evaluation module, and acquiring the harmonic abnormity frequency early warning grade. And finally, calibrating the harmonic abnormal frequency characteristic according to the harmonic abnormal frequency early warning level, calibrating the early warning level of the harmonic abnormal frequency, generating the harmonic current early warning frequency characteristic, and adding the early warning characteristic information. Harmonic abnormal frequency and corresponding early warning level existing in harmonic waves are determined by obtaining the harmonic current early warning frequency characteristics, and technical support is provided for determination of subsequent abnormal equipment.
The method provided by the embodiment of the present application further includes the following steps:
step 311: acquiring harmonic current monitoring record data, wherein the harmonic current monitoring record data is historical real-time harmonic current monitoring data;
step 312: obtaining a harmonic current frequency recording value, and calibrating a characteristic value of the harmonic current monitoring recording data according to the harmonic current frequency recording value to obtain a harmonic current frequency separation module identification data set;
step 313: and training the harmonic current frequency separation module according to the harmonic current monitoring record data and the harmonic current frequency separation module identification data set.
Specifically, the harmonic current frequency separation module acquires harmonic current monitoring record data, wherein the harmonic current monitoring record data are historical real-time harmonic current monitoring data. And processing the harmonic current monitoring record data, namely the historical real-time harmonic current monitoring data, in a Fourier transform or wavelet transform mode to obtain harmonic current frequency record values, wherein the frequency data of harmonic current in the harmonic current frequency record values and the record value data of the frequency value content and the frequency value duration jointly form the harmonic current frequency record values. And then, calibrating the characteristic value of the harmonic current monitoring recorded data according to the harmonic current frequency recorded value, calibrating the frequency data of the historical recorded real-time harmonic current monitoring data in the harmonic current monitoring recorded data, the frequency value content and the frequency value duration time, and acquiring a harmonic current frequency separation module identification data set. And then, taking the harmonic current monitoring record data and the harmonic current frequency separation module identification data set as input data to construct a decision tree to complete the construction of the harmonic current frequency separation module. The harmonic current frequency separation module is used for carrying out frequency separation on harmonic current to obtain frequency data, frequency value content and frequency value duration of harmonic waves.
The method step 313 provided by the embodiment of the present application further includes:
step 313-1: constructing a first decision tree according to the harmonic current monitoring record data and the harmonic current frequency separation module identification data set;
step 313-2: extracting a training data set which does not meet the preset accuracy rate in the first decision tree, and recording the training data set as a first data set;
step 313-3: and when the data volume of the first data set is less than or equal to a preset data volume, setting the first decision tree as the harmonic current frequency separation module.
Specifically, a first decision tree is constructed according to the fact that harmonic current monitoring record data serve as training data and a harmonic current frequency separation module identification data set serve as supervision data. The decision tree is a prediction model, and the acquisition of the frequency of the harmonic current frequency recorded value can be completed by inputting the time sequence information of the harmonic current. A decision tree is a process of supervised learning in which the harmonic current frequency separation module identifies a data set as the supervisory data for the decision tree. And acquiring a training data set which does not meet the preset accuracy in the first decision tree, and recording the training data set as a first data set. The training data set which does not meet the preset accuracy rate is data which is obtained by constructing a decision tree through the training data set, and the deviation between the obtained result and the supervision data exceeds a certain threshold, and at the moment, the data in the first data set cannot obtain an accurate result in the first decision tree. When the data volume of the first data set is less than or equal to the preset data volume, the output result of the first decision tree acquired at the moment can meet the requirement of accurately processing most data, and the first decision tree is set as a harmonic current frequency separation module at the moment. By acquiring the first data set and judging the data quantity in the first data set, the output result of the acquired harmonic current frequency separation module is more accurate.
The method provided by the embodiment of the present application further includes step 313-3:
step 313-31: when the data volume of the first data set is larger than the preset data volume, constructing a second decision tree through the first data set;
steps 313-32: extracting a training data set which does not meet the preset accuracy rate in the second decision tree, and recording the training data set as a second data set;
step 313-33: and repeating iteration based on the second data set until the data quantity of the Nth data set of the Nth decision tree is less than or equal to the preset data quantity, combining the first decision tree and the Nth decision tree, and generating the harmonic current frequency separation module.
