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CN119231768A - A kind of intelligent monitoring system and method of power grid data based on Internet of Things - Google Patents

A kind of intelligent monitoring system and method of power grid data based on Internet of Things Download PDF

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Publication number
CN119231768A
CN119231768A CN202411765995.0A CN202411765995A CN119231768A CN 119231768 A CN119231768 A CN 119231768A CN 202411765995 A CN202411765995 A CN 202411765995A CN 119231768 A CN119231768 A CN 119231768A
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equipment
operation parameter
fault
power
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汪彦
何源
刘恢
曹阳
黄斌
孙瑶
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Jiangsu Zhirong Energy Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/56—Testing of electric apparatus
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/30—Control
    • G16Y40/35—Management of things, i.e. controlling in accordance with a policy or in order to achieve specified objectives
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

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Abstract

本发明公开了一种基于物联网的电网数据智能化监管系统及方法，涉及信息管理技术领域，建立电力设备运行参数项集合，将目标故障发生前，电力设备的某一种运行状态作为目标状态，计算目标故障的故障参考值，获取两个运行参数项不同的电力设备，分别记为第一目标设备和第二目标设备，计算各运行参数项占目标故障的历史记录中的权重，计算各运行参数项的加权故障率，当第三目标设备处于目标状态时，获取相同运行参数项历史记录的相似程度，通过将第三目标设备的运行参数项与第一目标设备和第二目标设备的运行参数项进行比较，得到第三目标设备分别与第一目标设备和第二目标设备的关联程度，建立第三目标设备的状态评估模型。

The present invention discloses an intelligent monitoring system and method for power grid data based on the Internet of Things, which relates to the field of information management technology. A set of operating parameter items of power equipment is established, a certain operating state of the power equipment before a target fault occurs is used as a target state, a fault reference value of the target fault is calculated, two power equipment with different operating parameter items are obtained, which are respectively recorded as a first target device and a second target device, the weight of each operating parameter item in the historical record of the target fault is calculated, the weighted failure rate of each operating parameter item is calculated, and when a third target device is in the target state, the similarity of the historical records of the same operating parameter item is obtained, and the operating parameter items of the third target device are compared with the operating parameter items of the first target device and the second target device to obtain the association degree between the third target device and the first target device and the second target device, and a state evaluation model for the third target device is established.

Description

Intelligent power grid data supervision system and method based on Internet of things
Technical Field
The invention relates to the technical field of information management, in particular to an intelligent power grid data supervision system and method based on the Internet of things.
Background
As the power demand increases, the power grid is in a continuous development and capacity expansion stage, and the increasing power grid scale has a significant impact on fault detection of power equipment in the power grid system. In order to relieve the working pressure of power grid maintenance, in the prior art, power equipment is connected into an Internet of things system of power grid equipment, and the power equipment is remotely managed through a computer network.
In the prior art, a method of collecting fault records of a power grid system, extracting operation characteristics of power equipment related to faults and carrying out early warning on related faults is generally adopted, and a management method of one-to-one correspondence of fault data and the fault characteristics is often adopted in an existing power equipment management system.
Disclosure of Invention
The invention aims to provide an intelligent power grid data supervision system and method based on the Internet of things, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme that the intelligent monitoring method for the power grid data based on the Internet of things comprises the following steps:
Step S100, in a power system formed by a plurality of power equipment, establishing a power equipment operation parameter item set, setting a certain fault in the power system as a target fault, taking a certain operation state of the power equipment as a target state before the target fault occurs, and calculating a fault reference value of the target fault according to a history record of the operation state;
step 200, acquiring two pieces of electric power equipment with different operation parameter items from an electric power system, respectively marking the two pieces of electric power equipment as first target equipment and second target equipment, calculating weights of the operation parameter items in a history record of target faults in the first target equipment and the second target equipment, and calculating weighted fault rates of the operation parameter items;
Step S300, acquiring a third target device in the power system, wherein the third target device, the first target device and the second target device have at least one same operation parameter item, and when the third target device is detected to be in a target state, the similarity degree of the history records of the same operation parameter item between the third target device and the first target device and between the third target device and the second target device is acquired;
and step S400, comparing the operation parameter items of the third target equipment with the operation parameter items of the first target equipment and the second target equipment to obtain the association degree of the third target equipment with the first target equipment and the second target equipment respectively, establishing a state evaluation model of the third target equipment, and comparing the calculation result of the state evaluation model with the weighted failure rate of the operation parameter items to generate alarm information.
