Power grid variable monitoring system and method

Abstract

The invention discloses a power grid variable monitoring system and method, and relates to the technical field of power grid variable monitoring. The system comprises a data acquisition and processing module, a feature extraction and analysis module, a health assessment and risk judgment module and a real-time monitoring and early warning module. The data acquisition and processing module constructs a historical operation data set, the feature extraction and analysis module recognizes abnormal fluctuation of a power grid, the health assessment and risk judgment module assesses health and risks of energy storage equipment and establishes an early warning reference, and the real-time monitoring and early warning module analyzes real-time data according to history and matches the early warning reference to generate prompt information. According to the method, the operation state of the energy storage equipment can be monitored in real time, potential risks of power grid operation can be identified through analysis of historical data, decision support is provided for dispatching and management of the power grid, and safe and stable operation of the power grid is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid variable monitoring, and in particular to a power grid variable monitoring system and method. Background Art

[0002] As power systems continue to expand in size and complexity, grid stability and reliability have become a core challenge for power dispatch and management. To ensure efficient and stable grid operation, real-time monitoring and analysis of various operational data are essential. Traditional grid monitoring methods rely on static data and basic operational parameter monitoring, such as load, frequency, and voltage. While these methods can ensure grid operation to a certain extent, as grid complexity increases, traditional monitoring methods are no longer able to effectively address unexpected grid anomalies and operational risks.

[0003] Energy storage devices are playing an increasingly important role in grid operation, serving as a crucial tool for regulating grid load and balancing power supply and demand. However, changes in the state of energy storage devices during grid operation, such as abnormal fluctuations in the charging and discharging processes, can lead to load and frequency fluctuations, and even more serious grid failures. Therefore, a more in-depth analysis of the health of energy storage devices and their relationship to abnormal grid fluctuations is needed.

[0004] Existing technologies for monitoring energy storage devices and grid operational fluctuations primarily focus on the real-time collection and monitoring of single data points, lacking a comprehensive analysis of the overall grid operational trends and the status of energy storage devices. For example, traditional methods often focus solely on real-time fluctuations in grid load or frequency, while ignoring the role of energy storage devices and the potential impact of their health on grid operations. Furthermore, existing grid monitoring systems lack in-depth correlation analysis between historical dispatch instructions and actual operational data, making it impossible to accurately identify the inherent connection between the risk status of energy storage devices and abnormal grid fluctuations. Summary of the Invention

[0005] The object of the present invention is to provide a power grid variable monitoring system and method to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A method for monitoring power grid variables includes the following steps:

[0008] Step S100: Collect historical operation data and historical dispatch instruction records of the power grid over a past period of time, analyze the historical dispatch instruction records, extract historical dispatch instructions for charging and discharging energy storage devices, summarize and record them as a target instruction set; establish a one-to-one correspondence between the target instruction set and the corresponding historical operation data, thereby constructing a historical operation data set;

[0009] Step S200: For each element in the historical operation data set, extract the corresponding key trend feature and construct a key trend feature vector; based on the key trend feature vector, perform load fluctuation analysis and frequency fluctuation analysis of the power grid to identify abnormal fluctuations;

[0010] Step S300. Evaluate the health of the energy storage device corresponding to each target instruction based on the key trend feature vector; based on the evaluation results of the energy storage device health, determine whether the energy storage device is in a risk state of overcharging or over-discharging; if a risk state is determined to be present, mark the corresponding target instruction as a risk instruction, obtain the correlation pattern between the risk instruction and abnormal power grid fluctuations, and mark it as a risk warning reference;

[0011] Step S400. Collect real-time operation data of the power grid, analyze the real-time operation data with reference to the analysis method of historical operation data, and obtain the risk judgment result of the current energy storage device; compare and analyze the risk judgment result of the current energy storage device with the corresponding historical operation data set to match the risk warning reference and generate corresponding prompt information.

[0012] Furthermore, step S100 includes:

[0013] S101. Collect historical operation data and historical dispatch instruction records of the power grid in the past time period, wherein the historical operation data includes power grid load data, power grid frequency data, and energy storage system status data; the energy storage system status data includes the state of charge and charge / discharge power of the energy storage device; from the historical dispatch instruction records, according to pre-set screening keywords, filter out historical dispatch instructions for charging and discharging of the energy storage device and mark them as target instructions, summarize all target instructions to form a target instruction set, represented as {I1, I2, ..., In}, where I1 represents the first target instruction, I2 represents the second target instruction, and so on, In represents the nth target instruction, and n represents the total number of target instructions in the past time period;

[0014] S102. According to the call timestamp td of each target instruction in the target instruction set, find the historical operation data within the time period [td-T, td] to form a data pair (Ii, Di), where T represents the time step, Di represents the historical operation data corresponding to the i-th target instruction, and i ranges from 1 to n; summarize all data pairs to construct a historical operation data set S, and express it as: S = {(I1, D1), (I2, D2), ..., (In, Dn)}, where D1 represents the historical operation data corresponding to the first target instruction, D2 represents the historical operation data corresponding to the second target instruction, and so on, Dn represents the historical operation data corresponding to the n-th target instruction.

