Power system dispatching optimization method and system in abnormal state of transformer

Abstract

The invention relates to a power system dispatching optimization method and system in an abnormal state of transformers, and the method comprises the following steps: collecting high-dimensional data of each transformer in real time, and constructing a high-dimensional data matrix; performing characteristic spectrum analysis on the high-dimensional data matrix by adopting a random matrix theory, and identifying an abnormal time period; constructing an augmented matrix in the abnormal time period to perform characteristic spectrum analysis on the transformer, and positioning an abnormal transformer; and based on the abnormal time period and the positioned abnormal transformer, a scheduling optimization model is constructed and solved by taking operation cost and carbon emission minimization as targets, and a scheduling optimization process in an abnormal state is completed. Compared with the prior art, the method has the advantages of improving the abnormal precision of the transformer, realizing stable operation of a power system and the like.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a method and system for optimizing power system dispatching under abnormal transformer status. Background Art

[0002] With the continuous increase in the scale and complexity of power systems, transformers, as an important part of the power grid, have become an important task in ensuring the stability and reliability of power systems. Traditional transformer anomaly detection methods usually rely on threshold setting, simple statistical analysis or empirical rules, but these methods are often incapable of dealing with large-scale, high-dimensional data and are difficult to cope with the changing operating conditions and complex failure modes in the system. In modern power systems, the transformer operating environment involves multiple variables, such as load fluctuations, temperature changes, oil quality changes, etc. These factors make the transformer data highly dynamic, nonlinear and multidimensional. Therefore, traditional methods are prone to false positives and false negatives, and it is difficult to accurately and quickly detect transformer anomalies.

[0003] As an effective high-dimensional data analysis tool, random matrix theory (RMT) has been widely used in anomaly detection of complex systems in recent years. By analyzing the characteristic spectrum changes of transformer operation data, RMT can identify abnormal fluctuations in system status, especially showing its unique advantages in large-scale data environments. However, the direct application of random matrix theory can usually only detect the abnormal time period, but cannot accurately locate the specific components or locations where the abnormality occurs in the transformer. This makes it difficult for methods that rely solely on random matrices to provide clear positioning information for transformer fault diagnosis in complex power grid environments. To solve this problem, the augmented matrix method was introduced into transformer anomaly detection. The augmented matrix method can effectively refine the anomaly detection results by jointly analyzing the specific transformer data of the transformer with the overall system data, thereby improving the recognition accuracy of the specific fault location. With this method, the abnormal location of the transformer can be more accurate and has better adaptability to the nonlinear characteristics in the data, thereby improving the accuracy and timeliness of anomaly detection. However, the existing transformer anomaly recognition methods based on random matrices and augmented matrices still lack a systematic integrated solution in practical applications. Especially in power systems, an efficient and reliable high-dimensional anomaly recognition system has not yet been established to comprehensively solve the challenges in transformer anomaly detection. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing power system dispatch under abnormal transformer conditions, which can improve the operational stability of the power system under abnormal transformer conditions.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for optimizing power system dispatching under abnormal transformer state, characterized in that it comprises the following steps: Collect high-dimensional data of each transformer in real time and construct a high-dimensional data matrix; Using random matrix theory to perform characteristic spectrum analysis on the high-dimensional data matrix to identify abnormal time periods; Constructing an augmented matrix during the abnormal time period to perform characteristic spectrum analysis on the transformer and locate the abnormal transformer; Based on the abnormal time period and the located abnormal transformer, a scheduling optimization model is constructed and solved with the goal of minimizing operating costs and carbon emissions, thereby completing the scheduling optimization process under abnormal conditions.

[0007] Furthermore, the step of constructing a high-dimensional data matrix includes: The high-dimensional data of each transformer is constructed into an initial high-dimensional data matrix , where the initial high-dimensional data matrix for: In the formula, Represents the high-dimensional data of the operation of the i-th transformer at the j-th time point, and its dimension is , m is the number of transformers in the substation, n is the number of time sampling points; The initial high-dimensional data matrix is ​​preprocessed to obtain a final high-dimensional data matrix.