Specifically, when the data amount of the first data set is larger than the preset data amount, there are still more data that cannot obtain an accurate output result in the first decision tree, and at this time, the second decision tree is continuously constructed by using the first data set as input data. And when a second decision tree is constructed, the supervision data of the second decision tree identifies data in the data set for the harmonic current frequency separation module corresponding to the first data set. And continuously extracting a training data set which does not meet the preset accuracy in a second decision tree, recording the training data set as a second data set, continuously repeating iteration based on the second data set until the data quantity of an Nth data set of an Nth decision tree is less than or equal to the preset data quantity, merging the first decision tree and the Nth decision tree, and generating the harmonic current frequency separation module. By combining the Nth decision tree until the data volume of the Nth data set is less than or equal to the preset data volume, the obtained harmonic current frequency separation module can be further ensured to finish accurate processing of data, so that the model can accurately obtain the harmonic abnormal frequency, the content deviation degree or the duration deviation degree.
The method steps 340 provided by the embodiment of the present application further include:
step 341: acquiring a harmonic current frequency content threshold and a frequency duration threshold according to the harmonic current abnormity evaluation module;
step 342: judging whether the frequency value content meets the frequency content threshold of the harmonic current or not;
step 343: if not, judging whether the frequency value duration meets the frequency duration threshold or not;
step 344: and if so, adding the harmonic current frequency value into the harmonic abnormal frequency, and calculating the deviation degree of the duration according to the frequency value duration and the frequency duration threshold.
Specifically, according to the harmonic current abnormality evaluation module, a harmonic current frequency content threshold and a frequency duration threshold are obtained, wherein the harmonic current frequency content threshold is a threshold of a ratio of a root mean square value of harmonic current to a root mean square value of fundamental current, that is, a harmonic current content threshold, the frequency duration threshold is a threshold of duration of current frequency, and the harmonic current frequency content threshold and the frequency duration threshold can be set according to an actual situation. And then, judging whether the frequency value content in the harmonic current frequency distribution information meets the harmonic current frequency content threshold, namely judging whether the frequency value content is greater than or equal to the harmonic current frequency content threshold, and adding the harmonic current frequency value into the harmonic abnormal frequency if the harmonic current frequency content is greater than or equal to the harmonic current frequency content threshold, wherein the influence of the harmonic on the original fundamental wave is greater and the influence on the current waveform of the power grid is greater. When the frequency value content is not satisfied, namely the frequency value content is smaller than the wave current frequency content threshold value, the influence on the original fundamental wave is small at the time. And then judging whether the frequency value duration meets the frequency duration threshold or not, namely judging whether the duration of the current frequency is greater than or equal to the frequency duration threshold or not. When the frequency duration time is larger than or equal to the frequency duration time threshold, the influence time of the harmonic on the original fundamental wave is long, the current frequency is abnormal, and otherwise, the current frequency is not abnormal. And then adding the harmonic current frequency meeting the frequency continuous time length threshold into the harmonic abnormal frequency, and calculating the deviation of the continuous time length according to the frequency value continuous time length and the frequency continuous time length threshold, namely acquiring a deviation value between the frequency value continuous time length and the frequency continuous time length threshold so as to judge the influence degree of the harmonic current frequency value.
The method provided by the embodiment of the present application further includes steps 342:
step 342-1: and when the frequency value content meets the harmonic current frequency content threshold, adding the harmonic current frequency value into the harmonic abnormal frequency, and calculating the content deviation degree according to the frequency value content and the harmonic current frequency content threshold.
And if the frequency value content meets the harmonic current frequency content threshold, adding the harmonic current frequency value into the harmonic abnormal frequency, and calculating the content deviation degree according to the frequency value content and the harmonic current frequency content threshold, namely acquiring a deviation value between the frequency value content and the harmonic current frequency content threshold so as to judge the influence degree of the harmonic current frequency value.