[0015] Furthermore, step S200 includes:

[0016] S201. For each element in the historical operation data set, extract the corresponding key trend features; the key trend features include load fluctuation trend features, frequency fluctuation trend features and energy storage device status trend features; wherein the load fluctuation trend feature is the dynamic change rate of load power ΔP at adjacent time points, and the corresponding calculation formula is: ΔP = [P_load(t+Δt)-P_load(t)] / Δt, wherein P_load(t+Δt) represents the grid load power at time (t+Δt), P_load(t) represents the grid load power at time t, and Δt represents the time interval; similarly, the frequency fluctuation trend feature is the frequency change rate, and the frequency change rate Δfc is calculated by referring to the calculation formula of the dynamic change rate of load power ΔP; the energy storage device status trend features include the energy storage charge and discharge power ratio Pc and the charge state change rate S, the corresponding calculation formulas are: Pc = P_charge(t) / [P_discharge(t)+∈], where P_charge(t) represents the charging power of the energy storage device at time t, and P_discharge(t) represents the discharging power of the energy storage device at time t; ∈ represents the minimum value, which is greater than 0; S = [SOC(t+Δt)-SOC(t)] / Δt, where SOC(t+Δt) represents the state of charge of the energy storage device at time (t+Δt), and SOC(t) represents the state of charge of the energy storage device at time t; the extracted key trend features are averaged within the time period [td-T, td] and normalized, so as to construct a key feature vector Gi for each target instruction Ii, and Gi = [ΔP_load_i, Δf_i, P_i, SCR_i];

[0017] S202. Based on the key eigenvectors, statistical analysis is performed on the load power dynamic change rate ΔP_load and the frequency change rate Δf, respectively, to calculate thresholds at different confidence levels, which are the upper thresholds Tp1 and Tf1, and the lower thresholds Tp2 and Tf2. For load fluctuation anomaly analysis, ΔP_load_i in the key eigenvector Gi is compared with the upper threshold Tp1 and the lower threshold Tp2. If ΔP_load_i>Tp1 or ΔP_load_i<Tp2, a preliminary determination is made that load fluctuation anomaly exists. The corresponding energy storage charge-discharge power ratio P_i and state of charge change rate SCR_i are used for auxiliary judgment. When it is preliminarily determined that there is an abnormal load fluctuation, the load fluctuation is confirmed to be abnormal if the following conditions are met at the same time: If ΔP_load_i>Tp1, and P_i<1 and SCR_i<0, it means that the energy storage device may not respond to the load increase and discharge in time, and the load fluctuation is confirmed to be abnormal. If ΔP_load_i<Tp2, and P_i>1 and SCR_i>0, it means that the energy storage device may not effectively utilize the excess power, and the load fluctuation is confirmed to be abnormal.

[0018] S203. For frequency fluctuation anomaly analysis, the frequency change rate Δf_i in the key characteristic vector Gi is compared with the upper threshold Tf1 and the lower threshold Tf2 respectively. If Δf_i>Tf1 or Δf_i<Tf2, then there is a preliminary frequency fluctuation anomaly; calculate the Pearson correlation coefficient r(Δf_i,ΔP_load_i) between the frequency change rate Δf_i and the load power dynamic change rate ΔP_load_i. If |r(Δf_i,ΔP_load_i)|>r, and there is a preliminary frequency fluctuation anomaly, further confirm according to the change direction of load and frequency: when ΔP_load_i>0 and Δf_i<0, the frequency fluctuation anomaly is confirmed; when ΔP_load_i<0 and Δf_i>0, the frequency fluctuation anomaly is confirmed.

[0019] Furthermore, step S300 includes:

[0020] S301. Extract the energy storage charge-discharge power ratio and state-of-charge change rate from the key trend feature vector, evaluate the health of the energy storage device corresponding to each target instruction, and thus obtain a comprehensive health assessment score Hi. The corresponding calculation formula is:

[0021] Hi=w1·P_i+w2·SCR_i+w3·σ(P_i,SCR_i),

[0022] Wherein, σ(P_i, SCR_i) represents the standard deviation of the charge-discharge power ratio and the state-of-charge change rate of the energy storage device, w1, w2, and w3 represent weight coefficients, and w1+w2+w3=1; the comprehensive health assessment score Hi is compared with the threshold interval Q, and Q=[H_min,H_max], H_min represents the minimum value of the threshold interval Q, and H_max represents the maximum value of the threshold interval Q; if the comprehensive health assessment score Hi is not within the threshold interval Q, and P_i>1 and SCR_i>0, then it is judged as an overcharge risk; if the comprehensive health assessment score Hi is not within the threshold interval Q, and P_i<1 and SCR_i<0, then it is judged as an over-discharge risk;

[0023] S302. Based on the judgment result, mark the corresponding target instruction as a risk instruction; according to the identification result of the abnormal fluctuation in step S200, obtain the abnormal fluctuation timestamp tb, and associate the abnormal fluctuation timestamp tb with the time period of the historical operation data corresponding to the risk instruction, so as to obtain the association pattern between the risk instruction and the abnormal fluctuation of the power grid, and mark the association pattern between the risk instruction and the abnormal fluctuation of the power grid as a risk warning reference.

[0024] The association pattern refers to the relationship between risk instructions and abnormal fluctuations in the power grid. By associating the timestamp of the abnormal fluctuation with the corresponding historical operating data time period, a pattern is formed. This association pattern helps the system identify past abnormal situations and trigger early warnings in similar situations. For example, if there is a risk of overcharging or discharging of energy storage equipment within a specific time period, and abnormal fluctuations (such as voltage or frequency fluctuations) occur in the power grid during that time period, this time period can be used as a risk warning reference. When similar abnormal fluctuations in the power grid occur, the system will issue a corresponding warning based on this association pattern.

[0025] Furthermore, step S400 includes:

[0026] S401. Collect real-time operation data of the power grid and analyze the real-time operation data with reference to the analysis method of historical operation data to extract the real-time key feature vector SG, and SG = [ΔPs_load, Δfs, Ps, SCRs], where ΔPs_load represents the real-time load fluctuation trend characteristics, Δfs represents the real-time frequency fluctuation trend characteristics, and Ps and SCRs represent the real-time status trend characteristics of the energy storage device; based on the real-time key feature vector SG, refer to the calculation formula of the comprehensive health assessment score Hi to calculate the real-time health assessment score SH; compare the real-time health assessment score SH with the threshold interval Q; if the real-time health assessment score SH is not within the threshold interval Q, and Ps>1 and SCRs>0, it is determined to be an overcharging risk; if the comprehensive health assessment score Hi is not within the threshold interval Q, and Ps<1 and SCRs<0, it is determined to be an over-discharging risk;

[0027] S402. Based on the risk judgment result, extract the historical operation data of the same risk type in the risk instruction, calculate the similarity between the real-time operation data and the historical operation data in turn, and select the element with the largest similarity and the corresponding similarity value greater than the similarity threshold as the matching result; based on the matching result, obtain the corresponding association pattern between the risk instruction and the abnormal fluctuation of the power grid, and mark it as a risk warning reference; based on the risk warning reference, generate corresponding prompt information to relevant personnel, and the relevant personnel will further perform corresponding processing.

