CN115441581A - Large-scale power grid safety and intelligent scheduling system based on big data - Google Patents
Large-scale power grid safety and intelligent scheduling system based on big data Download PDFInfo
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- CN115441581A CN115441581A CN202211006342.5A CN202211006342A CN115441581A CN 115441581 A CN115441581 A CN 115441581A CN 202211006342 A CN202211006342 A CN 202211006342A CN 115441581 A CN115441581 A CN 115441581A
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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
- 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
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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
- H02J13/00006—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 information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
Abstract
The invention discloses a large-scale power grid safety and intelligent scheduling system based on big data, which relates to the technical field of intelligent power grid scheduling and solves the technical problem of large-scale power grid safety unified intelligent scheduling; the method comprises the following steps: the system comprises a data acquisition module, a scheduling control center and a scheduling execution module; the data acquisition module is used for acquiring various real-time electric power data representing the running state of the electric power system in the station; the data acquisition module packs the real-time power data, the area number and the number of the power data acquisition device and sends the packed data to the dispatching control center; the dispatching control center is used for receiving and processing the packed data sent by the data acquisition module; the dispatching control center sends the processed dispatching information to a dispatching execution module; the scheduling execution module is used for remotely controlling and processing the power problem or informing the staff in the corresponding scheduling area to process the power problem according to the abnormal result information processed by the scheduling control center.
Description
Technical Field
The invention belongs to the field of intelligent power grid dispatching, and particularly relates to a large-scale power grid safety and intelligent dispatching system based on big data.
Background
The power grid is an organic whole, the total quantity of production, transmission and use of alternating current electric energy in the power grid changes at any time, but balance must be kept at any moment, so that the quality index of the electric energy can be ensured to meet the standard, the modern power grid as a huge whole for producing, supplying and selling the electric energy is a necessary result of electric power development, and the network performance and the scale performance are more fully embodied when the power grid is larger. According to the rule that the generation and supply of power generation and instantaneous balance are completed simultaneously and the characteristic that the power grid sells power products and users in zero stock, the power grid system with complex technology needs to be strictly and scientifically managed, any equipment of the power generation and supply system breaks down, any local problem can be spread to the whole power grid, especially sudden accidents of the power grid can be correctly and quickly handled, the power supply needs to be recovered as soon as possible, and at the moment, the fault can be correctly and quickly eliminated only under unified command, and the normal operation of the power grid is kept.
Therefore, the invention provides a large-scale power grid safety and intelligent scheduling system based on big data.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a large-scale power grid safety and intelligent scheduling system based on big data, which solves the technical problem of large-scale power grid safety unified intelligent scheduling.
To achieve the above object, an embodiment according to a first aspect of the present invention provides a large-scale grid security and intelligent scheduling system based on big data, including: the system comprises a data acquisition module, a scheduling control center and a scheduling execution module;
the data acquisition module is used for acquiring various real-time power data representing the running state of the power system in the station; the data acquisition module is provided with n power data acquisition devices, n represents the number of the power data acquisition devices, and n is more than or equal to 1; marking the number of the region to be scheduled as k, wherein k is more than or equal to 1; the method comprises the steps that electric power data acquisition devices with different numbers acquire real-time electric power data in a dispatching area station corresponding to area numbers; the data acquisition module packs real-time power data, area numbers and numbers of the power data acquisition devices and sends the packed data to the dispatching control center;
the dispatching control center is used for receiving and processing the packed data sent by the data acquisition module; the dispatching control center sends the processed dispatching information to the dispatching execution module; the dispatching control center comprises a dispatching area data storage unit, a fault detection unit, a decision generation unit, a fault early warning unit and a dispatching prediction unit;
the scheduling execution module is used for remotely controlling and processing the power problem or informing the staff in the corresponding scheduling area to process the power problem according to the abnormal result information processed by the scheduling control center.