The method provided by the embodiment of the present application further includes the following steps:
step 314: constructing a first coordinate axis based on big data, wherein the first coordinate axis is used for carrying out early warning rating on the content rate deviation degree;
step 315: constructing a second coordinate axis based on big data, wherein the second coordinate axis is used for carrying out early warning rating on the duration deviation degree;
step 316: generating an early warning grade evaluation coordinate system according to the first coordinate axis and the second coordinate axis;
step 317: and setting the early warning grade evaluation coordinate system as the harmonic current early warning grade evaluation module.
Specifically, a first coordinate axis is constructed based on big data, the first coordinate axis is the content deviation degree, and the mapping relation between the content deviation degree and the early warning rating is obtained through the big data, namely, each content deviation degree corresponds to one early warning rating. And constructing a second coordinate axis based on the big data, wherein the relation between the duration deviation and the early warning rating is obtained through the big data, namely, each duration deviation corresponds to one early warning rating, and the second coordinate axis is the duration deviation. And generating an early warning grade evaluation coordinate system according to the first coordinate axis, the second coordinate axis and the corresponding relation of the early warning grades. In the early warning level evaluation coordinate system, the abscissa is a first coordinate axis, the ordinate is a second coordinate axis, each coordinate point corresponds to two early warning levels, namely a content deviation early warning level and a duration deviation early warning level, and the final calculation result is obtained by performing mean calculation on the two early warning levels, namely the actual early warning level of the point. And setting the early warning grade evaluation coordinate system as the harmonic current early warning grade evaluation module. The harmonic current early warning grade evaluation module is obtained through big data, so that the harmonic current early warning grade evaluation module has universality, and the obtained early warning grade evaluation result is more accurate.
Step 400: performing region segmentation on the power grid region to be monitored according to the public connection point to obtain a region segmentation result;
step 500: traversing the region segmentation result, and extracting region equipment harmonic characteristic information, wherein the region equipment harmonic characteristic information comprises harmonic current deviation frequency;
specifically, the area of the power grid to be detected is subjected to area segmentation according to the common connecting points, the monitoring area of the power quality monitoring module at each node is obtained, and an area segmentation result is obtained. Traversing all the region segmentation results, extracting harmonic characteristic information of equipment in the region, and when acquiring the harmonic characteristic information of the equipment in the region, acquiring characteristic information of a harmonic current frequency value generated under the working state of the characterization equipment confirmed according to historical monitoring data, namely acquiring waveform information of the harmonic current generated under the historical working state of the working equipment generating the harmonic in the region. And the harmonic characteristic information of the regional equipment comprises the harmonic current deviation frequency, wherein the harmonic current deviation frequency is a harmonic current frequency value of which the frequency of the equipment in the historical working state is greater than a preset value, namely the harmonic frequency with the longest harmonic generation time in the historical working state is obtained.
Step 600: positioning in the region segmentation result according to the harmonic current early warning frequency characteristic and the harmonic current deviation frequency, and determining harmonic abnormal equipment;
step 700: and carrying out harmonic abnormality early warning according to the harmonic current early warning frequency characteristic and the harmonic abnormality equipment.
Specifically, according to the obtained harmonic current early warning frequency characteristic, namely the harmonic current frequency and the harmonic current deviation frequency for early warning in the harmonic signal time sequence information obtained by each public connection point, namely the harmonic frequency with the longest harmonic duration generated by the equipment in the working state, the equipment is positioned in the region segmentation result of the region to be detected, and the harmonic abnormal equipment is determined. The harmonic current early warning frequency characteristics comprise the early warning harmonic current frequency, namely the frequency of the harmonic current and the corresponding early warning grade. And determining the early warning level of the equipment according to the determined harmonic abnormal equipment and the harmonic current early warning frequency characteristic, and performing harmonic abnormal early warning on the equipment according to the early warning level.
As shown in fig. 3, the method steps 600 provided in the embodiment of the present application further include:
step 610: matching according to the harmonic current deviation frequency and the harmonic current early warning frequency characteristic to obtain initial harmonic abnormal equipment;
step 620: detecting harmonic current values of the initial harmonic abnormal equipment through the distributed power quality monitoring module along the direction information of the harmonic current to obtain a plurality of groups of input harmonic current values and output harmonic current values, wherein the initial harmonic abnormal equipment corresponds to the plurality of groups of input harmonic current values and output harmonic current values one to one;
step 630: comparing any set of the input harmonic current values with the output harmonic current values;
step 640: adding the initial harmonic abnormal equipment of which the output harmonic current value is greater than the input harmonic current value into a list of equipment to be screened;
step 650: and adding the harmonic abnormal equipment according to the equipment in the equipment list to be screened.
