CN115313662A - Distribution automation intelligence auxiliary tool that patrols and examines - Google Patents
Distribution automation intelligence auxiliary tool that patrols and examines Download PDFInfo
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- CN115313662A CN115313662A CN202211087199.7A CN202211087199A CN115313662A CN 115313662 A CN115313662 A CN 115313662A CN 202211087199 A CN202211087199 A CN 202211087199A CN 115313662 A CN115313662 A CN 115313662A
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- 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
- H02J13/00001—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 the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02B—BOARDS, SUBSTATIONS OR SWITCHING ARRANGEMENTS FOR THE SUPPLY OR DISTRIBUTION OF ELECTRIC POWER
- H02B3/00—Apparatus specially adapted for the manufacture, assembly, or maintenance of boards or switchgear
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- 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
Abstract
The application provides distribution automation intelligence appurtenance that patrols and examines includes: the system comprises a cross-system information acquisition module, a big data intelligent analysis module and a remote stylized interaction module; the cross-system acquisition module is connected with the PMS system, the OMS system, the EMS system, the IMS system and the feeder line automation system. According to the method, the problem of single engraving of data sources and excessive manual dependence in the traditional technical scheme is solved, integration, processing and assembly are carried out on multi-channel information on the basis of an original feeder automation system, integrated comprehensive application is achieved, and the intelligent and automatic levels in the working flows of equipment inspection, signal monitoring, operation analysis, defect elimination debugging, state prediction and the like are improved.
Description
Technical Field
The application relates to the technical field of intelligent power distribution, in particular to an auxiliary tool for intelligent inspection of power distribution automation.
Background
The statements in this section merely provide background information related to the present application and may not constitute prior art.
In a specific scene of the application of the intelligent technology of the power distribution network, the intelligent technology mainly comprises a main station side, a terminal side and a network channel for communicating the main station side and the terminal side. The main station side comprises software and hardware such as a feeder automation System (SCADA), a security encryption system, a server, a storage system, a workstation and the like; the terminal side comprises various types of two-remote (telemetering and remote signaling) and three-remote (telemetering, remote signaling and remote control) terminals; the network channel comprises optical fiber and wireless, and corresponding safety protection measures such as a firewall, a safety isolation device, an intrusion detection device (IDS), an intrusion prevention device (IPS), a vulnerability scanning device and a network safety monitoring device.
Distribution automation is a system which can only exert practical value through precise matching of a main station side and a terminal side, and data errors, abnormal states, functional failures and even system breakdown can be caused when any link fails. In the traditional technical scheme, the acquisition of various data only depends on a limited number of simple sensors matched with a secondary terminal, and the acquired signals are single carved, so that the actual operation conditions of the distribution lines and the equipment still cannot be visually displayed after the signals are uploaded to a main station side, a considerable number of daily maintenance personnel are required to form an operation and maintenance team, a large amount of labor and time cost are invested, remote monitoring and manual analysis are carried out on remote measuring and remote signaling signals sent on the terminal side, comprehensive study and judgment are carried out on the online and offline conditions of the equipment, lean investigation is carried out on the actual operation process of the feeder automation function by combining data in other professional systems, manual analysis is carried out on various hidden defect risk points, and remote-field combined debugging and the like are carried out on multiple links of the terminal debugging and defect elimination process.
The daily work is complicated in category, real information represented by various monitoring signals in the working process is not visual enough, a large amount of manual participation analysis is needed, higher requirements are provided for professional ability of practitioners, and a large amount of daily operation and maintenance work easily causes human errors.
Disclosure of Invention
This application has provided distribution automation intelligence and has patrolled and examined appurtenance in order to solve above-mentioned problem, solve the single board of carving of data source among the traditional technical scheme and to artifical excessive reliance, on original feeder automation system's basis, integrate, process, assemble multi-channel information, realize the integration and use synthetically to the lifting means patrols and examines, signal monitoring, operational analysis, disappear intelligent, the automation level in workflow such as scarce debugging, state prediction.