[0028] A power grid variable monitoring system includes: a data acquisition and processing module, a feature extraction and analysis module, a health assessment and risk judgment module, and a real-time monitoring and early warning module;

[0029] The data acquisition and processing module collects historical operation data and historical dispatch instruction records of the power grid over the past period of time, analyzes the historical dispatch instruction records, extracts historical dispatch instructions for charging and discharging energy storage devices, summarizes them and records them as a target instruction set; and establishes a one-to-one correspondence between the target instruction set and the corresponding historical operation data to construct a historical operation data set.

[0030] The feature extraction and analysis module extracts the corresponding key trend features for each element in the historical operation data set and constructs a key trend feature vector. Based on the key trend feature vector, it performs load fluctuation analysis and frequency fluctuation analysis on the power grid to identify abnormal fluctuations.

[0031] The health assessment and risk judgment module evaluates the health of the energy storage device corresponding to each target instruction based on the key trend feature vector. Based on the health assessment results of the energy storage device, it determines whether the energy storage device is in a risk state of overcharging or overdischarging. If a risk state is determined to be present, the corresponding target instruction is marked as a risk instruction. The correlation pattern between the risk instruction and abnormal power grid fluctuations is obtained and marked as a risk warning reference.

[0032] The real-time monitoring and early warning module collects real-time operating data of the power grid and analyzes the real-time operating data with reference to the analysis method of historical operating data to obtain the risk judgment result of the current energy storage equipment; the risk judgment result of the current energy storage equipment is compared and analyzed with the corresponding historical operating data set to match the risk warning reference and generate corresponding prompt information.

[0033] Furthermore, the data acquisition and processing module includes a data acquisition unit and a data processing unit;

[0034] The data acquisition unit collects historical operating data and historical dispatch instruction records of the power grid in the past time period; the data processing unit analyzes the historical dispatch instruction records, extracts the historical dispatch instruction records for charging and discharging of energy storage equipment, summarizes them and records them as the target instruction set; and establishes a one-to-one correspondence between the target instruction set and the corresponding historical operating data to construct a historical operation data set.

[0035] Furthermore, the feature extraction and analysis module includes a feature extraction unit and an abnormal fluctuation analysis unit;

[0036] The feature extraction unit extracts the corresponding key trend features for each element in the historical operation data set and constructs a key trend feature vector; the abnormal fluctuation analysis unit performs load fluctuation analysis and frequency fluctuation analysis of the power grid based on the key trend feature vector, thereby identifying abnormal fluctuations.

[0037] Furthermore, the health assessment and risk judgment module includes a health assessment unit, a risk judgment unit, and a risk instruction marking and association unit;

[0038] The health assessment unit evaluates the health of the energy storage device corresponding to each target instruction based on the key trend feature vector; the risk judgment unit determines whether the energy storage device is in a risk state of overcharging or over-discharging based on the assessment result of the health of the energy storage device; if the risk instruction marking and association unit determines that a risk state exists, it marks the corresponding target instruction as a risk instruction, obtains the association pattern between the risk instruction and abnormal fluctuations in the power grid, and marks it as a risk warning reference.

[0039] Furthermore, the real-time monitoring and early warning module includes a real-time operation data collection and analysis unit and an early warning generation unit;

[0040] The real-time operation data acquisition and analysis unit collects the real-time operation data of the power grid and analyzes the real-time operation data with reference to the analysis method of historical operation data, thereby obtaining the risk judgment result of the current energy storage device; the early warning generation unit compares and analyzes the risk judgment result of the current energy storage device with the corresponding historical operation data set to match the risk warning reference and generate corresponding prompt information.

[0041] Compared with the existing technology, the present invention has the following advantages: through in-depth analysis of historical grid operation data and historical dispatch instructions, combined with the health status of energy storage devices, the present invention comprehensively monitors grid load fluctuations, frequency fluctuations, and the charging and discharging of energy storage devices, enabling more accurate identification of abnormal grid fluctuations and potential risks of energy storage devices. This method breaks through the limitations of traditional grid monitoring systems that rely solely on single parameters (such as load and frequency), providing more comprehensive risk identification capabilities. The present invention not only analyzes grid load and frequency fluctuations, but also combines the health status of energy storage devices with grid fluctuations to form a correlation model between risk instructions and abnormal grid fluctuations, thereby providing more accurate early warnings and treatment recommendations. This analysis method can help predict potential risks in grid operation, take timely measures to regulate and control, and effectively reduce the probability of grid failures. By evaluating the real-time health status of energy storage devices and comparing them with historical operation data, the present invention can promptly detect abnormal conditions of energy storage devices, such as overcharging or over-discharging risks, to ensure stable operation of the grid. The matching analysis of real-time and historical data not only improves the accuracy of risk warnings but also increases the response speed of the system. By generating risk warning references related to abnormal fluctuations in the power grid and combining them with real-time monitoring data, the present invention can provide power dispatchers with intelligent early warning information, helping them to identify possible power grid failure risks in advance and take corresponding measures, thereby improving the safety and stability of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 It is a module schematic diagram of a power grid variable monitoring system of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0045] See also Figure 1 , the present invention provides a technical solution:

[0046] A power grid variable monitoring system includes: a data acquisition and processing module, a feature extraction and analysis module, a health assessment and risk judgment module, and a real-time monitoring and early warning module;

[0047] The data acquisition and processing module collects historical operation data and historical dispatch instruction records of the power grid over the past period of time, analyzes the historical dispatch instruction records, extracts historical dispatch instructions for charging and discharging energy storage devices, summarizes them and records them as a target instruction set; and establishes a one-to-one correspondence between the target instruction set and the corresponding historical operation data to construct a historical operation data set.