Specifically, matching is performed according to the harmonic current deviation frequency and the harmonic current early warning frequency characteristic, namely, the harmonic current early warning frequency characteristic is matched according to the harmonic current deviation frequency obtained according to the historical operating data of the equipment, so that initial harmonic abnormal equipment is obtained. And then, carrying out harmonic current value detection on the initial harmonic abnormal equipment through the distributed power quality monitoring module along the harmonic current direction information to obtain multiple groups of input harmonic current values and output harmonic current values, wherein the initial harmonic abnormal equipment corresponds to the multiple groups of input harmonic current values and output harmonic current values one to one. Comparing any group of the input harmonic current values with the output harmonic current values, and when the output harmonic current values are larger than the input harmonic current values, directly generating harmonic waves by the equipment, so that harmonic wave abnormality exists in the equipment. At this time, the initial harmonic abnormal device of which the output harmonic current value is greater than the input harmonic current value is added into a device list to be screened. And then adding the harmonic abnormal equipment according to the equipment in the equipment list to be screened. By monitoring the harmonic current value of the operating equipment, the harmonic abnormal equipment in the region segmentation result is accurately determined.
In summary, the method provided by the embodiment of the present application obtains the harmonic signal timing information, extracts the harmonic current timing information, inputs the harmonic current timing information into the early warning feature analysis model, performs early warning evaluation on the harmonic current timing information, and performs early warning level evaluation on the harmonic abnormal frequency with abnormal frequency deviation or abnormal duration deviation to obtain the harmonic current early warning frequency feature, that is, obtain the abnormal harmonic abnormal frequency and the corresponding harmonic abnormal frequency early warning level. And then, obtaining the harmonic characteristic information of the equipment in the region according to the region segmentation result to obtain the deviation frequency of the harmonic current. And determining harmonic abnormal equipment according to the harmonic current early warning frequency characteristic and the harmonic current deviation frequency. And carrying out harmonic anomaly early warning through the harmonic anomaly frequency early warning level and harmonic anomaly equipment. Due to the adoption of the intelligent early warning characteristic analysis model, the harmonic signals are separated, the harmonic current characteristic information is acquired more accurately, the accurate evaluation of the harmonic signal time sequence information is realized, and the technical effect of the accuracy of early warning judgment is improved. And then the technical problems that the false alarm probability is high and the early warning accuracy is low in the power grid safety early warning based on harmonic wave monitoring in the prior art are solved.
Example two
Based on the same inventive concept as the power grid safety early warning method based on harmonic monitoring in the foregoing embodiment, as shown in fig. 4, the present application provides a power grid safety early warning system based on harmonic monitoring, where the system includes a distributed power quality monitoring module, and the system includes:
the harmonic signal timing information acquisition module 11 is used for uploading harmonic signal timing information of a public connection point of a power grid area to be monitored through the distributed power quality monitoring module;
a harmonic current timing information extraction module 12, configured to extract harmonic current timing information from the harmonic signal timing information of any one of the common connection points;
the early warning characteristic information acquisition module 13 is configured to input the harmonic current timing sequence information into an early warning characteristic analysis model to obtain early warning characteristic information, where the early warning characteristic information includes a harmonic current early warning frequency characteristic;
the region segmentation module 14 is configured to perform region segmentation on the power grid region to be monitored according to the public connection point, and obtain a region segmentation result;
the regional equipment harmonic characteristic information acquiring module 15 is configured to traverse the regional division result and extract regional equipment harmonic characteristic information, where the regional equipment harmonic characteristic information includes harmonic current deviation frequency;
the harmonic abnormal equipment determining module 16 is configured to locate in the region segmentation result according to the harmonic current early warning frequency feature and the harmonic current deviation frequency, and determine harmonic abnormal equipment;
and the abnormity early warning module 17 is used for carrying out harmonic abnormity early warning according to the harmonic current early warning frequency characteristic and the harmonic abnormity equipment.