The application provides appurtenance is patrolled and examined to distribution automation intelligence, include: the system comprises a cross-system information acquisition module, a big data intelligent analysis module and a remote stylized interaction module; the cross-system acquisition module is connected with the PMS system, the OMS system, the EMS system, the IMS system and the feeder line automatic system and is used for acquiring marketing, scheduling and operation and inspection data; the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system acquisition module, so that logical association is established among all data, and dispatching personnel are assisted in carrying out power distribution inspection; the remote programming interaction module is used for automatically processing remote and field cooperative work according to a preset automatic processing model.
Preferably, the cross-system acquisition module is connected with the PMS system, the OMS system, the EMS system, and the IMS system through cross-regional security isolation equipment.
Preferably, the data collected by the cross-system collection module comprises customer complaints, heavy line overload, severe weather early warning, fault repair work orders, field inspection task orders, load prediction, maintenance plans, line loads, terminal brand models, terminal commissioning years, line distribution area power loss ranges, the number of power loss users and line historical load rates.
Preferably, the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the dispatcher to carry out power distribution inspection comprises the following steps:
and (4) tripping alarm:
s101: the cross-system information acquisition module acquires customer complaints in the IMS system and line load information in the EMS system and transmits the complaints and the line load information to the big data intelligent analysis module;
s102: the big data intelligent analysis module carries out statistic monitoring on customer complaint information of each area, if the number of power failure complaints in a preset time range of any area exceeds a preset value N, line load information of the corresponding time range of the corresponding area is called, whether voltage mutation exists or not is checked, if yes, a suspected tripping alarm is sent to a person on duty to be dispatched, and a suspected alarm grade is determined according to the number of the power failure complaints.
Preferably, in step S101, the cross-system information collecting module further collects event information in the OMS system;
in the step S102, if the number of power failure complaints in a certain area exceeds the preset value N and a voltage mutation exists within a preset time range, the big data intelligent analysis module integrates the OMS historical events to check whether an event affecting the voltage mutation exists, if not, a suspected trip alarm is sent to a scheduling operator, and if so, the suspected trip alarm is cancelled.
Preferably, the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the scheduling personnel in carrying out power distribution inspection further comprises the following steps:
monitoring the running state of the terminal:
s201: the cross-system information acquisition module acquires the brand and model of a retrieval terminal of the PMS, the operation period, a fault report and repair work order, a field inspection task order and a maintenance plan in the OMS, and transmits the information to the big data intelligent analysis module;
s202: the big data intelligent analysis module comprehensively judges the fault rate and the maintenance frequency of each terminal, and the specific analysis is as follows:
if the number of faults is higher than a preset value M1 and the number of maintenance times is lower than a preset value M2 within a preset time range, the inspection and maintenance plan is recommended to be adjusted;
if the number of times of faults in the preset time range is higher than the preset value M1 and the number of times of maintenance is higher than the preset value M2, the fact that whether the quality of the terminal is suitable for continuous operation or not is recommended to be checked;
and if the number of faults in the preset time range is higher than a preset value M1 and the commissioning life is less than a preset value M3, calling other terminals of the brand and model to which the terminal belongs to judge whether the common problem exists.
Preferably, the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the scheduling personnel in carrying out power distribution inspection further comprises the following steps:
analysis of the feeder automation self-healing process:
s301: the cross-system information acquisition module acquires the power loss range, the number of power loss users and the historical load rate of a line of a comprehensive line platform area in an EMS (energy management system) and OMS (operation management system), acquires real-time monitoring data in a feeder automation system and transmits the real-time monitoring data to the big data intelligent analysis module;
s302: the big data intelligent analysis module extracts fields which can directly reflect the self-healing action process in the trip fault information list through a preset strategy to visually display workers, meanwhile, the fields are matched with a preset diagnosis template, matched diagnosis information and the fields are synchronously displayed, and reference opinions are provided for judging whether the self-healing process is suspected to have problems.