Furthermore, the scheduling area data storage unit is used for receiving and storing the packed data sent by the data acquisition module; the fault detection unit is used for carrying out abnormity detection on the power data stored in the dispatching area data storage unit and predicting future abnormal power data; the decision generation unit is used for analyzing the abnormal power data detected by the fault detection unit into specific content of abnormal power information; the fault early warning unit is used for marking the area number of the scheduling area where the abnormal power information is located in red and giving an alarm, and the fault early warning unit is also used for detecting whether all numbered power data acquisition devices are abnormal or not and giving an alarm; and the scheduling prediction unit displays the prediction result of the future abnormal power data for the staff.
Further, the receiving and processing procedures of the dispatching control center are as follows:
the method comprises the following steps: the scheduling area data storage unit receives and stores the packed data sent by the data acquisition module;
step two: the fault detection unit sends a data extraction signal to the scheduling area data storage unit, and the scheduling area data storage unit sends the stored packed data to the fault detection unit;
step three: the fault detection unit analyzes the power data in the packaged data, an abnormal power data detection model is established in the fault detection unit, and the abnormal power data detection model analyzes the power data; the output abnormal power data combined area number and the number of the power data acquisition device are sent to the decision generation unit; the fault detection unit is also used for predicting future abnormal power data according to historical power data and sending prediction information to the scheduling prediction unit, and the scheduling prediction unit is used for displaying the prediction result of the future abnormal power data for workers;
step four: the decision generation unit analyzes the abnormal power data into corresponding abnormal power information; the decision generation unit sends the abnormal power information, the area number and the number of the power data acquisition device to the fault early warning unit;
step five: the fault early warning unit is a human-computer interaction interface and comprises area numbers of all scheduling areas and the number of the power data acquisition device, and the fault early warning unit marks the area number corresponding to abnormal power information red and gives an alarm, so that a user or a worker can know that a power fault occurs in a certain scheduling area in time;
the fault early warning unit also inquires the electric power data acquisition device at intervals of t time to inquire whether the electric power data acquisition device works normally or not; if the operation is normal, no operation is performed; and if the power data acquisition device is abnormal or has no reply, the fault early warning unit marks the number of the corresponding power data acquisition device with red and gives an alarm.
Further, the abnormal power data detection model is provided with normal ranges of different types of power data, and power data exceeding the normal ranges are regarded as abnormal data; the abnormal power data detection model trains the abnormal power data detection model by continuously learning the input normal power data set and the input abnormal power data set, and finally obtains an accurate abnormal power data detection model.
Further, the abnormal power information includes problems generated during power generation, power transmission, power transformation, power distribution, and power supply.
Further, the workflow of the scheduling execution module is as follows:
the scheduling execution module detects a scheduling area which is alarmed by the fault early warning unit, analyzes the content of abnormal power information, if the existing technical solution exists, the scheduling execution module is confirmed by a worker, and after the solution is confirmed, the scheduling execution module automatically processes the abnormal problem of the scheduling area through remote control and feeds back a processing result; if no prior art solution exists, sending the abnormal power information to the networking equipment of the staff in the corresponding scheduling area, processing the problem reflected by the abnormal power information by the staff in the corresponding scheduling area, and requiring the staff to feed back a processing result;
when the scheduling execution module detects the electric power data acquisition device which is alarmed by the fault early warning unit, the scheduling execution module informs maintenance personnel to check or maintain the alarmed electric power data acquisition device and requires the maintenance personnel to feed back a processing result;
and the scheduling execution module packs and stores the abnormal power information, the area number, the number of the power data acquisition device and the problem processing feedback result.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps that power data acquisition devices with different numbers in a data acquisition module are used for acquiring various real-time power data representing the operation state of a power system in a station of a scheduling area with different area numbers, the acquired power data, the area numbers and the numbers of the power data acquisition devices are packaged and then sent to a scheduling control center, the scheduling control center comprises a scheduling area data storage unit, a fault detection unit, a decision generation unit, a fault early warning unit and a scheduling prediction unit, the data packaged by the power data, the area numbers and the numbers of the power data acquisition devices are processed through cooperation among the units, and a scheduling execution module is used for remotely controlling and processing power problems or informing workers corresponding to the scheduling area to process the power problems according to abnormal results processed by the scheduling control center. Through the technical scheme described by the invention, workers in the field can quickly acquire the abnormal power information of the power grid, timely and remotely process the abnormal power information, and timely inform the workers of processing the abnormal power problem, so that the technical problem of safe and unified intelligent scheduling of the large-scale power grid is solved.
