CN115224684A - Intelligent power distribution network risk state identification method and system based on immune hazard theory - Google Patents
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Abstract
The invention discloses an immune hazard theory-based intelligent power distribution network risk state identification method and system, wherein a risk signal and an antigen identification signal in risk assessment are defined based on the immune hazard theory, a power distribution network risk state identification mechanism is established, a risk index is selected and a quantitative scheme design is carried out from the two aspects of the fault probability and the fault consequence severity of equipment, and the real-time operation risk degree of a power distribution network is comprehensively evaluated; the method for identifying the risk state can be used for online safety monitoring of the power distribution network and guiding a prevention and control reconstruction strategy in the risk state, and can also be used for adjusting the short-term operation planning of the power distribution network according to historical data stored by an information physical platform and a safe and reliable operation mode of an offline computing system.
Description
The technical field is as follows:
the invention relates to the technical field of power distribution network self-healing control and power system automation, in particular to a method and a system for identifying a risk state of an intelligent power distribution network based on an immune hazard theory.
Background art:
the power distribution system power failure accident has an evolution development process of state deterioration and risk increase, and the probability and loss of the power failure accident can be effectively reduced by adopting targeted control measures. Risk assessment is taken as a prerequisite basis of preventive control measures and has important research significance.
The risk operation of the power distribution network is specifically characterized in that the probability of failure occurrence is high and the caused consequences are serious in the current system operation mode. The evaluation to equipment running state is also in order to obtain equipment failure probability, is convenient for in time carry out maintenance, but, every time maintain to equipment and overhaul just need influence the power supply of system, influence user's power consumption and experience, moreover, sometimes running state between the equipment is different, when can have maintenance part equipment unavoidably, the condition that some equipment broke down takes place in addition, causes the continuity of maintenance, increases the degree that influences user's power consumption.
The invention content is as follows:
the technical problem to be solved by the invention is as follows: the intelligent power distribution network risk state identification method based on the immune hazard theory overcomes the defects of the prior art, has higher functional similarity in the antigen identification process in the power distribution network risk identification and evaluation and immunological theory, establishes a power distribution network real-time operation risk evaluation system based on the immune hazard theory, selects risk indexes and carries out quantitative scheme design from the two aspects of the failure probability and the failure consequence severity of equipment, and comprehensively evaluates the real-time operation risk degree of the power distribution network.
The technical scheme of the invention is as follows: an intelligent power distribution network risk state identification method based on immune hazard theory comprises the following steps:
step one, acquiring the fault probability of equipment in a power distribution network according to the information of the equipment, taking the fault probability exceeding a threshold value as a dangerous signal, and marking corresponding equipment as fault equipment;
step two, acquiring a fault consequence of the fault equipment, and taking the fault consequence and the fault probability as risk indexes to synthesize a risk value of the equipment, wherein the equipment with a higher risk value considers that the operation reliability is lower, and sends out a 'danger signal' early warning;
step three, synthesizing the risk values of all the devices to obtain a real-time operation risk value of the system, and sending an antigen identification signal when the real-time operation risk value of the system exceeds a safety threshold value;
and step four, when the danger signal and the antigen recognition signal are provided at the same time, the risk prevention and control function of the power distribution network is started, and a prevention control strategy is generated while alarming.
Further, in the first step, the fault probability includes a fault probability value under the influence of a historical record, a fault probability value under the influence of an out-of-limit electric quantity of the equipment and a fault probability value under the influence of severe weather;
the failure probability value under the influence of the historical records is obtained in the following mode: collecting the previous defects and fault conditions of each part of the distribution network equipment in the maintenance process, calculating part state evaluation scores according to the defect overhaul conditions, and synthesizing equipment state evaluation scores;
the method for obtaining the fault probability value under the influence of the out-of-limit electrical quantity of the equipment comprises the following steps: collecting power flow data of a power distribution network, and calculating an equipment overload fault risk value and an equipment voltage out-of-limit fault risk value;
the failure probability value under the influence of severe weather is obtained in the following mode: and obtaining the fault rate of each meteorological factor under different meteorological grades according to fault data provided by a power supply bureau and the weather data of a meteorological department.
Further, in the second step, the fault consequences include a load power loss rate calculated according to economic losses caused by power outage of the users and a user number loss rate calculated according to a weight coefficient of a power user load grade.