Further, step S100 includes:
Step S101, acquiring operation parameters of each power device in a power system, and collecting the operation parameters of the power devices into a set W, W (W 1,w2,w3,……,wp), wherein the set W is an operation parameter item set of the power devices, W 1,w2,w3, and W p respectively represent operation parameters of the 1 st, 2 nd, 3 rd, and p-th power devices, and any one of the power devices at least comprises device operation parameters in one operation parameter item set;
Step S102, acquiring running state records of each power equipment in a time period with a time length T1 before occurrence of a target fault from a management log of the power equipment, and setting a certain abnormal state of the power equipment as a target state;
the target state represents the macroscopically changed running state of the power equipment, the state is easy to detect, but the risk in the power system cannot be accurately pre-warned, and the condition that a certain equipment is in the target state is influenced by the running state of the equipment and is also influenced by the external interference, so that after the power equipment is detected to be in the target state, the power system can possibly generate faults or can not generate faults;
step S103, acquiring two pieces of electric equipment in an electric power system, namely a first target equipment and a second target equipment, wherein the first target equipment and the second target equipment comprise different operation parameter items;
Step S104, obtaining m 1 historical records from the running state record of the first target equipment, wherein n 1 historical records of the target faults of the power system occur in a T1 time period after the target state of the first target equipment occurs in m 1 historical records;
Acquiring m 2 historical records from the running state record of the second target equipment, wherein n 2 historical records of the target faults of the power system occur in the time period T1 after the first target equipment is in the target state in the m 2 historical records;
Step S105, calculating a first fault reference value C 1,C1=n1/m1, calculating a second fault reference value C 2,C2=n2/m2, and calculating a comprehensive fault reference value C 0,C0=（n1+n2）/（m1+m2).
Further, step S200 includes:
Step S201, acquiring operation parameter items of first target equipment, collecting the operation parameter items of the first target equipment into a first equipment item set, wherein the first equipment item set is marked as D EV1, acquiring operation parameter items of second target equipment, collecting the operation parameter items of the second target equipment into a second equipment item set, and the second equipment item set is marked as D EV2;
Step S202, acquiring total times of operation parameter items in a first equipment item set and a second equipment item set, namely g 0, acquiring times of occurrence of a kth operation parameter item in the first equipment item set, namely g 1k, and times of occurrence in the second equipment item set, namely g 2k;
Step S203, calculating the weight gamma k,γk=（g1k+g2k）/g0 of the kth operation parameter item, and calculating the weighted failure rate R k,Rk=γk×C0 of the kth operation parameter item;
The weighted failure rate of the operation parameter item indicates that, for the degree of association between the same type of failure of the power grid and the operation parameter item of the power equipment, when the number of occurrences of a certain operation parameter item in the failure record becomes large, the weight of the operation parameter item becomes high, and when the power equipment includes the operation parameter item, the probability of occurrence of the failure increases.
Further, step S300 includes:
Step 301, monitoring the state of a third target device, when the third target device is in a target running state, recording a time point T0, setting a time period with a termination time of T0 and a time length of T2 as a target time period, wherein T2 is more than or equal to T1;
Step S302, acquiring operation parameter items of third target equipment, collecting the operation parameter items to a third equipment item set, wherein the third equipment item set is marked as D EV3, acquiring a first comparison set R EP1 and a second comparison set R EP2,REP1=DEV1∩DEV3,REP2=DEV2∩DEV3, taking the operation parameter items in the first comparison set as first comparison parameter items, and taking the operation parameter items in the second comparison set as second comparison parameter items;
Step S303, acquiring a history record of a first comparison parameter item of first target equipment in a target time period, drawing a function image of the change of the numerical value of the first comparison parameter item along with time, recording the function image as a first change function, acquiring a history record of a second comparison parameter item of second target equipment in the target time period, and drawing a function image of the change of the numerical value of the second comparison parameter item along with time, recording the function image as a second change function;
Acquiring a history record of a first comparison parameter item of a third target device in a target time period, drawing a function image of the change of the numerical value of the first comparison parameter item along with time, recording as a third change function, acquiring the history record of the first comparison parameter item of the first target device in the target time period, drawing a function image of the change of the numerical value of the second comparison parameter item along with time, and recording as a fourth change function;
step S304, obtaining the similarity of the first change function and the third change function as alpha, and obtaining the similarity of the second change function and the fourth change function as beta.
