Intelligent power plant remote scheduling management system

Abstract

The invention discloses an intelligent power plant remote scheduling management system which comprises an information acquisition module, a processing module, an information transmission module and a storage module. The method comprises the following steps: 1, acquiring electric power data through an information acquisition module; step 2, the information acquisition module sends the acquired power data to an information storage module and a processing module; 3, the processing module analyzes the electric power data, then makes an electric power dispatching graph and sends the electric power dispatching graph to the information transmission module, an electric power dispatching graph changing period is designed in the processing module, and the information acquisition module automatically obtains new electric power data again; and new power dispatching graph construction is automatically executed. According to the remote scheduling management system for the intelligent power plant, the operation of the system is more real-time and the output result is more reliable through logic limitation of a power scheduling graph replacement period and a system operation period.

Description

Technical Field

[0001] The present invention relates to the technical field of dispatching and management systems, and in particular to a remote dispatching and management system for an intelligent power plant. Background Art

[0002] Power dispatching management is an important part of ensuring the smooth operation of the power system, optimizing resource utilization and meeting power demand. It ensures that the power system provides users with power efficiently and reliably through the management of supply and demand balance dispatching and power supply resource dispatching. It has a very important application prospect in hospitals, schools, factories and other power users with high dependence on electricity. Among them, power supply resource dispatching relies on the reasonable dispatching of power supply lines of different power generation energy sources, including thermal energy, wind energy, solar energy, etc., which can minimize energy waste and reduce power supply costs, and has become the current preferred dispatching direction.

[0003] After searching, the existing patent (publication number: CN116505595B) discloses a power monitoring and dispatching management system based on a virtual power plant. By analyzing the load level and operation tendency of the power equipment owned by the target power user, the target power user's demand analysis for power supply stability is reflected. At the same time, the current power supply stability and transmission loss of each power supply line existing in the target power user are monitored and analyzed, and then the required power supply stability of the target power user is compared with the current power supply stability of each power supply line, and a comprehensive screening is performed in combination with the transmission loss of each power supply line. The power supply line is dispatched, and the scientific and reasonable dispatch of the power supply resources of the target power user is realized, so that the dispatched power supply line can not only effectively meet the power supply stability requirements of the power user, but also can choose renewable energy as the power supply energy to a certain extent, realizing the combination of power supply stability and energy saving.

[0004] However, it has its shortcomings. Its power data cannot be automatically updated, which will lead to lagging power information and inaccurate power dispatching. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent power plant remote dispatching and management system, which solves the problems raised by the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent power plant remote dispatching and management system, including an information acquisition module, a processing module, an information transmission module and a storage module; Step 1: acquiring power data through the information acquisition module; Step 2: The information acquisition module sends the acquired power data to the information storage module and the processing module; Step 3: The processing module analyzes the power data, and then makes a power dispatching graph, and sends the power dispatching graph to the information transmission module. The processing module is internally designed with a power dispatching graph replacement cycle, which automatically enables the information acquisition module to reacquire new power data and automatically executes the construction of a new power dispatching graph; Step 4: The information transmission module sends the power dispatching graph to the remote device of the power plant staff, and the staff will control the power dispatching through the power dispatching graph operation.

[0007] Preferably, the information acquisition module includes feature extraction and data mining.

[0008] Preferably, the working steps of the information acquisition module are: Step 31: Input some power data into the storage module, extract a feature from the power data in the storage module, and normalize the mined data by using the feature to make the data value range corresponding to each feature of the mined data consistent, thereby forming compressed code training data: Using the compressed code training data, the encoder training is performed according to the following formula that minimizes the objective function of the classification error: in, is the weight coefficient, is the loss function, and the expression of the loss function is , for In the the category identifier of the category, For the compressed code training data, , For the In the category, The classification parameters corresponding to the features are is the bias parameter, is the number of features, is the projection matrix, For the The projection matrix corresponding to the features is is a hash function, is the number of the compressed code training data, is the number of categories of the compressed code training data, and are two normalization functions, which are used to adjust the classification parameter matrix and the projection matrix The role of and are two real numbers, used to adjust the normalization function and ; After training, the projection matrix is ​​obtained , the classification parameter matrix and the bias matrix , ; and the hash function: As a binary compression code encoder; Step 32: Use a classifier based on binary compression code to classify the binary compression code of the mined data to obtain a category, and classify the binary compression code of the mined data through the following function: ; Step 33: Calculate the similarity between the power data features inside the storage module and the data of each category by using the cosine method, and select the category with the highest similarity. The formula for calculating the similarity by the cosine method is as follows: ,in To store the power data characteristic data inside the module, For each category of data, The smaller the value, the higher the similarity.