Preferably, in step S302, the big data intelligent analysis module performs instant load prediction on the line by integrating the power loss range, the number of users of power loss, and the historical load rate of the line in the line distribution platform area in the EMS and OMS systems through real-time monitoring data in the line automation system, and automatically generates an optimum transfer policy for the scheduling staff to select.
Preferably, the specific method of the remote programming interaction module for automatically processing the remote and field cooperative work is as follows:
s401: the remote programming interaction module pre-compiles a plurality of sets of selectable programming models for various operations;
s402: after the scheduling personnel selects the programming model, the programming model automatically checks abnormal conditions found in the operation process by comparing various signals fed back on site in the feeder automation system with a preset signal template in real time, and sends an alarm to operation and maintenance personnel.
Compared with the prior art, the beneficial effect of this application is:
this application is on original signal access and data acquisition's basis, integrate multi-channel information, processing, the assembly, realize integration comprehensive application, patrol and examine with lifting means, signal monitoring, operation analysis, disappear the debugging, intellectuality among the work flow such as state prediction, the automation level, greatly reduced the dependence to manual operation, the risk of artificial misoperation has been reduced, the cost of labor has been dropped, the stability and the practicality of distribution automation system operation have been promoted, the steady promotion of joining in marriage net power supply reliability has effectively been helped.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application, and the description of the exemplary embodiments and illustrations of the application are intended to explain the application and are not intended to limit the application.
Figure 1 is a schematic diagram of a system connection according to one embodiment of the present application,
figure 2 is a schematic diagram of the system components of one embodiment of the present application,
figure 3 is a flow chart of a method according to one embodiment of the present application,
figure 4 is a flow chart of a method according to one embodiment of the present application,
figure 5 is a flow chart diagram three of the method of one embodiment of the present application,
FIG. 6 is a flow chart of a method according to an embodiment of the present application.
The specific implementation mode is as follows:
the present application will be further described with reference to the following drawings and examples.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only relational terms determined for convenience in describing structural relationships of the parts or elements of the present disclosure, and do not refer to any parts or elements of the present disclosure, and are not to be construed as limiting the present disclosure.
As shown in fig. 1 to 6, the application provides an auxiliary tool is patrolled and examined to distribution automation intelligence, its characterized in that includes: the cross-system information acquisition module is connected with a PMS system, an OMS system, an EMS system, an IMS system and a feeder automation system and used for acquiring marketing, scheduling and operation and inspection data, the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, establishing logic association among all data, assisting a scheduler to carry out power distribution inspection, and the remote stylized interaction module is used for carrying out automatic processing on remote and field cooperative work according to a preset automatic processing model.
The distribution automation profession is an important business field directly influencing the power consumption experience of distribution network users in various social circles, related data acquisition is carried out, besides terminal remote measurement and remote signaling signals related to the traditional technical scheme, multidimensional data of three professions of marketing, scheduling and operation and inspection are often required to be quoted in an actual application scene, and the multidimensional data comprises information such as customer complaints, line heavy overload, bad weather early warning, fault report and repair work orders, field inspection task orders, load prediction, maintenance plans and the like.
The cross-system acquisition module is connected with the PMS system, the OMS system, the EMS system and the IMS system through cross-regional safety isolation equipment so as to ensure that information is transmitted to encryption safety.
The PMS is a device (asset) operation and maintenance lean management system, and belongs to an operation and inspection system; the EMS system is an intelligent dispatching control system, the OMS system is a Shandong power grid dispatching management system, and the OMS and the EMS belong to regulation and control specialties; the IMS system is an electric power marketing service application system and belongs to marketing major.
The data collected by the cross-system collection module comprise customer complaints, line heavy overload, severe weather early warning, fault report repair work orders, field inspection task orders, load prediction, maintenance plans, load prediction, terminal brand models, terminal commissioning life, line distribution room power loss ranges, the number of power loss users and line historical load rates, wherein the customer complaints belong to an IMS system, the line heavy overload, severe weather early warning, load prediction, line distribution room power loss ranges, the number of power loss users and the line historical load rates belong to an OMS system and an EMS system, and the terminal brand models and the terminal commissioning life belong to the IMS system.