[0048] The feature extraction and analysis module extracts the corresponding key trend features for each element in the historical operation data set and constructs a key trend feature vector. Based on the key trend feature vector, it performs load fluctuation analysis and frequency fluctuation analysis on the power grid to identify abnormal fluctuations.

[0049] The health assessment and risk judgment module evaluates the health of the energy storage device corresponding to each target instruction based on the key trend feature vector. Based on the health assessment results of the energy storage device, it determines whether the energy storage device is in a risk state of overcharging or overdischarging. If a risk state is determined to be present, the corresponding target instruction is marked as a risk instruction. The correlation pattern between the risk instruction and abnormal power grid fluctuations is obtained and marked as a risk warning reference.

[0050] The real-time monitoring and early warning module collects real-time operating data of the power grid and analyzes the real-time operating data with reference to the analysis method of historical operating data to obtain the risk judgment result of the current energy storage equipment; the risk judgment result of the current energy storage equipment is compared and analyzed with the corresponding historical operating data set to match the risk warning reference and generate corresponding prompt information.

[0051] The data acquisition and processing module includes a data acquisition unit and a data processing unit;

[0052] The data acquisition unit collects historical operating data and historical dispatch instruction records of the power grid in the past time period; the data processing unit analyzes the historical dispatch instruction records, extracts the historical dispatch instruction records for charging and discharging of energy storage equipment, summarizes them and records them as the target instruction set; and establishes a one-to-one correspondence between the target instruction set and the corresponding historical operating data to construct a historical operation data set.

[0053] The feature extraction and analysis module includes a feature extraction unit and an abnormal fluctuation analysis unit;

[0054] The feature extraction unit extracts the corresponding key trend features for each element in the historical operation data set and constructs a key trend feature vector; the abnormal fluctuation analysis unit performs load fluctuation analysis and frequency fluctuation analysis of the power grid based on the key trend feature vector, thereby identifying abnormal fluctuations.

[0055] The health assessment and risk judgment module includes a health assessment unit, a risk judgment unit, and a risk instruction marking and association unit;

[0056] The health assessment unit evaluates the health of the energy storage device corresponding to each target instruction based on the key trend feature vector; the risk judgment unit determines whether the energy storage device is in a risk state of overcharging or over-discharging based on the assessment result of the health of the energy storage device; if the risk instruction marking and association unit determines that a risk state exists, it marks the corresponding target instruction as a risk instruction, obtains the association pattern between the risk instruction and abnormal fluctuations in the power grid, and marks it as a risk warning reference.

[0057] The real-time monitoring and early warning module includes a real-time operation data collection and analysis unit and an early warning generation unit;

[0058] The real-time operation data acquisition and analysis unit collects the real-time operation data of the power grid and analyzes the real-time operation data with reference to the analysis method of historical operation data, thereby obtaining the risk judgment result of the current energy storage device; the early warning generation unit compares and analyzes the risk judgment result of the current energy storage device with the corresponding historical operation data set to match the risk warning reference and generate corresponding prompt information.

[0059] A method for monitoring power grid variables includes the following steps:

[0060] Step S100: Collect historical operation data and historical dispatch instruction records of the power grid over a past period of time, analyze the historical dispatch instruction records, extract historical dispatch instructions for charging and discharging energy storage devices, summarize and record them as a target instruction set; establish a one-to-one correspondence between the target instruction set and the corresponding historical operation data, thereby constructing a historical operation data set;

[0061] Step S200: For each element in the historical operation data set, extract the corresponding key trend feature and construct a key trend feature vector; based on the key trend feature vector, perform load fluctuation analysis and frequency fluctuation analysis of the power grid to identify abnormal fluctuations;

[0062] Step S300. Evaluate the health of the energy storage device corresponding to each target instruction based on the key trend feature vector; based on the evaluation results of the energy storage device health, determine whether the energy storage device is in a risk state of overcharging or over-discharging; if a risk state is determined to be present, mark the corresponding target instruction as a risk instruction, obtain the correlation pattern between the risk instruction and abnormal power grid fluctuations, and mark it as a risk warning reference;

[0063] Step S400. Collect real-time operation data of the power grid, analyze the real-time operation data with reference to the analysis method of historical operation data, and obtain the risk judgment result of the current energy storage device; compare and analyze the risk judgment result of the current energy storage device with the corresponding historical operation data set to match the risk warning reference and generate corresponding prompt information.

[0064] Step S100 includes:

[0065] S101. Collect historical operation data and historical dispatch instruction records of the power grid in the past time period, wherein the historical operation data includes power grid load data, power grid frequency data, and energy storage system status data; the energy storage system status data includes the state of charge and charge / discharge power of the energy storage device; from the historical dispatch instruction records, according to pre-set screening keywords, filter out historical dispatch instructions for charging and discharging of the energy storage device and mark them as target instructions, summarize all target instructions to form a target instruction set, represented as {I1, I2, ..., In}, where I1 represents the first target instruction, I2 represents the second target instruction, and so on, In represents the nth target instruction, and n represents the total number of target instructions in the past time period;

[0066] S102. According to the call timestamp td of each target instruction in the target instruction set, find the historical operation data within the time period [td-T, td] to form a data pair (Ii, Di), where T represents the time step, Di represents the historical operation data corresponding to the i-th target instruction, and i ranges from 1 to n; summarize all data pairs to construct a historical operation data set S, and express it as: S = {(I1, D1), (I2, D2), ..., (In, Dn)}, where D1 represents the historical operation data corresponding to the first target instruction, D2 represents the historical operation data corresponding to the second target instruction, and so on, Dn represents the historical operation data corresponding to the n-th target instruction.