[0009] Preferably, the feature extraction method is: An iterative formula for constructing a sample matrix based on the power data inside the storage module The sample matrix is ​​iteratively updated according to the iterative formula to obtain a feature matrix, where is a constant, giving a random initial value for iteration , , iteratively update according to the iterative formula, when the number of iterations reaches the set threshold k, the iteration is terminated, and the result is That is, k represents the product of the matrices, Y is the intermediate parameter, P represents the projection gradient algorithm, and P means projecting all negative numbers in the matrix Z to 0.

[0010] Preferably, the design logic of the power dispatching graph iteration cycle is: Where: Set the value for the previous iteration cycle; The number of sources of output power data for the model components; is the total value of the power output operation time in the previous iteration cycle setting value; n is the total power output; δ is the total value of the number of power users served by the power output end of the model output power data source; The peak value of electricity consumption of electricity users in the previous cycle; The average power consumption of power users in the previous cycle; in, Recorded as , When , the above formula is simplified to , When When the information acquisition module acquires power data, the acquisition cycle is manually set by the system end, and the set acquisition cycle is always less than the minimum cycle obtained by the iterative cycle design logic.

[0011] Beneficial Effects The present invention provides a remote dispatching and management system for intelligent power plants. Compared with the prior art, it has the following beneficial effects: 1. The intelligent power plant remote dispatching and management system makes the system operation more real-time and the output results more reliable through the logical limitation of the power dispatching graph update cycle and the system operation cycle.

[0012] 2. The intelligent power plant remote dispatching and management system iteratively updates the sample matrix according to the iterative formula to obtain the optimal characteristic matrix and the optimal characteristic vector, thereby improving the accuracy of comparative calculation, so that power data can be accurately mined. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the structure of the present invention.

[0014] In the figure: 1. Information acquisition module; 2. Processing module; 3. Information transmission module; 4. Storage module. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] See also Figure 1The present invention provides a technical solution: a remote dispatching management system for an intelligent power plant, comprising an information acquisition module 1, a processing module 2, an information transmission module 3 and a storage module 4; step 1, obtaining power data through the information acquisition module 1; step 2, the information acquisition module 1 sends the obtained power data to the information storage module 4 and the processing module 2; step 3, the processing module 2 analyzes the power data, and then makes it into a power dispatching graph, and sends the power dispatching graph to the information transmission module 3, and the processing module 2 is internally designed with a power dispatching graph iteration cycle, which automatically enables the information acquisition module 1 to re-acquire new power data, and automatically executes a new power dispatching graph to construct a power dispatching graph iteration cycle. The design logic is: , where: Set the value for the previous iteration cycle; The number of sources of output power data for the model components; is the total value of the power output operation time in the previous iteration cycle setting value; n is the total power output; δ is the total value of the number of power users served by the power output end of the model output power data source; The peak value of electricity consumption of electricity users in the previous cycle; is the average power consumption of power users in the previous cycle; Recorded as , When , the above formula is simplified to , When ; When the information acquisition module acquires power data, the acquisition cycle is manually set by the system end, and the set acquisition cycle is always less than the minimum cycle obtained by the iteration cycle design logic; Step 4, the information transmission module 3 sends the power dispatching graph to the remote device of the power plant staff, and the staff will control the cable dispatching through the power dispatching graph operation.

[0017] By logically limiting the power dispatching graph update cycle and the system operation cycle, the system operation is made more real-time and the output results are more reliable.

[0018] Preferably, the information acquisition module 1 includes feature extraction and data mining, and the working steps of the information acquisition module 1 are: Step 31, input some power data into the storage module 4, extract a feature from the power data in the storage module 4, and normalize the mined data through the feature to make the data value range corresponding to each feature of the mined data consistent, forming compressed code training data: Using the compressed code training data, the encoder is trained according to the following formula that minimizes the objective function of the classification error: ,in, is the weight coefficient, is the loss function, and the expression of the loss function is , for In the the category identifier of the category, For the compressed code training data, , For the In the category, The classification parameters corresponding to the features are is the bias parameter, is the number of features, is the projection matrix, For the The projection matrix corresponding to the features is is a hash function, is the number of compressed code training data, is the number of categories of compressed code training data, and are two normalization functions, which are used to adjust the classification parameter matrix and the projection matrix The role of and are two real numbers, used to adjust the normalization function and ; After training, the projection matrix is ​​obtained , classification parameter matrix and the bias matrix , ; and the hash function: As a binary compression code encoder; Step 32, using a classifier based on the binary compression code to classify the mined data binary compression code to obtain a category, and classifying the mined data binary compression code through the following function: Step 33, the power data features inside the storage module 4 and the data of each category are calculated by the cosine method to perform similarity calculation, and the category with the highest similarity is selected, wherein the cosine method similarity calculation formula is as follows: ,in It is the power data characteristic data inside the storage module 4, For each category of data, The smaller the similarity, the higher the feature extraction method is: construct an iterative formula of the sample matrix according to the power data inside the storage module 4: , according to the iterative formula, the sample matrix is ​​iteratively updated to obtain the feature matrix, where is a constant, giving a random initial value for iteration , , iteratively update according to the iterative formula, when the number of iterations reaches the set threshold k, the iteration is terminated, and we get That is, the feature matrix, k represents the matrix product, Y is the intermediate parameter, P represents the projection gradient algorithm, and PZ means projecting all negative numbers in the matrix Z to 0; By iteratively updating the sample matrix according to the iterative formula, an optimal characteristic matrix and an optimal characteristic vector are obtained, thereby improving the accuracy of comparative calculation, so that power data can be accurately mined.