Further, in the third step, the fault probability and the fault consequence are numerically synthesized by using a product form, so as to obtain a real-time operation risk value of the system.
The utility model provides an intelligent power distribution network risk state identification system based on immune danger theory, characterized by: the risk state identification module is included: the risk is divided into a danger signal and an antigen recognition signal by constructing a power distribution network risk state identification mechanism based on an immune risk theory, and the severity of distribution network risk operation is comprehensively judged under the synergistic stimulation effect of the danger signal and the antigen recognition signal;
the equipment operation reliability diagnosis module: quantifying the risk running state of the power distribution network equipment, obtaining equipment fault probability, and taking the equipment fault probability exceeding a threshold value as a danger signal;
the equipment failure consequence evaluation module: after equipment fails, quantitatively analyzing the failure consequence and severity of the equipment according to two conditions of load power loss rate and power consumption user number loss rate;
the overall risk value calculation module of the power distribution network: the equipment is taken as a basic unit, the fault probability and the fault consequence are synthesized into a risk value of the equipment, and the equipment with higher risk value sends out a 'danger signal' early warning; and synthesizing the risk values of the devices to obtain a real-time operation risk value of the system, and sending an antigen recognition signal when the real-time operation risk value of the system exceeds a safety threshold value.
Further, the risk operation state of the power distribution network equipment is quantified as a fault probability value under the influence of historical records, a fault probability value under the influence of out-of-limit electric quantity of the equipment and a fault probability value under the influence of severe weather.
Further, the load power loss rate is calculated according to economic loss caused by power failure of the users, and the power user number loss rate is calculated according to the weight coefficient of the power user load grade.
Further, the fault probability and the fault consequence are numerically synthesized by using a product form to obtain a real-time operation risk value of the system.
The beneficial effects of the invention are:
the method is based on the immune hazard theory, establishes a power distribution network risk state identification mechanism, divides risks into 'danger signals' and 'antigen recognition signals', and comprehensively evaluates the real-time operation risk degree of the power distribution network under the synergistic stimulation effect of the danger signals and the antigen recognition signals. The 'danger signal' refers to a failure probability risk value of equipment under risk disturbance, and comprises four aspects of historical record, operation condition, severe weather (typhoon and ice disaster) and construction influence. The 'antigen recognition signal' is added with equipment failure consequence severity evaluation on the basis of the former, the evaluation comprises two aspects of economic loss and household loss of load power loss, the real-time risk condition of the power distribution network is evaluated by comprehensively considering the failure probability and the failure consequence, the risk prevention and control system can be used for online safety monitoring of the power distribution network and guiding a prevention and control reconstruction strategy in a risk state, and can also be used as an offline computing platform, and the offline computing system is matched with and adjusts the short-term operation planning of the power distribution network according to the influences of historical data, construction plans and the like stored by an information physical platform.
Description of the drawings:
fig. 1 is a schematic diagram of a power distribution network risk state identification mechanism.
Fig. 2 is an IEEE 33 node power distribution network model.
The specific implementation mode is as follows:
example (b): see fig. 1 and 2.
The risk state identification method and system of the intelligent power distribution network based on the immune hazard theory define a hazard signal and an antigen identification signal in risk assessment based on the immune hazard theory, establish a power distribution network risk state identification mechanism, select a risk index and carry out quantitative scheme design from the two aspects of the fault probability and the fault consequence severity of equipment, and comprehensively evaluate the real-time operation risk degree of the power distribution network. The method can be used for online safety monitoring of the power distribution network and guidance of a prevention and control reconstruction strategy in a risk state, and can also be used for matching adjustment of short-term operation planning of the power distribution network according to historical data stored by an information physical platform and a safe and reliable operation mode of an offline computing system.
The present application will be described in detail below with reference to the drawings and examples.
