Pumped storage plant state real-time evaluation system and method based on data analysis

Abstract

The application discloses a kind of real-time evaluation system and method of pumped storage equipment state based on data analysis, it is related to big data analysis technical field, the present application is by setting unit speed data acquisition cycle, using PLC control system is combined with OPC single unit speed fluctuation peak value UA protocol and reluctance type speed sensor respectively obtains historical and real-time unit speed data, constructs equipment state analysis first set and real-time equipment state analysis second set, again by data traversal analysis, weighted fusion calculation and time series prediction model, obtains fluctuation peak value, standard unit speed, speed change trend, combined with unit speed upper limit value and standard unit speed is monitored, constructs analysis set and predicts trend with monitoring result, realize to pumped storage equipment unit speed fluctuation characteristics of accurate capture, dynamic evaluation and abnormal early warning of operating state are realized, improve data reliability and analysis accuracy, provide support for equipment state monitoring.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, specifically to a real-time status assessment system and method for pumped storage equipment based on data analysis. Background Technology

[0002] Pumped storage hydroelectric power systems are core facilities for the flexible storage and regulation of electrical energy in power systems. They play a crucial role in ensuring stable grid operation, enhancing the absorption capacity of renewable energy, and optimizing the energy structure. Their importance lies in their ability to effectively mitigate grid load fluctuations, improve power supply reliability, reduce operational risks caused by peak-valley differences, and provide peak-shaving and frequency regulation support for large-scale grid integration of new energy sources.

[0003] The operating speed of pumped storage units is affected by multiple factors, including grid load fluctuations, unit start-up and shutdown switching, changes in water flow conditions, and equipment mechanical wear. Its stability directly determines the unit's operating efficiency and energy conversion accuracy. Abnormal operating speed can lead to increased unit vibration, accelerated component wear, and even shutdown failures, thereby affecting the grid's peak-shaving and frequency regulation capabilities, renewable energy absorption efficiency, and the safety and reliability of the power system. Therefore, a real-time dynamic analysis method for evaluating unit operating speed is urgently needed to accurately capture speed fluctuation characteristics and predict equipment operating status, providing technical support for the safe and efficient operation of pumped storage units and intelligent grid control. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time status assessment system and method for pumped storage equipment based on data analysis, so as to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a real-time status assessment method for pumped storage equipment based on data analysis, the real-time status assessment method for pumped storage equipment comprising the following steps: Step S1: Set the unit speed data acquisition cycle, acquire historical unit speed data of each pumped storage unit to construct the first set of equipment status analysis; acquire unit speed data of each pumped storage unit within the unit speed data acquisition cycle to construct the second set of real-time equipment status analysis. Step S1-1: Using the data export tool of the PLC control system in conjunction with the OPC single unit speed fluctuation peak UA interface protocol, historical unit speed data of each pumped storage device in the current unit speed data acquisition cycle is obtained. The unit speed data is represented as the number of rotations of the pumped storage device unit spindle per unit time. Step S1-2: Select the unique identifier of the pumped storage equipment as the primary key and the unit speed data corresponding to the timestamp as the value for storage to construct the first set of equipment status analysis; Step S1-3: Obtain the unit speed data of the pumped storage equipment during the data acquisition period through the magnetoresistive speed sensor, and construct the second set of real-time equipment status analysis according to the storage method of step S1-2; By setting a data acquisition cycle and combining the PLC control system with the OPC single-time unit speed fluctuation peak UA protocol to obtain historical data, and using a magnetoresistive speed sensor to collect real-time data, two sets of equipment status analysis (first set) and real-time equipment status analysis (second set) are constructed for analysis. This allows for trend analysis and fault prediction based on historical data, as well as real-time monitoring of equipment operating status based on real-time data. This enables a comprehensive, accurate, and efficient understanding of the pumped storage unit speed, ensuring the safe and stable operation of the equipment.

