CN112054510A

CN112054510A - Method for estimating abnormal operation state of power system

Info

Publication number: CN112054510A
Application number: CN202010839118.9A
Authority: CN
Inventors: 袁甄; 刘以成; 游木森
Original assignee: Xiamen Yingshengjie Electric Technology Co ltd
Current assignee: Xiamen Yingshengjie Electric Technology Co ltd
Priority date: 2020-08-19
Filing date: 2020-08-19
Publication date: 2020-12-08
Anticipated expiration: 2040-08-19
Also published as: CN112054510B

Abstract

The method for estimating the abnormal operation state of the power system comprises the following steps: creating a historical case library diagnosed by an expert, wherein the historical case library is divided into a voltage level hierarchy, a substation hierarchy and an interval hierarchy, and each hierarchy is provided with a plurality of hierarchy nodes; filling the data content of each historical case into the level nodes corresponding to each level respectively; in the historical case library, performing big data analysis on the operation state values of the hierarchy nodes of various types of disturbances before the disturbances occur, and calculating disturbance early warning threshold values of the operation state values; collecting panoramic information of a user power grid in real time; and when a certain running state value of the panoramic information exceeds the corresponding disturbance early warning threshold value, sending out a disturbance early warning. The estimation method can provide estimation of disturbance occurrence for the user, remind the user to take measures in time and prevent disturbance occurrence.

Description

Method for estimating abnormal operation state of power system

Technical Field

The invention relates to the field of power system fault diagnosis, in particular to a method for estimating an abnormal operation state of a power system.

Background

An electric power system is a system in which a large number of power stations, substations, distribution stations, users, and the like are connected by transmission and distribution lines. It is usually composed of generator, transformer, bus, transmission and distribution line and electric equipment. Electrical components, equipment and systems are normally in normal operation, but may also be in fault or abnormal operation.

The power system fault refers to a state that the electrical elements and equipment cannot work according to expected indexes, that is, the electrical elements and equipment do not reach the functions which the electrical elements and equipment should achieve, and the faults include generator set faults, transformer faults, transmission line faults, substation faults, bus faults and the like.

As the scale of the power system becomes larger and larger, the structure becomes more and more complex, and the occurrence of a fault is inevitable. The power system fault processing process may be that a topology change is detected from an operating state of the system, fault symptom information is detected from an area (unit) associated with the topology change, and after analyzing and processing the information, a specific area and a specific position (such as a fault range or a fault point) where a fault occurs are determined according to a signal of a protection action. After the fault range or fault point is determined, the fault area (unit) is ensured to be reliably cut off or isolated, then the power supply recovery of the power-off load is completed, and finally fault reason checking and fault elimination processing are carried out.

The special system for power system diagnosis is a corresponding power system fault diagnosis expert system.

Disclosure of Invention

The invention aims to provide a method for estimating the abnormal operation state of an electric power system, so as to better estimate the abnormal state (fault state, disturbance event and the like) of the electric power system and provide early warning.

In order to solve the above problems, the present invention provides a method for estimating an abnormal operation state of an electrical power system, comprising: creating a historical case library diagnosed by an expert, wherein the historical case library is divided into a voltage level hierarchy, a substation hierarchy and an interval hierarchy, and each hierarchy is provided with a plurality of hierarchy nodes; filling the data content of each historical case into a level node corresponding to each level according to the voltage level, the substation level and the interval level; in the historical case library, performing big data analysis on the operation state values of the hierarchy nodes of various types of disturbances before the disturbances occur, and calculating disturbance early warning threshold values of the operation state values; collecting panoramic information of a user power grid in real time, and comparing the panoramic information with the disturbance early warning threshold value in real time; and when a certain operation state value of the panoramic information exceeds the corresponding disturbance early warning threshold value, predicting that the power system will be disturbed, and sending out disturbance early warning.

Optionally, the estimation method further includes: and performing big data analysis on the operation state values of the level nodes before the disturbance occurs through the historical case library, and counting the times or probability of the abnormal state values under various types of disturbance to obtain a typical state value matrix of the abnormal state occurring before various types of disturbance occur.

Optionally, the level nodes of the interval level include interval disturbances, and the interval disturbances include a disturbance type subclass, a disturbance frequency subclass, and a disturbance environment subclass.

Optionally, the disturbance environment subclass includes a state value matrix, and the state values included in the state value matrix include current, voltage, frequency, power, device state values, and process parameter values.

