CN116805210B - Intelligent power grid risk identification management and control method based on big data - Google Patents
Intelligent power grid risk identification management and control method based on big data Download PDFInfo
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Abstract
The invention belongs to the technical field of power grid risk management and control, and particularly discloses a power grid risk intelligent identification and management and control method based on big data.
Description
Technical Field
The invention belongs to the technical field of power grid risk management and control, and relates to a power grid risk intelligent identification management and control method based on big data.
Background
The power industry occupies a very important position in the development of society, and once an accident occurs in a power grid, the power grid brings a great deal of inconvenience to the life of people and also causes great social influence to power supply enterprises. Along with the rapid development of urban construction, the structure of the existing power supply network is gradually expanded, and the risk factors influencing the operation safety in the power grid are more and more increased, so that the identification management and control on the operation risk of the power grid are necessary for improving the reliability and the security guarantee of the power supply.
However, the risk generated by the operation of the power grid is generally focused when the operation risk identification of the power grid is performed at present, such as overload and overvoltage of the operation of the circuit, operation faults of circuit equipment and the like, the operation risk caused by severe weather, such as strong wind, heavy rain, snow storm and the like, can be ignored, the circuit is damaged, important power equipment fails, fire disaster can be caused by serious people, casualties are caused, and therefore the operation hazard of the power grid caused by severe weather is relatively large, if the monitoring of the severe weather is ignored in the operation of the power grid, the coverage of the operation risk identification of the power grid can be reduced intangibly, the identification is limited and is not comprehensive enough, so that the timeliness and accuracy of the operation risk identification of the power grid are affected to a certain extent, and the follow-up operation risk management and control treatment of the power grid are not facilitated.
Moreover, at present, when the operation risk management and control of the power grid is carried out, only the identified operation risk is taken as the management and control basis, the power-off treatment is basically adopted for facilitating the power grid management when the operation risk is large, and the demand consideration of the power supply continuity of the power utilization end is lacked, because the demands of different power utilization ends on the power supply continuity are different, the power supply continuity and stability of some precise electric equipment are very sensitive, the equipment is damaged due to the trade outage, and the equipment can not be repaired or is expensive, so that the identified operation risk is taken as the management and control basis obviously and is not scientific and reasonable enough, a cut phenomenon is easily formed, the management and control result and the power utilization end adaptation degree are not high, the management and control effect is poor, and the incidence of invalid management and control is increased intangibly.
Disclosure of Invention
In view of the above, an intelligent power grid risk identification management and control method based on big data is provided, and the problems in the background technology are effectively solved.
The aim of the invention can be achieved by the following technical scheme: the invention provides a power grid risk intelligent identification management and control method based on big data, which comprises the following steps: A. and counting the number of the power supply networks in the target area, and acquiring power utilization information and a power supply management and control end corresponding to each power supply network, wherein the power utilization information comprises a power utilization area, a power utilization period of the power utilization area and a main body load grade corresponding to each power utilization period.
B. And monitoring line operation information, equipment operation information and line passing region weather information corresponding to each power supply network in real time, judging whether each power supply network has operation risk at each monitoring moment according to the line operation information, if so, marking the power supply network as a risk power supply network, marking the monitoring moment as a risk moment, and identifying the operation risk direction of the risk power supply network at the risk moment.
C. The outage demand index of the risk power supply network at the risk moment is predicted based on the running risk direction of the risk power supply network at the risk moment.
D. And evaluating the allowable outage index of the risk power supply network at the risk moment based on the risk moment corresponding to the risk power supply network and the power consumption information.
E. Judging whether the risk power supply network needs power-off processing based on the power-off demand index and the allowable power-off index of the risk power supply network at the risk moment, and analyzing a non-power-off processing mode corresponding to the risk power supply network when judging that the risk power supply network does not need power-off processing.
F. Uploading the power-off processing judgment result and the non-power-off processing mode corresponding to the risk power supply network to the corresponding power supply management and control end.
In an optional manner, the electricity consumption region electricity consumption period includes an electricity consumption region peak electricity consumption period, an electricity consumption region time period and an electricity consumption region valley electricity consumption period, wherein the acquisition process corresponding to the main body load level corresponding to each electricity consumption period is as follows: and counting the number of the power equipment existing in the power utilization area, and identifying the load level of each power equipment.
Setting a monitoring time period, and counting the power equipment in an operation state in each power utilization period in each monitoring day corresponding to the monitoring time period as the operation power equipment corresponding to each power utilization period.