[0008] Furthermore, the pre-processing step comprises: For the initial high-dimensional data matrix Normalization is performed, where the normalized expression is: In the formula, and Respectively represent the minimum and maximum values ​​of all time data of transformer i; is the normalized data value, satisfying ; According to the normalized results, the sliding average method is used to filter the noise and obtain the final high-dimensional data matrix, where the expression of the sliding average method is: In the formula, is the data after denoising; k is the sliding window radius, which determines the degree of smoothing; is the normalized data of transformer i at the pth time point.

[0009] Furthermore, the step of identifying the abnormal time period includes: Construct a covariance matrix: Based on the high-dimensional data matrix, construct a covariance matrix , where the covariance matrix for: In the formula, n is the number of time sampling points, is a high-dimensional data matrix; Calculate the eigenvalue distribution: calculate the eigenvalue of the covariance matrix to obtain a set of eigenvalues ,in m is the number of transformers, is the kth eigenvalue of the covariance matrix; Calculating the average spectrum radius: Based on the characteristic value distribution, the average spectrum radius is calculated as a monitoring indicator of the transformer state, wherein the calculation expression of the average spectrum radius is: In the formula, MSR is the average spectral radius; Identify abnormal time periods: determine the average spectrum radius MSR Whether it exceeds the set threshold ,If so, it means that the substation is in an abnormal state and is marked as an abnormal time period. If not, it is not marked.

[0010] Furthermore, the step of locating the abnormal transformer includes: Constructing an augmented matrix: combining the high-dimensional data of a separate transformer with the high-dimensional data matrix to construct an augmented matrix, wherein the augmented matrix is: In the formula, Represents the data vector of the i-th transformer at time, with dimension , is a high-dimensional data matrix, is the augmented matrix with dimension , n is the number of time sampling points, m is the number of transformers; Constructing an augmented covariance matrix: Based on the augmented matrix, constructing an augmented covariance matrix, wherein the augmented covariance matrix is: In the formula, is the augmented covariance matrix, with dimension ; Calculate the eigenvalue distribution of the augmented matrix: Calculate the eigenvalue set of the augmented covariance matrix ,in is the kth eigenvalue of the augmented covariance matrix; Calculate the augmented average spectral radius: Calculate the average spectral radius of the augmented covariance matrix based on the eigenvalue distribution of the augmented matrix, wherein the calculation expression of the average spectral radius is: In the formula, AMSR is the average spectral radius; Identifying abnormal transformers: determining the average spectral radius AMSR Whether it exceeds the set threshold If so, the transformer i There is an abnormality, if not, it is considered that the transformer i No abnormalities.

[0011] Furthermore, the scheduling optimization model includes an objective function and corresponding constraints, and the objective function is: Where Z is the total optimization target value; is the unit power cost coefficient of the i-th energy source; is the dispatch power of the i-th energy source at time t; is the carbon emission weight coefficient; is the unit power carbon emission coefficient of the i-th energy type; is the load and voltage variation coefficient of the i-th type transformer, reflecting the impact of transformer abnormality on dispatch; is the weight coefficient of the transformer abnormal state, which controls the priority of the transformer abnormality in the optimization target; The constraints include: (1) Power limit constraints (2) Load demand balancing constraints, (3) Energy storage constraints (4) Transformer safe operation constraints In the formula, is the maximum output power of the jth energy source, is the output power of the jth energy source at time t, m is the number of energy types, is the total load demand of the system at time t, is the storage state of the energy storage device at time t, is the change of charge and discharge capacity, and are the temperature and voltage of the transformer, and The maximum value for its safe operation.

[0012] Furthermore, an abnormal alarm step is also included, specifically including: Based on the identified abnormal time period and the location of the abnormal transformer, alarm information is automatically generated and output, wherein the alarm information includes the abnormal time period, the location of the abnormal transformer and the degree of abnormality, wherein the degree of abnormality is determined according to the degree of deviation of the distribution of the transformer characteristic values.

[0013] Furthermore, the calculation expression of the deviation degree is: In the formula, is the deviation index, M is the number of transformers, is the actual value of transformer MSR, d is the ideal value of transformer MSR, is the measurement function.