Further, step S400 includes:
Step S401, calculating a fault risk value of a target running state, obtaining a feature set of a third target equipment running parameter item, and D EVf=REP1∪REP2, wherein D EVf represents the feature set, obtaining weighted fault rates of various running parameter items in the feature set, obtaining the sum of weighted fault rates in the feature set, marking the sum as H, and taking the H as the fault risk value of the target running state;
step S402, calculating the membership degree F 1,F1=Nr1/Nv3 of the third target device relative to the first target device, wherein Nr1 represents the number of operation parameter items in the first comparison set, N v3 represents the number of operation parameter items in the third device item set, and calculating the membership degree F 2,F2=Nr2/Nv3 of the third device relative to the second target device, wherein N r3 represents the number of operation parameter items in the second comparison set;
Step S403 of establishing a state evaluation model Q of the third target device with respect to the target operation state,
;
AndThe two parameters are used for comparing the association degree of the third target equipment with the first target equipment and the second target, wherein the association degree is the association degree between each running state of the running data, so that the state of the third target equipment can be better presumed, the main reference equipment and the auxiliary reference equipment of the third target equipment are distinguished through the two parameters, and the reference degree of the corresponding power equipment is obtained;
Step S404, the parameters are brought into a state evaluation model, state evaluation parameters q of the third target device are calculated, and when q > H, risk warning is carried out to relevant management personnel.
In order to better realize the method, the system also provides a power grid data intelligent supervision system of the power grid data intelligent supervision method based on the Internet of things, and the system comprises a fault record management module, a weighted fault rate calculation module, a device comparison module and a state evaluation module, wherein the fault record management module is used for acquiring a historical operation record of the power equipment, calculating a fault reference value of a target fault, the weighted fault rate calculation module is used for calculating a weighted fault rate of each operation parameter item, the device comparison module is used for comparing the association degree of the third target equipment with the first target equipment and the second target equipment, and the state evaluation module is used for carrying out state evaluation on the third target equipment.
Further, the fault record management module comprises a power equipment management unit, a target state management unit, a history record management unit and a fault reference value calculation unit, wherein the power equipment management unit is used for acquiring operation parameters of the power equipment, collecting and obtaining a power equipment operation parameter item set, the target state management unit is used for acquiring a target state of the power equipment, the history record management unit is used for acquiring a history operation record of the power equipment, and the fault reference value calculation unit is used for calculating a first fault reference value, a second fault reference value and a comprehensive fault reference value.
The weighted failure rate calculation module further comprises an operation parameter item management unit, a parameter item set management unit and a weight calculation unit, wherein the operation parameter item management unit is used for managing operation parameter items of the first target device and the second target device, the parameter item set management unit is used for managing device item sets corresponding to the first target device and the second target device, and the weight calculation unit is used for calculating the weight of the operation parameter item and the weighted failure rate of the operation parameter item.
The device comparison module further comprises a device detection unit, a comparison parameter acquisition unit and a parameter comparison unit, wherein the device detection unit is used for carrying out state monitoring on the third target device, the comparison parameter acquisition unit is used for acquiring a first comparison parameter item and a second comparison parameter item, and the parameter comparison unit is used for comparing the similarity of the third target device and the operation parameters of the first target device and the second target device.
Further, the state evaluation module comprises a risk management unit, a membership degree calculation unit, an evaluation model management unit and an information reminding unit, wherein the risk management unit is used for obtaining a fault risk value, the membership degree calculation unit is used for calculating membership degrees of the third target equipment, the first target equipment and the second target equipment, the evaluation model management unit is used for managing a state evaluation model of the third target equipment, the information reminding unit is used for calculating state evaluation parameters of the third target equipment, and when an alarm condition is met, risk warning is carried out on related management personnel.