[0019] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0020] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions. The sentence "includes an element defined by ... does not exclude the existence of other identical elements in the process, method, article or device including the element".

[0021] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote dispatching and management system for an intelligent power plant, characterized in that: It comprises an information acquisition module (1), a processing module (2), an information transmission module (3) and a storage module (4); Step 1: acquiring power data through the information acquisition module (1); Step 2: the information acquisition module (1) sends the acquired power data to the information storage module (4) and the processing module (2); Step 3: The processing module (2) analyzes the power data, and then generates a power dispatching graph, and sends the power dispatching graph to the information transmission module (3). The processing module (2) is internally designed with a power dispatching graph update cycle, which automatically enables the information acquisition module (1) to re-acquire new power data and automatically executes the construction of a new power dispatching graph. Step 4: The information transmission module (3) sends the power dispatching graph to the remote device of the power plant staff, and the staff will control the power dispatching through the power dispatching graph operation.

2. According to claim 1, the intelligent power plant remote dispatching and management system is characterized by: The information acquisition module (1) includes feature extraction and data mining.

3. The intelligent power plant remote dispatching and management system according to claim 2 is characterized by: The working steps of the information acquisition module (1) are: Step 31: input some power data into the storage module (4), extract a feature from the power data in the storage module (4), and perform normalization processing on the mined data by using the feature so that the data value range corresponding to each feature of the mined data is consistent, thereby forming compressed code training data: Using the compressed code training data, the encoder training is performed according to the following formula that minimizes the objective function of the classification error: in, is the weight coefficient, is the loss function, and the expression of the loss function is , for In the the category identifier of the category, For the compressed code training data, , For the In the category, The classification parameters corresponding to the features are is the bias parameter, is the number of features, is the projection matrix, For the The projection matrix corresponding to the features is is a hash function, is the number of the compressed code training data, is the number of categories of the compressed code training data, and are two normalization functions, which are used to adjust the classification parameter matrix and the projection matrix The role of and are two real numbers, used to adjust the normalization function and ; After training, the projection matrix is ​​obtained , the classification parameter matrix and the bias matrix , ; and the hash function: As a binary compression code encoder; Step 32: Use a classifier based on binary compression code to classify the binary compression code of the mined data to obtain a category, and classify the binary compression code of the mined data through the following function: ； Step 33: Calculate the similarity between the power data features inside the storage module (4) and the data of each category by using the cosine method, and select the category with the highest similarity. The formula for calculating the similarity using the cosine method is as follows: ,in The power data characteristic data inside the storage module (4) is For each category of data, The smaller the value, the higher the similarity.

4. The intelligent power plant remote dispatching and management system according to claim 3 is characterized by: The feature extraction method is: An iterative formula for constructing a sample matrix based on the power data inside the storage module (4) is: The sample matrix is ​​iteratively updated according to the iterative formula to obtain a feature matrix, where is a constant, giving a random initial value for iteration , , iteratively update according to the iterative formula, when the number of iterations reaches the set threshold k, the iteration is terminated, and the result is That is the characteristic matrix, k represents the product of the matrix, Y is the intermediate parameter, P() represents the projection gradient algorithm, and P(Z) means projecting all negative numbers in the matrix Z to 0.

5. The intelligent power plant remote dispatching and management system according to claim 4 is characterized by: The design logic of the power dispatching graph iteration cycle is: Where: Set the value for the previous iteration cycle; The number of sources of output power data for the model components; is the total value of the power output operation time in the previous cycle setting value; n is the total power output; δ is the total number of electricity users served by the power output terminal from which the output power data of the model is derived; The peak value of electricity consumption of electricity users in the previous cycle; The average power consumption of power users in the previous cycle; in, Recorded as , When , the above formula is simplified to , When ; When the information acquisition module acquires power data, the acquisition cycle is manually set by the system end, and the set acquisition cycle is always less than the minimum cycle obtained by the iterative cycle design logic.

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