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Fig. 1 is a schematic diagram of a module structure according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, the large-scale grid security and intelligent scheduling system based on big data includes: the system comprises a data acquisition module, a scheduling control center and a scheduling execution module;
the data acquisition module is used for acquiring various real-time electric power data representing the running state of the electric power system in the station; the data acquisition module is provided with n power data acquisition devices, n represents the number of the power data acquisition devices, and n is more than or equal to 1; marking the number of the area needing scheduling as k, wherein k is more than or equal to 1; the power data acquisition devices with different numbers acquire real-time power data in the dispatching area station with the corresponding area number; the data acquisition module packs real-time power data, area numbers and numbers of the power data acquisition devices and sends the packed data to the dispatching control center;
the dispatching control center is used for receiving and processing the packed data sent by the data acquisition module; the dispatching control center sends the processed dispatching information to the dispatching execution module; the dispatching control center comprises a dispatching area data storage unit, a fault detection unit, a decision generation unit, a fault early warning unit and a dispatching prediction unit;
the scheduling execution module is used for remotely controlling and processing the power problem or informing the staff in the corresponding scheduling area to process the power problem according to the abnormal result information processed by the scheduling control center.
In one embodiment of the invention, the scheduling control center is used for receiving and processing the packed data sent by the data acquisition module; the dispatching control center comprises a dispatching area data storage unit, a fault detection unit, a decision generation unit, a fault early warning unit and a dispatching prediction unit; the scheduling area data storage unit is used for receiving and storing the packed data sent by the data acquisition module; the fault detection unit is used for carrying out abnormity detection on the power data stored in the dispatching area data storage unit and predicting future abnormal power data; the decision generation unit is used for analyzing the abnormal power data detected by the fault detection unit into specific content of abnormal power information; the fault early warning unit is used for marking the area number of the scheduling area where the abnormal power information is located in red and giving an alarm, and the fault early warning unit is also used for detecting whether all numbered power data acquisition devices are abnormal or not and giving an alarm; the scheduling prediction unit displays the prediction result of future abnormal power data for the staff;
the receiving and processing process of the dispatching control center is as follows:
the method comprises the following steps: the scheduling area data storage unit receives and stores the packed data sent by the data acquisition module;
step two: the fault detection unit sends a data extraction signal to the scheduling area data storage unit, and the scheduling area data storage unit sends the stored packed data to the fault detection unit;
step three: the fault detection unit analyzes the power data in the packaged data, an abnormal power data detection model is established in the fault detection unit, and the abnormal power data detection model analyzes the power data; the output abnormal power data joint area number and the number of the power data acquisition device are sent to the decision generation unit; the fault detection unit is also used for predicting future abnormal power data according to historical power data and sending prediction information to the scheduling prediction unit, and the scheduling prediction unit is used for displaying the prediction result of the future abnormal power data for workers;
it should be noted that the abnormal power data detection model is provided with normal ranges of different types of power data, and power data exceeding the normal ranges are regarded as abnormal data; the abnormal power data detection model trains the abnormal power data detection model by continuously learning the input normal power data set and the input abnormal power data set, and finally obtains an accurate abnormal power data detection model;
step four: the decision generation unit analyzes the abnormal power data into corresponding abnormal power information, wherein the abnormal power information comprises problems generated in the power generation, power transmission, power transformation, power distribution and power supply processes, such as information about load and power generation conditions, voltage, active or reactive power flow, stability limit, power grid frequency and the like; the decision generation unit sends the abnormal power information, the area number and the number of the power data acquisition device to the fault early warning unit;
step five: the fault early warning unit is a human-computer interaction interface and comprises area numbers of all scheduling areas and the number of the electric power data acquisition device, and the fault early warning unit marks the area number corresponding to abnormal electric power information in red and gives an alarm, so that a user or a worker can know that an electric power fault occurs in a certain scheduling area in time;
the fault early warning unit also inquires the electric power data acquisition device at intervals of t time to inquire whether the electric power data acquisition device works normally or not; if the operation is normal, no operation is performed; and if the power data acquisition device is abnormal or has no reply, the fault early warning unit marks the number of the corresponding power data acquisition device with red and gives an alarm.