Compared with the prior art, the method has the beneficial effects that the method constructs the relation between the operation parameters of the power equipment, the equipment states and the power grid faults through the fault records of the existing power equipment, compares the types of the power equipment, and under the condition of lacking the abnormal records of the equipment, presumes the possible risks in the power grid system by detecting the same abnormal states. The risk of the power grid is warned under the condition that the running record of the power equipment is not updated timely, and the data acquisition quantity of the power equipment comparison data in the construction of the power system running management system is reduced.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic structural diagram of an intelligent monitoring system for power grid data based on the internet of things;
fig. 2 is a flow chart of an intelligent monitoring method for power grid data based on the internet of things.
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 and 2, the present invention provides the following technical solutions:
Step S100, in a power system formed by a plurality of power equipment, establishing a power equipment operation parameter item set, setting a certain fault in the power system as a target fault, taking a certain operation state of the power equipment as a target state before the target fault occurs, and calculating a fault reference value of the target fault according to a history record of the operation state;
Wherein, step S100 includes:
Step S101, acquiring operation parameters of each power device in a power system, and collecting the operation parameters of the power devices into a set W, W (W 1,w2,w3,……,wp), wherein the set W is an operation parameter item set of the power devices, W 1,w2,w3, and W p respectively represent operation parameters of the 1 st, 2 nd, 3 rd, and p-th power devices, and any one of the power devices at least comprises device operation parameters in one operation parameter item set;
Step S102, acquiring running state records of each power equipment in a time period with a time length T1 before occurrence of a target fault from a management log of the power equipment, and setting a certain abnormal state of the power equipment as a target state;
target states that may be employed are, for example, the temperature of the electrical device, vibration of the electrical device, and humidity in the electrical device environment;
step S103, acquiring two pieces of electric equipment in an electric power system, namely a first target equipment and a second target equipment, wherein the first target equipment and the second target equipment comprise different operation parameter items;
Step S104, obtaining m 1 historical records from the running state record of the first target equipment, wherein n 1 historical records of the target faults of the power system occur in a T1 time period after the target state of the first target equipment occurs in m 1 historical records;
Acquiring m 2 historical records from the running state record of the second target equipment, wherein n 2 historical records of the target faults of the power system occur in the time period T1 after the first target equipment is in the target state in the m 2 historical records;
Step S105, calculating a first fault reference value C 1,C1=n1/m1, calculating a second fault reference value C 2,C2=n2/m2 and calculating a comprehensive fault reference value C 0,C0=（n1+n2）/（m1+m2);
step 200, acquiring two pieces of electric power equipment with different operation parameter items from an electric power system, respectively marking the two pieces of electric power equipment as first target equipment and second target equipment, calculating weights of the operation parameter items in a history record of target faults in the first target equipment and the second target equipment, and calculating weighted fault rates of the operation parameter items;
Wherein, step S200 includes:
Step S201, acquiring operation parameter items of first target equipment, collecting the operation parameter items of the first target equipment into a first equipment item set, wherein the first equipment item set is marked as D EV1, acquiring operation parameter items of second target equipment, collecting the operation parameter items of the second target equipment into a second equipment item set, and the second equipment item set is marked as D EV2;
Step S202, acquiring total times of operation parameter items in a first equipment item set and a second equipment item set, namely g 0, acquiring times of occurrence of a kth operation parameter item in the first equipment item set, namely g 1k, and times of occurrence in the second equipment item set, namely g 2k;
Step S203, calculating the weight gamma k,γk=（g1k+g2k）/g0 of the kth operation parameter item, and calculating the weighted failure rate R k,Rk=γk×C0 of the kth operation parameter item.