[0006] Step S2: Perform data traversal analysis based on the first set of equipment status analysis to obtain the peak fluctuation of the unit speed data; and calculate the standard unit speed of the pumped storage equipment within the current unit speed data acquisition period by weighted fusion calculation based on the first set of equipment status analysis and the second set of real-time equipment status analysis. Step S2-1: Iterate through and read the unit speed data in the first set of equipment status analysis. Calculate the absolute value of the difference between two sets of unit speed data at adjacent times in the first set of equipment status analysis, and select the maximum absolute value of the pumped storage unit speed using the bubble sort algorithm. This maximum value is recorded as the comprehensive unit speed fluctuation peak. The comprehensive unit speed fluctuation peak represents the maximum instantaneous fluctuation of the number of rotations of the pumped storage unit's main shaft between two adjacent sampling times during unit operation. Step S2-2: Calculate the average value of the unit speed data in the first set of equipment status analysis, and record it as the first unit speed average; calculate the average value of the unit speed data in the second set of real-time equipment status analysis, and record it as the second unit speed average. Step S2-3: Based on the average speed of the first unit and the average speed of the second unit, the standard unit speed of the pumped storage equipment within the current unit speed data collection period is obtained through weighted fusion calculation. The standard unit speed of pumped storage hydroelectric power equipment is calculated using the following formula: ; In the formula, S tandard V represents the standard unit speed of the pumped storage hydroelectric power plant; 'a' represents the weighting coefficient for the weighted fusion calculation, with a value range of 0 ≤ w ≤ 1; 'n' represents the number of sample unit speed data in the first set of equipment status analysis; h，i Represented as the first set V of equipment status analysis h The unit speed value at the i-th time point; m represents the number of unit speed data samples in the second set of real-time equipment status analysis; Vr，i Represented as the second set V of real-time device status analysis r The unit speed value at the i-th time point; This calculation method obtains the peak value of the unit speed fluctuation through traversal analysis, grasps the instantaneous fluctuation of the unit operation, and then calculates the mean by combining historical data and real-time data. The standard unit speed is obtained by weighted fusion. It takes into account both the stability of historical data and the dynamism of real-time data, which can accurately reflect the real state of the pumped storage equipment unit speed, effectively improve the reliability of data and the accuracy of analysis, and provide strong support for equipment operation status monitoring and fault diagnosis.

[0007] Step S3: Obtain historical unit speed data based on the unique identifier of the pumped storage equipment to construct a single unit speed analysis set. Analyze the unit speed change trend of the pumped storage equipment through the single unit speed analysis set, and extract the maximum value of the single unit speed fluctuation, which is recorded as the single unit speed fluctuation peak value. Step S3-1: Select the unique identifier of any pumped storage device within the current unit speed data acquisition period as the index. Extract the historical unit speed data of the corresponding pumped storage device from the first set of equipment status analysis using the index. Construct a single unit speed analysis set according to the storage method of step S1-2. Specifically, the single unit speed analysis set is an independent dataset established for each pumped storage device. This set uses the unique identifier of the device as the primary key index and associates and stores all unit speed data within each unit speed data acquisition period to form a one-to-one mapping time-series data structure. Step S3-2: By using a time series prediction model based on the single unit speed analysis set, the changing trend of the pumped storage unit speed is predicted. Step S3-3: Iterate through and read the unit speed data in the single unit speed analysis set. Calculate the absolute value of the difference between two sets of unit speed data at adjacent times, and use it as the maximum value of the single unit speed fluctuation of the pumped storage equipment, which is recorded as the peak value of the single unit speed fluctuation. By constructing a single-unit speed analysis set, using a time series prediction model to analyze the speed change trend, and extracting the maximum value of a single speed fluctuation, we can not only predict the equipment's operating trend, but also capture instantaneous fluctuation extremes. This provides data support for accurately grasping equipment performance and providing early warning of abnormal fluctuations, thereby improving equipment operating stability and maintenance efficiency.

[0008] Step S4: Obtain the overspeed value of the pumped storage unit based on the trend of unit speed change and the peak value of single unit speed fluctuation; monitor the overspeed value of the unit speed using the standard unit speed of the pumped storage unit; monitor the pumped storage unit based on the overspeed value of the unit speed and the standard unit speed; analyze and store the monitoring results to construct a unit speed analysis set. Step S4-1: Calculate the average unit speed by predicting the change trend of the pumped storage unit speed. Step S4-2: Using the combined unit speed fluctuation peak and the single unit speed fluctuation peak, the standard unit speed fluctuation peak of the pumped storage equipment is obtained by weighted fusion calculation. The standard unit speed fluctuation peak represents the instantaneous maximum value of the number of rotations of the unit's main shaft per unit time after the pumped storage equipment is affected by negative fluctuation factors during operation. The peak speed fluctuation of a standard pumped storage unit is calculated using the following formula: ; In the formula, F tandard The standard unit speed fluctuation peak value of the pumped storage hydroelectric power plant is represented by F; w represents the weighting coefficient in the weighted fusion calculation of the standard unit speed fluctuation peak value, with a value range of 0 ≤ w ≤ 1; max This represents the peak value of the combined unit speed fluctuation of the pumped storage hydroelectric power plant; F single This represents the peak value of the single-cycle unit speed fluctuation of a pumped storage hydroelectric power plant. Step S4-3: The upper limit of the unit speed is obtained by adding the average unit speed of the pumped storage equipment to the peak value of the standard unit speed fluctuation. The upper limit of the unit speed represents the maximum limit value that the pumped storage equipment can reach under safe operating conditions. The upper limit of the unit speed of pumped storage hydroelectric power equipment is calculated using the following formula: ; In the formula, f tandard This represents the peak value of the standard unit speed fluctuation of a pumped storage hydroelectric power plant; F avg This represents the average unit speed of the pumped storage energy equipment within the single-unit speed analysis set.