Comparing the operation power equipment of each monitoring day in the same power utilization period, and further de-duplicating the same operation power equipment to obtain a plurality of operation power equipment corresponding to each power utilization period.
And acquiring the load levels of the operation power equipment corresponding to each power utilization period based on the load levels of the power equipment, arranging the load levels of the operation power equipment corresponding to each power utilization period according to the order of importance from high to low, and taking the load level arranged at the first position as the main load level corresponding to each power utilization period.
In an alternative manner, the line operation information includes a line load rate, a line overvoltage rate and a line power supply fluctuation degree, the device operation information includes operation indicators of devices on the line, and the weather information includes a weather type and a weather expression degree value.
In an optional manner, the specific dividing manner of the dividing of the monitoring time is as follows: setting a monitoring starting time, and dividing the monitoring starting time within one day according to set time intervals to obtain a plurality of monitoring times.
In an optional manner, the step of evaluating whether the operation risk exists in each power supply network at each monitoring moment specifically includes the following steps: and extracting the line limiting operation information and the normal operation indication of each device of the line corresponding to each power supply network in the target area from the power supply management library.
Comparing the line operation information of each power supply network at each monitoring moment with the line limit operation information corresponding to each power supply network, and calculating the line operation limit index of each power supply network at each monitoring momentThe expression isIn the formula->、/>、/>Respectively representing the line load rate, the line overvoltage rate and the line power supply fluctuation degree of the ith power supply network at the t monitoring moment, wherein i represents the number of the power supply network and +.>T is denoted as the number of monitoring instants, +.>，/>、、/>The load rate, overvoltage rate and power supply fluctuation degree of the limiting line corresponding to the ith power supply network are respectively expressed, and e is expressed as a natural constant.
Comparing the equipment operation information of each power supply network at each monitoring moment with the normal operation index of each equipment of the corresponding line of each power supply network, and calculating the equipment operation abnormality index of each power supply network at each monitoring moment, wherein the expression is as followsIn the formula->Denoted as the operational indication of the kth device of the ith supply network at the tth monitoring instant, k denoted as device number,/->，/>Indicated as normal operation indication of the kth device on the corresponding line of the ith power supply network.
Extracting weather types from weather information, and screening weather influence factors of each power supply network at each power supply moment from a power supply management library。
Extracting weather expression degree values from the weather information, and combining weather influence factors of all power supply networks at all monitoring moments with the weather expression degree values to obtain an expressionObtainingWeather severity index for each power supply network at each monitoring point>，/>And the weather performance degree value of the ith power supply network at the t monitoring moment is expressed.
Comparing the line operation limit index, the equipment operation abnormality index and the weather severity index of each power supply network at each monitoring moment with the allowable line operation limit index, the allowable equipment operation abnormality index and the allowable weather severity index corresponding to each power supply network in the power supply management library, and judging that the power supply network has no operation risk if the line operation limit index, the equipment operation abnormality index and the weather severity index of a certain power supply network at a certain monitoring moment are smaller than or equal to the allowable line operation limit index, the allowable equipment operation abnormality index and the allowable weather severity index corresponding to the power supply network, otherwise judging that the power supply network has operation risk.
In an optional mode, the specific identification mode of the running risk direction of the risk power supply network at the risk moment is to import the line running limit index, the equipment running abnormality index and the weather severity index of the risk power supply network at the risk moment into an identification modelObtaining the running risk direction of the risk power supply network at the risk moment>，/>、/>、/>Respectively expressed as line operation limitation index, equipment operation abnormality index and weather severity index of a risk power supply network at risk moment, +>、/>、/>The system is respectively expressed as an allowable line operation overrun index, an allowable equipment operation abnormality index and an allowable weather severity index corresponding to a risk power supply network.
In an alternative way, the outage demand index of the predicted risk power supply network at the risk moment is referred to the following procedure: matching the running risk directions of the risk power supply network at the risk moment with the power-off influence duty factors corresponding to the various running risk directions stored in the power supply management library, and matching the power-off influence duty factors of the risk power supply network at the risk moment。
Based on the running risk direction of the risk power supply network at the risk moment, the running risk index corresponding to the risk power supply network is screened from the line running limit index, the equipment running abnormality index and the weather severity index of the risk power supply network at the risk moment。
Using predictive expressionsObtaining a power-off demand index of a risk power supply network at risk moment>，/>The running risk points to the corresponding allowed running risk index, which is indicated as the running risk to which the risk power supply network belongs.