[0014] Furthermore, an optimization algorithm is used for solving the problem, and the optimization algorithm includes one of a linear programming algorithm, a genetic algorithm, an ant colony algorithm, and a particle swarm algorithm.

[0015] The present invention also provides a power system dispatch optimization system under abnormal transformer state, comprising: Matrix construction module: used to collect high-dimensional data of each transformer in real time and construct a high-dimensional data matrix; Abnormal time period identification module: used to perform characteristic spectrum analysis on the high-dimensional data matrix using random matrix theory to identify abnormal time periods; Abnormal transformer positioning module: used to construct an augmented matrix within the abnormal time period to perform characteristic spectrum analysis on the transformer and locate the abnormal transformer; Dispatch optimization module: It is used to build and solve a dispatch optimization model based on the abnormal time period and the located abnormal transformers, with the goal of minimizing operating costs and carbon emissions, and complete the dispatch optimization process under abnormal conditions.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] (1) The present invention constructs the real-time high-dimensional data of each substation into a high-dimensional data matrix, and uses random matrix characteristic spectrum analysis technology and augmented matrix characteristic spectrum analysis technology to capture the abnormal time period and abnormal location of the transformer through characteristic analysis in turn, thereby improving the accuracy of abnormal transformer detection, and by optimizing the power system under abnormal conditions, the stability of the power system under abnormal transformer conditions is improved.

[0018] (2) Compared with simple abnormality identification methods, the augmented matrix characteristic spectrum analysis technology adopted in the present invention can accurately locate abnormal transformers through the combination of specific transformer data and substation overall data, greatly improving the detection accuracy.

[0019] (3) The dispatch optimization model of the present invention ensures that the dispatch power meets the power and load balance requirements of the system while minimizing the operating cost and carbon emissions, so that reasonable dispatch and risk control can be carried out through real-time dispatch optimization even when the transformer is in an abnormal state.

[0020] (4) Compared with traditional single-dimensional data anomaly monitoring, the present invention adopts a multi-dimensional data-driven alarm mechanism and uses high-dimensional data for real-time dynamic monitoring, ensuring that control personnel can quickly and intuitively understand the abnormal status of the system and improve the level of situation awareness of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 This is a flow chart of abnormal transformer locating according to the present invention. DETAILED DESCRIPTION

[0022] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0023] Example 1

[0024] This embodiment provides a method for optimizing power system dispatching under abnormal transformer conditions. Figure 1 As shown, the method comprises the following steps:

[0025] Step 1: Collect and preprocess high-dimensional data for each transformer.

[0026] For substations in the power system, high-dimensional data is collected in real time from each transformer in the substation, including oil temperature, carbon dioxide, carbon monoxide and other gas concentrations. These data form a big data matrix for subsequent characteristic spectrum analysis. The steps for high-dimensional data collection and preprocessing based on substation operation are as follows: Step 1.1: Data collection, assuming that the collected substation data matrix is ​​recorded as , whose dimensions are , where: m represents the number of transformers in the substation; n represents the number of time sampling points.

[0027] Data Matrix In , each row represents the operating status data of a transformer at different time points, and each column represents the data of different transformers at the same time point. Data matrix The form is as follows:

[0028] in, Represents the operating data of the i-th transformer at the j-th time point.

[0029] Step 1.2: Data normalization, the original data matrix Normalization is performed to eliminate the differences caused by different dimensions and ensure that the data is analyzed on the same scale. Normalization is achieved using the following formula:

[0030] in: is the original data value of the i-th transformer at the j-th time point; and Respectively represent the minimum and maximum values ​​of all time data of transformer i; is the normalized data value, satisfying .

[0031] Normalization processing can limit all data to the same range, making subsequent analysis more stable and reliable.

[0032] Step 1.3: Data denoising: To improve the stability and accuracy of the data, a denoising method is used to normalize the data matrix. Perform noise filtering. Assuming that the noise in the data conforms to the Gaussian distribution, the sliding average method can be used to smooth the data and remove the noise interference caused by short-term fluctuations. The formula is as follows:

[0033] in: is the data after denoising; k is the sliding window radius, which determines the degree of smoothing; represents the normalized data of transformer i at the pth time point.