[0009] Step S4-4: Monitor the pumped storage equipment by comparing the upper limit of the unit speed with the standard unit speed, and analyze and store the monitoring results to form a unit speed analysis set. The specific process of the data analysis and processing is as follows: Step S4-4-1: Subtract the standard unit speed from the upper limit of the pumped storage unit's speed, and process the absolute value of the difference to obtain the absolute value deviation of the upper limit of the unit speed from the standard value. Step S4-4-2: Select the timestamp data of the pumped storage equipment as the primary key, select the absolute value deviation of the upper limit of the unit speed from the standard value as the value, and store it after time sequence to build a unit speed analysis set; By combining the unit speed change trend with the single peak value to calculate the overspeed value, and monitoring with the standard speed as the benchmark, it is possible to determine the limit threshold for safe operation of pumped storage equipment, and to quantify the degree of speed fluctuation deviation. The resulting unit speed analysis set can intuitively reflect the equipment status change trend, providing data basis for precise control and preventive maintenance of the equipment, and ensuring the efficient and stable operation of pumped storage equipment.

[0010] Step S5: Predict the unit speed change trend of pumped storage equipment through group speed analysis and ensemble analysis; evaluate and warn the pumped storage equipment based on the unit speed change trend of each pumped storage equipment within the current unit speed data collection period. Step S5-1: Iterate through and read the absolute value deviation of the upper limit of the unit speed from the standard value in the group speed analysis set, and input them into the time series prediction model to analyze and predict the change trend of the unit speed of the pumped storage equipment. Step S5-2: Using the data analysis process from Step S3 to Step S4, obtain the unit speed change trend of each pumped storage unit within the current unit speed data acquisition cycle; Step S5-3: Extract the unit speed value corresponding to the maximum value of the horizontal axis in the trend of pumped storage unit speed change. Compare the extracted unit speed value with the upper limit of the unit speed. Based on the comparison result, evaluate and issue an early warning for the pumped storage equipment. The specific process of the comparison is as follows: Step S5-3-1: When the unit speed of the pumped storage equipment exceeds the upper limit of the unit speed, it is judged as an abnormal unit speed state and a warning signal is issued. Step S5-3-2: When the unit speed of the pumped storage power station does not exceed the upper limit of the unit speed, it is judged that the unit speed is in a normal state, no warning signal is issued, and the unit speed is continuously monitored. By analyzing the unit speed analysis set through time series prediction model and combining the results of multi-stage data analysis, the speed change trend of each piece of equipment is obtained. The predicted extreme values ​​are compared with the safety thresholds to achieve dynamic early warning of the operating status of pumped storage equipment. This can not only detect abnormal speed in time to avoid equipment damage, but also ensure the efficient and stable operation of equipment through continuous monitoring, thereby improving the safety and reliability of pumped storage system.

[0011] Furthermore, a real-time status assessment system for pumped storage equipment based on data analysis is provided, which includes a data acquisition module, a data processing module, a single-machine analysis module, a threshold monitoring module, and an assessment and early warning module. The data acquisition module is used to acquire historical and real-time unit speed data of pumped storage equipment and construct a set; the data processing module is used to calculate and analyze the acquired historical and real-time data and generate standard unit speeds; the single-unit analysis module is used to predict speed trends and analyze fluctuation characteristics based on single-unit historical data; the threshold monitoring module is used to combine the average speed and peak speed to calculate the safe operation threshold of the unit and analyze the deviation; the evaluation and early warning module is used to predict future trends based on deviation data and trigger abnormal early warnings. The output of the data acquisition module is electrically connected to the input of the data processing module; the output of the data processing module is electrically connected to the input of the stand-alone analysis module; the output of the stand-alone analysis module is electrically connected to the input of the threshold monitoring module; and the output of the threshold monitoring module is electrically connected to the input of the evaluation and early warning module. The data acquisition module includes a historical data acquisition unit and a real-time data acquisition unit; the historical data acquisition unit is used to acquire historical unit speed data of the pumped storage equipment through the PLC control system and the OPC single unit speed fluctuation peak UA protocol; the real-time data acquisition unit is used to acquire real-time unit speed data of the pumped storage equipment in the current cycle through a magnetoresistive speed sensor. The data processing module includes a peak value calculation unit and a mean value fusion unit; the peak value calculation unit is used to traverse historical data and calculate the peak value of the comprehensive unit speed fluctuation of the pumped storage equipment using the bubble sort algorithm; the mean value fusion unit is used to calculate the average value of historical and real-time data and generate a standard unit speed through weighted fusion. The single-unit analysis module includes a trend prediction unit and a fluctuation analysis unit; the trend prediction unit is used to predict the change trend of the unit speed of the pumped storage equipment through a time series model; the fluctuation analysis unit is used to calculate the absolute value of the difference in speed between adjacent moments of a single unit and extract the peak value of the single unit speed fluctuation. The threshold monitoring module includes a threshold calculation unit and a deviation analysis unit. The threshold calculation unit is used to calculate the upper limit of safe operation of the pumped storage equipment by combining the average unit speed with the standard peak speed. The deviation analysis unit is used to compare the upper limit of speed with the standard speed and calculate the deviation, store it, and construct an analysis set. The assessment and early warning module includes a trend prediction unit and an early warning triggering unit. The trend prediction unit is used to predict the future rotational speed change trend of the pumped storage equipment based on historical deviation data. The early warning triggering unit is used to compare the real-time rotational speed with the upper limit value, determine the unit status, and trigger abnormal early warning or continuous monitoring.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a multi-dimensional data analysis system, comprehensively utilizing historical and real-time data, to accurately capture the speed fluctuation characteristics of pumped storage units and dynamically predict speed change trends, providing comprehensive and reliable data support for the evaluation of pumped storage equipment operation status, and significantly improving the accuracy and foresight of the evaluation results.

