Large-scale load balancing response system for power grid

Abstract

The invention discloses a large-scale load balancing response system for a power grid, and relates to the technical field of power grids, and the system comprises a real-time monitoring module of which the input end is connected with a frequency adjustment module, and the input end of the frequency adjustment module is connected with a data analysis module; the frequency adjusting module comprises an equipment power range identifying module, a power grading module, a power grade defining module, a grade limiting module and a monitoring frequency adjusting module, the output end of the equipment power range identifying module is connected with the power grading module, and the output end of the power grading module is connected with the power grade defining module; the output end of the power level defining module is connected with a level limiting module, and the output end of the level limiting module is connected with a monitoring frequency adjusting module. According to the invention, the monitoring computing power is preferentially distributed to the nodes at the security edge, and the power grid data is predicted and analyzed in combination with the regional load analysis and the LSTM prediction model, so that the high-efficiency balance of the power grid load is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a large-scale load balancing response system for power grids. Background Art

[0002] Large-scale load balancing response system for power grid is an important technical system aimed at ensuring the stability, reliability and efficiency of power grid operation.

[0003] The large-scale load balancing response system for power grid has the following characteristics:

[0004] Large-scale: Able to handle large-scale power grids and complex network structures.

[0005] Efficiency: Quickly respond to and handle load changes to reduce the impact on users.

[0006] Intelligence: Rely on advanced algorithms and technologies to achieve precise decision-making and control.

[0007] Compatibility: Compatible with various types of power equipment and systems, easy to integrate and work together.

[0008] Such a system is of vital importance in meeting the growing demand for electricity, improving the resilience of the power grid, and promoting the effective access of renewable energy. It helps to optimize the allocation of power resources, reduce the cost of power grid operation, and improve the overall power supply quality and reliability.

[0009] The existing large-scale load balancing response system continuously performs real-time monitoring on each node during operation, but this will result in a large amount of computing power to process the detection results, thereby increasing the computing pressure of the entire system and making it inconvenient for the continuous operation of the system. To this end, in response to the above-mentioned defects, the present application proposes a large-scale load balancing response system for a power grid. Summary of the invention

[0010] In view of the above-mentioned problems, the present invention is proposed.

[0011] Therefore, the present invention provides a large-scale load balancing response system for a power grid, which can solve the problems mentioned in the background technology.

[0012] To solve the above technical problems, the present invention provides the following technical solutions: A large-scale load balancing response system for a power grid, comprising: a real-time monitoring module, an input end of which is connected to a frequency regulation module, and the input end of the frequency regulation module is connected to a data analysis module; the frequency regulation module comprises an equipment power range identification module, a power grading module, a power level definition module, a level limitation module and a monitoring frequency adjustment module, and the output end of the equipment power range identification module is connected to the power grading module, the output end of the power grading module is connected to the power level definition module, and the output end of the power level definition module is connected to the level limitation module, and the output end of the level limitation module is connected to the monitoring frequency adjustment module.

[0013] As a preferred solution of the large-scale load balancing response system for power grids described in the present invention, wherein: the output end of the real-time monitoring module is connected to a data segmentation module, and the real-time monitoring module, the data segmentation module and the data analysis module are connected in series;

[0014] The output end of the data analysis module is connected to a regional load analysis module, and the regional load analysis module and the frequency regulation module are connected in parallel;

[0015] The output end of the regional load analysis module is connected to the change frequency prediction module, and the output end of the change frequency prediction module is connected to the plan formulation module.

[0016] As a preferred solution of the large-scale load balancing response system for power grids described in the present invention, the output end of the regional load analysis module is connected to an overload reminder module, and the overload reminder module and the change frequency prediction module are connected in parallel.

[0017] As a preferred solution of the large-scale load balancing response system for power grid described in the present invention, the overload reminder module includes an area limitation module and an over-limit alarm module, and the output end of the area limitation module is connected to the over-limit alarm module.

[0018] As a preferred solution of the large-scale load balancing response system for power grid described in the present invention, the output end of the regional load analysis module is connected to a display intervention module, and the display intervention module and the change frequency prediction module are connected in parallel.