In an alternative way, the evaluation of the permissible outage fingers of the risk power supply network at the moment of riskSee the following steps: extracting the area of the power utilization area from the power utilization information corresponding to the risk power supply network, and calculating the area of the power utilization area by combining the area of the power utilization area with the area of the target area to obtain the ratio of the power utilization area corresponding to the risk power supply network。
And extracting the electricity consumption time period of the electricity consumption region from the electricity consumption information corresponding to the risk power supply network, further matching the risk time corresponding to the risk power supply network with the electricity consumption time period of the electricity consumption region corresponding to the risk power supply network, and obtaining the electricity consumption time period of the electricity consumption region of the risk time corresponding to the risk power supply network from the matching, and recording the electricity consumption time period as a specific electricity consumption time period.
Matching a specific power consumption period corresponding to the risk power supply network with power interruption hazard factors corresponding to various power consumption periods stored in a power supply management library, and matching the power interruption hazard factors of the risk power supply network at risk moments。
And extracting the main body load grade of the risk power supply network in the specific power consumption period from the power consumption information based on the specific power consumption period corresponding to the risk power supply network.
Matching the main body load grade of the risk power supply network in a specific power utilization period with the required power supply reliability corresponding to various load grades in a power supply management library, and matching the required power supply reliability of the risk power supply network at the risk moment。
Will be、/>And->Inlet expression->Calculating the demand power supply continuous index of the risk power supply network at the risk moment>U is expressed as a preset constant, and U>1。
And comparing the required power supply duration index of the risk power supply network at the risk moment with the allowable power outage indexes corresponding to the various required power supply duration indexes stored in the power supply management library, and obtaining the allowable power outage index of the risk power supply network at the risk moment.
In an optional mode, the implementation mode of evaluating whether the risk power supply network needs to be powered off is that the power-off demand index of the risk power supply network at the risk moment is compared with the allowed power-off index, if the power-off demand index is greater than the allowed power-off index, the risk power supply network is evaluated to need to be powered off, and otherwise, the risk power supply network is evaluated to not need to be powered off.
In an optional manner, the corresponding uninterruptible power processing manner of the analytic risk power supply network is referred to as the following process: guiding operation risk direction of risk power supply network at risk moment into analysis algorithmAnd obtaining an uninterruptible power treatment mode R corresponding to the risk power supply network.
Compared with the prior art, the invention has the following beneficial effects: (1) According to the invention, when the power grid operation risk is identified, the risk judgment and identification are carried out according to the monitoring results by starting from the three dimensions of the power supply line operation state, the equipment operation state and the weather state of the area where the line passes, the comprehensive identification of the power grid operation risk is realized by expanding the coverage range of the power grid operation risk identification, the defect that the power grid risk identification is limited in the prior art is effectively avoided, the timeliness and the accuracy of the power grid operation risk identification are improved to a certain extent, and reliable reference can be provided for the power grid operation risk management and control processing.
(2) According to the invention, the power consumption end information is obtained when the power grid operation risk management and control is processed, so that the power consumption end is analyzed for the power supply sustainability requirement, the identified operation risk is fused with the power consumption end for the power supply sustainability requirement, and the power grid risk management and control is comprehensively carried out, so that the power grid risk management and control is more scientific and reasonable, the adaptation degree of the management and control result and the power consumption end is greatly improved, the management and control effect is better, the occurrence rate of ineffective management and control is greatly reduced, the operation safety of electric equipment is ensured to a certain extent, and the loss caused to the power consumption end due to trade outage is reduced.
(3) According to the invention, after the management and control processing is performed based on the identified power grid operation risk and the requirements of the power utilization end on the power supply continuity, the analysis of the uninterruptible processing mode is increased when the management and control processing result is uninterruptible processing, so that targeted and reliable guarantee measures can be provided for the power grid operation safety of uninterruptible processing, the power grid operation risk can be reduced as much as possible in the uninterruptible state, and the continuous operation of the power grid can be maintained.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is 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.
FIG. 1 is a schematic diagram of the method steps of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a power grid risk intelligent identification management and control method based on big data, which comprises the following steps: A. and counting the number of the power supply networks in the target area, and acquiring power utilization information and a power supply management and control end corresponding to each power supply network, wherein the power utilization information comprises a power utilization area, a power utilization period of the power utilization area and a main body load grade corresponding to each power utilization period.