Optionally, the estimation method further includes: counting the probability of each type of disturbance occurring when each interval is switched under different weather conditions, months or operation modes through the historical case library; and when the probability is higher than a preset threshold value and three disturbance environments of weather conditions, months or operation mode switching occur, corresponding early warning information is given.

Optionally, the early warning information is given, and the user is prompted about the transaction typical state value of the fault type corresponding to the early warning information.

Optionally, each level node of the substation level is divided into an attribute name, an attribute value and an attribute operation; each level node of the interval level is divided into an attribute name, an attribute value and an attribute operation.

Optionally, the level node of the voltage level includes 220kV, 110kV, 35kV, 10kV, 6kV, 1kV, and 0.4 kV.

Optionally, the panoramic information includes: the system comprises a transformer substation SCADA system data, a power centralized control SCADA system data, a power dispatching SCADA system data, a protection information system data, a relay protection device data, a safety, stability and automatic control device data, an intelligent measurement and control device data and a fault recording device data.

Optionally, the panoramic information further includes: power equipment state monitoring data and production process data.

In one aspect of the technical scheme, the method comprises the steps of establishing a historical case base for expert diagnosis, filling corresponding data into each level node according to the level node of the established historical case base, analyzing and processing the data, calculating a disturbance early warning threshold value of each operation state value, comparing the disturbance early warning threshold value with panoramic information adopted in real time, and carrying out corresponding disturbance early warning according to a comparison result. The estimation method can estimate the abnormal operation state of the power system and timely perform early warning on the user, so that the user can take effective measures in time to prevent disturbance events.

The method for estimating the abnormal operation state of the power system can fully and effectively utilize the value of a historical case (typical case), realize the estimation function of the abnormal operation state of the power system with more specialization and pertinence, and provide more accurate and reliable early warning for users.

Drawings

Fig. 1 is a configuration structure of a diagnostic system master station and a dispatching center (or a centralized control center) in a fault diagnosis expert system of an electric power system in a first embodiment;

FIG. 2 is a first embodiment of a diagnostic system master station deployment structure of a fault diagnosis expert system of an electrical power system;

fig. 3 is a schematic diagram illustrating a step of a method for estimating an abnormal operation state of an electrical power system according to a second embodiment.

Detailed Description

For a more clear presentation, the invention is described in detail below with reference to the accompanying drawings.

Example one

Referring to fig. 1 and fig. 2, a fault diagnosis expert system for an electric power system according to the present invention is shown.

The power system fault diagnosis expert system comprises a diagnosis system main station, and the diagnosis system main station of the embodiment is directly arranged by using a network of a scheduling main station (or called a centralized control main station) (hereinafter, referred to as the scheduling main station).

In fig. 1, the left side of the dotted line is the structure of the scheduling master station, and the right side of the dotted line is the diagnostic system master station of the fault diagnosis expert system.

As can be seen from fig. 1, the master station of the diagnostic system of this embodiment is hung in the network structure of the scheduling master station.

As shown in fig. 1, the corresponding scheduling master station may include: the system comprises a scheduling main station data storage structure, an engineer station, an operator station, a telecontrol forwarding/scheduling communication unit, a scheduling main station server and the like.

The telecontrol forwarding/scheduling communication unit of the scheduling master station can be accessed to the power scheduling network. And the server of the dispatching master station is accessed to the SCADA information of each transformer substation in the centralized control area.

The diagnosis system master station may directly access the power system by using the communication Device of the scheduling master station, for example, the diagnosis system master station accesses the scheduling master station system by using a station Control layer switch of the scheduling master station, that is, the diagnosis system master station accesses the corresponding power system And power detection system, such as an SCADA system (Supervisory Control And Data Acquisition system, that is, Data Acquisition And monitoring Control system) or an IED system (Intelligent Electronic Device), for example. The SCADA system is a DCS (distributed control system) and an electric power automatic monitoring system based on a computer, and can be applied to data acquisition and monitoring control, process control and the like in various industrial fields.

Figure 2 shows a particular deployment configuration of the diagnostic system master station.

As shown in fig. 2, the diagnostic system master station includes: a data storage structure (shown as a dashed box in fig. 2), an expert knowledge base, a front-end server, an analysis engine, and a running workstation.

The data storage structure is used for storing data. The expert knowledge base is used for storing the expert knowledge. The front-end server is used for collecting the operation parameters of the power system and executing data preprocessing. The analysis engine is used as a real-time inference engine, acquires observation information required by cache inference from the front-end server, searches appropriate expert knowledge from the expert knowledge base, completes inference and stores inference processes and inference results. The workstation is operative to act as a user client for displaying information and the like.