[0019] As a preferred solution of the large-scale load balancing response system for power grids described in the present invention, the display intervention module includes a load change prediction module, a pre-alert module and a display module, and the output end of the load change prediction module is connected to the pre-alert module, and the output end of the pre-alert module is connected to the display module.

[0020] As a preferred solution of the large-scale load balancing response system for power grids described in the present invention, the display intervention module further includes an intervention locking module and a manual intervention module, and the output end of the pre-alert module is connected to the intervention locking module, the output end of the intervention locking module is connected to the manual intervention module, and the intervention locking module and the display module are connected in parallel;

[0021] The output end of the plan formulation module is connected to the power distribution module, and the change frequency prediction module, the plan formulation module and the power distribution module are connected in series.

[0022] To further solve the above technical problems, the present invention provides the following technical solutions: a large-scale load balancing response method for a power grid, comprising: monitoring the load, voltage and current of each node in the power grid through a real-time monitoring module, and segmenting the monitoring data according to the data type through a data segmentation module and transmitting the monitoring data to a data analysis module for analysis; transmitting the analysis results of the data analysis module to a regional load analysis module and a frequency adjustment module respectively, wherein the frequency adjustment module adjusts the detection frequency of the real-time monitoring module, and the regional load analysis module divides the monitoring data into regions; transmitting the analysis data of the regional load analysis module to a change frequency prediction module, the change frequency prediction module uses a long short-term memory network LSTM to predict the analysis data, and transmits the prediction results to a plan formulation module, and the plan formulation module formulates a power distribution plan according to the prediction results and transmits it to the power distribution module.

[0023] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein when the processor executes the computer program, the steps of the large-scale load balancing response system for a power grid as described above are implemented.

[0024] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the large-scale load balancing response system for a power grid as described above are implemented.

[0025] Beneficial effects of the present invention: 1. The present invention can adjust the detection frequency of the real-time monitoring module through the frequency adjustment module, so that the main computing power of the real-time monitoring module is first allocated to the node detection at the safety edge. This can enable the system to spare a large amount of computing power to respond to the nodes with problems in time, avoiding the waste of computing power and the resulting slow response speed.

[0026] 2. The present invention has a pre-processing plan formulation function through the plan formulation module, which can formulate response plans in advance to facilitate timely response when problems occur in the power grid. At the same time, the area limitation module can actively divide the safe and dangerous areas of each data in the power grid, so that the data of each node can be discovered in time when they are close to each other, and an alarm can be issued, which is convenient for the staff to respond to the problem.

[0027] 3. The present invention can predict subsequent workload changes through the load change prediction module, and then display the prediction structure through the display module. Dangerous data and areas can be marked through the pre-warning module to facilitate staff response. It can also facilitate staff to intervene in the plan-making module through the manual intervention module, so that the processing plan generated by the plan-making module is more perfect. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0029] Figure 1 It is the overall flow chart of the present invention;

[0030] Figure 2 This is a flow chart of the frequency adjustment module of the present invention;

[0031] Figure 3 This is a flow chart of the overload reminder module of the present invention;

[0032] Figure 4 An intervention module flow chart is shown for the present invention. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] Example 1, reference Figure 1 to Figure 4 , as an embodiment of the present invention, provides a large-scale load balancing response system for a power grid.

[0036] See also Figure 1 and Figure 2 The present invention provides a technical solution: comprising a real-time monitoring module, the input end of the real-time monitoring module is connected to a frequency adjustment module, and the input end of the frequency adjustment module is connected to a data analysis module, the frequency adjustment module comprises an equipment power range identification module, a power classification module, a power level definition module, a level limitation module and a monitoring frequency adjustment module, and the output end of the equipment power range identification module is connected to a power classification module, the output end of the power classification module is connected to a power level definition module, and the output end of the power level definition module is connected to a level limitation module, and the output end of the level limitation module is connected to a monitoring frequency adjustment module;

[0037] The frequency regulation module can be used to adjust the detection frequency of the real-time monitoring module, so that the main computing power of the real-time monitoring module can be first allocated to the node detection at the safety edge. This can free up a large amount of computing power for the system to respond to nodes with problems in a timely manner, avoiding the waste of computing power and resulting in slow response speed.