In further embodiments, the power usage region power usage period includes a power usage region peak power usage period, a power usage region normal power usage period, and a power usage region valley power usage period.
It should be noted that, the above-mentioned dividing of the electricity consumption period of the electricity consumption area is to divide 24H corresponding to one day, and an exemplary electricity consumption period of the electricity consumption area peak is 9:00-17:00, an electricity consumption period of the electricity consumption area at ordinary times is 17:00-22:00, and an electricity consumption period of the electricity consumption area valley is 22:00-8:00.
The acquisition process corresponding to the main body load level corresponding to each electricity utilization period is as follows: and counting the number of the power equipment in the power utilization area, and identifying the load grade of each power equipment, wherein the specific identification mode is to match the names of each power equipment in the power utilization area with a plurality of power equipment names corresponding to various load grades in a power supply management library, so as to identify the load grade of each power equipment.
It should be noted that the load level is a level of the electric load classified according to the requirement for the reliability of power supply and the degree of loss or influence caused by politics and economy by interrupting power supply, and may be divided into a primary load, a secondary load, and a tertiary load, wherein the primary load has an importance greater than the secondary load, and the secondary load has an importance greater than the tertiary load.
Setting a monitoring time period, and counting the power equipment in an operation state in each power utilization period in each monitoring day corresponding to the monitoring time period as the operation power equipment corresponding to each power utilization period.
Comparing the operation power equipment of each monitoring day in the same power utilization period, and further de-duplicating the same operation power equipment to obtain a plurality of operation power equipment corresponding to each power utilization period.
And acquiring the load levels of the operation power equipment corresponding to each power utilization period based on the load levels of the power equipment, arranging the load levels of the operation power equipment corresponding to each power utilization period according to the order of importance from high to low, and taking the load level arranged at the first position as the main load level corresponding to each power utilization period.
B. The method comprises the steps of monitoring line operation information, equipment operation information and line passing region weather information corresponding to each power supply network in real time, wherein the line operation information comprises line load rate, line overvoltage rate and line power supply fluctuation degree, the equipment operation information comprises operation instructions of each equipment on a line, the weather information comprises weather type and weather expression degree values, whether each power supply network has operation risks at each monitoring moment is judged according to the weather information, if the operation risks of a certain power supply network exist at a certain monitoring moment, the power supply network is judged to be a risk power supply network, the monitoring moment is recorded as risk moment, and meanwhile the operation risk direction of the risk power supply network at the risk moment is recognized.
In a specific embodiment of the present invention, the specific dividing manner of the monitoring time is: setting a monitoring starting time, and dividing the monitoring starting time within one day according to set time intervals to obtain a plurality of monitoring times.
As an example of the above-described scheme, assuming that the start monitoring time is 7:00, the set time interval is 10 minutes, the divided monitoring times in this case are 7:10 to 7:20, 7:20 to 7:30, 7:30 to 7:40.
Further, the circuit load rate of the circuit operation information is obtained by obtaining the actual load of the circuit by using the formulaThe line overvoltage rate is obtained by obtaining the actual operating voltage of the line and using the formula +.>The acquisition mode of the line power supply fluctuation degree is to acquire line power quality indexes, specifically including frequency deviation, voltage deviation and power grid harmonic ratio, and the expression is utilized
Still further, devices present on the line include, but are not limited to, circuit breakers, transformers.
Still further, the weather types include rainy days, wind days, sunny days, snowy days, etc., the weather expression level value is the expression level under the corresponding weather type, and when the weather type is rainy days, the weather expression level value isWhen the weather type is wind, the weather expression level value is +.>When the weather type is sunny, the weather expression level value isWhen the weather type is snowy, the weather expression level value is +.>。
The weather information is obtained from a weather center in a region where the route passes.
In the above technical solution, the judging whether the operation risk exists in each power supply network at each monitoring time specifically includes the following steps: and extracting the line limiting operation information and the normal operation indication of each device of the line corresponding to each power supply network in the target area from the power supply management library.
Comparing the line operation information of each power supply network at each monitoring moment with the line limit operation information corresponding to each power supply network, and calculating the line operation limit index of each power supply network at each monitoring momentThe expression isIn the formula->、/>、/>Respectively representing the line load rate, the line overvoltage rate and the line power supply fluctuation degree of the ith power supply network at the t monitoring moment, wherein i represents the number of the power supply network and +.>T is denoted as the number of monitoring instants, +.>，/>、、/>The load rate, overvoltage rate and power supply fluctuation degree of the limiting line corresponding to the ith power supply network are respectively expressed, and e is expressed as a natural constant.