The workflow of the scheduling execution module is as follows:
the scheduling execution module detects a scheduling area which is alarmed by the fault early warning unit, analyzes the content of abnormal power information, if the prior art solution exists, the scheduling execution module is confirmed by a worker, and after the solution is confirmed, the scheduling execution module automatically processes the abnormal problem of the scheduling area through remote control and feeds back a processing result; if no prior art solution exists, the abnormal power information is sent to the networking equipment of the staff in the corresponding scheduling area, the staff in the corresponding scheduling area processes the problem reflected by the abnormal power information, and the staff is required to feed back a processing result;
when the scheduling execution module detects the electric power data acquisition device which is alarmed by the fault early warning unit, the scheduling execution module informs maintenance personnel to check or maintain the alarmed electric power data acquisition device and requires the maintenance personnel to feed back a processing result;
and the scheduling execution module packs and stores the abnormal power information, the area number, the number of the power data acquisition device and the problem processing feedback result.
The working principle of the invention is as follows: the method comprises the steps that power data acquisition devices with different numbers in a data acquisition module acquire various real-time power data representing the running states of power systems in stations of scheduling areas with different area numbers, the acquired power data, the area numbers and the numbers of the power data acquisition devices are packaged and then are sent to a scheduling control center, the scheduling control center comprises a scheduling area data storage unit, a fault detection unit, a decision generation unit, a fault early warning unit and a scheduling prediction unit, the data packaged by the power data, the area numbers and the numbers of the power data acquisition devices are processed through cooperation among the units, and a scheduling execution module is used for remotely controlling and processing power problems or informing workers corresponding to the scheduling areas to process the power problems according to abnormal result information processed by the scheduling control center.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (6)
1. Large-scale electric wire netting safety and intelligent scheduling system based on big data, its characterized in that includes: the system comprises a data acquisition module, a scheduling control center and a scheduling execution module;
the data acquisition module is used for acquiring various real-time power data representing the running state of the power system in the station; the data acquisition module is provided with n power data acquisition devices, n represents the number of the power data acquisition devices, and n is more than or equal to 1; marking the number of the area needing scheduling as k, wherein k is more than or equal to 1; the power data acquisition devices with different numbers acquire real-time power data in the dispatching area station with the corresponding area number; the data acquisition module packs real-time power data, area numbers and numbers of the power data acquisition devices and sends the packed data to the dispatching control center;
the dispatching control center is used for receiving and processing the packed data sent by the data acquisition module; the dispatching control center sends the processed dispatching information to the dispatching execution module; the dispatching control center comprises a dispatching area data storage unit, a fault detection unit, a decision generation unit, a fault early warning unit and a dispatching prediction unit;
the scheduling execution module is used for remotely controlling and processing the power problem or informing the staff in the corresponding scheduling area to process the power problem according to the abnormal result information processed by the scheduling control center.
2. The big-data-based large-scale power grid security and intelligent scheduling system according to claim 1, wherein the scheduling area data storage unit is configured to receive and store packed data sent by the data acquisition module; the fault detection unit is used for carrying out abnormity detection on the power data stored in the dispatching area data storage unit and predicting future abnormal power data; the decision generation unit is used for analyzing the abnormal power data detected by the fault detection unit into specific content of abnormal power information; the fault early warning unit is used for marking the area number of the scheduling area where the abnormal power information is located in red and giving an alarm, and the fault early warning unit is also used for detecting whether all numbered power data acquisition devices are abnormal or not and giving an alarm; and the scheduling prediction unit displays the prediction result of the future abnormal power data for the staff.