In the traditional technical scheme, whether a line trips or not is judged mainly by sending fault signals and protection action information to an intelligent switch with a protection function in a transformer substation or on the line, and when both the signals are sent correctly, a system can send tripping alarm to a dispatching attendant. However, when the network channel is unstable or the state of the signal acquisition device is abnormal, the timeliness and accuracy of the fault signal and the protection action information are affected, so that the system cannot give any alarm, and the discovery of the trip fault is greatly delayed.
The big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the dispatching personnel in carrying out power distribution inspection comprises the following steps:
and (4) tripping alarm:
s101: the cross-system information acquisition module acquires customer complaints in the IMS system and line load information in the EMS system and transmits the customer complaints and the line load information to the big data intelligent analysis module;
s102: the big data intelligent analysis module carries out statistical monitoring on customer complaint information of each area, if the number of power failure complaints in a preset time range of any area exceeds a preset value N, line load information of a corresponding time range of the corresponding area is called, whether voltage mutation exists or not is checked, if yes, a suspected tripping alarm is sent to a scheduling attendant, and a suspected alarm grade is determined according to the number of the power failure complaints.
In step S101, the cross-system information acquisition module further acquires event information in the OMS system;
in the step S102, if the number of power failure complaints in a certain area within a preset time range exceeds a preset value N and a voltage mutation exists, the big data intelligent analysis module integrates OMS historical events to check whether an event affecting the voltage mutation exists, if not, a suspected trip alarm is sent to a dispatching attendant, and if so, the suspected trip alarm is cancelled.
In the conventional technical scheme, the monitoring content of the terminal operation state is basically limited to "average online rate in a certain time period", and important data such as possible reasons causing offline, fault frequency of a specific terminal, overall operation condition and the like are obtained by manual analysis and sorting.
The big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the scheduling personnel in carrying out power distribution inspection further comprises the following steps:
monitoring the running state of the terminal:
s201: the cross-system information acquisition module acquires the brand and model of a retrieval terminal of the PMS, the operation period, a fault repair work order, a field inspection task order and a repair plan in the OMS and transmits the fault repair work order, the field inspection task order and the repair plan to the big data intelligent analysis module;
s202: the big data intelligent analysis module comprehensively judges the fault rate and the maintenance frequency of each terminal, and the specific analysis is as follows:
if the number of faults is higher than a preset value M1 and the number of maintenance times is lower than a preset value M2 within a preset time range, the inspection and maintenance plan is recommended to be adjusted;
if the number of times of faults is higher than a preset value M1 within a preset time range and the number of times of maintenance is higher than a preset value M2, suggesting to check whether the quality of the terminal is suitable for continuous operation;
if the number of faults in the preset time range is higher than the preset value M1 and the commissioning life is smaller than the preset value M3, calling other terminals of the brand and model to which the terminal belongs to judge whether the common problem exists.
In the traditional technical scheme, the system lists all telemetering, telesignalling and remote control information sent by intelligent switches in the transformer substation and on a line after faults occur in a time sequence, and staff screen corresponding information from a large amount of information to perform manual trip analysis. Although the system can roughly judge the trip self-healing process according to the uploading condition of part of key signals, the system has insufficient practicability and is easy to cause misjudgment (such as whether the line outgoing switch in the station is successfully superposed or not, whether the self-healing action is successful or not and the like) due to the instability of uploading of a single signal.
The big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the dispatching personnel in carrying out power distribution inspection further comprises the following steps:
analysis of the feeder automation self-healing process:
s301: the cross-system information acquisition module acquires the power loss range, the number of power loss users and the historical load rate of a line of a comprehensive line platform area in an EMS (energy management system) and an OMS (operation management system), acquires real-time monitoring data in a feeder automation system and transmits the real-time monitoring data to the big data intelligent analysis module;
s302: the big data intelligent analysis module extracts a field which can directly reflect a self-healing action process in a trip fault information list through a preset strategy to visually display workers, and meanwhile, the field is matched with a preset diagnosis template, matched diagnosis information and the field are synchronously displayed, and reference opinions are provided for the suspected problem existing in the self-healing process.