Further, the early warning feature information obtaining module 13 is further configured to:
acquiring a harmonic current frequency separation module, a harmonic current abnormity evaluation module and a harmonic current early warning grade evaluation module according to the early warning characteristic analysis model;
inputting the harmonic current time sequence information into the harmonic current frequency separation module to obtain harmonic current frequency distribution information, wherein the harmonic current frequency distribution information comprises a harmonic current frequency value, a frequency value content and a frequency value duration;
inputting the harmonic current frequency value, the frequency value content and the frequency value duration into the harmonic current abnormity evaluation module to obtain harmonic abnormal frequency, content deviation or duration deviation;
inputting the content deviation degree, the duration deviation degree and the harmonic abnormal frequency into the harmonic current early warning grade evaluation module to obtain a harmonic abnormal frequency early warning grade;
and calibrating the harmonic abnormal frequency characteristic according to the harmonic abnormal frequency early warning grade, generating the harmonic current early warning frequency characteristic, and adding the early warning characteristic information.
Further, the early warning feature information obtaining module 13 is further configured to:
acquiring harmonic current monitoring record data, wherein the harmonic current monitoring record data is historical real-time harmonic current monitoring data;
obtaining a harmonic current frequency recording value, and calibrating a characteristic value of the harmonic current monitoring recording data according to the harmonic current frequency recording value to obtain a harmonic current frequency separation module identification data set;
and training the harmonic current frequency separation module according to the harmonic current monitoring record data and the harmonic current frequency separation module identification data set.
Further, the early warning feature information obtaining module 13 is further configured to:
constructing a first decision tree according to the harmonic current monitoring record data and the harmonic current frequency separation module identification data set;
extracting a training data set which does not meet the preset accuracy rate in the first decision tree, and recording the training data set as a first data set;
and when the data volume of the first data set is less than or equal to a preset data volume, setting the first decision tree as the harmonic current frequency separation module.
Further, the early warning feature information obtaining module 13 is further configured to:
when the data volume of the first data set is larger than the preset data volume, constructing a second decision tree through the first data set;
extracting a training data set which does not meet the preset accuracy rate in the second decision tree, and recording the training data set as a second data set;
and repeating iteration based on the second data set until the data quantity of the Nth data set of the Nth decision tree is less than or equal to the preset data quantity, combining the first decision tree and the Nth decision tree, and generating the harmonic current frequency separation module.
Further, the early warning feature information obtaining module 13 is further configured to:
acquiring a harmonic current frequency content threshold and a frequency duration threshold according to the harmonic current abnormity evaluation module;
judging whether the frequency value content meets the frequency content threshold of the harmonic current or not;
if not, judging whether the frequency value duration meets the frequency duration threshold or not;
and if so, adding the harmonic current frequency value into the harmonic abnormal frequency, and calculating the deviation degree of the duration according to the frequency value duration and the frequency duration threshold.
Further, the early warning feature information obtaining module 13 is further configured to:
and when the frequency value content meets the harmonic current frequency content threshold, adding the harmonic current frequency value into the harmonic abnormal frequency, and calculating the content deviation degree according to the frequency value content and the harmonic current frequency content threshold.
Further, the early warning feature information obtaining module 13 is further configured to:
constructing a first coordinate axis based on big data, wherein the first coordinate axis is used for carrying out early warning rating on the content rate deviation degree;
constructing a second coordinate axis based on big data, wherein the second coordinate axis is used for carrying out early warning rating on the duration deviation degree;
generating an early warning grade evaluation coordinate system according to the first coordinate axis and the second coordinate axis;
and setting the early warning grade evaluation coordinate system as the harmonic current early warning grade evaluation module.