[0067] Step S200 includes:

[0068] S201. For each element in the historical operation data set, extract the corresponding key trend features; the key trend features include load fluctuation trend features, frequency fluctuation trend features and energy storage device status trend features; wherein the load fluctuation trend feature is the dynamic change rate of load power ΔP at adjacent time points, and the corresponding calculation formula is: ΔP = [P_load(t+Δt)-P_load(t)] / Δt, wherein P_load(t+Δt) represents the grid load power at time (t+Δt), P_load(t) represents the grid load power at time t, and Δt represents the time interval; similarly, the frequency fluctuation trend feature is the frequency change rate, and the frequency change rate Δfc is calculated by referring to the calculation formula of the dynamic change rate of load power ΔP; the energy storage device status trend features include the energy storage charge and discharge power ratio Pc and the charge state change rate S, the corresponding calculation formulas are: Pc = P_charge(t) / [P_discharge(t)+∈], where P_charge(t) represents the charging power of the energy storage device at time t, and P_discharge(t) represents the discharging power of the energy storage device at time t; ∈ represents the minimum value, which is greater than 0; S = [SOC(t+Δt)-SOC(t)] / Δt, where SOC(t+Δt) represents the state of charge of the energy storage device at time (t+Δt), and SOC(t) represents the state of charge of the energy storage device at time t; the extracted key trend features are averaged within the time period [td-T, td] and normalized, so as to construct a key feature vector Gi for each target instruction Ii, and Gi = [ΔP_load_i, Δf_i, P_i, SCR_i];

[0069] S202. Based on the key eigenvectors, statistical analysis is performed on the load power dynamic change rate ΔP_load and the frequency change rate Δf, respectively, to calculate thresholds at different confidence levels, which are the upper thresholds Tp1 and Tf1, and the lower thresholds Tp2 and Tf2. For load fluctuation anomaly analysis, ΔP_load_i in the key eigenvector Gi is compared with the upper threshold Tp1 and the lower threshold Tp2. If ΔP_load_i>Tp1 or ΔP_load_i<Tp2, a preliminary determination is made that load fluctuation anomaly exists. The corresponding energy storage charge-discharge power ratio P_i and state of charge change rate SCR_i are used for auxiliary judgment. When it is preliminarily determined that there is an abnormal load fluctuation, the load fluctuation is confirmed to be abnormal if the following conditions are met at the same time: If ΔP_load_i>Tp1, and P_i<1 and SCR_i<0, it means that the energy storage device may not respond to the load increase and discharge in time, and the load fluctuation is confirmed to be abnormal. If ΔP_load_i<Tp2, and P_i>1 and SCR_i>0, it means that the energy storage device may not effectively utilize the excess power, and the load fluctuation is confirmed to be abnormal.

[0070] S203. For frequency fluctuation anomaly analysis, the frequency change rate Δf_i in the key characteristic vector Gi is compared with the upper threshold Tf1 and the lower threshold Tf2 respectively. If Δf_i>Tf1 or Δf_i<Tf2, then there is a preliminary frequency fluctuation anomaly; calculate the Pearson correlation coefficient r(Δf_i,ΔP_load_i) between the frequency change rate Δf_i and the load power dynamic change rate ΔP_load_i. If |r(Δf_i,ΔP_load_i)|>r, and there is a preliminary frequency fluctuation anomaly, further confirm according to the change direction of load and frequency: when ΔP_load_i>0 and Δf_i<0, the frequency fluctuation anomaly is confirmed; when ΔP_load_i<0 and Δf_i>0, the frequency fluctuation anomaly is confirmed.

[0071] In this embodiment, the conditions for confirming abnormal load fluctuation and abnormal frequency fluctuation are based on the dynamic response of the power grid system and the role of the energy storage device, which are specifically explained as follows:

[0072] Conditions for confirming abnormal load fluctuation:

[0073] 1. Threshold determination of load power dynamic change rate:

[0074] The load fluctuation trend is measured by the dynamic rate of change of load power ΔP_load, which reflects the instantaneous fluctuation of the grid load. If ΔP_load_i>Tp1 or ΔP_load_i<Tp2, it can be considered that the load fluctuation exceeds the normal range and a load fluctuation anomaly is preliminarily determined.

[0075] 2. Auxiliary judgment of energy storage equipment:

[0076] When the load fluctuation exceeds the threshold, the status of the energy storage device is analyzed to further confirm whether the load fluctuation is abnormal. The responsiveness of the energy storage device is crucial to the balance of the power grid. If the energy storage device's charge-discharge power ratio P is small (i.e., the energy storage device is not discharging in time to respond to the load increase) and the state of charge change rate (SCR) is negative (i.e., the energy storage device is not charging effectively), it can be confirmed that the load fluctuation is abnormal.

[0077] Conversely, if the load fluctuation is small (ΔP_load_i<Tp2), but the charge-discharge power ratio of the energy storage device is high (i.e., the energy storage device has excess power that is not released in time), and the state of charge change rate is positive (i.e., the energy storage device has the ability to increase discharge), then it can also be considered that there is an abnormal load fluctuation, indicating that the energy storage device is not fully utilizing its stored energy to balance the load.

[0078] Conditions for confirming abnormal frequency fluctuation:

[0079] 1. Threshold determination of frequency change rate:

[0080] The frequency fluctuation trend is measured by the frequency change rate Δf. If the frequency change rate exceeds the upper threshold Tf1 or is less than the lower threshold Tf2, it is preliminarily determined that there is a frequency fluctuation anomaly, indicating that the frequency of the power grid deviates too much from the normal value.

[0081] 2. Correlation analysis between load and frequency changes:

[0082] Load and frequency are interrelated: an increase in load typically causes a decrease in frequency, while a decrease in load typically causes an increase in frequency. To further confirm whether there is abnormal frequency fluctuation, the Pearson correlation coefficient r(Δf, ΔPload) between the frequency change rate Δf and the load power dynamic change rate ΔPload can be calculated.

[0083] If |r(Δf,ΔPload)| is greater than a set threshold r, and the frequency decreases when the load increases (when ΔP_load_i>0 and Δf_i<0), or the frequency increases when the load decreases (ΔP_load_i<0 and Δf_i>0), it can be confirmed that the frequency fluctuation is abnormal.