Intelligent distribution network risk state identification system based on immune danger theory includes risk state identification module: the risk is divided into a danger signal and an antigen recognition signal by constructing a power distribution network risk state identification mechanism based on an immune risk theory, and the severity of distribution network risk operation is comprehensively judged under the synergistic stimulation effect of the danger signal and the antigen recognition signal;
the equipment operation reliability diagnosis module: quantifying the risk running state of the power distribution network equipment, obtaining the equipment fault probability, and taking the equipment fault probability exceeding a threshold value as a dangerous signal;
the equipment failure consequence evaluation module: after equipment fails, quantitatively analyzing the failure consequence and severity of the equipment according to two conditions of load power loss rate and power consumption user number loss rate;
the overall risk value calculation module of the power distribution network: the equipment is taken as a basic unit, the fault probability and the fault consequence are synthesized into a risk value of the equipment, and the equipment with higher risk value sends out a 'danger signal' early warning; and synthesizing the risk values of all the devices to obtain a real-time operation risk value of the system, and sending an antigen recognition signal when the real-time operation risk value of the system exceeds a safety threshold value.
The modules are matched with each other, and the method for identifying the risk state of the intelligent power distribution network comprises the following steps:
firstly, by taking the immune hazard theory as reference, main equipment in the power distribution network is regarded as organism cells, and signals sent by the equipment in the risk assessment process of the power distribution network are divided into two types, namely a hazard signal (sig 1) and an antigen recognition signal (sig 2).
Wherein the danger signal (sig 1): the intelligent sensor based on the physical power distribution information platform feeds back the operation situation of n monitoring points in the system in real time, and when the main equipment (organism cells) of the immune system of the power distribution network reacts and is reduced in operation reliability, the equipment is considered to send out a danger signal. In the analysis process, risk source data of n monitoring points are extracted firstly and a set X = { X } is formed 1 ,x 2 ,...,x n }. Processing risk source data by using a risk evaluation system to obtain a function mapping Y = { f (x) of equipment reliability 1 ),f(x 2 ),...,f(x n )}。
Definition of danger signal:
wherein, antigen recognition signal (sig 2): the damage degree of cells in the power distribution immune system cannot represent the overall risk of the system, so that the position of equipment in the network topology layer is combined as the severity degree of the fault consequence of the equipment, and the overall risk value of the system is obtained. When the overall risk value of the system exceeds a risk threshold value, the antigen is considered to have a large-scale accident power loss risk to the immune system of the power distribution network, and an antigen recognition signal is sent out.
Definition of antigen recognition signal:
and applying a danger theory in the power distribution network immune system to coordinate the relationship between the individual damage degree and the overall risk value of the system. The starting of immune response requires that a danger signal and an antigen recognition signal exist at the same time, and when individual equipment sends the danger signal and the system does not send the antigen recognition signal, the prevention and control measures of the power distribution network cannot be started, so that the power distribution network system has certain immune tolerance capability, the prevention and control strategy is prevented from being started frequently, and the running economy of the system is reduced; and for the conditions that the reliability of the equipment is low and the consequences caused by the failure are serious, the risk value of the system exceeds the set safety threshold value, and an antigen recognition signal is sent out. The risk prevention and control strategy of the power distribution network can be started in time by the coordination and matching of the antigen recognition signal and the danger signal, and the accident risk is reduced.
Step one, acquiring the fault probability of equipment in a power distribution network according to the information of the equipment, taking the fault probability exceeding a threshold value as a dangerous signal, and marking the corresponding equipment as fault equipment; the fault probability comprises a fault probability value under the influence of historical records, a fault probability value under the influence of equipment electrical quantity out-of-limit and a fault probability value under the influence of severe weather.
The obtaining mode of the fault probability value under the influence of the historical records is as follows: and collecting the defects and fault conditions of each part of the distribution network equipment in the maintenance process, calculating the state evaluation score of the part according to the defect overhauling condition, and synthesizing the state evaluation score of the equipment.
The method specifically comprises the following steps: the distribution network equipment can accumulate operation information in the maintenance process, wherein the operation information comprises the previous defect and the fault condition, and the historical defect records have corresponding defect level L test Or repair grade M test . Referring to 'Q/GDW 645-2011-distribution network equipment state evaluation guide rule' of national grid company, for single part of equipment, fault/defect levels and corresponding deduction values S are set D Repair grade and corresponding repair factor F D 。
After the defects are found, the inspection or defect elimination is not carried out, and the calculation formula of the state evaluation scores of all the components is as follows:
S i ＝(100-S D )×K n (3)
after maintenance or defect elimination, the calculation formula of the state evaluation score of each component is as follows:
S i ＝(100-S D ×F D )×K n (4)
in the formula, K is a defect coefficient, which indicates the degree of deterioration of the device state after a failure or defect occurs, and is generally 0.95; n is the number of times that the same type of fault or defect occurs in the same component of the same equipment. When the device is replaced with a new one, the recording evaluation is performed again.