Comparing the equipment operation information of each power supply network at each monitoring moment with the normal operation index of each equipment of the corresponding line of each power supply network, and calculating the equipment operation abnormality index of each power supply network at each monitoring moment, wherein the expression is as followsIn the formula->Denoted as the operational indication of the kth device of the ith supply network at the tth monitoring instant, k denoted as device number,/->，/>Indicated as normal operation indication of the kth device on the corresponding line of the ith power supply network.
Extracting weather types from weather information, and screening weather influence factors of each power supply network at each power supply moment from a power supply management libraryWherein the worse the weather type, the greater the weather influencing factor.
Extracting weather expression degree values from the weather information, and combining weather influence factors of all power supply networks at all monitoring moments with the weather expression degree values to obtain an expressionObtaining weather severity index of each power supply network at each monitoring moment>，/>And the weather performance degree value of the ith power supply network at the t monitoring moment is expressed.
Comparing the line operation limit index, the equipment operation abnormality index and the weather severity index of each power supply network at each monitoring moment with the allowable line operation limit index, the allowable equipment operation abnormality index and the allowable weather severity index corresponding to each power supply network in the power supply management library, and judging that the power supply network has no operation risk if the line operation limit index, the equipment operation abnormality index and the weather severity index of a certain power supply network at a certain monitoring moment are smaller than or equal to the allowable line operation limit index, the allowable equipment operation abnormality index and the allowable weather severity index corresponding to the power supply network, otherwise judging that the power supply network has operation risk.
On the basis of the scheme, the specific identification mode of the running risk direction of the risk power supply network at the risk moment is that the line running limit index, the equipment running abnormality index and the weather severity index of the risk power supply network at the risk moment are imported into an identification modelObtaining the operation risk direction of the risk power supply network at the risk moment，/>、/>、/>Respectively expressed as line operation limitation index, equipment operation abnormality index and weather severity index of a risk power supply network at risk moment, +>、/>、/>The system is respectively expressed as an allowable line operation overrun index, an allowable equipment operation abnormality index and an allowable weather severity index corresponding to a risk power supply network.
According to the invention, when the power grid operation risk is identified, the risk judgment and identification are carried out according to the monitoring results by starting from the three dimensions of the power supply line operation state, the equipment operation state and the weather state of the area where the line passes, the comprehensive identification of the power grid operation risk is realized by expanding the coverage range of the power grid operation risk identification, the defect that the power grid risk identification is limited in the prior art is effectively avoided, the timeliness and the accuracy of the power grid operation risk identification are improved to a certain extent, and reliable reference can be provided for the power grid operation risk management and control processing.
C. Based on the running risk direction of the risk power supply network at the risk moment, predicting the outage demand index of the risk power supply network at the risk moment, wherein the specific prediction process is as follows: matching the running risk directions of the risk power supply network at the risk moment with the power-off influence duty ratio factors corresponding to the various running risk directions stored in the power supply management library, and matching the power-off influence duty ratio factors of the risk power supply network at the risk momentRatio factor。
The values of the corresponding outage influence factors of various operation risk orientations are between 0 and 1.
Based on the running risk direction of the risk power supply network at the risk moment, the running risk index corresponding to the risk power supply network is screened from the line running limit index, the equipment running abnormality index and the weather severity index of the risk power supply network at the risk moment。
Using predictive expressionsObtaining the power-off demand index of a risk power supply network at risk moment，/>The running risk points to the corresponding allowed running risk index, which is indicated as the running risk to which the risk power supply network belongs.
Specifically, when the operation risk direction of the risk power supply network at the risk moment is the line operation overrun, the operation risk index corresponding to the risk power supply network is the line operation overrun index, the allowable operation risk index is the allowable line operation overrun index, when the operation risk direction of the risk power supply network at the risk moment is the equipment operation anomaly index, the operation risk index corresponding to the risk power supply network is the equipment operation anomaly index, the allowable operation risk index is the allowable equipment operation anomaly index, and when the operation risk direction of the risk power supply network at the risk moment is the weather severity index, the operation risk index corresponding to the risk power supply network is the weather severity index, and the allowable operation risk index is the allowable weather severity index.