[0084] Step S300 includes:

[0085] S301. Extract the energy storage charge-discharge power ratio and state-of-charge change rate from the key trend feature vector, evaluate the health of the energy storage device corresponding to each target instruction, and thus obtain a comprehensive health assessment score Hi. The corresponding calculation formula is:

[0086] Hi=w1·P_i+w2·SCR_i+w3·σ(P_i,SCR_i),

[0087] Wherein, σ(P_i, SCR_i) represents the standard deviation of the charge-discharge power ratio and the state-of-charge change rate of the energy storage device, w1, w2, and w3 represent weight coefficients, and w1+w2+w3=1; the comprehensive health assessment score Hi is compared with the threshold interval Q, and Q=[H_min,H_max], H_min represents the minimum value of the threshold interval Q, and H_max represents the maximum value of the threshold interval Q; if the comprehensive health assessment score Hi is not within the threshold interval Q, and P_i>1 and SCR_i>0, then it is judged as an overcharge risk; if the comprehensive health assessment score Hi is not within the threshold interval Q, and P_i<1 and SCR_i<0, then it is judged as an over-discharge risk;

[0088] S302. Based on the judgment result, mark the corresponding target instruction as a risk instruction; according to the identification result of the abnormal fluctuation in step S200, obtain the abnormal fluctuation timestamp tb, and associate the abnormal fluctuation timestamp tb with the time period of the historical operation data corresponding to the risk instruction, so as to obtain the association pattern between the risk instruction and the abnormal fluctuation of the power grid, and mark the association pattern between the risk instruction and the abnormal fluctuation of the power grid as a risk warning reference.

[0089] The association pattern refers to the relationship between risk instructions and abnormal fluctuations in the power grid. By associating the timestamp of the abnormal fluctuation with the corresponding historical operating data time period, a pattern is formed. This association pattern helps the system identify past abnormal situations and trigger early warnings in similar situations. For example, if there is a risk of overcharging or discharging of energy storage equipment within a specific time period, and abnormal fluctuations (such as voltage or frequency fluctuations) occur in the power grid during that time period, this time period can be used as a risk warning reference. When similar abnormal fluctuations in the power grid occur, the system will issue a corresponding warning based on this association pattern.

[0090] In this embodiment, it is assumed that the specific data of a certain energy storage device during the charging and discharging process at a certain moment is as follows:

[0091] The energy storage device charge-discharge power ratio Pi = 1.2, the energy storage device state of charge change rate SCRi = 0.05, and the standard deviation σ(Pi, SCRi) calculated from historical data = 0.2. According to the above formula, the comprehensive health assessment score Hi is:

[0092] Hi=0.4×1.2+0.4×0.05+0.2×0.2=0.48+0.02+0.04=0.54; the score Hi=0.54 falls within the threshold interval Q=[0.5,1.5], and therefore it is determined that there is no risk state.

[0093] Step S400 includes:

[0094] S401. Collect real-time operation data of the power grid and analyze the real-time operation data with reference to the analysis method of historical operation data to extract the real-time key feature vector SG, and SG = [ΔPs_load, Δfs, Ps, SCRs], where ΔPs_load represents the real-time load fluctuation trend characteristics, Δfs represents the real-time frequency fluctuation trend characteristics, and Ps and SCRs represent the real-time status trend characteristics of the energy storage device; based on the real-time key feature vector SG, refer to the calculation formula of the comprehensive health assessment score Hi to calculate the real-time health assessment score SH; compare the real-time health assessment score SH with the threshold interval Q; if the real-time health assessment score SH is not within the threshold interval Q, and Ps>1 and SCRs>0, it is determined to be an overcharging risk; if the comprehensive health assessment score Hi is not within the threshold interval Q, and Ps<1 and SCRs<0, it is determined to be an over-discharging risk;

[0095] S402. Based on the risk judgment result, extract the historical operation data of the same risk type in the risk instruction, calculate the similarity between the real-time operation data and the historical operation data in turn, and select the element with the largest similarity and the corresponding similarity value greater than the similarity threshold as the matching result; based on the matching result, obtain the corresponding association pattern between the risk instruction and the abnormal fluctuation of the power grid, and mark it as a risk warning reference; based on the risk warning reference, generate corresponding prompt information to relevant personnel, and the relevant personnel will further perform corresponding processing.

[0096] In this embodiment, based on the matching results, risk instructions and associated patterns related to abnormal power grid fluctuations in historical operation data are obtained; prompt information related to risk warnings is generated by the system, and the specific content may include:

[0097] Risk type: "Overcharging risk"

[0098] Risk Description: "The current charging state of the grid energy storage device is abnormal, which may cause equipment damage or safety issues. The energy storage device status Ps>1 and SCRs>0."

[0099] Historical Case Study: "Based on historical similar data, overcharging problems have occurred under similar load fluctuations and frequency fluctuations."

[0100] Prompt message: "Please check the charging status of the energy storage device immediately and adjust the charging strategy to prevent overcharging and device failure."

[0101] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0102] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for monitoring power grid variables, characterized by: The method comprises the following steps: Step S100: Collect historical operation data and historical dispatch instruction records of the power grid over a past period of time, analyze the historical dispatch instruction records, extract historical dispatch instructions for charging and discharging energy storage devices, summarize and record them as a target instruction set; establish a one-to-one correspondence between the target instruction set and the corresponding historical operation data, thereby constructing a historical operation data set; Step S200: For each element in the historical operation data set, extract the corresponding key trend feature and construct a key trend feature vector; based on the key trend feature vector, perform load fluctuation analysis and frequency fluctuation analysis of the power grid to identify abnormal fluctuations; Step S300. Evaluate the health of the energy storage device corresponding to each target instruction based on the key trend feature vector; based on the evaluation results of the energy storage device health, determine whether the energy storage device is in a risk state of overcharging or over-discharging; if a risk state is determined to be present, mark the corresponding target instruction as a risk instruction, obtain the correlation pattern between the risk instruction and abnormal power grid fluctuations, and mark it as a risk warning reference; Step S400. Collect real-time operation data of the power grid, analyze the real-time operation data with reference to the analysis method of historical operation data, and obtain the risk judgment result of the current energy storage device; compare and analyze the risk judgment result of the current energy storage device with the corresponding historical operation data set to match the risk warning reference and generate corresponding prompt information.