For the equipment, if the health state score of a part is less than 85 points, the total score of the equipment is based on the score of the part; otherwise, the total score of the equipment is obtained by weighting the component score according to the weight of each component specified by the existing state evaluation guide rule. The calculation formula of the equipment state evaluation score S is as follows:
and scoring the state of the equipment according to the historical records, and carrying out a reliability diagnosis formula on the commissioned equipment under the influence of the historical records:
the method for obtaining the fault probability value under the influence of the out-of-limit electrical quantity of the equipment comprises the following steps: and collecting power flow data of the power distribution network, and calculating an equipment overload fault risk value and an equipment voltage out-of-limit fault risk value.
The method comprises the following specific steps: defining a device overload fault risk value P e,I The probability of equipment failure due to overload is characterized. When the device current is less than or equal to a certain proportion a of the rated current (which may be set according to the evaluation target, typically taking a = 0.8), the probability of causing the device fault is 0; as the current flowing through the device increases, the probability of device failure increases and the rate of increase becomes faster. Risk value P of equipment overload fault e,I Calculating the formula:
in the formula of lambda I For the current out-of-limit risk probability coefficient, L is the proportion of the current flowing through the device to its rated current.
Defining a device voltage out-of-limit fault risk value P e,V And characterizing the probability of equipment failure caused by voltage line crossing. Setting the severity function to be 0 when the voltage is 1.0 p.u; as the voltage threshold increases, the severity of the node voltage threshold risk also increases. Device voltage out-of-limit fault risk value P e,V Calculating the formula:
P e,V ＝λ V (e |1-V| -1) (8)
in the formula, λ V V is the node voltage amplitude.
Assuming that the probabilities of faults caused by power out-of-limit and voltage out-of-line are mutually independent, the reliability diagnosis formula of the operated equipment under the influence of the operation condition is as follows:
P e ＝1-(1-P e,I )(1-P e,V ) (9)。
the failure probability value under the influence of severe weather is obtained in the following mode: and obtaining the fault rate of each meteorological factor under different meteorological grades according to fault data provided by a power supply bureau and the weather data of a meteorological department.
The method specifically comprises the following steps: considering eight meteorological factors (thunder, icing, rainfall, wind, typhoon, hail, snow and air temperature) causing line faults, establishing fault rate models of the single meteorological factors under different meteorological levels:
in the formula:
the fault rate of the line of the ith meteorological factor under the meteorological parameter level xi is a function of the meteorological parameter level xi;
the number of times of line faults of the ith meteorological factor under the meteorological level xi;
n =8, which is the total number of occurrences of the weather grade xi under the ith weather factor.
Risk value due to bad weather
And step two, acquiring the fault consequence of the fault equipment, taking the fault consequence and the fault probability as risk indexes, synthesizing to obtain a risk value of the equipment, considering that the equipment with higher risk value has lower operation reliability, and sending out a 'danger signal' early warning.
The fault consequences comprise a load power loss rate calculated according to economic losses caused by power failure of users and a power user number loss rate calculated according to a weight coefficient of a power user load grade.
The method specifically comprises the following steps: load loss power loss rate: and representing the proportion of the load loss of the downstream of the feeder line where the shutdown equipment is located. Considering that the economic losses brought to different power consumers by fault power failure are different, the economic losses caused by user power failure are introduced, and the load power loss rate is defined as follows:
in the formula, RF is the load loss rate caused by equipment failure; si is the ith load capacity of the downstream of the equipment; ci is the power failure economic loss of the ith downstream user; m is the total downstream load; sj is the jth load capacity in the power distribution system; cj is the power failure economic loss of the jth user in the system; and N is the total load number of the power distribution system.
The method specifically comprises the following steps: loss rate of number of users: and characterizing the loss of the number of power consumers caused by the equipment failure and the corresponding load grade weight. Introducing a weight coefficient of a power consumer load grade, defining a power consumer number loss rate RU:
in the formula, gamma i is the weight coefficient of the ith user with power loss caused by equipment quit; gamma j is the load weight coefficient of the jth user of the feeder line section where the equipment is located; m is the total number of downstream loads; and N is the total number of loads on the feeder where the equipment is located.