D. Evaluating the allowable outage index of the risk power supply network at the risk moment based on the risk moment corresponding to the risk power supply network and the power consumption information, wherein the allowable outage index comprises the following steps: from the power utilization information corresponding to the risk power supply networkExtracting the area of the electricity utilization area, dividing the area by the area of the target area, and calculating to obtain the corresponding electricity utilization area occupation ratio of the risk power supply network。
And extracting the electricity consumption time period of the electricity consumption region from the electricity consumption information corresponding to the risk power supply network, further matching the risk time corresponding to the risk power supply network with the electricity consumption time period of the electricity consumption region corresponding to the risk power supply network, and obtaining the electricity consumption time period of the electricity consumption region of the risk time corresponding to the risk power supply network from the matching, and recording the electricity consumption time period as a specific electricity consumption time period.
Matching a specific power consumption period corresponding to the risk power supply network with power interruption hazard factors corresponding to various power consumption periods stored in a power supply management library, and matching the power interruption hazard factors of the risk power supply network at risk moments。
And extracting the main body load grade of the risk power supply network in the specific power consumption period from the power consumption information based on the specific power consumption period corresponding to the risk power supply network.
Matching the main body load grade of the risk power supply network in a specific power utilization period with the required power supply reliability corresponding to various load grades in a power supply management library, and matching the required power supply reliability of the risk power supply network at the risk moment。
Will be、/>And->Inlet expression->Calculating the risk moment of the risk power supply networkDemand power supply duration index->U is expressed as a preset constant, and U>1。
And comparing the required power supply duration index of the risk power supply network at the risk moment with the allowable power outage indexes corresponding to the various required power supply duration indexes stored in the power supply management library, and obtaining the allowable power outage index of the risk power supply network at the risk moment.
E. Judging whether the risk power supply network needs power-off processing based on the power-off demand index and the allowable power-off index of the risk power supply network at the risk moment, and analyzing a non-power-off processing mode corresponding to the risk power supply network when judging that the risk power supply network does not need power-off processing.
In the above scheme, the implementation mode of judging whether the risk power supply network needs to be powered off is that the power-off demand index of the risk power supply network at the risk moment is compared with the allowable power-off index, if the power-off demand index is larger than the allowable power-off index, the risk power supply network is judged to need to be powered off, otherwise, the risk power supply network is judged not to need to be powered off.
According to the invention, the power consumption end information is obtained when the power grid operation risk management and control is processed, so that the power consumption end is analyzed for the power supply sustainability requirement, the identified operation risk is fused with the power consumption end for the power supply sustainability requirement, and the power grid risk management and control is comprehensively carried out, so that the power grid risk management and control is more scientific and reasonable, the adaptation degree of the management and control result and the power consumption end is greatly improved, the management and control effect is better, the occurrence rate of ineffective management and control is greatly reduced, the operation safety of electric equipment is ensured to a certain extent, and the loss caused to the power consumption end due to trade outage is reduced.
Preferably, the corresponding uninterruptible power supply processing mode of the analytic risk power supply network is as follows:
guiding operation risk direction of risk power supply network at risk moment into analysis algorithmObtaining the corresponding risk power supply networkUnpowered processing mode R.
According to the invention, after the management and control processing is performed based on the identified power grid operation risk and the requirements of the power utilization end on the power supply continuity, the analysis of the uninterruptible processing mode is increased when the management and control processing result is uninterruptible processing, so that targeted and reliable guarantee measures can be provided for the power grid operation safety of uninterruptible processing, the power grid operation risk can be reduced as much as possible in the uninterruptible state, and the continuous operation of the power grid can be maintained.
F. Uploading the power-off processing judgment result and the non-power-off processing mode corresponding to the risk power supply network to the corresponding power supply management and control end.