As shown in fig. 2, in this embodiment, the data storage structure may include a data server and a disk array. The data storage structure of the present embodiment includes two data servers. The two data servers can be used as historical data servers to store historical cases, historical reports, and statics analysis historical data. The redundant configuration of the two data servers can ensure the safety of data existence. The disk array may be used for separate preservation of long-term historical data. The number of the magnetic disks can be selected according to needs. In other embodiments, other data storage structures may be used, for example, a disk array may be omitted, or only one data server may be used.

As shown in fig. 2, in this embodiment, the expert knowledge base is used to store and update expert knowledge for diagnosing faults of various power systems, and the corresponding expert knowledge may be stored according to a certain rule for easy calling. The expert knowledge base is adapted for independent configuration.

As shown in fig. 2, in this embodiment, the front-end server may collect the operating parameters of the power system in real time and perform the relevant data preprocessing. The front-end server is adapted to employ a standalone deployment.

As shown in fig. 2, in this embodiment, the analysis engine is used as a real-time inference engine, and can collect each observation information required for cache inference from the corresponding front-end server, and can search for appropriate expert knowledge from the expert knowledge base, thereby completing inference, and storing inference processes and intermediate conclusions in real time (i.e., the inference result of the analysis engine may include diagnosis intermediate conclusions). The analysis engine is preferably deployed independently to make the analytical reasoning process of the diagnostic system more efficient and reliable.

As shown in fig. 2, in the present embodiment, the operation workstation serves as a user client, and the displayed information includes operation information of a user system (client system). The operation workstation can specifically display real-time operation information of a user system, can also be used for displaying expert early warning information and expert diagnosis reports, and can also be used for starting functions such as diagnosis tracking, case inversion and the like. And the operation workstation can be used for starting the remote inquiry cloud expert system function. The operation workstation is arranged in a manner of being separately deployed from the server.

It should be noted that, with reference to fig. 1 and fig. 2, the diagnostic system arrangement scheme of the present embodiment is a station-side deployment scheme (disposed at a station side of a station control layer). However, in other embodiments, the diagnostic system arrangement may be deployed in other structural locations.

With continued reference to FIG. 2, the diagnostic system master station may also include a maintenance workstation. The maintenance workstation is used for realizing the maintenance of the diagnosis system. The maintenance workstation may be used for a user engineer (knowledge engineer) to perform maintenance on the diagnostic system. For example, modeling configuration of the power system and expert base knowledge maintenance are realized. In this embodiment, the maintenance workstations are independently deployed, which is beneficial to better implement their maintenance functions. In other embodiments, the maintenance workstation may also be incorporated with the operational workstation of the diagnostic system.

With continued reference to fig. 2, the diagnostic system master station may also include an emergency command center interface server. And the emergency command center interface server is used for being in communication connection with the enterprise emergency command center. The emergency command center interface server may be specifically responsible for real-time communication with the enterprise emergency command center. In this embodiment, the emergency command center interface server is deployed independently, and this structure can exert its effect more. In other embodiments, the emergency command center interface server may also be incorporated with the analysis engine or the operation workstation.

With continued reference to FIG. 2, the diagnostic system master site may also include a WEB server. The WEB server is used for realizing WEB publishing of information and short message (mobile information) pushing. The WEB server may specifically issue a report of the electronic system fault through WEB, and may notify relevant personnel of the corresponding fault information in time through a short message (mobile information) push mode or the like. In other embodiments, the WEB server may not be necessary, i.e., omitted.

With continued reference to fig. 2, the diagnostic system host may further include a cloud expert system interface server. And the cloud expert system interface server is used for being in communication connection with the cloud expert system. When the cloud expert system interface server is communicated with the cloud expert system, the fault diagnosis capability of the diagnosis system is expanded, and the fault cloud diagnosis is guaranteed. In the embodiment, the independent server is adopted, namely, the independent deployment structure is adopted, so that the cloud diagnosis is more efficient, safe, reliable and timely. In other embodiments, the cloud expert system interface server may also be merged with the WEB server.

With continued reference to fig. 2, the diagnostic system master station may also include a firewall. The WEB server and the cloud expert system interface server are isolated outside the firewall. Firewall is used for the safe subregion of system, and other parts of WEB server and high in the clouds expert system interface server and system are separated to this embodiment, reach the better protection to other structures, make the system more stable.