Step S300, acquiring a third target device in the power system, wherein the third target device, the first target device and the second target device have at least one same operation parameter item, and when the third target device is detected to be in a target state, the similarity degree of the history records of the same operation parameter item between the third target device and the first target device and between the third target device and the second target device is acquired;
wherein, step S300 includes:
Step 301, monitoring the state of a third target device, when the third target device is in a target running state, recording a time point T0, setting a time period with a termination time of T0 and a time length of T2 as a target time period, wherein T2 is more than or equal to T1;
Step S302, acquiring operation parameter items of third target equipment, collecting the operation parameter items to a third equipment item set, wherein the third equipment item set is marked as D EV3, acquiring a first comparison set R EP1 and a second comparison set R EP2,REP1=DEV1∩DEV3,REP2=DEV2∩DEV3, taking the operation parameter items in the first comparison set as first comparison parameter items, and taking the operation parameter items in the second comparison set as second comparison parameter items;
Step S303, acquiring a history record of a first comparison parameter item of first target equipment in a target time period, drawing a function image of the change of the numerical value of the first comparison parameter item along with time, recording the function image as a first change function, acquiring a history record of a second comparison parameter item of second target equipment in the target time period, and drawing a function image of the change of the numerical value of the second comparison parameter item along with time, recording the function image as a second change function;
Acquiring a history record of a first comparison parameter item of a third target device in a target time period, drawing a function image of the change of the numerical value of the first comparison parameter item along with time, recording as a third change function, acquiring the history record of the first comparison parameter item of the first target device in the target time period, drawing a function image of the change of the numerical value of the second comparison parameter item along with time, and recording as a fourth change function;
Step S304, obtaining the similarity of the first change function and the third change function as alpha, and obtaining the similarity of the second change function and the fourth change function as beta;
The method for comparing the similarity of the functions comprises the steps of comparing the similarity of the images of the functions, collecting data on the images of the functions, calculating the data distance between the data, carrying out spectrum analysis on the functions, and comparing the similarity of the spectrums.
Step S400, comparing the operation parameter items of the third target equipment with the operation parameter items of the first target equipment and the second target equipment to obtain the association degree of the third target equipment with the first target equipment and the second target equipment respectively, establishing a state evaluation model of the third target equipment, and comparing the calculation result of the state evaluation model with the weighted failure rate of the operation parameter items to generate alarm information;
Wherein, step S400 includes:
Step S401, calculating a fault risk value of a target running state, obtaining a feature set of a third target equipment running parameter item, and D EVf=REP1∪REP2, wherein D EVf represents the feature set, obtaining weighted fault rates of various running parameter items in the feature set, obtaining the sum of weighted fault rates in the feature set, marking the sum as H, and taking the H as the fault risk value of the target running state;
step S402, calculating the membership degree F 1,F1=Nr1/Nv3 of the third target device relative to the first target device, wherein Nr1 represents the number of operation parameter items in the first comparison set, N v3 represents the number of operation parameter items in the third device item set, and calculating the membership degree F 2,F2=Nr2/Nv3 of the third device relative to the second target device, wherein N r3 represents the number of operation parameter items in the second comparison set;
Step S403 of establishing a state evaluation model Q of the third target device with respect to the target operation state,
,
Step S404, the parameters are brought into a state evaluation model, state evaluation parameters q of the third target device are calculated, and when q > H, risk warning is carried out to relevant management personnel.
In an embodiment:
the first target equipment comprises an operation parameter 1, an operation parameter 2, an operation parameter 3, an operation parameter 4 and an operation parameter 5;
the second target equipment comprises an operation parameter 2, an operation parameter 3, an operation parameter 4, an operation parameter 6 and an operation parameter 7;
The third target equipment comprises an operation parameter 2, an operation parameter 3, an operation parameter 5, an operation parameter 6 and an operation parameter 8;
Collecting 4 pieces of historical data of the target state of the first target equipment, wherein 1 piece of target faults are generated in the power grid system, and calculating a first fault reference value C 1 =1/4=0.25;
Collecting 10 pieces of historical data of the target state of the second target equipment, wherein 3 pieces of target faults are generated in the power grid system, and calculating a second fault reference value C 2 =3/10=0.3;
Calculate the integrated fault reference value C 0 = (1+3)/(4+10) = 0.2857;
Acquiring a first comparison set R EP1 = (operation parameter 2, operation parameter 3 and operation parameter 5) by referring to operation parameter items included in the third target device, and acquiring a second comparison set R EP2 = (operation parameter 2, operation parameter 3 and operation parameter 6) and a feature set D EVf = (operation parameter 2, operation parameter 3, operation parameter 5 and operation parameter 6);
Calculating a weighted failure rate for the operating parameter items in feature set D EVf:
operating parameters 2:0.2857 x 2 +.10= 0.05714, operating parameters 3:0.2857 x 2 +.10= 0.05714,
Operating parameters 5:0.28572 x 1 ≡10= 0.02857, operating parameters 6:0.28572 x 1 ≡10= 0.02857;
Fault risk value h= 0.17142;
In an embodiment, membership F 1= F2 = 3/5;
Obtaining the similarity of the first group of parameter items, wherein alpha 1=0.4,β1 =0.3;
Calculating a state evaluation parameter value q 1=0.1628,q1 < H, and not giving an alarm at the moment;
Obtaining the similarity of the second group of parameter items, wherein alpha 2=0.2,β1 =0.6;
The state evaluation parameter value q 2=0.1725,q2 > H is calculated, at which point an alarm is raised.