The invention also uses a power supply management library in the implementation process, and is used for storing the allowable line operation limit index, the allowable equipment operation abnormality index and the allowable weather severity index corresponding to each power supply network, storing the line rated load, the line rated operation voltage and the normal frequency deviation, the normal voltage deviation and the normal power grid harmonic rate corresponding to each power supply network, storing the influence factors corresponding to various weather types, storing the line limited operation information and the normal operation index of each equipment of each power supply network, storing the power-off influence proportion factors corresponding to various operation risk directions, storing the power interruption hazard factors corresponding to various power consumption time periods, storing the required power supply reliability corresponding to various load levels, storing a plurality of power equipment names corresponding to various load levels and storing the allowable power-off index corresponding to various required power supply duration indexes.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (6)
1. The intelligent power grid risk identification management and control method based on big data is characterized by comprising the following steps of:
A. counting the number of power supply networks existing in a target area, and acquiring power utilization information and a power supply management and control end corresponding to each power supply network, wherein the power utilization information comprises a power utilization area, a power utilization period of the power utilization area and a main body load grade corresponding to each power utilization period;
B. the method comprises the steps of monitoring line operation information, equipment operation information and line passing region weather information corresponding to each power supply network in real time, judging whether each power supply network has operation risk at each monitoring moment according to the line operation information, the equipment operation information and the line passing region weather information, if so, marking the power supply network as a risk power supply network, marking the monitoring moment as a risk moment, and identifying the operation risk direction of the risk power supply network at the risk moment;
the step of evaluating whether the operation risk exists in each power supply network at each monitoring moment specifically comprises the following steps:
extracting line limiting operation information and normal operation instructions of all equipment of the line corresponding to all power supply networks in a target area from a power supply management library;
comparing the line operation information of each power supply network at each monitoring moment with the line limit operation information corresponding to each power supply network, and calculating the line operation limit index of each power supply network at each monitoring momentThe expression isIn the formula->、/>、Respectively representing the line load rate, the line overvoltage rate and the line power supply fluctuation degree of the ith power supply network at the t monitoring moment, wherein i represents the number of the power supply network and +.>T is denoted as the number of monitoring instants, +.>，/>、/>、/>The load rate, the overvoltage rate and the power supply fluctuation degree of the limiting line corresponding to the ith power supply network are respectively expressed, and e is expressed as a natural constant;
comparing the equipment operation information of each power supply network at each monitoring moment with the normal operation index of each equipment of the corresponding line of each power supply network, and calculating the equipment operation abnormality index of each power supply network at each monitoring moment, wherein the expression is as followsIn the formula->Denoted as the operational indication of the kth device of the ith supply network at the tth monitoring instant, k denoted as device number,/->，/>The normal operation indication of the kth equipment on the corresponding line of the ith power supply network is shown;
extracting weather types from weather information, and screening weather influence factors of each power supply network at each power supply moment from a power supply management library；
Extracting from weather informationTaking a weather expression degree value, and combining weather influence factors of all power supply networks at all monitoring moments with the weather expression degree value to pass through an expressionObtaining weather severity index of each power supply network at each monitoring moment>，/>The weather expression degree value of the ith power supply network at the t monitoring moment is expressed;
comparing the line operation limit index, the equipment operation abnormality index and the weather severity index of each power supply network at each monitoring moment with the allowable line operation limit index, the allowable equipment operation abnormality index and the allowable weather severity index corresponding to each power supply network in the power supply management library, and judging that the power supply network has no operation risk if the line operation limit index, the equipment operation abnormality index and the weather severity index of a certain power supply network at a certain monitoring moment are all smaller than or equal to the allowable line operation limit index, the allowable equipment operation abnormality index and the allowable weather severity index corresponding to the power supply network, otherwise judging that the power supply network has operation risk;
the specific identification mode of the running risk direction of the risk power supply network at the risk moment is that the line running limit index, the equipment running abnormality index and the weather severity index of the risk power supply network at the risk moment are imported into an identification modelObtaining the operation risk direction of the risk power supply network at the risk moment，/>、/>、/>Respectively expressed as line operation limitation index, equipment operation abnormality index and weather severity index of a risk power supply network at risk moment, +>、/>、/>The allowable line operation limit index, the allowable equipment operation abnormality index and the allowable weather severity index corresponding to the risk power supply network are respectively expressed;
predicting a power-off demand index of the risk power supply network at the risk moment based on the running risk direction of the risk power supply network at the risk moment;
the outage demand index of the predicted risk power supply network at the risk moment is shown in the following process:
matching the running risk directions of the risk power supply network at the risk moment with the power-off influence duty factors corresponding to the various running risk directions stored in the power supply management library, and matching the power-off influence duty factors of the risk power supply network at the risk moment；
Based on the running risk direction of the risk power supply network at the risk moment, the running risk index corresponding to the risk power supply network is screened from the line running limit index, the equipment running abnormality index and the weather severity index of the risk power supply network at the risk moment；