Further, the harmonic anomaly device determination module 16 is further configured to:
matching according to the harmonic current deviation frequency and the harmonic current early warning frequency characteristic to obtain initial harmonic abnormal equipment;
detecting harmonic current values of the initial harmonic abnormal equipment through the distributed power quality monitoring module along the direction information of the harmonic current to obtain a plurality of groups of input harmonic current values and output harmonic current values, wherein the initial harmonic abnormal equipment corresponds to the plurality of groups of input harmonic current values and output harmonic current values one to one;
comparing any set of the input harmonic current values with the output harmonic current values;
adding the initial harmonic abnormal equipment of which the output harmonic current value is greater than the input harmonic current value into a list of equipment to be screened;
and adding the harmonic abnormal equipment according to the equipment in the equipment list to be screened.
The second embodiment is used for executing the method as in the first embodiment, and both the execution principle and the execution basis can be obtained through the content recorded in the first embodiment, which is not described herein again. Although the present application has been described in connection with particular features and embodiments thereof, the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, those skilled in the art may make various changes and modifications to the present application without departing from the scope of the present application, and what is obtained in this way also belongs to the protection scope of the present application.
Claims (10)
1. A power grid safety early warning method based on harmonic wave monitoring is characterized in that the method is applied to a power grid safety early warning system based on harmonic wave monitoring, the system comprises distributed power quality monitoring modules, and the method comprises the following steps:
harmonic signal time sequence information of a public connection point of a power grid area to be monitored is uploaded through a distributed power quality monitoring module;
extracting harmonic current timing information from the harmonic signal timing information of any one of the common connection points;
inputting the harmonic current time sequence information into an early warning characteristic analysis model to obtain early warning characteristic information, wherein the early warning characteristic information comprises harmonic current early warning frequency characteristics;
performing region segmentation on the power grid region to be monitored according to the public connection point to obtain a region segmentation result;
traversing the region segmentation result, and extracting region equipment harmonic characteristic information, wherein the region equipment harmonic characteristic information comprises harmonic current deviation frequency;
positioning in the region segmentation result according to the harmonic current early warning frequency characteristic and the harmonic current deviation frequency, and determining harmonic abnormal equipment;
and carrying out harmonic abnormality early warning according to the harmonic current early warning frequency characteristic and the harmonic abnormality equipment.
2. The method of claim 1, wherein the inputting the harmonic current timing information into an early warning feature analysis model to obtain early warning feature information comprises:
acquiring a harmonic current frequency separation module, a harmonic current abnormity evaluation module and a harmonic current early warning grade evaluation module according to the early warning characteristic analysis model;
inputting the harmonic current time sequence information into the harmonic current frequency separation module to obtain harmonic current frequency distribution information, wherein the harmonic current frequency distribution information comprises a harmonic current frequency value, a frequency value content and a frequency value duration;
inputting the harmonic current frequency value, the frequency value content and the frequency value duration into the harmonic current abnormity evaluation module to obtain harmonic abnormal frequency, content deviation or duration deviation;
inputting the content deviation degree, the duration deviation degree and the harmonic abnormal frequency into the harmonic current early warning grade evaluation module to obtain a harmonic abnormal frequency early warning grade;
and calibrating the harmonic abnormal frequency characteristic according to the harmonic abnormal frequency early warning level, generating the harmonic current early warning frequency characteristic, and adding the early warning characteristic information.
3. The method of claim 2, wherein the obtaining a harmonic current frequency separation module comprises:
acquiring harmonic current monitoring record data, wherein the harmonic current monitoring record data is historical real-time harmonic current monitoring data;
obtaining a harmonic current frequency recording value, and calibrating a characteristic value of the harmonic current monitoring recording data according to the harmonic current frequency recording value to obtain a harmonic current frequency separation module identification data set;
and training the harmonic current frequency separation module according to the harmonic current monitoring record data and the harmonic current frequency separation module identification data set.
4. The method of claim 3, wherein training the harmonic current frequency separation module from the harmonic current monitoring log data and the harmonic current frequency separation module identification data set comprises:
constructing a first decision tree according to the harmonic current monitoring record data and the harmonic current frequency separation module identification data set;
extracting a training data set which does not meet the preset accuracy rate in the first decision tree, and recording the training data set as a first data set;
and when the data volume of the first data set is less than or equal to a preset data volume, setting the first decision tree as the harmonic current frequency separation module.