The system comprises:
the system comprises a fault record management module, a weighted fault rate calculation module, a device comparison module and a state evaluation module;
The fault record management module is used for acquiring a historical operation record of the power equipment and calculating a fault reference value of a target fault, wherein the fault record management module comprises a power equipment management unit, a target state management unit, a historical record management unit and a fault reference value calculation unit, wherein the power equipment management unit is used for acquiring operation parameters of the power equipment and collecting to obtain a power equipment operation parameter item set, the target state management unit is used for acquiring a target state of the power equipment, the historical record management unit is used for acquiring the historical operation record of the power equipment, and the fault reference value calculation unit is used for calculating a first fault reference value, a second fault reference value and a comprehensive fault reference value;
the weighted failure rate calculation module is used for calculating the weighted failure rate of each operation parameter item, wherein the weighted failure rate calculation module comprises an operation parameter item management unit, a parameter item set management unit and a weight calculation unit, wherein the operation parameter item management unit is used for managing operation parameter items of first target equipment and second target equipment, the parameter item set management unit is used for managing equipment item sets corresponding to the first target equipment and the second target equipment, and the weight calculation unit is used for calculating the weight of the operation parameter item and the weighted failure rate of the operation parameter item;
The device comparison module is used for comparing the association degree of the third target device with the first target device and the second target device, and comprises a device detection unit, a comparison parameter acquisition unit and a parameter comparison unit, wherein the device detection unit is used for carrying out state monitoring on the third target device, the comparison parameter acquisition unit is used for acquiring a first comparison parameter item and a second comparison parameter item, and the parameter comparison unit is used for comparing the similarity of the third target device and the operation parameters of the first target device and the second target device;
The state evaluation module is used for performing state evaluation on the third target equipment, and comprises a risk management unit, a membership degree calculation unit, an evaluation model management unit and an information reminding unit, wherein the risk management unit is used for acquiring fault risk values, the membership degree calculation unit is used for calculating membership degrees of the third target equipment, the first target equipment and the second target equipment, the evaluation model management unit is used for managing a state evaluation model of the third target equipment, the information reminding unit is used for calculating state evaluation parameters of the third target equipment, and when an alarm condition is met, risk warning is performed on related management staff.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the above-mentioned embodiments are merely preferred embodiments of the present invention, and the present invention is not limited thereto, but may be modified or substituted for some of the technical features thereof by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The intelligent power grid data supervision method based on the Internet of things is characterized by comprising the following steps of:
step S100, in a power system formed by a plurality of power equipment, establishing a power equipment operation parameter item set, setting a certain fault in the power system as a target fault, taking a certain operation state of the power equipment as a target state before the target fault occurs, and calculating a fault reference value of the target fault according to a history record of the operation state;
Step 200, acquiring two pieces of power equipment with different operation parameter items from the power system, respectively marking the two pieces of power equipment as first target equipment and second target equipment, calculating weights of the operation parameter items in a history record of target faults in the first target equipment and the second target equipment, and calculating weighted fault rates of the operation parameter items;
Step S300, obtaining third target equipment in the power system, wherein the third target equipment and the first target equipment and the second target equipment have at least one same operation parameter item, and when the third target equipment is detected to be in a target state, obtaining the similarity degree of the history records of the same operation parameter item between the third target equipment and the first target equipment and between the third target equipment and the second target equipment;
And step S400, comparing the operation parameter items of the third target equipment with the operation parameter items of the first target equipment and the second target equipment to obtain the association degree of the third target equipment with the first target equipment and the second target equipment respectively, establishing a state evaluation model of the third target equipment, and comparing the calculation result of the state evaluation model with the weighted failure rate of the operation parameter items to generate alarm information.