[0013] 2. By combining historical stability with real-time dynamics, this invention scientifically calculates the standard unit speed and peak fluctuation, effectively quantifies the degree of deviation of equipment operating status, provides clear and specific reference indicators for monitoring and early warning of pumped storage equipment, and enhances the scientificity and practicality of the evaluation system.

[0014] 3. This invention establishes an intelligent early warning mechanism and predicts abnormal rotation speed in advance based on a time series prediction model, thereby enabling preventive maintenance of pumped storage equipment. This not only avoids sudden failures of pumped storage equipment but also optimizes equipment operation and maintenance plans, significantly improving the operational safety and economy of pumped storage equipment and effectively ensuring the stable operation of the power grid. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a real-time status assessment method for pumped storage equipment based on data analysis, according to the present invention. Figure 2 This is a schematic diagram of the structure of a real-time status assessment system for pumped storage equipment based on data analysis, according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: As Figure 1 As shown, the present invention provides a technical solution: a real-time status assessment method for pumped storage equipment based on data analysis. This real-time status assessment method for pumped storage equipment includes the following steps: Step S1: Set the unit speed data acquisition cycle, acquire historical unit speed data of each pumped storage unit to construct the first set of equipment status analysis; acquire unit speed data of each pumped storage unit within the unit speed data acquisition cycle to construct the second set of real-time equipment status analysis. Step S1-1: Using the data export tool of the PLC control system in conjunction with the OPC single unit speed fluctuation peak UA interface protocol, historical unit speed data of each pumped storage device in the current unit speed data acquisition cycle is obtained. The unit speed data is represented as the number of rotations of the pumped storage device unit spindle per unit time. Step S1-2: Select the unique identifier of the pumped storage equipment as the primary key and the unit speed data corresponding to the timestamp as the value for storage to construct the first set of equipment status analysis; Step S1-3: Obtain the unit speed data of the pumped storage equipment during the data acquisition period through the magnetoresistive speed sensor, and construct the second set of real-time equipment status analysis according to the storage method of step S1-2; In practical implementation, the data export tool of the PLC control system, combined with the OPC single-time unit speed fluctuation peak UA interface protocol, batches of unit speed data per minute for each device over the past hour are retrieved from the historical database, such as 1000 r / min, 1005 r / min, etc., to construct the first set of equipment status analysis. At the same time, the current speed data per minute is collected in real time by a magnetoresistive speed sensor to construct the second set. The principle is to use industrial protocols to achieve efficient acquisition of historical data and sensors to achieve real-time data acquisition. Step S2: Perform data traversal analysis based on the first set of equipment status analysis to obtain the peak fluctuation of the unit speed data; and calculate the standard unit speed of the pumped storage equipment within the current unit speed data acquisition period by weighted fusion calculation based on the first set of equipment status analysis and the second set of real-time equipment status analysis. Step S2-1: Iterate through and read the unit speed data in the first set of equipment status analysis. Calculate the absolute value of the difference between two sets of unit speed data at adjacent times in the first set of equipment status analysis, and select the maximum absolute value of the pumped storage unit speed using the bubble sort algorithm. This maximum value is recorded as the comprehensive unit speed fluctuation peak. The comprehensive unit speed fluctuation peak represents the maximum instantaneous fluctuation of the number of rotations of the pumped storage unit's main shaft between two adjacent sampling times during unit operation. Step S2-2: Calculate the average value of the unit speed data in the first set of equipment status analysis, and record it as the first unit speed average; calculate the average value of the unit speed data in the second set of real-time equipment status analysis, and record it as the second unit speed average. Step S2-3: Based on the average speed of the first unit and the average speed of the second unit, the standard unit speed of the pumped storage equipment within the current unit speed data collection period is obtained through weighted fusion calculation. In practical implementation, taking the speed data (1000, 1008, 1003, 995, 1010...) of a certain unit at 10 consecutive single-time unit speed fluctuation peaks as an example, the single-time unit speed fluctuation peak with the largest adjacent difference of 15 r / min is found by bubble sort as the comprehensive fluctuation peak. Then, the standard speed (e.g., a single-time unit speed fluctuation peak of 1002 r / min) is calculated by using the historical data average (weighted at 0.6) and the real-time data average (weighted at 0.4). The principle is to improve accuracy by dynamically integrating the statistical characteristics of historical data with real-time data.