[0034] The matrix after denoising is recorded as ,in It is high-dimensional denoised data and is used for further characteristic spectrum analysis to ensure the reliability of the analysis results.

[0035] Step 2: Preliminary identification of transformer abnormal time periods based on random matrix characteristic spectrum analysis.

[0036] After completing the data preprocessing, the random matrix theory is used to perform characteristic spectrum analysis on the high-dimensional data matrix to identify the abnormal time period of the transformer. This step determines whether the transformer is abnormal by calculating the characteristic spectrum index (such as the average spectrum radius) of the data matrix.

[0037] Step 2.1: Construct the covariance matrix. Let the processed denoised data matrix be , whose dimensions are , where m is the number of transformers and n is the number of time sampling points. First, construct the covariance matrix of the data , the calculation formula is as follows:

[0038] in, The dimension is , reflecting the relationship between the transformers.

[0039] Step 2.2: Calculate the eigenvalue distribution by Find the eigenvalue and get a set of eigenvalues ,These eigenvalues ​​reflect different dimensions of the transformer state.,The distribution of eigenvalues ​​can reveal the changes in the transformer operating state,,and the changes in the characteristic spectrum may indicate the occurrence of transformer anomalies.

[0040] Step 2.3: Calculation of mean spectral radius. To further quantify the distribution of eigenvalues, the mean spectral radius (MSR) is calculated as a monitoring indicator of transformer status. The calculation formula of the mean spectral radius is as follows:

[0041] in, is the covariance matrix The kth eigenvalue of , where m is the number of transformers.

[0042] Step 2.4: Identify abnormal time periods, when the average spectrum radius MSR exceeds a certain set threshold When the transformer is considered to be in an abnormal state, the time period is marked as an abnormal time period. The sensitivity and accuracy of the detection can be ensured through statistical analysis of historical data or experimental settings.

[0043] Step 2.5: Dynamic monitoring and updating. This step calculates and monitors the MSR value in real time during the operation of the substation to achieve dynamic anomaly detection. If the MSR exceeds the threshold , the time period is automatically marked as abnormal and the subsequent abnormal location analysis is triggered.

[0044] Through random matrix characteristic spectrum analysis, this part can quickly identify abnormal time periods in high-dimensional data, providing a basis for the next step of abnormal transformer positioning.

[0045] Step 3: Augmented matrix construction and anomaly transformer location.

[0046] After identifying the abnormal time period, in order to further determine the specific transformer in the substation where the abnormality occurs, the present invention adopts an augmented matrix method to accurately locate the abnormal transformer by performing characteristic spectrum analysis on the data of the specific transformer. Figure 2 As shown, the step includes: Step 3.1: Construct an augmented matrix. After detecting the abnormal time period, select the transformers that may have abnormalities in the system and construct an augmented matrix , combine the data of this transformer with the overall data matrix. Let the data vector of the abnormal transformer be , the augmented matrix The form is:

[0047] in: is the processed high-dimensional denoised data matrix, with dimension ; Represents the data vector of the i-th transformer at time t, with dimension ; Augmented matrix The dimension is .

[0048] Step 3.2: Augmented covariance matrix calculation, through the augmented matrix , construct the augmented covariance matrix , used for characteristic spectrum analysis, the formula is as follows:

[0049] Augmented covariance matrix The dimension is , which contains the association information between the overall data and the target transformer data.

[0050] Step 3.3: Augmented matrix eigenspectral analysis, calculate augmented covariance matrix The set of eigenvalues ​​of These eigenvalues ​​reflect the performance of the target transformer in the overall state of the system. When the target transformer is abnormal, the distribution of its eigenvalues ​​will shift significantly.

[0051]

[0052] Step 3.4: Calculate the augmented mean spectral radius. Calculate the augmented mean spectral radius (AMSR) of the augmented matrix for quantitative analysis of the state of the target transformer. The formula is as follows:

[0053] in, is the augmented covariance matrix The kth eigenvalue of .