2. The intelligent supervision method of power grid data based on the Internet of things according to claim 1, wherein the step S100 comprises the following steps:
Step S101, acquiring operation parameters of each power device in a power system, and collecting the operation parameters of the power devices into a set W, W (W 1,w2,w3,……,wp), wherein the set W is an operation parameter item set of the power devices, W 1,w2,w3, and W p respectively represent operation parameters of the 1 st, 2 nd, 3 rd, and p-th power devices, and any one of the power devices at least comprises equipment operation parameters in one operation parameter item set;
Step S102, acquiring running state records of each power equipment in a time period with a time length T1 before occurrence of a target fault from a management log of the power equipment, and setting a certain abnormal state of the power equipment as a target state;
step S103, acquiring two pieces of electric equipment in an electric power system, namely a first target equipment and a second target equipment, wherein the first target equipment and the second target equipment comprise different operation parameter items;
Step S104, obtaining m 1 historical records from the running state record of the first target equipment, wherein n 1 historical records of the target faults of the power system occur in a T1 time period after the target state of the first target equipment occurs in the m 1 historical records;
Obtaining m 2 historical records from the running state record of the second target equipment, wherein n 2 historical records of the target faults of the power system occur in the time period T1 after the first target equipment occurs in the target state in the m 2 historical records;
Step S105, calculating a first fault reference value C 1,C1=n1/m1, calculating a second fault reference value C 2,C2=n2/m2, and calculating a comprehensive fault reference value C 0,C0=（n1+n2）/（m1+m2).
3. The intelligent supervision method of the power grid data based on the Internet of things according to claim 2, wherein the step S200 comprises the following steps:
Step S201, acquiring operation parameter items of first target equipment, collecting the operation parameter items of the first target equipment into a first equipment item set, wherein the first equipment item set is marked as D EV1, acquiring operation parameter items of second target equipment, collecting the operation parameter items of the second target equipment into a second equipment item set, and the second equipment item set is marked as D EV2;
Step S202, acquiring total times of operation parameter items in a first equipment item set and a second equipment item set, namely g 0, acquiring times of occurrence of a kth operation parameter item in the first equipment item set, namely g 1k, and times of occurrence in the second equipment item set, namely g 2k;
And S203, calculating the weight gamma k,γk=（g1k+g2k）/g0 of the kth operation parameter item, and calculating the weighted failure rate R k,Rk=γk×C0 of the kth operation parameter item.
4. The intelligent supervision method of power grid data based on the Internet of things according to claim 3, wherein the step S300 comprises the following steps:
Step 301, monitoring the state of a third target device, when the third target device is in a target running state, recording a time point T0, setting a time period with a termination time of T0 and a time length of T2 as a target time period, wherein T2 is more than or equal to T1;
Step S302, acquiring an operation parameter item of a third target device, collecting the operation parameter item to a third device item set, wherein the third device item set is marked as D EV3, acquiring a first comparison set R EP1 and a second comparison set R EP2,REP1=DEV1∩DEV3,REP2=DEV2∩DEV3, taking the operation parameter item in the first comparison set as a first comparison parameter item, and taking the operation parameter item in the second comparison set as a second comparison parameter item;
Step S303, acquiring a history record of a first comparison parameter item of first target equipment in a target time period, drawing a function image of the change of the numerical value of the first comparison parameter item along with time, recording the function image as a first change function, acquiring a history record of a second comparison parameter item of second target equipment in the target time period, and drawing a function image of the change of the numerical value of the second comparison parameter item along with time, recording the function image as a second change function;
Acquiring a history record of a first comparison parameter item of a third target device in a target time period, drawing a function image of the change of the numerical value of the first comparison parameter item along with time, recording as a third change function, acquiring the history record of the first comparison parameter item of the first target device in the target time period, drawing a function image of the change of the numerical value of the second comparison parameter item along with time, and recording as a fourth change function;
step S304, obtaining the similarity of the first change function and the third change function as alpha, and obtaining the similarity of the second change function and the fourth change function as beta.