[0018] Step S3: Obtain historical unit speed data based on the unique identifier of the pumped storage equipment to construct a single unit speed analysis set. Analyze the unit speed change trend of the pumped storage equipment through the single unit speed analysis set, and extract the maximum value of the single unit speed fluctuation, which is recorded as the single unit speed fluctuation peak value. Step S3-1: Select the unique identifier of any pumped storage device within the current unit speed data acquisition period as the index. Extract the historical unit speed data of the corresponding pumped storage device from the first set of equipment status analysis using the index. Construct a single unit speed analysis set according to the storage method of step S1-2. Specifically, the single unit speed analysis set is an independent dataset established for each pumped storage device. This set uses the unique identifier of the device as the primary key index and associates and stores all unit speed data within each unit speed data acquisition period to form a one-to-one mapping time-series data structure. Step S3-2: By using a time series prediction model based on the single unit speed analysis set, the changing trend of the pumped storage unit speed is predicted. Step S3-3: Iterate through and read the unit speed data in the single unit speed analysis set. Calculate the absolute value of the difference between two sets of unit speed data at adjacent times, and use it as the maximum value of the single unit speed fluctuation of the pumped storage equipment, which is recorded as the peak value of the single unit speed fluctuation. In practical implementation, for example, for unit number PS-001, its rotational speed data over the past 24 hours is extracted to construct a single-unit analysis set. The ARIMA model is used to predict that the rotational speed will show an upward trend in the next 2 hours. At the same time, the maximum difference between adjacent times of 20 r / min is calculated as the peak value of a single fluctuation. Trend prediction is achieved based on time series modeling of the historical data of a single unit.

[0019] Step S4: Obtain the overspeed value of the pumped storage unit based on the trend of unit speed change and the peak value of single unit speed fluctuation; monitor the overspeed value of the unit speed using the standard unit speed of the pumped storage unit; monitor the pumped storage unit based on the overspeed value of the unit speed and the standard unit speed; analyze and store the monitoring results to construct a unit speed analysis set. Step S4-1: Calculate the average unit speed by predicting the change trend of the pumped storage unit speed. Step S4-2: Using the combined unit speed fluctuation peak and the single unit speed fluctuation peak, the standard unit speed fluctuation peak of the pumped storage equipment is obtained by weighted fusion calculation. The standard unit speed fluctuation peak represents the instantaneous maximum value of the number of rotations of the unit's main shaft per unit time after the pumped storage equipment is affected by negative fluctuation factors during operation. Step S4-3: The upper limit of the unit speed is obtained by adding the average unit speed of the pumped storage equipment to the peak value of the standard unit speed fluctuation. The upper limit of the unit speed represents the maximum limit value that the pumped storage equipment can reach under safe operating conditions. Step S4-4: Monitor the pumped storage equipment by comparing the upper limit of the unit speed with the standard unit speed, and analyze and store the monitoring results to form a unit speed analysis set. The specific process of the data analysis and processing is as follows: Step S4-4-1: Subtract the standard unit speed from the upper limit of the pumped storage unit's speed, and process the absolute value of the difference to obtain the absolute value deviation of the upper limit of the unit speed from the standard value. Step S4-4-2: Select the timestamp data of the pumped storage equipment as the primary key, select the absolute value deviation of the upper limit of the unit speed from the standard value as the value, and store it after time sequence to build a unit speed analysis set; In practical implementation, a certain unit predicts an average speed of 1005 r / min, a comprehensive fluctuation peak of 15 r / min, and a single fluctuation peak of 20 r / min, with weights of 0.7 and 0.3 respectively. The standard fluctuation peak is calculated to be 16.5 r / min, resulting in an upper limit of 1021.5 r / min for the speed. When the current speed is monitored at 1018 r / min, the deviation is calculated to be 3.5 r / min and stored. A safety threshold is set by superimposing the average and the fluctuation peak.

[0020] Step S5: Predict the unit speed change trend of pumped storage equipment through group speed analysis and ensemble analysis; evaluate and warn the pumped storage equipment based on the unit speed change trend of each pumped storage equipment within the current unit speed data collection period. Step S5-1: Iterate through and read the absolute value deviation of the upper limit of the unit speed from the standard value in the group speed analysis set, and input them into the time series prediction model to analyze and predict the change trend of the unit speed of the pumped storage equipment. Step S5-2: Using the data analysis process from Step S3 to Step S4, obtain the unit speed change trend of each pumped storage unit within the current unit speed data acquisition cycle; Step S5-3: Extract the unit speed value corresponding to the maximum value of the horizontal axis in the trend of pumped storage unit speed change. Compare the extracted unit speed value with the upper limit of the unit speed. Based on the comparison result, evaluate and issue an early warning for the pumped storage equipment. The specific process of the comparison is as follows: Step S5-3-1: When the unit speed of the pumped storage equipment exceeds the upper limit of the unit speed, it is judged as an abnormal unit speed state and a warning signal is issued. Step S5-3-2: When the unit speed of the pumped storage power station does not exceed the upper limit of the unit speed, it is judged that the unit speed is in a normal state, no warning signal is issued, and the unit speed is continuously monitored. In practical implementation, taking the predicted speed curve of a certain unit for the next hour as an example, the endpoint speed value of 1025 r / min is extracted and compared with the upper limit value of 1021.5 r / min. When the limit is exceeded, an audible and visual warning is triggered. The principle is to achieve early warning by comparing the predicted value with the threshold. It should be noted that the time series prediction model needs to be updated with the latest data regularly.