[0038] like Figure 1 As shown, the output end of the real-time monitoring module is connected to the data segmentation module, and the real-time monitoring module, the data segmentation module and the data analysis module are connected in series, the output end of the data analysis module is connected to the regional load analysis module, and the regional load analysis module and the frequency regulation module are connected in parallel, the output end of the regional load analysis module is connected to the change frequency prediction module, and the output end of the change frequency prediction module is connected to the plan formulation module;

[0039] The data analyzed by the data analysis module will also be transmitted to the regional load analysis module for analysis according to the region. Based on the data structure analyzed historically by the regional load analysis module, the change frequency prediction module can calculate the subsequent data change frequency of the region.

[0040] like Figure 1 As shown, the output end of the regional load analysis module is connected to the overload reminder module, and the overload reminder module is connected in parallel with the change frequency prediction module, the output end of the regional load analysis module is connected to the display intervention module, and the display intervention module is connected in parallel with the change frequency prediction module, the output end of the plan formulation module is connected to the power distribution module, and the change frequency prediction module, the plan formulation module and the power distribution module are connected in series;

[0041] The power distribution plan is formulated through the plan formulation module, and the power distribution module is controlled to work according to the plan formulated by the plan formulation module. The overload reminder module can alarm when the data exceeds the limit, and the display intervention module can display the data and enable manual intervention in the plan.

[0042] like Figure 3 As shown, the overload reminder module includes an area limitation module and an over-limit alarm module, and the output end of the area limitation module is connected to the over-limit alarm module;

[0043] The plan-making module has the function of pre-processing plan-making, which can formulate response plans in advance to facilitate timely response when problems occur in the power grid. At the same time, the area limitation module can actively divide the safe and dangerous areas of each data in the power grid, so that the data of each node can be discovered in time when they are close to each other, and an alarm can be issued, which is convenient for staff to respond to problems.

[0044] like Figure 4 As shown, the display intervention module includes a load change prediction module, a pre-alert module and a display module, and the output end of the load change prediction module is connected to the pre-alert module, and the output end of the pre-alert module is connected to the display module, the display intervention module also includes an intervention locking module and a manual intervention module, and the output end of the pre-alert module is connected to the intervention locking module, the output end of the intervention locking module is connected to the manual intervention module, and the intervention locking module and the display module are connected in parallel;

[0045] The present invention predicts the frequency of data changes as follows:

[0046] Forget Gate: determines how much information to forget from the cell state;

[0047] Input Gate: determines the current input x t How much new information is added to the cell state;

[0048] Unit state update: Update the unit state through the forget gate and input gate;

[0049] Output Gate: determines how much information to output;

[0050] The final hidden state output is:

[0051] Data preparation: Preprocess the raw power grid data, including normalization, detrending, and splitting into training and test sets.

[0052] Build an LSTM model: define the LSTM network structure and set the input layer, LSTM layer, and output layer.

[0053] Training model: Use the training data set to train the model and optimize the network parameters through back propagation.

[0054] Predict frequency changes: Use the trained model to predict new power grid data and obtain the frequency of changes in the future.

[0055] Capturing time series characteristics. The algorithm of the present invention can effectively capture the time dependency and sequence characteristics in power grid data, and is suitable for processing data such as power load and power generation with time series properties. Power grid data is usually continuous time series data, and the design of LSTM is naturally suitable for this type of problem.

[0056] Long-term dependencies. Compared with traditional RNNs, LSTM can better handle long-term dependencies through its memory unit structure. This is crucial for predicting the frequency of power grid data changes, because changes in the power grid are often affected by historical data.

[0057] Robustness: The algorithm of the present invention has a certain robustness to noise and missing values ​​in the input data, and can make predictions more stably. This cannot be ignored for actual power grid data, because power grid data may be interfered by various factors during the collection process.

[0058] Flexibility: The algorithm model of the present invention can easily adjust the network structure and parameters to adapt to different power grid data characteristics. By adjusting the number of layers, units and training parameters, the model performance can be optimized to improve the prediction accuracy.

[0059] The load change prediction module can predict subsequent workload changes, and then the display module can display the predicted structure. The pre-warning module can mark dangerous data and areas to facilitate staff response. It can also facilitate staff to intervene in the plan-making module through the manual intervention module, so that the processing plan generated by the plan-making module is more perfect.