2. A power grid variable monitoring method according to claim 1, characterized in that: The step S100 includes: S101. Collect historical operation data and historical dispatch instruction records of the power grid in the past time period, wherein the historical operation data includes power grid load data, power grid frequency data, and energy storage system status data; the energy storage system status data includes the state of charge and charge / discharge power of the energy storage device; from the historical dispatch instruction records, according to pre-set screening keywords, filter out historical dispatch instructions for charging and discharging of the energy storage device and mark them as target instructions, summarize all target instructions to form a target instruction set, represented as {I1, I2, ..., In}, where I1 represents the first target instruction, I2 represents the second target instruction, and so on, In represents the nth target instruction, and n represents the total number of target instructions in the past time period; S102. According to the call timestamp td of each target instruction in the target instruction set, find the historical operation data within the time period [td-T, td] to form a data pair (Ii, Di), where T represents the time step, Di represents the historical operation data corresponding to the i-th target instruction, and i ranges from 1 to n; summarize all data pairs to construct a historical operation data set S, and express it as: S = {(I1, D1), (I2, D2), ..., (In, Dn)}, where D1 represents the historical operation data corresponding to the first target instruction, D2 represents the historical operation data corresponding to the second target instruction, and so on, Dn represents the historical operation data corresponding to the n-th target instruction.

3. A method for monitoring power grid variables according to claim 2, characterized in that: The step S200 includes: S201. For each element in the historical operation data set, extract the corresponding key trend features; the key trend features include load fluctuation trend features, frequency fluctuation trend features and energy storage device status trend features; wherein the load fluctuation trend feature is the dynamic change rate of load power ΔP at adjacent time points, and the corresponding calculation formula is: ΔP = [P_load(t+Δt)-P_load(t)] / Δt, wherein P_load(t+Δt) represents the grid load power at time (t+Δt), P_load(t) represents the grid load power at time t, and Δt represents the time interval; similarly, the frequency fluctuation trend feature is the frequency change rate, and the frequency change rate Δfc is calculated by referring to the calculation formula of the dynamic change rate of load power ΔP; the energy storage device status trend features include the energy storage charge and discharge power ratio Pc and the charge state change rate S, the corresponding calculation formulas are: Pc = P_charge(t) / [P_discharge(t)+∈], where P_charge(t) represents the charging power of the energy storage device at time t, and P_discharge(t) represents the discharging power of the energy storage device at time t; ∈ represents the minimum value, which is greater than 0; S = [SOC(t+Δt)-SOC(t)] / Δt, where SOC(t+Δt) represents the state of charge of the energy storage device at time (t+Δt), and SOC(t) represents the state of charge of the energy storage device at time t; the extracted key trend features are averaged within the time period [td-T, td] and normalized, so as to construct a key feature vector Gi for each target instruction Ii, and Gi = [ΔP_load_i, Δf_i, P_i, SCR_i]; S202. Based on the key eigenvectors, the load power dynamic change rate ΔP_load and the frequency change rate Δf are statistically analyzed respectively, thereby calculating the thresholds at different confidence levels, which are the upper thresholds Tp1 and Tf1, and the lower thresholds Tp2 and Tf2 respectively. For load fluctuation abnormality analysis, ΔP_load_i in the key eigenvector Gi is compared with the upper threshold Tp1 and the lower threshold Tp2 respectively. If ΔP_load_i>Tp1 or ΔP_load_i<Tp2, it is preliminarily determined that there is a load fluctuation abnormality. The corresponding energy storage charge-discharge power ratio P_i and state of charge change rate SCR_i are introduced for auxiliary judgment. When it is preliminarily determined that there is a load fluctuation abnormality, the load fluctuation abnormality is confirmed if the following conditions are met at the same time: if ΔP_load_i>Tp1, P_i<1, and SCR_i<0, the load fluctuation abnormality is confirmed; if ΔP_load_i<Tp2, P_i>1, and SCR_i>0, the load fluctuation abnormality is confirmed. S203. For frequency fluctuation anomaly analysis, the frequency change rate Δf_i in the key characteristic vector Gi is compared with the upper threshold Tf1 and the lower threshold Tf2 respectively. If Δf_i>Tf1 or Δf_i<Tf2, then there is a preliminary frequency fluctuation anomaly; calculate the Pearson correlation coefficient r(Δf_i,ΔP_load_i) between the frequency change rate Δf_i and the load power dynamic change rate ΔP_load_i. If |r(Δf_i,ΔP_load_i)|>r, and there is a preliminary frequency fluctuation anomaly, further confirm according to the change direction of load and frequency: when ΔP_load_i>0 and Δf_i<0, the frequency fluctuation anomaly is confirmed; when ΔP_load_i<0 and Δf_i>0, the frequency fluctuation anomaly is confirmed.

4. A method for monitoring power grid variables according to claim 3, characterized in that: The step S300 includes: S301. Extract the energy storage charge-discharge power ratio and state-of-charge change rate from the key trend feature vector, evaluate the health of the energy storage device corresponding to each target instruction, and thus obtain a comprehensive health assessment score Hi. The corresponding calculation formula is: Hi=w1·P_i+w2·SCR_i+w3·σ(P_i,SCR_i), Wherein, σ(P_i, SCR_i) represents the standard deviation of the charge-discharge power ratio and the state-of-charge change rate of the energy storage device, w1, w2, and w3 represent weight coefficients, and w1+w2+w3=1; the comprehensive health assessment score Hi is compared with the threshold interval Q, and Q=[H_min,H_max], H_min represents the minimum value of the threshold interval Q, and H_max represents the maximum value of the threshold interval Q; if the comprehensive health assessment score Hi is not within the threshold interval Q, and P_i>1 and SCR_i>0, then it is judged as an overcharge risk; if the comprehensive health assessment score Hi is not within the threshold interval Q, and P_i<1 and SCR_i<0, then it is judged as an over-discharge risk; S302. Based on the judgment result, mark the corresponding target instruction as a risk instruction; according to the identification result of the abnormal fluctuation in step S200, obtain the abnormal fluctuation timestamp tb, and associate the abnormal fluctuation timestamp tb with the time period of the historical operation data corresponding to the risk instruction, so as to obtain the association pattern between the risk instruction and the abnormal fluctuation of the power grid, and mark the association pattern between the risk instruction and the abnormal fluctuation of the power grid as a risk warning reference.