[0021] Example 2, as Figure 2 As shown, the present invention provides a real-time status assessment system for pumped storage equipment based on data analysis. The real-time status assessment system for pumped storage equipment includes a data acquisition module, a data processing module, a single-machine analysis module, a threshold monitoring module, and an assessment and early warning module. The data acquisition module is used to acquire historical and real-time unit speed data of pumped storage equipment and construct a set; the data processing module is used to calculate and analyze the acquired historical and real-time data and generate standard unit speeds; the single-unit analysis module is used to predict speed trends and analyze fluctuation characteristics based on single-unit historical data; the threshold monitoring module is used to combine the average speed and peak speed to calculate the safe operation threshold of the unit and analyze the deviation; the evaluation and early warning module is used to predict future trends based on deviation data and trigger abnormal early warnings. The output of the data acquisition module is electrically connected to the input of the data processing module; the output of the data processing module is electrically connected to the input of the stand-alone analysis module; the output of the stand-alone analysis module is electrically connected to the input of the threshold monitoring module; and the output of the threshold monitoring module is electrically connected to the input of the evaluation and early warning module. The data acquisition module includes a historical data acquisition unit and a real-time data acquisition unit; the historical data acquisition unit is used to acquire historical unit speed data of the pumped storage equipment through the PLC control system and the OPC single unit speed fluctuation peak UA protocol; the real-time data acquisition unit is used to acquire real-time unit speed data of the pumped storage equipment in the current cycle through a magnetoresistive speed sensor. The data processing module includes a peak value calculation unit and a mean value fusion unit; the peak value calculation unit is used to traverse historical data and calculate the peak value of the comprehensive unit speed fluctuation of the pumped storage equipment using the bubble sort algorithm; the mean value fusion unit is used to calculate the average value of historical and real-time data and generate a standard unit speed through weighted fusion. The single-unit analysis module includes a trend prediction unit and a fluctuation analysis unit; the trend prediction unit is used to predict the change trend of the unit speed of the pumped storage equipment through a time series model; the fluctuation analysis unit is used to calculate the absolute value of the difference in speed between adjacent moments of a single unit and extract the peak value of the single unit speed fluctuation. The threshold monitoring module includes a threshold calculation unit and a deviation analysis unit. The threshold calculation unit is used to calculate the upper limit of safe operation of the pumped storage equipment by combining the average unit speed with the standard peak speed. The deviation analysis unit is used to compare the upper limit of speed with the standard speed and calculate the deviation, store it, and construct an analysis set. The assessment and early warning module includes a trend prediction unit and an early warning triggering unit. The trend prediction unit is used to predict the future rotational speed change trend of the pumped storage equipment based on historical deviation data. The early warning triggering unit is used to compare the real-time rotational speed with the upper limit value, determine the unit status, and trigger abnormal early warning or continuous monitoring.

[0022] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for real-time status assessment of pumped storage equipment based on data analysis, characterized in that: The real-time status assessment method for pumped storage equipment includes the following steps: Step S1: Set the unit speed data acquisition cycle, acquire historical unit speed data of each pumped storage unit to construct the first set of equipment status analysis; acquire unit speed data of each pumped storage unit within the unit speed data acquisition cycle to construct the second set of real-time equipment status analysis. Step S2: Perform data traversal analysis based on the first set of equipment status analysis, calculate the absolute value of the difference between the two sets of unit speed data at adjacent times, select the maximum absolute value of the pumped storage equipment unit speed to obtain the comprehensive unit speed fluctuation peak; and obtain the standard unit speed of the pumped storage equipment within the current unit speed data acquisition period by weighted fusion calculation based on the first set of equipment status analysis and the second set of real-time equipment status analysis. Step S3: Obtain historical unit speed data based on the unique identifier of the pumped storage equipment to construct a single unit speed analysis set. Analyze the unit speed change trend of the pumped storage equipment through the single unit speed analysis set. Calculate the absolute value of the difference between two sets of unit speed data at adjacent times as the maximum value of the single unit speed fluctuation of the pumped storage equipment, and record it as the peak value of the single unit speed fluctuation. Step S4: Using the combined peak value of unit speed fluctuation and the peak value of single unit speed fluctuation, the standard peak value of pumped storage equipment is obtained by weighted fusion calculation. The upper limit of unit speed is obtained by adding the average unit speed of pumped storage equipment to the peak value of standard unit speed fluctuation. The upper limit of unit speed is monitored using the standard unit speed of pumped storage equipment. The pumped storage equipment is monitored based on the upper limit of unit speed and the standard unit speed. The monitoring results are analyzed and stored to construct a unit speed analysis set. Step S5: Predict the changing trend of pumped storage unit speed through unit speed analysis set analysis; evaluate and warn of pumped storage equipment based on the changing trend of unit speed of each pumped storage unit within the current unit speed data collection period.