With continued reference to fig. 2, the diagnostic system master station may also include various network devices. These network devices are used to ensure communication of the system. As shown in fig. 2, the network device is specifically implemented by using a switch, and the main station of the diagnostic system shown in fig. 2 includes a first front-end switch, a second switch, and a third switch. For the first front-end switch of the main station of the diagnostic system, an optical fiber interface can be adopted according to the specific situation of an access system, and a switch with gigabit bandwidth is preferably selected. The second switch and the third switch can adopt the switch with the gigabit bandwidth.

With continued reference to fig. 2, the diagnostic system master station may also include an output device. The output device may specifically be a printer, as shown in fig. 2. The printer is used for printing corresponding fault reports, diagnosis reports and the like at any time.

Referring to fig. 2, in this embodiment, the system for accessing the master station of the diagnostic system includes a synchronous clock (system), an SCADA system, an IED (system), a security system, a security management and control platform system, and the like, through the front-end server. The synchronous clock is a power system synchronous clock and is used for ensuring the clock synchronization of data. The information protection system is a relay protection information processing system and is used for managing relay protection setting values, fault message information and the like.

As shown in fig. 2, the present embodiment uses a single front-end server, so this structure can be referred to as a single front-end single network structure. The single-preposition single-network structure enables the internal network structure of the diagnosis system main station to be a single-network structure, and the structure is simpler, so the system cost can be reduced.

It should be noted that, as can be seen from the above contents in fig. 1 and fig. 2, each node in fig. 2 is a logic function defining node, and when actually deployed, the logic function nodes and the physical nodes may be completely in one-to-one correspondence according to the scheme in the figure, or the functional nodes may be tailored, the physical nodes may be merged, and the like according to needs. For example, as described above, for two logical function nodes, namely the operation workstation and the maintenance workstation, in the physical implementation, one workstation computer can be used for implementation.

As can be seen from fig. 1 and fig. 2, in this embodiment, a station end of an expert system for power system fault diagnosis is deployed at a station control layer, and a diagnosis system master station may be specifically deployed at a scheduling center station end, a centralized control center station end, or a substation station end. A forwarding channel between the SCADA system and the expert system station side is opened, and real-time information required by the expert system in each substation of the whole plant can be forwarded to the expert system station side by an IEC 60870-5-104 or IEC61850 standard protocol. The deployment scheme can fully reuse resources and has good practicability for both new projects and existing project reconstruction.

Example two

The second embodiment of the invention provides a method for estimating the abnormal operation state of an electric power system. Referring to fig. 3, the estimating method includes:

s1, creating a historical case library for expert diagnosis, wherein the historical case library is divided into a voltage level hierarchy, a substation hierarchy and an interval hierarchy, and each hierarchy is provided with a plurality of hierarchy nodes;

s2, filling the data content of each historical case into the level nodes corresponding to each level according to the voltage level, the substation level and the interval level;

s3, in the historical case library, performing big data analysis on the operation state values of the hierarchy nodes before disturbance occurs in each type of disturbance, and calculating disturbance early warning threshold values of the operation state values;

s4, collecting panoramic information of a user power grid in real time, and comparing the panoramic information with the disturbance early warning threshold value in real time; and when a certain operation state value of the panoramic information exceeds the corresponding disturbance early warning threshold value, predicting that the power system will be disturbed, and sending out disturbance early warning.

As can be seen from the above process, in the present embodiment, the history case library is created in three levels (three levels) of "voltage level-substation-interval". For the specific cases (historical cases) diagnosed by the expert system, the three levels can be known from the state information of the cases (corresponding data and information), so that when the diagnosed cases (historical cases) are stored in the historical case base, the diagnosed cases (historical cases) can be automatically and correspondingly filled in each level node of the historical case base. At this time, the step S1 and the step S2 complete the joining.

In this embodiment, the hierarchy nodes of the voltage class hierarchy may include different hierarchies such as 220kV, 110kV, 35kV, 10kV, 6kV, 1kV, and 0.4kV, and of course, other different voltage class hierarchies may be set according to needs.

In this embodiment, each level node of the substation level is divided into an attribute name, an attribute value, and an attribute operation.