5. The intelligent supervision method of the power grid data based on the Internet of things according to claim 4, wherein the step S400 comprises the following steps:
Step S401, calculating a fault risk value of a target running state, obtaining a feature set of a third target equipment running parameter item, and D EVf=REP1∪REP2, wherein D EVf represents the feature set, obtaining weighted fault rates of various running parameter items in the feature set, obtaining the sum of weighted fault rates in the feature set, marking the sum as H, and taking the H as the fault risk value of the target running state;
step S402, calculating the membership degree F 1,F1=Nr1/Nv3 of the third target device relative to the first target device, wherein Nr1 represents the number of operation parameter items in the first comparison set, N v3 represents the number of operation parameter items in the third device item set, and calculating the membership degree F 2,F2=Nr2/Nv3 of the third device relative to the second target device, wherein N r3 represents the number of operation parameter items in the second comparison set;
Step S403 of establishing a state evaluation model Q of the third target device with respect to the target operation state,
;
Step S404, the parameters are brought into a state evaluation model, state evaluation parameters q of the third target device are calculated, and when q > H, risk warning is carried out to relevant management personnel.
6. An intelligent monitoring system for network data based on the Internet of things is used for executing the intelligent monitoring method for network data based on the Internet of things according to any one of claims 1-5, and is characterized by comprising a fault record management module, a weighted fault rate calculation module, a device comparison module and a state evaluation module, wherein the fault record management module is used for acquiring historical operation records of power devices, calculating fault reference values of target faults, the weighted fault rate calculation module is used for calculating weighted fault rates of operation parameter items, the device comparison module is used for comparing the association degree of third target devices with first target devices and second target devices, and the state evaluation module is used for carrying out state evaluation on the third target devices.
7. The intelligent supervisory system for power grid data based on the Internet of things, wherein the fault record management module comprises a power equipment management unit, a target state management unit, a history record management unit and a fault reference value calculation unit, the power equipment management unit is used for acquiring operation parameters of power equipment and collecting an operation parameter item set of the power equipment, the target state management unit is used for acquiring a target state of the power equipment, the history record management unit is used for acquiring a history operation record of the power equipment, and the fault reference value calculation unit is used for calculating a first fault reference value, a second fault reference value and a comprehensive fault reference value.
8. The intelligent supervisory system for electric network data based on the Internet of things, wherein the weighted failure rate calculation module comprises an operation parameter item management unit, a parameter item set management unit and a weight calculation unit, wherein the operation parameter item management unit is used for managing operation parameter items of the first target device and the second target device, the parameter item set management unit is used for managing equipment item sets corresponding to the first target device and the second target device, and the weight calculation unit is used for calculating weights of the operation parameter items and weighted failure rates of the operation parameter items.
9. The intelligent monitoring system of the power grid data based on the Internet of things, which is characterized in that the equipment comparison module comprises an equipment detection unit, a comparison parameter acquisition unit and a parameter comparison unit, wherein the equipment detection unit is used for monitoring the state of third target equipment, the comparison parameter acquisition unit is used for acquiring a first comparison parameter item and a second comparison parameter item, and the parameter comparison unit is used for comparing the similarity of the third target equipment and the operation parameters of the first target equipment and the second target equipment.
10. The intelligent monitoring system of the power grid data based on the Internet of things is characterized in that the state evaluation module comprises a risk management unit, a membership degree calculation unit, an evaluation model management unit and an information reminding unit, wherein the risk management unit is used for acquiring fault risk values, the membership degree calculation unit is used for calculating membership degrees of third target equipment, first target equipment and second target equipment, the evaluation model management unit is used for managing a state evaluation model of the third target equipment, the information reminding unit is used for calculating state evaluation parameters of the third target equipment, and when an alarm condition is met, risk warning is conducted to relevant management staff.
CN202411765995.0A 2024-12-04 2024-12-04 A kind of intelligent monitoring system and method of power grid data based on Internet of Things Pending CN119231768A (en)

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