2. The method for real-time status assessment of pumped storage equipment based on data analysis according to claim 1, characterized in that: The specific steps of step S1 are as follows: Step S1-1: Using the data export tool of the PLC control system in conjunction with the OPC single unit speed fluctuation peak UA interface protocol, historical unit speed data of each pumped storage device in the current unit speed data acquisition cycle is obtained. The unit speed data is represented as the number of rotations of the pumped storage device unit spindle per unit time. Step S1-2: Select the unique identifier of the pumped storage equipment as the primary key and the unit speed data corresponding to the timestamp as the value for storage to construct the first set of equipment status analysis; Step S1-3: Obtain the unit speed data of the pumped storage equipment during the data acquisition period through the magnetoresistive speed sensor, and construct the second set of real-time equipment status analysis according to the storage method of step S1-2.

3. The method for real-time status assessment of pumped storage equipment based on data analysis according to claim 2, characterized in that: The specific steps of step S2 are as follows: Step S2-1: Iterate through and read the unit speed data in the first set of equipment status analysis. Calculate the absolute value of the difference between two sets of unit speed data at adjacent times in the first set of equipment status analysis, and select the maximum absolute value of the pumped storage unit speed using the bubble sort algorithm. This maximum value is recorded as the comprehensive unit speed fluctuation peak. The comprehensive unit speed fluctuation peak represents the maximum instantaneous fluctuation of the number of rotations of the pumped storage unit's main shaft between two adjacent sampling times during unit operation. Step S2-2: Calculate the average value of the unit speed data in the first set of equipment status analysis, and record it as the average value of the first unit speed; Calculate the average value of the unit speed data in the second set of real-time equipment status analysis, and record it as the average value of the second unit speed. Step S2-3: Based on the average speed of the first unit and the average speed of the second unit, the standard unit speed of the pumped storage equipment within the current unit speed data collection period is obtained through weighted fusion calculation.

4. The method for real-time status assessment of pumped storage equipment based on data analysis according to claim 3, characterized in that: The specific steps of step S3 are as follows: Step S3-1: Select the unique identifier of any pumped storage device within the current unit speed data acquisition period as the index. Extract the historical unit speed data of the corresponding pumped storage device from the first set of equipment status analysis using the index. Construct a single unit speed analysis set according to the storage method of step S1-2. Specifically, the single unit speed analysis set is an independent dataset established for each pumped storage device. This set uses the unique identifier of the device as the primary key index and associates and stores all unit speed data within each unit speed data acquisition period to form a one-to-one mapping time-series data structure. Step S3-2: By using a time series prediction model based on the single unit speed analysis set, the changing trend of the pumped storage unit speed is predicted. Step S3-3: Iterate through and read the unit speed data in the single unit speed analysis set. Calculate the absolute value of the difference between two sets of unit speed data at adjacent times, and use it as the maximum value of the single unit speed fluctuation of the pumped storage equipment, which is recorded as the peak value of the single unit speed fluctuation.

5. The method for real-time status assessment of pumped storage equipment based on data analysis according to claim 4, characterized in that: The specific steps of step S4 are as follows: Step S4-1: Calculate the average unit speed by predicting the change trend of the pumped storage unit speed. Step S4-2: Using the combined unit speed fluctuation peak and the single unit speed fluctuation peak, the standard unit speed fluctuation peak of the pumped storage equipment is obtained by weighted fusion calculation. The standard unit speed fluctuation peak represents the instantaneous maximum number of rotations of the unit's main shaft per unit time after the pumped storage equipment is affected by negative fluctuation factors during operation.

6. The method for real-time status assessment of pumped storage equipment based on data analysis according to claim 5, characterized in that: Step S4 also includes: Step S4-3: The upper limit of the unit speed is obtained by adding the average unit speed of the pumped storage equipment to the peak value of the standard unit speed fluctuation. The upper limit of the unit speed represents the maximum limit value that the pumped storage equipment can reach under safe operating conditions. Step S4-4: Monitor the pumped storage equipment by comparing the upper limit of the unit speed with the standard unit speed, and analyze and store the monitoring results to form a unit speed analysis set. The specific process of the data analysis and processing is as follows: Step S4-4-1: Subtract the standard unit speed from the upper limit of the pumped storage unit's speed, and process the absolute value of the difference to obtain the absolute value deviation of the upper limit of the unit speed from the standard value. Step S4-4-2: Select the timestamp data of the pumped storage equipment as the primary key, select the absolute value deviation of the upper limit of the unit speed from the standard value as the value, and store it after time sequence to build a unit speed analysis set.