3. The big-data-based large-scale power grid security and intelligent scheduling system of claim 1, wherein the receiving and processing process of the scheduling control center is as follows:
the method comprises the following steps: the scheduling area data storage unit receives and stores the packed data sent by the data acquisition module;
step two: the fault detection unit sends a data extraction signal to the scheduling area data storage unit, and the scheduling area data storage unit sends the stored packed data to the fault detection unit;
step three: the fault detection unit analyzes the power data in the packaged data, an abnormal power data detection model is established in the fault detection unit, and the abnormal power data detection model analyzes the power data; the output abnormal power data combined area number and the number of the power data acquisition device are sent to the decision generation unit; the fault detection unit is also used for predicting future abnormal power data according to historical power data and sending prediction information to the scheduling prediction unit, and the scheduling prediction unit is used for displaying the prediction result of the future abnormal power data for workers;
step four: the decision generation unit analyzes the abnormal power data into corresponding abnormal power information; the decision generation unit sends the abnormal power information, the area number and the number of the power data acquisition device to the fault early warning unit;
step five: the fault early warning unit is a human-computer interaction interface and comprises area numbers of all scheduling areas and the number of the power data acquisition device, and the fault early warning unit marks the area number corresponding to abnormal power information red and gives an alarm, so that a user or a worker can know that a power fault occurs in a certain scheduling area in time;
the fault early warning unit also inquires the electric power data acquisition device at intervals of t time to inquire whether the electric power data acquisition device works normally; if the operation is normal, no operation is performed; and if the power data acquisition device is abnormal or has no reply, the fault early warning unit marks the number of the corresponding power data acquisition device with red and gives an alarm.
4. The big-data-based large-scale power grid security and intelligent scheduling system according to claim 3, wherein the abnormal power data detection model is provided with normal ranges of different types of power data, and power data exceeding the normal ranges are regarded as abnormal data; the abnormal power data detection model trains the abnormal power data detection model by continuously learning the input normal power data set and the input abnormal power data set, and finally obtains an accurate abnormal power data detection model.
5. The big data-based large-scale grid security and intelligent scheduling system of claim 3, wherein the abnormal power information comprises problems generated during power generation, power transmission, power transformation, power distribution and power supply.
6. The big-data-based large-scale power grid security and intelligent scheduling system of claim 1, wherein the scheduling execution module has the following workflow:
the scheduling execution module detects a scheduling area which is alarmed by the fault early warning unit, analyzes the content of abnormal power information, if the existing technical solution exists, the scheduling execution module is confirmed by a worker, and after the solution is confirmed, the scheduling execution module automatically processes the abnormal problem of the scheduling area through remote control and feeds back a processing result; if no prior art solution exists, sending the abnormal power information to the networking equipment of the staff in the corresponding scheduling area, processing the problem reflected by the abnormal power information by the staff in the corresponding scheduling area, and requiring the staff to feed back a processing result;
when the scheduling execution module detects the electric power data acquisition device which is alarmed by the fault early warning unit, the scheduling execution module informs maintenance personnel to check or maintain the alarmed electric power data acquisition device and requires the maintenance personnel to feed back a processing result;
and the scheduling execution module packs and stores the abnormal power information, the area number, the number of the power data acquisition device and the problem processing feedback result.
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| CN116880395A (en) * | 2023-07-19 | 2023-10-13 | 天津市易控科技发展有限公司 | Monitoring method, device, equipment and medium based on DCS system |
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| CN116880395A (en) * | 2023-07-19 | 2023-10-13 | 天津市易控科技发展有限公司 | Monitoring method, device, equipment and medium based on DCS system |
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