A substation level node has attributes (including attribute names and corresponding and attribute values) and supported operations as shown in table 1 below:

TABLE 1

In this embodiment, each level node of the interval level is also divided into an attribute name, an attribute value, and an attribute operation. The attributes and supported operations of a compartment level node can be as shown in table 2 below:

TABLE 2

In this embodiment, the level nodes of the interval level include interval disturbances, and the interval disturbances include a disturbance type subclass, a disturbance frequency subclass, and a disturbance environment subclass. I.e., the "interval perturbation" attribute of a level node of the interval level is a class. This class in turn contains various subclasses and their attributes as shown in table 3 below:

TABLE 3

The sub-category of disturbance environments includes a matrix of state values, which contains state values including current, voltage, frequency, power, equipment state values, and process parameter values. Specifically, the "operation state value of each type of disturbance before the disturbance occurs" in the disturbance environment sub-class is a state value matrix, and according to the interval type, as described above, each value of the state value matrix includes, but is not limited to, a current, a voltage, a frequency, a power, an equipment state value (temperature, partial discharge, vibration, rotation speed, etc.), and a process parameter value (flow, pressure, etc.).

In this embodiment, the method further includes: and performing big data analysis on the operation state values of the level nodes before the disturbance occurs through the historical case library, and counting the times or probability of the abnormal (abnormally changed) state values under various types of disturbance to obtain a typical state value matrix of the abnormal state before the various types of disturbance occur.

In summary, in this embodiment, the voltage hierarchy may have a plurality of voltage hierarchy nodes, the substation hierarchy may have a plurality of substation hierarchy nodes, and the bay hierarchy may have a plurality of bay hierarchy nodes.

In step S3, after the history case deposition, the big data analysis is performed on the "operation state values of each type of disturbance before the disturbance occurs", and the disturbance early warning threshold value of each operation state value is calculated.

The "operation state value of each type of disturbance before the disturbance occurs" is simply referred to as "state value". Typically, these state values are above a certain range, i.e. disturbances may occur. Therefore, the big data can be used for analyzing the specific size of the state values in the disturbance events (historical events) which occur once, so as to determine the disturbance early warning threshold value corresponding to each state value.

For example, for a certain state value, big data analysis can be used to obtain what its lowest value is in all history cases caused by its abnormality, so as to set its disturbance early warning threshold lower than this lowest value, for example, 0.9 times of the lowest value; or, for a certain state value, the average value of the state value in all historical cases caused by the abnormality is obtained by utilizing big data analysis, and the disturbance early warning threshold value is further set to be lower than the average value, for example, 0.7-0.8 times of the average value.

In step S4, panoramic information of the user power grid is collected in real time, where the panoramic information includes: the system comprises a transformer substation SCADA system data, a power centralized control SCADA system data, a power dispatching SCADA system data, a protection information system data, a relay protection device data, a safety, stability and automatic control device data, an intelligent measurement and control device data and a fault recording device data. The data can reflect the real-time electrical characteristics of the operation of the power grid such as topological connection, tide distribution and fault information of the power grid more completely, and the time window synchronism among the data is better because the current transformer substation comprehensive automation system generally applies a synchronous clock timing technology. By comparing the data (comparing the real-time panoramic data with disturbance early warning threshold values of nodes of each level of the historical database), better estimation (estimation of abnormal operation state of the power system) can be made.

The panoramic information further includes: power equipment state monitoring data and production process data. For example, when the further reason excavation is carried out on the motor winding temperature rise (or overload behavior), the further shafting load analysis can be carried out by combining the technical process data (such as the flow of a pump) of mechanical equipment driven by the motor, and the data have corresponding values for the estimation of the abnormal operation state of the corresponding power system.

In this embodiment, the method further includes: counting the probability of each type of disturbance occurring when each interval is switched under different weather conditions, months or operation modes through the historical case library; and when the probability is higher than a preset threshold value and three disturbance environments of weather conditions, months or operation modes are switched to occur (namely when the corresponding disturbance environments occur), corresponding early warning information is given.

In this step, through historical case deposition, big data analysis is performed on the operation state value of each type of disturbance before the disturbance occurs, the times (or probability value) of the abnormal motion state value under each type of disturbance is counted, and a typical state value matrix of the abnormal motion before each type of disturbance occurs is obtained.

For example, after big data analysis, the probability of the high joint temperature before the insulation breakdown occurs in a power cable of a certain specification is high, and the state value of "joint temperature" is listed as a transaction typical state value of the power cable of the type, and the value becomes one of the values in the typical state value matrix.

In this embodiment, the user is prompted with the transaction typical state value of the fault type corresponding to the warning information while the warning information is given. Namely, prompting the user to focus on the typical state value of the abnormal movement counted by the typical state value matrix of the abnormal movement.