7. The method for real-time status assessment of pumped storage equipment based on data analysis according to claim 6, characterized in that: The specific steps of step S5 are as follows: Step S5-1: Iterate through and read the absolute value deviation of the upper limit of the unit speed from the standard value in the unit speed analysis set, and input them into the time series prediction model to analyze and predict the change trend of the unit speed of the pumped storage equipment. Step S5-2: Using the data analysis process from Step S3 to Step S4, obtain the trend of unit speed change of each pumped storage unit within the current unit speed data acquisition cycle; Step S5-3: Extract the unit speed value corresponding to the maximum value of the horizontal axis in the trend of pumped storage unit speed change. Compare the extracted unit speed value with the upper limit of the unit speed. Based on the comparison result, evaluate and issue an early warning for the pumped storage equipment. The specific process of the comparison is as follows: Step S5-3-1: When the unit speed of the pumped storage power station exceeds the upper limit of the unit speed, it is judged as an abnormal unit speed state and an early warning signal is issued. Step S5-3-2: When the unit speed of the pumped storage power station does not exceed the upper limit of the unit speed, it is judged that the unit speed is in a normal state, no warning signal is issued, and the unit speed is continuously monitored.

8. A real-time status assessment system for pumped storage equipment based on data analysis, applied to the real-time status assessment method for pumped storage equipment based on data analysis as described in any one of claims 1-7, characterized in that: The real-time status assessment system for pumped storage equipment includes a data acquisition module, a data processing module, a single-machine analysis module, a threshold monitoring module, and an assessment and early warning module. The data acquisition module is used to acquire historical and real-time unit speed data of pumped storage equipment and construct a set; the data processing module is used to calculate the absolute value of the difference between two sets of unit speed data at adjacent times, select the maximum absolute value of the pumped storage equipment unit speed to obtain the comprehensive unit speed fluctuation peak; and perform weighted fusion calculation on the acquired historical and real-time data to obtain the standard unit speed of pumped storage equipment within the current unit speed data acquisition period. The single-unit analysis module is used to obtain historical unit speed data based on the unique identifier of the pumped storage equipment to construct a single-unit speed analysis set. The single-unit speed analysis set is used to analyze the unit speed change trend of the pumped storage equipment. The absolute value of the difference between two sets of unit speed data at adjacent times is calculated as the maximum value of the single unit speed fluctuation of the pumped storage equipment, which is recorded as the peak value of the single unit speed fluctuation. The threshold monitoring module is used to calculate the standard unit speed fluctuation peak of the pumped storage equipment by using the comprehensive unit speed fluctuation peak and the single unit speed fluctuation peak, and then to obtain the unit speed upper limit value by adding the average unit speed of the pumped storage equipment to the standard unit speed fluctuation peak value. The module monitors the unit speed upper limit value using the standard unit speed of the pumped storage equipment, monitors the pumped storage equipment based on the unit speed upper limit value and the standard unit speed, and analyzes and stores the monitoring results to construct a unit speed analysis set. The assessment and early warning module is used to predict future trends based on deviation data and trigger anomaly warnings. The output of the data acquisition module is electrically connected to the input of the data processing module; the output of the data processing module is electrically connected to the input of the stand-alone analysis module; the output of the stand-alone analysis module is electrically connected to the input of the threshold monitoring module; and the output of the threshold monitoring module is electrically connected to the input of the evaluation and early warning module.

9. A real-time status assessment system for pumped storage equipment based on data analysis according to claim 8, characterized in that: The data acquisition module includes a historical data acquisition unit and a real-time data acquisition unit; the historical data acquisition unit is used to acquire historical unit speed data of the pumped storage equipment through the PLC control system and the OPC single unit speed fluctuation peak UA protocol; the real-time data acquisition unit is used to acquire real-time unit speed data of the pumped storage equipment in the current cycle through a magnetoresistive speed sensor. The data processing module includes a peak value calculation unit and a mean value fusion unit; the peak value calculation unit is used to traverse historical data and calculate the peak value of the comprehensive unit speed fluctuation of the pumped storage equipment using the bubble sort algorithm; the mean value fusion unit is used to calculate the average value of historical and real-time data and generate a standard unit speed through weighted fusion. The single-unit analysis module includes a trend prediction unit and a fluctuation analysis unit; the trend prediction unit is used to predict the change trend of the unit speed of the pumped storage equipment through a time series model. The fluctuation analysis unit is used to calculate the absolute value of the speed difference between adjacent moments of a single unit and to extract the peak value of the single unit speed fluctuation.

10. A real-time status assessment system for pumped storage equipment based on data analysis according to claim 8, characterized in that: The threshold monitoring module includes a threshold calculation unit and a deviation analysis unit; the threshold calculation unit is used to calculate the upper limit of safe operation of the pumped storage equipment by combining the average unit speed and the standard peak value. The deviation analysis unit is used to compare the upper limit of the rotational speed with the standard rotational speed, calculate the deviation, store it, and construct an analysis set. The assessment and early warning module includes a trend prediction unit and an early warning triggering unit; The trend prediction unit is used to predict the future rotational speed trend of the pumped storage equipment based on historical deviation data; the early warning triggering unit is used to compare the real-time rotational speed with the upper limit value, determine the unit status, and trigger abnormal early warning or continuous monitoring.

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