[0054] Step 3.5: Abnormal transformer identification, if the augmented mean spectrum radius AMSR exceeds the set threshold of the augmented matrix , then it is determined that the target transformer i is abnormal. It can be set based on historical data or experiments to ensure the sensitivity and accuracy of transformer anomaly detection.

[0055] Through the analysis of the augmented matrix characteristic spectrum, this part can further identify the specific abnormal transformers within the abnormal time period, realize the accurate positioning of the power system anomaly, and provide a reliable basis for the system's abnormal response and dispatching decisions.

[0056] Step 4: Abnormal alarm and decision support.

[0057] After detecting the abnormal time period and locating the abnormal transformer, the present invention generates alarm information and provides corresponding dispatch decision support for the power system. This step combines the abnormal detection results with the dispatch strategy to ensure reasonable dispatch and risk control under abnormal conditions.

[0058] Step 4.1: Abnormal alarm generation, once the abnormal transformer and abnormal time period are identified, the system automatically generates alarm information and notifies the relevant control personnel of the abnormal situation. The alarm information includes the abnormal time period, abnormal transformer location and abnormal degree, providing intuitive real-time feedback.

[0059] Step 4.2: Dispatch optimization objective. In order to ensure the stable operation of the power system and minimize carbon emissions under abnormal conditions, the present invention proposes an optimization objective function that combines operating costs and carbon emissions. The objective function is as follows:

[0060] Where: Z is the total optimization target value of the system; is the unit power cost coefficient of the j-th energy type; is the dispatch power of the j-th energy source at time t; is the carbon emission weight coefficient, which controls the priority of carbon emissions; is the unit power carbon emission coefficient of the j-th energy type.

[0061] Step 4.3: Constraints,The dispatch optimization needs to satisfy the following constraints to ensure the physical feasibility and safety of the system, especially considering the operating limitations of the transformer.

[0062] 1) Power limit constraints:

[0063] in, is the maximum output power of the jth type of energy, ensuring that the dispatching power is within the allowable range.

[0064] 2) Load demand balancing constraints:

[0065] in, is the total load demand of the system at time t, ensuring that the load demand of the system is met at each time point.

[0066] 3) Energy storage constraints: For energy storage equipment, the dynamic balance of energy storage must be met. The formula is as follows:

[0067] in, is the storage state of the energy storage device at time t, is the change of charge and discharge capacity.

[0068] 4) Transformer safe operation constraints. For transformer equipment, safety constraints must be met during operation. For example, parameters such as temperature, oil pressure, and voltage must be within a safe range. The formula is as follows:

[0069] in, and are the temperature and voltage of the transformer, and The maximum value for its safe operation.

[0070] Step 4.4: Solve the optimal dispatch strategy. Based on the above objective function and constraints, use an optimization algorithm (such as a linear programming algorithm or a genetic algorithm or an ant colony algorithm or a particle swarm algorithm) to solve and obtain the optimal dispatch power distribution. , the formula is as follows:

[0071] The optimal scheduling strategy achieves the rational allocation of resources under abnormal conditions, meeting load demand while minimizing carbon emissions and operating costs.

[0072] Step 4.5: Dynamic feedback and adjustment. While executing scheduling decisions, the system monitors the data changes of various evaluation indicators in real time, and adjusts the scheduling strategy when new abnormal situations occur, forming a closed-loop feedback to ensure the dynamic adaptability and intelligence of the scheduling plan.

[0073] The technical solution of the present invention is described below with reference to a specific embodiment.

[0074] (1) Obtain high-dimensional operating data of each transformer in the substation, including oil temperature, carbon dioxide, carbon monoxide and other gas concentrations, to form an original data matrix, and normalize the data to ensure that the data is consistent in the same dimension; (2) Based on random matrix characteristic spectrum analysis, the covariance matrix of the data is constructed and the eigenvalue set is calculated. The transformer status is monitored by calculating the average spectrum radius. When the value exceeds the set threshold, the time period is marked as an abnormal time period; (3) During the abnormal time period, select transformers that may be abnormal, construct an augmented matrix, combine the transformer data with the overall data, and calculate the average spectrum radius of the augmented matrix. When the augmented spectrum radius exceeds the set threshold, the transformer is determined to be an abnormal transformer, achieving accurate positioning; (4) Generate alarm information based on anomaly detection results and provide scheduling suggestions. Through scheduling optimization objectives, ensure that the scheduling power meets the power and load balance requirements of the system while minimizing operating costs and carbon emissions, and ultimately obtain the optimal scheduling solution.