Specifically, the method comprises the following steps: risk value of the device: the equipment failure probability indexes mainly include operation conditions, historical records and severe weather. Assuming that four risk factors causing equipment failure are independent of each other, a calculation formula of a comprehensive risk value of equipment operation can be obtained:
P F,i ＝1-(1-P h,i )(1-P e,i )(1-P w,i ) (13)
in the formula, PF, i is a failure probability risk value of the equipment i; ph and i are fault probability values under the influence of historical records; pe, i is the fault probability value under the out-of-limit influence of the electrical quantity of the equipment; pw, i is the probability value of the fault under the influence of severe weather.
The equipment fault consequence influence indexes comprise load loss rate and user number loss rate after fault, and the fault consequence risk value of the equipment is obtained by weighting:
C F,i ＝β 1 R F,i +β 2 R U,i (14)
in the formula, CF, i is a fault consequence risk value of the equipment i; RF, i is the load power loss rate of the equipment i; RU, i is the loss rate of the number of users after the equipment i fails; beta is the weight of the index and can be weighted by an analytic hierarchy process.
And evaluating the operation reliability of the equipment and the influence of the fault consequence thereof, and defining the product of the operation reliability and the fault consequence as the overall risk value of the equipment. When the equipment has low operation reliability and serious fault consequences, the risk value of the equipment is increased remarkably, and the calculation formula is as follows:
VAR i ＝P F,i ×C F,i (15)
in the formula, VARi is the risk value of the power distribution equipment i; PF, i is a failure probability risk value of the equipment i; CF, i is the risk value of the fault consequence of the equipment i.
And step three, synthesizing the risk values of the devices to obtain a real-time operation risk value of the system, and sending an antigen recognition signal when the real-time operation risk value of the system exceeds a safety threshold value.
Specifically, the method comprises the following steps: and (3) synthesizing all the distribution equipment risk conditions in the distribution network system to obtain the real-time operation risk value of the distribution system:
in the formula, VAR is a real-time operation risk value of the power distribution system; q is the total number of devices in the system.
And step four, when the danger signal and the antigen recognition signal are provided at the same time, the risk prevention and control function of the power distribution network is started, and a prevention control strategy is generated while alarming.
The present application is now verified with reference to the figures.
The invention will now be described by way of example with reference to the IEEE 33 node power distribution network model shown in figure 2. The system has 37 branches, 5 interconnection switches and 33 load nodes, and the loads are divided into four types of residential areas, commercial areas, industrial areas and agricultural areas. According to the early warning information of the weather forecast center, the city is about to face typhoon weather and affects the equipment of the industrial area in the map.
The equipment danger signal safety threshold value of the IEEE 33 node model is set to be 0.1, and the antigen recognition signal safety threshold value of the system is set to be 1 (namely, about 1/3 of equipment is in a risk operation state). And quantitatively analyzing the operation reliability and the fault consequence influence degree of each device in the simulation model to obtain that the risk value of the current system is 1.391. Table 1 lists the risk data for a typical device.
TABLE 1 Risk calculation results for typical devices
Analyzing the above risk quantification data, the following results can be obtained:
(1) The operation reliability of the devices 9-17 is obviously reduced under the influence of severe weather (typhoon), and in addition, the devices are in an industrial area, so that once the load is lost, the economic loss caused by power failure is high, and the failure result is serious. Due to the fact that the important loads of the low-reliability equipment are large, the real-time risk values of the equipment exceed the safety threshold value, and a danger signal is sent out.
(2) Under the current operation mode, the electrical quantity of the equipment 27-31 is more obvious in out-of-limit, but because the equipment does not have bad historical records, namely the physical state is good, the equipment is considered to have certain tolerance capacity to the out-of-limit operation condition of the electrical quantity, the risk value of the equipment does not exceed the safety threshold value of the danger signal, and the danger signal is not sent out.
(3) And (3) comprehensively calculating the risk operation conditions of all equipment in the system to obtain that the risk value of the system is 1.391, and when the risk value exceeds the set safety threshold value of the antigen identification signal, the system sends the antigen identification signal and waits for the risk prevention and control strategy to be started.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent variations and modifications made to the above embodiment according to the technical spirit of the present invention still fall within the scope of the technical solution of the present invention.