5. The method of claim 4, wherein the method further comprises:
when the data volume of the first data set is larger than the preset data volume, constructing a second decision tree through the first data set;
extracting a training data set which does not meet the preset accuracy rate in the second decision tree, and recording the training data set as a second data set;
and repeating iteration based on the second data set until the data quantity of the Nth data set of the Nth decision tree is less than or equal to the preset data quantity, combining the first decision tree and the Nth decision tree, and generating the harmonic current frequency separation module.
6. The method of claim 2, wherein said inputting said harmonic current frequency value, said frequency value content and said frequency value duration into said harmonic current anomaly evaluation module, obtaining a harmonic anomaly frequency, content rate deviation or duration deviation, comprises:
acquiring a harmonic current frequency content threshold and a frequency duration threshold according to the harmonic current abnormity evaluation module;
judging whether the frequency value content meets the frequency content threshold of the harmonic current or not;
if not, judging whether the frequency value duration meets the frequency duration threshold or not;
and if so, adding the harmonic current frequency value into the harmonic abnormal frequency, and calculating the deviation degree of the duration according to the frequency value duration and the frequency duration threshold.
7. The method of claim 6, wherein said determining whether the frequency content rate satisfies the harmonic current frequency content threshold comprises:
and when the frequency value content meets the harmonic current frequency content threshold, adding the harmonic current frequency value into the harmonic abnormal frequency, and calculating the content deviation according to the frequency value content and the harmonic current frequency content threshold.
8. The method of claim 2, wherein obtaining the harmonic current warning level assessment module comprises:
constructing a first coordinate axis based on big data, wherein the first coordinate axis is used for carrying out early warning rating on the content rate deviation degree;
constructing a second coordinate axis based on big data, wherein the second coordinate axis is used for carrying out early warning rating on the duration deviation degree;
generating an early warning grade evaluation coordinate system according to the first coordinate axis and the second coordinate axis;
and setting the early warning level evaluation coordinate system as the harmonic current early warning level evaluation module.
9. The method of claim 1, wherein the locating in the region segmentation result according to the harmonic current warning frequency feature and the harmonic current deviation frequency to determine harmonic anomaly equipment comprises:
matching according to the harmonic current deviation frequency and the harmonic current early warning frequency characteristic to obtain initial harmonic abnormal equipment;
detecting harmonic current values of the initial harmonic abnormal equipment through the distributed power quality monitoring module along the direction information of the harmonic current to obtain a plurality of groups of input harmonic current values and output harmonic current values, wherein the initial harmonic abnormal equipment corresponds to the plurality of groups of input harmonic current values and output harmonic current values one to one;
comparing any set of the input harmonic current values with the output harmonic current values;
adding the initial harmonic abnormal equipment of which the output harmonic current value is greater than the input harmonic current value into a list of equipment to be screened;
and adding the harmonic abnormal equipment according to the equipment in the equipment list to be screened.
10. The utility model provides a power grid safety precaution system based on harmonic monitoring, its characterized in that, the system includes distributed power quality monitoring module, the system includes:
the harmonic signal time sequence information acquisition module is used for uploading harmonic signal time sequence information of a public connection point of a power grid area to be monitored through the distributed power quality monitoring module;
the harmonic current timing sequence information extraction module is used for extracting harmonic current timing sequence information from the harmonic signal timing sequence information of any one public connection point;
the early warning characteristic information acquisition module is used for inputting the harmonic current time sequence information into an early warning characteristic analysis model to acquire early warning characteristic information, wherein the early warning characteristic information comprises harmonic current early warning frequency characteristics;
the region segmentation module is used for performing region segmentation on the power grid region to be monitored according to the public connection point to obtain a region segmentation result;
the regional equipment harmonic characteristic information acquisition module is used for traversing the regional division result and extracting regional equipment harmonic characteristic information, wherein the regional equipment harmonic characteristic information comprises harmonic current deflection frequency;
the harmonic abnormal equipment determining module is used for positioning in the region segmentation result according to the harmonic current early warning frequency characteristic and the harmonic current deviation frequency and determining harmonic abnormal equipment;
and the abnormity early warning module is used for carrying out harmonic abnormity early warning according to the harmonic current early warning frequency characteristic and the harmonic abnormity equipment.
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