[0075] Example 2

[0076] This embodiment provides a power system dispatch optimization system under abnormal transformer status, including: Matrix construction module: used to collect high-dimensional data of each transformer in real time and construct a high-dimensional data matrix; Abnormal time period identification module: used to perform characteristic spectrum analysis on the high-dimensional data matrix using random matrix theory to identify abnormal time periods; Abnormal transformer positioning module: used to construct an augmented matrix within the abnormal time period to perform characteristic spectrum analysis on the transformer and locate the abnormal transformer; Dispatch optimization module: It is used to build and solve a dispatch optimization model based on the abnormal time period and the located abnormal transformers, with the goal of minimizing operating costs and carbon emissions, and complete the dispatch optimization process under abnormal conditions.

[0077] The rest is the same as in Example 1.

[0078] If the above functions are implemented in the form of software functional units and sold or used as independent products, they 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. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or 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, etc., which can store program codes.

[0079] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0080] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0081] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0083] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0084] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing power system dispatching under abnormal transformer conditions, characterized in that: The following steps are involved: Collect high-dimensional data of each transformer in real time and construct a high-dimensional data matrix; Using random matrix theory to perform characteristic spectrum analysis on the high-dimensional data matrix to identify abnormal time periods; Constructing an augmented matrix during the abnormal time period to perform characteristic spectrum analysis on the transformer and locate the abnormal transformer; Based on the abnormal time period and the located abnormal transformer, a scheduling optimization model is constructed and solved with the goal of minimizing operating costs and carbon emissions, thereby completing the scheduling optimization process under abnormal conditions.

2. The method for optimizing power system dispatching under abnormal transformer state according to claim 1, characterized in that: The step of constructing a high-dimensional data matrix comprises: The high-dimensional data of each transformer is constructed into an initial high-dimensional data matrix , where the initial high-dimensional data matrix for: In the formula, Represents the high-dimensional data of the operation of the i-th transformer at the j-th time point, and its dimension is , m is the number of transformers in the substation, n is the number of time sampling points; The initial high-dimensional data matrix is ​​preprocessed to obtain a final high-dimensional data matrix.

3. The method for optimizing power system dispatching under abnormal transformer state according to claim 2, characterized in that: The pre-processing step comprises: For the initial high-dimensional data matrix Normalization is performed, where the normalized expression is: In the formula, and Respectively represent the minimum and maximum values ​​of all time data of transformer i; is the normalized data value, satisfying ; According to the normalized results, the sliding average method is used to filter the noise and obtain the final high-dimensional data matrix, where the expression of the sliding average method is: In the formula, is the data after denoising; k is the sliding window radius, which determines the degree of smoothing; is the normalized data of transformer i at the pth time point.

4. The method for optimizing power system dispatching under abnormal transformer state according to claim 1, characterized in that: The step of identifying an abnormal time period comprises: Construct a covariance matrix: Based on the high-dimensional data matrix, construct a covariance matrix , where the covariance matrix for: In the formula, n is the number of time sampling points, is a high-dimensional data matrix; Calculate the eigenvalue distribution: calculate the eigenvalue of the covariance matrix to obtain a set of eigenvalues ,in m is the number of transformers, is the kth eigenvalue of the covariance matrix; Calculating the average spectrum radius: Based on the characteristic value distribution, the average spectrum radius is calculated as a monitoring indicator of the transformer state, wherein the calculation expression of the average spectrum radius is: In the formula, MSR is the average spectral radius; Identify abnormal time periods: determine the average spectrum radius MSR Whether it exceeds the set threshold ,If so, it means that the substation is in an abnormal state and is marked as an abnormal time period. If not, it is not marked.