In step S302, the big data intelligent analysis module performs immediate load prediction on the line by integrating the power loss range, the number of power loss users, and the historical load rate of the line in the line distribution room in the EMS and OMS systems through real-time monitoring data in the line automation system, and automatically generates an optimal transfer strategy for the scheduling staff to select.
For mass information acquired by crossing systems, a big data technology is used for developing intelligent analysis, so that logical association is established among all data, real information behind the data is mined, a signal monitoring mode of 'number matching and seat entering' in the traditional technical scheme is changed, and the data practicability level is improved
In daily work of distribution automation major, a large number of services are completed through cooperative work of a remote place (a distribution automation master station operation and maintenance unit) and a site (a line terminal operation and maintenance administration unit). In the traditional technical scheme, the interaction processes are mainly carried out in a telephone communication mode by two persons of two parties, the labor consumption and the time cost are high, meanwhile, the information effectiveness in the communication process is low, and particularly when the debugging, acceptance inspection and maintenance tasks are heavy, the arrangement of the personnel tasks is close to the oversaturation state, and the work efficiency is seriously influenced.
The specific method for the remote programming interaction module to automatically process the remote and field cooperative operation comprises the following steps:
s401: the remote programming interaction module pre-compiles a plurality of sets of selectable programming models for various operations;
s402: after the scheduling personnel select the programming model, the programming model automatically checks abnormal conditions found in the operation process by comparing various signals fed back on site in the feeder automation system with a preset signal template in real time, and sends an alarm to operation and maintenance personnel.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the specific embodiments of the present application have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present application, and it should be understood that those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present application.
Claims (9)
1. Auxiliary tool is patrolled and examined to distribution automation intelligence, its characterized in that includes: the system comprises a cross-system information acquisition module, a big data intelligent analysis module and a remote stylized interaction module;
the cross-system acquisition module is connected with the PMS system, the OMS system, the EMS system, the IMS system and the feeder line automatic system and is used for acquiring marketing, scheduling and operation and inspection data;
the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system acquisition module, establishing logical association among all data and assisting a dispatcher in carrying out power distribution inspection;
the remote programming interaction module is used for automatically processing remote and field cooperative work according to a preset automatic processing model.
2. The power distribution automation intelligence inspection auxiliary tool of claim 1, characterized in that:
the cross-system acquisition module is connected with the PMS system, the OMS system, the EMS system and the IMS system through cross-region safety isolation equipment.
3. The power distribution automation intelligence inspection auxiliary tool of claim 2, characterized in that:
the data acquired by the cross-system acquisition module comprise customer complaints, line heavy overload, severe weather early warning, fault repair work orders, field inspection task orders, load prediction, maintenance plans, line loads, terminal brand models, terminal commissioning years, line transformer district power loss ranges, the number of power loss users and line historical load rates.
4. The power distribution automation intelligence inspection auxiliary tool of claim 3, characterized in that:
the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the dispatching personnel in carrying out power distribution inspection comprises the following steps:
and (4) tripping alarm:
s101: the cross-system information acquisition module acquires customer complaints in the IMS system and line load information in the EMS system and transmits the complaints and the line load information to the big data intelligent analysis module;
s102: the big data intelligent analysis module carries out statistic monitoring on customer complaint information of each area, if the number of power failure complaints in a preset time range of any area exceeds a preset value N, line load information of the corresponding time range of the corresponding area is called, whether voltage mutation exists or not is checked, if yes, a suspected tripping alarm is sent to a person on duty to be dispatched, and a suspected alarm grade is determined according to the number of the power failure complaints.
5. The power distribution automation intelligence inspection auxiliary tool of claim 4, characterized in that:
in step S101, the cross-system information acquisition module further acquires event information in the OMS system;
in the step S102, if the number of power failure complaints in a certain area exceeds the preset value N and a voltage mutation exists within a preset time range, the big data intelligent analysis module integrates the OMS historical events to check whether an event affecting the voltage mutation exists, if not, a suspected trip alarm is sent to a scheduling operator, and if so, the suspected trip alarm is cancelled.