Using predictive expressionsObtaining a power-off demand index of a risk power supply network at risk moment>，Representing that the running risk of the risk power supply network points to a corresponding allowed running risk index;
evaluating an allowable outage index of the risk power supply network at the risk moment based on the risk moment corresponding to the risk power supply network and the power consumption information;
the evaluation of the allowable outage index of the risk power supply network at the risk moment is carried out by the following steps:
extracting the area of the power utilization area from the power utilization information corresponding to the risk power supply network, and calculating the area of the power utilization area by combining the area of the power utilization area with the area of the target area to obtain the ratio of the power utilization area corresponding to the risk power supply network；
Extracting power consumption time periods of power consumption areas from power consumption information corresponding to a risk power supply network, matching risk moments corresponding to the risk power supply network with the power consumption time periods of the power consumption areas corresponding to the risk power supply network, and recording the power consumption time periods of the power consumption areas, to which the risk moments corresponding to the risk power supply network belong, as specific power consumption time periods;
matching a specific power consumption period corresponding to the risk power supply network with power interruption hazard factors corresponding to various power consumption periods stored in a power supply management library, and matching the power interruption hazard factors of the risk power supply network at risk moments；
Extracting the main body load level of the risk power supply network in the specific power consumption period from the power consumption information based on the specific power consumption period corresponding to the risk power supply network;
body load class of risk power supply network in specific power utilization periodMatching the required power supply reliability corresponding to various load levels in the power supply management library, and matching the required power supply reliability of the risk power supply network at risk moment；
Will be、/>And->Inlet expression->Calculating the demand power supply continuous index of the risk power supply network at the risk moment>U is expressed as a preset constant, and U>1；
Comparing the required power supply continuous index of the risk power supply network at the risk moment with the allowable power outage indexes corresponding to the various required power supply continuous indexes stored in the power supply management library, and acquiring the allowable power outage index of the risk power supply network at the risk moment;
E. judging whether the risk power supply network needs power-off processing based on the power-off demand index and the allowable power-off index of the risk power supply network at the risk moment, and analyzing a non-power-off processing mode corresponding to the risk power supply network when judging that the risk power supply network does not need power-off processing;
F. uploading the power-off processing judgment result and the non-power-off processing mode corresponding to the risk power supply network to the corresponding power supply management and control end.
2. The intelligent power grid risk identification and management method based on big data as set forth in claim 1, wherein the method comprises the following steps: the electricity consumption period of the electricity consumption area comprises a peak electricity consumption period of the electricity consumption area, a normal electricity consumption period of the electricity consumption area and a valley electricity consumption period of the electricity consumption area, wherein the acquisition process corresponding to the main body load grade corresponding to each electricity consumption period is as follows:
counting the number of the power equipment existing in the power utilization area, and identifying the load level of each power equipment;
setting a monitoring time period, and counting the power equipment in an operation state in each power utilization period in each monitoring day corresponding to the monitoring time period as operation power equipment corresponding to each power utilization period;
comparing the operation power equipment of each monitoring day in the same power utilization period, and further de-duplicating the same operation power equipment to obtain a plurality of operation power equipment corresponding to each power utilization period;
and acquiring the load levels of the operation power equipment corresponding to each power utilization period based on the load levels of the power equipment, arranging the load levels of the operation power equipment corresponding to each power utilization period according to the order of importance from high to low, and taking the load level arranged at the first position as the main load level corresponding to each power utilization period.
3. The intelligent power grid risk identification and management method based on big data as set forth in claim 1, wherein the method comprises the following steps: the line operation information comprises line load rate, line overvoltage rate and line power supply fluctuation degree, the equipment operation information comprises operation instructions of all equipment on the line, and the weather information comprises weather type and weather expression degree values.
4. The intelligent power grid risk identification and management method based on big data as set forth in claim 1, wherein the method comprises the following steps: the specific dividing mode of the dividing of the monitoring time is as follows: setting a monitoring starting time, and dividing the monitoring starting time within one day according to set time intervals to obtain a plurality of monitoring times.
5. The intelligent power grid risk identification and management method based on big data as set forth in claim 1, wherein the method comprises the following steps: the implementation mode of judging whether the risk power supply network needs power-off processing is that the power-off demand index of the risk power supply network at the risk moment is compared with the allowable power-off index, if the power-off demand index is larger than the allowable power-off index, the risk power supply network is judged to need power-off processing, and otherwise, the risk power supply network is judged not to need power-off processing.
6. The intelligent power grid risk identification and management method based on big data as set forth in claim 1, wherein the method comprises the following steps: the corresponding uninterruptible power treatment mode of the analytic risk power supply network is as follows:
guiding operation risk direction of risk power supply network at risk moment into analysis algorithmAnd obtaining an uninterruptible power treatment mode R corresponding to the risk power supply network.
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