5. The method for optimizing power system dispatching under abnormal transformer state according to claim 1, characterized in that: The step of locating the abnormal transformer comprises: Constructing an augmented matrix: combining the high-dimensional data of a separate transformer with the high-dimensional data matrix to construct an augmented matrix, wherein the augmented matrix is: In the formula, Represents the data vector of the i-th transformer at time, with dimension , is a high-dimensional data matrix, is the augmented matrix with dimension , n is the number of time sampling points, m is the number of transformers; Constructing an augmented covariance matrix: Based on the augmented matrix, constructing an augmented covariance matrix, wherein the augmented covariance matrix is: In the formula, is the augmented covariance matrix, with dimension ; Calculate the eigenvalue distribution of the augmented matrix: Calculate the eigenvalue set of the augmented covariance matrix ,in is the kth eigenvalue of the augmented covariance matrix; Calculate the augmented average spectral radius: Calculate the average spectral radius of the augmented covariance matrix based on the eigenvalue distribution of the augmented matrix, wherein the calculation expression of the average spectral radius is: In the formula, AMSR is the average spectral radius; Identifying abnormal transformers: determining the average spectral radius AMSR Whether it exceeds the set threshold If so, the transformer i There is an abnormality, if not, it is considered that the transformer i No abnormalities.

6. The method for optimizing power system dispatching under abnormal transformer state according to claim 1, characterized in that: The scheduling optimization model includes an objective function and corresponding constraints, and the objective function is: Where Z is the total optimization target value; is the unit power cost coefficient of the i-th energy source; is the dispatch power of the i-th energy source at time t; is the carbon emission weight coefficient; is the unit power carbon emission coefficient of the i-th energy type; is the load and voltage variation coefficient of the i-th type transformer, reflecting the impact of transformer abnormality on dispatch; is the weight coefficient of the transformer abnormal state, which controls the priority of the transformer abnormality in the optimization target; The constraints include: (1) Power limit constraints (2) Load demand balancing constraints, (3) Energy storage constraints (4) Transformer safe operation constraints In the formula, is the maximum output power of the jth energy source, is the output power of the jth energy source at time t, m is the number of energy types, is the total load demand of the system at time t, is the storage state of the energy storage device at time t, is the change of charge and discharge capacity, and are the temperature and voltage of the transformer, and The maximum value for its safe operation.

7. The method for optimizing power system dispatching under abnormal transformer state according to claim 1, characterized in that: It also includes abnormal alarm steps, including: Based on the identified abnormal time period and the location of the abnormal transformer, alarm information is automatically generated and output, wherein the alarm information includes the abnormal time period, the location of the abnormal transformer and the degree of abnormality, wherein the degree of abnormality is determined according to the degree of deviation of the distribution of the transformer characteristic values.

8. The method for optimizing power system dispatching under abnormal transformer state according to claim 7, characterized in that: The calculation expression of the deviation degree is: In the formula, is the deviation index, M is the number of transformers, is the actual value of transformer MSR, d is the ideal value of transformer MSR, is the measurement function.

9. The method for optimizing power system dispatching under abnormal transformer state according to claim 1, characterized in that: An optimization algorithm is used for solving the problem, and the optimization algorithm includes one of a linear programming algorithm, a genetic algorithm, an ant colony algorithm, and a particle swarm algorithm.

10. A power system dispatch optimization system under abnormal transformer status, characterized in that: include: Matrix construction module: used to collect high-dimensional data of each transformer in real time and construct a high-dimensional data matrix; Abnormal time period identification module: used to perform characteristic spectrum analysis on the high-dimensional data matrix using random matrix theory to identify abnormal time periods; Abnormal transformer positioning module: used to construct an augmented matrix within the abnormal time period to perform characteristic spectrum analysis on the transformer and locate the abnormal transformer; Dispatch optimization module: It is used to build and solve a dispatch optimization model based on the abnormal time period and the located abnormal transformers, with the goal of minimizing operating costs and carbon emissions, and complete the dispatch optimization process under abnormal conditions.

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