[0060] Working principle: First, the real-time monitoring module is used to monitor the power grid in real time, and the load conditions, voltage, current and other data of each node in the power grid are detected. Then, the data detected by the real-time monitoring module is divided according to the type through the data segmentation module, and transmitted to the data analysis module for analysis. At this time, the analysis results of the data analysis module will be transmitted to the equipment power range identification module to identify the load conditions, voltage, current and other data of each node in the power grid. The working range is then divided into levels through the power classification module, and then the load conditions, voltage, current, etc. of each node in the power grid are classified according to the classification of the power classification module through the power classification definition module. Level definition is used to determine the level of the load, voltage, current, etc. of each node in the power grid. Then, the level of each node in the power grid is limited by the level limitation module. If the monitoring frequency adjustment module adjusts the detection frequency of the real-time monitoring module according to the level of each node in the power grid, such as the load, voltage, current and other data of a certain node are at a very low level, it means that the node will not be overloaded, over-voltage, over-current, etc. in a short time, so the detection frequency of the node here can be reduced, thereby saving detection resources to supply nodes on the edge of overload, overvoltage, overcurrent, etc., so that it can be more convenient to timely discover overload, overvoltage, overcurrent nodes, and increase the response speed of the system;

[0061] The data analyzed by the data analysis module will also be transmitted to the regional load analysis module for analysis according to the region, so as to analyze the load distribution and change trend of a certain region. The data obtained by the regional load analysis module will be transmitted to the change frequency prediction module. According to the data structure analyzed historically by the regional load analysis module, the change frequency prediction module can calculate the subsequent data change frequency of the region, so as to achieve a simple advance prediction effect for the data of each region in the power grid. Then, according to the prediction results, the power distribution plan is formulated by the plan formulation module, so that when problems occur in the power grid, the power distribution module can be controlled to work according to the plan formulated by the plan formulation module, so as to ensure the normal operation of the power grid, and then the plan is formed through specific detection data to adjust the power grid;

[0062] The regional load analysis module will also transmit the data analyzed by the region to the over-limit alarm module, and the over-limit alarm module will compare the range set in advance by the regional limitation module. When the load situation analyzed by the regional load analysis module exceeds the limit set by the regional limitation module, the over-limit alarm module will play an alarm role. At this time, the alarm will be directly issued and the control plan formulation module will be started;

[0063] The data analyzed by the regional load analysis module will also be transmitted to the load change prediction module to predict the load situation. The prediction of the load change prediction module must be before the prediction of the change frequency prediction module, so the load change prediction module can predict for a longer time. At this time, the load change prediction module will form a change trend through the pre-alert module according to the prediction structure, and then transmit it to the display module for display through the pre-alert module. At the same time, the pre-alert module will mark the places that need attention, so that the staff can directly observe the places that need attention when observing the data through the display module, which is convenient for manual intervention;

[0064] The staff can intervene in the plan formulated by the plan formulation module through the manual intervention module, so as to make the plan formulated by the plan formulation module more perfect. At the same time, the intervention locking module will lock the manual intervention module. Only when the intervention reminder module predicts that there are precautions, the intervention locking module will release the operation authority of the manual intervention module. At this time, the staff can intervene in the plan of the plan formulation module through the manual intervention module.

[0065] In summary, the present invention can adjust the detection frequency of the real-time monitoring module through the frequency adjustment module, so that the main computing power of the real-time monitoring module is first allocated to the node detection at the safety edge. In this way, the system can spare a large amount of computing power to deal with the nodes with problems in time, avoiding the waste of computing power and the slow response speed. The present invention has a pre-processing plan formulation function through the plan formulation module, which can formulate a response plan in advance, so as to facilitate timely response when problems occur in the power grid. At the same time, the area limitation module can actively divide the safe and dangerous intervals of each data in the power grid, so that the data of each node can be discovered in time when it is close, and an alarm can be issued, which is convenient for the staff to respond to the problem. The present invention can predict the subsequent workload changes through the load change prediction module, and then display the prediction structure through the display module. The dangerous data and areas are marked through the pre-alert module to facilitate the staff to respond, and it is also convenient for the staff to intervene in the plan formulation through the manual intervention module.