6. The power distribution automation intelligence inspection auxiliary tool of claim 4, characterized in that:
the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the dispatching personnel in carrying out power distribution inspection further comprises the following steps:
monitoring the running state of the terminal:
s201: the cross-system information acquisition module acquires the brand and model of a retrieval terminal of the PMS, the operation period, a fault repair work order, a field inspection task order and a repair plan in the OMS and transmits the fault repair work order, the field inspection task order and the repair plan to the big data intelligent analysis module;
s202: the big data intelligent analysis module comprehensively studies and judges the fault rate and the maintenance frequency of each terminal, and the specific analysis is as follows:
if the number of faults is higher than a preset value M1 and the number of maintenance times is lower than a preset value M2 within a preset time range, suggesting to adjust a patrol maintenance plan;
if the number of times of faults is higher than a preset value M1 within a preset time range and the number of times of maintenance is higher than a preset value M2, suggesting to check whether the quality of the terminal is suitable for continuous operation;
if the number of faults in the preset time range is higher than the preset value M1 and the commissioning life is smaller than the preset value M3, calling other terminals of the brand and model to which the terminal belongs to judge whether the common problem exists.
7. The power distribution automation intelligence inspection auxiliary tool of claim 6, characterized in that:
the big data intelligent analysis module is used for carrying out intelligent analysis on the information acquired by the cross-system information acquisition module, and the specific method for assisting the dispatching personnel in carrying out power distribution inspection further comprises the following steps:
analysis of the feeder automation self-healing process:
s301: the cross-system information acquisition module acquires the power loss range, the number of power loss users and the historical load rate of a line of a comprehensive line platform area in an EMS (energy management system) and OMS (operation management system), acquires real-time monitoring data in a feeder automation system and transmits the real-time monitoring data to the big data intelligent analysis module;
s302: the big data intelligent analysis module extracts a field which can directly reflect a self-healing action process in a trip fault information list through a preset strategy to visually display workers, and meanwhile, the field is matched with a preset diagnosis template, matched diagnosis information and the field are synchronously displayed, and reference opinions are provided for the suspected problem existing in the self-healing process.
8. The power distribution automation intelligent inspection auxiliary tool according to claim 7, characterized in that:
in step S302, the big data intelligent analysis module performs real-time load prediction on the line by using real-time monitoring data in the line automation system and integrating the power loss range, the number of power loss users, and the historical load rate of the line in the line station area in the EMS and OMS systems, and automatically generates an optimum transfer strategy for a scheduling attendant to select.
9. The power distribution automation intelligence inspection auxiliary tool of claim 3, characterized in that:
the specific method for the remote programming interaction module to automatically process the remote and field cooperative operation comprises the following steps:
s401: the remote programming interaction module pre-compiles a plurality of sets of selectable programming models for various operations;
s402: after the scheduling personnel select the programming model, the programming model automatically checks abnormal conditions found in the operation process by comparing various signals fed back on site in the feeder automation system with a preset signal template in real time, and sends an alarm to operation and maintenance personnel.
Priority Applications (1)
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| CN202211087199.7A CN115313662A (en) | 2022-09-07 | 2022-09-07 | Distribution automation intelligence auxiliary tool that patrols and examines |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116131295A (en) * | 2023-04-14 | 2023-05-16 | 国网山西省电力公司临汾供电公司 | Grid-connected phase selection method based on power grid future state evaluation |
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2022
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116131295A (en) * | 2023-04-14 | 2023-05-16 | 国网山西省电力公司临汾供电公司 | Grid-connected phase selection method based on power grid future state evaluation |
| CN116131295B (en) * | 2023-04-14 | 2023-06-30 | 国网山西省电力公司临汾供电公司 | Grid-connected phase selection method based on power grid future state evaluation |
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