Claims (8)
1. An intelligent power distribution network risk state identification method based on immune hazard theory comprises the following steps:
step one, acquiring the fault probability of equipment in a power distribution network according to the information of the equipment, taking the fault probability exceeding a threshold value as a dangerous signal, and marking the corresponding equipment as fault equipment;
step two, acquiring a fault consequence of the fault equipment, and taking the fault consequence and the fault probability as risk indexes to synthesize a risk value of the equipment, wherein the equipment with the risk value exceeding a threshold value considers that the operation reliability is low, and sends out a 'danger signal' early warning;
step three, synthesizing the risk values of all the devices to obtain a real-time operation risk value of the system, and sending an antigen identification signal when the real-time operation risk value of the system exceeds a safety threshold value;
and step four, when the danger signal and the antigen recognition signal are provided at the same time, the risk prevention and control function of the power distribution network is started, and a prevention control strategy is generated while alarming.
2. The method for identifying the risk state of the intelligent power distribution network based on the immune hazard theory as claimed in claim 1, wherein: in the first step, the fault probability comprises a fault probability value under the influence of historical records, a fault probability value under the influence of out-of-limit equipment electrical quantity and a fault probability value under the influence of severe weather;
the failure probability value under the influence of the historical records is obtained in the following mode: collecting the previous defects and fault conditions of each part of the distribution network equipment in the maintenance process, calculating part state evaluation scores according to the defect overhaul conditions, and synthesizing equipment state evaluation scores;
the method for obtaining the fault probability value under the influence of the out-of-limit electrical quantity of the equipment comprises the following steps: collecting power flow data of a power distribution network, and calculating an equipment overload fault risk value and an equipment voltage out-of-limit fault risk value;
the failure probability value under the influence of severe weather is obtained in the following mode: and obtaining the fault rate of each meteorological factor under different meteorological grades according to fault data provided by a power supply bureau and the weather data of a meteorological department.
3. The method for identifying the risk state of the intelligent distribution network based on the immune hazard theory as claimed in claim 1, wherein the method comprises the following steps: in the second step, the fault consequences comprise a load power loss rate calculated according to economic loss caused by power failure of users and a user number loss rate calculated according to a weight coefficient of a power user load grade.
4. The method for identifying the risk state of the intelligent power distribution network based on the immune hazard theory as claimed in claim 1, wherein: and in the third step, numerically synthesizing the fault probability and the fault consequence by using a product form to obtain a real-time operation risk value of the system.
5. The utility model provides an intelligent power distribution network risk state identification system based on immune danger theory, characterized by: the risk state identification module is included: the risk is divided into a danger signal and an antigen recognition signal by constructing a power distribution network risk state identification mechanism based on an immune risk theory, and the severity of distribution network risk operation is comprehensively judged under the synergistic stimulation effect of the danger signal and the antigen recognition signal;
the equipment operation reliability diagnosis module: quantifying the risk running state of the power distribution network equipment, obtaining the equipment fault probability, and taking the equipment fault probability exceeding a threshold value as a dangerous signal;
the equipment fault consequence evaluation module: after equipment fails, quantitatively analyzing the failure consequence and severity of the equipment according to two conditions of load power loss rate and power consumption user number loss rate;
the overall risk value calculation module of the power distribution network: the equipment is taken as a basic unit, the fault probability and the fault consequence are synthesized into a risk value of the equipment, and the equipment with higher risk value sends out a 'danger signal' early warning; and synthesizing the risk values of the devices to obtain a real-time operation risk value of the system, and sending an antigen recognition signal when the real-time operation risk value of the system exceeds a safety threshold value.
6. The immune hazard theory-based intelligent power distribution network risk state identification system according to claim 5, wherein: and quantifying the risk running state of the power distribution network equipment, wherein the risk running state is a fault probability value under the influence of a historical record, a fault probability value under the influence of an out-of-limit electric quantity of the equipment and a fault probability value under the influence of severe weather.
7. The immune hazard theory-based intelligent power distribution network risk state identification system according to claim 5, wherein: and calculating the loss rate of the power loss of the load according to the economic loss caused by the power failure of the user, and calculating the loss rate of the number of the users according to the weight coefficient of the load grade of the power user.
8. The immune hazard theory-based intelligent power distribution network risk state identification system according to claim 5, wherein: and numerically synthesizing the fault probability and the fault consequence by using a product form to obtain a real-time operation risk value of the system.
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