[0066] Embodiment 2 is an embodiment of the present invention, which provides a large-scale load balancing response method for a power grid, including:

[0067] S1: Monitor the load, voltage and current of each node in the power grid through the real-time monitoring module, and segment the monitoring data according to the data type through the data segmentation module and transmit it to the data analysis module for analysis;

[0068] S2: Transmitting the analysis results of the data analysis module to the regional load analysis module and the frequency adjustment module respectively, wherein the frequency adjustment module adjusts the detection frequency of the real-time monitoring module, and the regional load analysis module divides the monitoring data into regions;

[0069] S3: The analysis data of the regional load analysis module is transmitted to the change frequency prediction module. The change frequency prediction module uses the long short-term memory network LSTM to predict the analysis data and transmits the prediction results to the plan formulation module. The plan formulation module formulates a power distribution plan according to the prediction results and transmits it to the power distribution module.

[0070] Embodiment 3 is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0071] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0072] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0073] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A large-scale load balancing response system for a power grid, characterized in that: include: A real-time monitoring module, wherein an input end thereof is connected to a frequency regulation module, and an input end of the frequency regulation module is connected to a data analysis module; the frequency regulation module comprises an equipment power range identification module, a power grading module, a power level definition module, a level limitation module and a monitoring frequency regulation module, and an output end of the equipment power range identification module is connected to a power grading module, an output end of the power grading module is connected to a power level definition module, and an output end of the power level definition module is connected to a level limitation module, and an output end of the level limitation module is connected to a monitoring frequency regulation module.

2. The large-scale load balancing response system for a power grid as claimed in claim 1, characterized in that: The output end of the real-time monitoring module is connected to the data segmentation module, and the real-time monitoring module, the data segmentation module and the data analysis module are connected in series; The output end of the data analysis module is connected to a regional load analysis module, and the regional load analysis module and the frequency regulation module are connected in parallel; The output end of the regional load analysis module is connected to the change frequency prediction module, and the output end of the change frequency prediction module is connected to the plan formulation module.

3. The large-scale load balancing response system for a power grid as claimed in claim 2, characterized in that: The output end of the regional load analysis module is connected to the overload reminder module, and the overload reminder module is connected in parallel with the change frequency prediction module.

4. The large-scale load balancing response system for a power grid as claimed in claim 3, characterized in that: The overload reminder module includes an area limitation module and an over-limit alarm module, and the output end of the area limitation module is connected to the over-limit alarm module.

5. The large-scale load balancing response system for a power grid as claimed in claim 4, characterized in that: The output end of the regional load analysis module is connected to the display intervention module, and the display intervention module and the change frequency prediction module are connected in parallel.

6. The large-scale load balancing response system for a power grid as claimed in claim 5, characterized in that: The display intervention module includes a load change prediction module, a pre-alert module and a display module, and the output end of the load change prediction module is connected to the pre-alert module, and the output end of the pre-alert module is connected to the display module.

7. The large-scale load balancing response system for a power grid as claimed in claim 6, characterized in that: The display intervention module further includes an intervention locking module and a manual intervention module, and the output end of the pre-warning module is connected to the intervention locking module, the output end of the intervention locking module is connected to the manual intervention module, and the intervention locking module and the display module are connected in parallel; The output end of the plan formulation module is connected to the power distribution module, and the change frequency prediction module, the plan formulation module and the power distribution module are connected in series.

8. A large-scale load balancing response method for a power grid, based on the large-scale load balancing response system for a power grid according to any one of claims 1 to 7, characterized in that: include, The load, voltage and current of each node in the power grid are monitored through the real-time monitoring module, and the monitoring data is segmented according to the data type through the data segmentation module and then transmitted to the data analysis module for analysis; Transmitting the analysis results of the data analysis module to the regional load analysis module and the frequency adjustment module respectively, wherein the frequency adjustment module adjusts the detection frequency of the real-time monitoring module, and the regional load analysis module divides the monitoring data into regions; The analysis data of the regional load analysis module is transmitted to the change frequency prediction module, and the change frequency prediction module uses the long short-term memory network LSTM to predict the analysis data, and transmits the prediction result to the plan formulation module, and the plan formulation module formulates a power distribution plan according to the prediction result and transmits it to the power distribution module.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the large-scale load balancing response system for a power grid according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the large-scale load balancing response system for a power grid according to any one of claims 1 to 7 are implemented.

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