5. A method for monitoring power grid variables according to claim 4, characterized in that: The step S400 includes: S401. Collect real-time operation data of the power grid and analyze the real-time operation data with reference to the analysis method of historical operation data to extract the real-time key feature vector SG, and SG = [ΔPs_load, Δfs, Ps, SCRs], where ΔPs_load represents the real-time load fluctuation trend characteristics, Δfs represents the real-time frequency fluctuation trend characteristics, and Ps and SCRs represent the real-time status trend characteristics of the energy storage device; based on the real-time key feature vector SG, refer to the calculation formula of the comprehensive health assessment score Hi to calculate the real-time health assessment score SH; compare the real-time health assessment score SH with the threshold interval Q; if the real-time health assessment score SH is not within the threshold interval Q, and Ps>1 and SCRs>0, it is determined to be an overcharging risk; if the comprehensive health assessment score Hi is not within the threshold interval Q, and Ps<1 and SCRs<0, it is determined to be an over-discharging risk; S402. Based on the risk judgment result, extract the historical operation data of the same risk type in the risk instruction, calculate the similarity between the real-time operation data and the historical operation data in turn, and select the element with the largest similarity and the corresponding similarity value greater than the similarity threshold as the matching result; based on the matching result, obtain the corresponding association pattern between the risk instruction and the abnormal fluctuation of the power grid, and mark it as a risk warning reference; based on the risk warning reference, generate corresponding prompt information to relevant personnel, and the relevant personnel will further perform corresponding processing.

6. A power grid variable monitoring system, applied to a power grid variable monitoring method according to any one of claims 1 to 5, characterized in that: The system includes: a data acquisition and processing module, a feature extraction and analysis module, a health assessment and risk judgment module, and a real-time monitoring and early warning module; The data acquisition and processing module collects historical operation data and historical dispatch instruction records of the power grid in the past time period, analyzes the historical dispatch instruction records, extracts historical dispatch instructions for charging and discharging of energy storage devices, summarizes and records them as a target instruction set; and establishes a one-to-one correspondence between the target instruction set and the corresponding historical operation data, thereby constructing a historical operation data set; The feature extraction and analysis module extracts the corresponding key trend features for each element in the historical operation data set and constructs a key trend feature vector; based on the key trend feature vector, it performs load fluctuation analysis and frequency fluctuation analysis of the power grid to identify abnormal fluctuations; The health assessment and risk judgment module evaluates the health of the energy storage device corresponding to each target instruction based on the key trend feature vector; based on the evaluation results of the health of the energy storage device, it determines whether the energy storage device is in a risk state of overcharging or overdischarging; if a risk state is determined to be present, the corresponding target instruction is marked as a risk instruction, and the correlation pattern between the risk instruction and abnormal power grid fluctuations is obtained and marked as a risk warning reference; The real-time monitoring and early warning module collects real-time operation data of the power grid, analyzes the real-time operation data with reference to the analysis method of historical operation data, and thus obtains the risk judgment result of the current energy storage device; compares and analyzes the risk judgment result of the current energy storage device with the corresponding historical operation data set to match the risk warning reference and generate corresponding prompt information.

7. A power grid variable monitoring system according to claim 6, characterized in that: The data acquisition and processing module includes a data acquisition unit and a data processing unit; The data acquisition unit collects historical operation data and historical dispatch instruction records of the power grid in the past time period; the data processing unit analyzes the historical dispatch instruction records, extracts historical dispatch instructions for charging and discharging energy storage devices, summarizes and records them as a target instruction set; and establishes a one-to-one correspondence between the target instruction set and the corresponding historical operation data, thereby constructing a historical operation data set.

8. The power grid variable monitoring system according to claim 6, characterized in that: The feature extraction and analysis module includes a feature extraction unit and an abnormal fluctuation analysis unit; The feature extraction unit extracts corresponding key trend features for each element in the historical operation data set and constructs a key trend feature vector; The abnormal fluctuation analysis unit performs load fluctuation analysis and frequency fluctuation analysis of the power grid based on the key trend feature vector, thereby identifying abnormal fluctuations.

9. The power grid variable monitoring system according to claim 6, characterized in that: The health assessment and risk judgment module includes a health assessment unit, a risk judgment unit, and a risk instruction marking and association unit; The health assessment unit assesses the health of the energy storage device corresponding to each target instruction based on the key trend feature vector; The risk judgment unit judges whether the energy storage device is in a risk state of overcharging or overdischarging based on the evaluation result of the health of the energy storage device; If the risk instruction marking and association unit determines that a risk state exists, it marks the corresponding target instruction as a risk instruction, obtains an association pattern between the risk instruction and abnormal power grid fluctuations, and marks it as a risk warning reference.

10. The power grid variable monitoring system according to claim 6, characterized in that: The real-time monitoring and early warning module includes a real-time operation data acquisition and analysis unit and an early warning generation unit; The real-time operation data acquisition and analysis unit acquires real-time operation data of the power grid, and analyzes the real-time operation data with reference to the analysis method of historical operation data, thereby obtaining a risk judgment result of the current energy storage device; The warning generation unit compares and analyzes the risk judgment result of the current energy storage device with the corresponding historical operation data set to match the risk warning reference and generate corresponding prompt information.

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