For example, through historical case statistical analysis, for a transformer interval in a substation, the probability of overload operation in 7-10 months per year is high, and when the transformer interval enters 7 months, a power system fault diagnosis expert system (the system can be the system of the first embodiment, or other systems, that is, the method of the first embodiment can be implemented by using a plurality of different systems, the system of the first embodiment is only one of the feasible systems) gives statistical early warning information, and prompts that abnormal typical state values such as winding temperature, oil temperature, current and the like of the transformer are focused, and meanwhile, early warning thresholds of the calculated state values are given.

Therefore, the statistical early warning information can be used as the key content of the monthly work plan and the operation and maintenance inspection plan, and a specific overload operation early warning report can refer to the following table 4:

TABLE 4

As can be seen from the above steps and contents, in the method for estimating the abnormal operation state of the power system provided by this embodiment, a historical case base for expert diagnosis is created, then, the data of the historical case is filled in each level node according to the level node of the created historical case base, then, analysis and processing are performed, a disturbance early warning threshold value of each operation state value is obtained through calculation, then, the panoramic information adopted in real time is used to compare with the disturbance early warning threshold value, and corresponding disturbance early warning is performed according to the comparison result. The estimation method can estimate the abnormal operation state of the power system and timely perform early warning on the user, so that the user can take effective measures in time to prevent disturbance events.

Meanwhile, the method for estimating the abnormal operation state of the power system provided by the embodiment can fully and effectively utilize the value of a historical case (typical case), realize a more specialized and targeted estimation function of the abnormal operation state of the power system, and provide more accurate and reliable early warning for users.

Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for predicting abnormal operation state of an electric power system is characterized by comprising the following steps:

creating a historical case library diagnosed by an expert, wherein the historical case library is divided into a voltage level hierarchy, a substation hierarchy and an interval hierarchy, and each hierarchy is provided with a plurality of hierarchy nodes;

filling the data content of each historical case into a level node corresponding to each level according to the voltage level, the substation level and the interval level;

in the historical case library, performing big data analysis on the operation state values of the hierarchy nodes of various types of disturbances before the disturbances occur, and calculating disturbance early warning threshold values of the operation state values;

collecting panoramic information of a user power grid in real time, and comparing the panoramic information with the disturbance early warning threshold value in real time; and when a certain operation state value of the panoramic information exceeds the corresponding disturbance early warning threshold value, predicting that the power system will be disturbed, and sending out disturbance early warning.

2. The method for estimating the abnormal operation state of the power system according to claim 1, further comprising: and performing big data analysis on the operation state values of the level nodes before the disturbance occurs through the historical case library, and counting the times or probability of the abnormal state values under various types of disturbance to obtain a typical state value matrix of the abnormal state occurring before various types of disturbance occur.

3. The method for estimating the abnormal operation state of the power system according to claim 2, wherein the interval hierarchy level nodes comprise interval disturbances, and the interval disturbances comprise a disturbance type subclass, a disturbance frequency subclass and a disturbance environment subclass.

4. The method of claim 3, wherein the disturbance environment subclass comprises a state value matrix, and the state value matrix comprises state values including current, voltage, frequency, power, equipment state values and process parameter values.

5. The method for estimating the abnormal operation state of the power system according to claim 4, further comprising: counting the probability of each type of disturbance occurring when each interval is switched under different weather conditions, months or operation modes through the historical case library; and when the probability is higher than a preset threshold value and three disturbance environments of weather conditions, months or operation mode switching occur, corresponding early warning information is given.

6. The method for estimating the abnormal operation state of the power system as claimed in claim 5, wherein the abnormal typical state value of the fault type corresponding to the early warning information is prompted to a user while the early warning information is given.

7. The method for predicting the abnormal operation state of the power system as claimed in claim 1, wherein each level node of the substation level is divided into an attribute name, an attribute value and an attribute operation; each level node of the interval level is divided into an attribute name, an attribute value and an attribute operation.

8. The method for estimating the abnormal operation state of the power system according to claim 1, wherein the nodes in the hierarchy of the voltage classes include 220kV, 110kV, 35kV, 10kV, 6kV, 1kV and 0.4 kV.

9. The method for estimating the abnormal operation state of the power system according to claim 1, wherein the panoramic information includes: the system comprises a transformer substation SCADA system data, a power centralized control SCADA system data, a power dispatching SCADA system data, a protection information system data, a relay protection device data, a safety, stability and automatic control device data, an intelligent measurement and control device data and a fault recording device data.

10. The method for estimating the abnormal operation state of the power system according to claim 9, wherein the panoramic information further includes: power equipment state monitoring data and production process data.