Early warning control method based on power equipment fault

Abstract

The invention belongs to the technical field of power equipment fault control, and discloses a power equipment fault-based early warning control method, which comprises the following steps of: analyzing an operation efficiency abnormal condition, an output power abnormal condition and an operation state abnormal condition of each specified power equipment during abnormal analysis of the specified power equipment; according to the analysis mode, the comprehensiveness and the accuracy of abnormity analysis of the specified power equipment are improved, potential abnormal conditions can be found and processed in time, and then the accident occurrence probability is remarkably reduced. According to the invention, when the output power deviation condition analysis of each specified device is carried out, the habitual power demand load of the current power station in the next monitoring period is analyzed based on the historical demand load record, and then the output power deviation condition analysis is carried out, such an analysis mode can more accurately predict the future power demand; and resources can be reasonably allocated according to the expected power demand.

Description

Technical Field

[0001] The present invention belongs to the technical field of power equipment fault control, and relates to a warning control method based on power equipment faults. Background Technique

[0002] Power equipment refers to various devices and facilities used for power generation, transmission, distribution, and power consumption. Among them, power generation equipment plays a crucial role in modern society. With the development of clean and sustainable energy, hydro-generating equipment has an extremely important position in power generation equipment. Power equipment faults usually include abnormal operation of power equipment and deviation of power equipment output power. Power equipment faults have a direct and significant impact on the operation of the power system and users. Therefore, warning control based on power equipment faults is of great significance.

[0003] When analyzing the abnormal conditions of each specified power equipment in the traditional warning control of power equipment faults, it usually only analyzes the operation state parameters of each specified power equipment, while ignoring the analysis of the operation efficiency of the specified power equipment. This analysis method reduces the comprehensiveness and accuracy of the abnormal analysis of the specified power equipment. The decline in equipment efficiency may be a precursor to faults. Monitoring only based on state parameters may ignore the maintenance requirements caused by the decline in efficiency, and may also result in low energy utilization efficiency, increasing energy waste and operating costs.

[0004] When analyzing the deviation between the output power and the demand load in the traditional power equipment faults, it is usually based on the real-time output power and the known current demand load, without performing a warning analysis on the demand composite. This analysis method reduces the predictability and flexibility of the analysis of the output power deviation situation, and cannot take measures in advance to cope with the upcoming load changes, which may lead to unstable system operation. At the same time, the power system may generate electricity excessively or insufficiently, resulting in energy waste or insufficient supply. Summary of the Invention

[0005] In view of this, to solve the problems raised in the above background technique, a warning control method based on power equipment faults is proposed.

[0006] The object of the present invention can be achieved by the following technical solutions: A warning control method based on power equipment faults, including: S1. Abnormal operation efficiency analysis: Denote the already-operated hydro-generating equipment of the current power station as the specified power equipment, and at the same time divide a single-day time into several monitoring periods. Based on the historical data monitoring records of each specified power equipment, analyze the reference operation efficiency of each specified power equipment in the current monitoring period, thereby obtaining the abnormal operation efficiency situation of each specified power equipment.

[0007] S2. Output power anomaly analysis: Analyze the output power anomalies of each specified power device during the current monitoring period based on the power demand load during the current monitoring period.

[0008] S3. Operating status anomaly analysis: Obtain the operating status data of each specified power device in real time. The operating status data includes the rotational speed anomaly degree and the vibration frequency anomaly degree, and then analyze the operating status anomalies of each specified power device during the current monitoring period.

[0009] S4. Equipment operation anomaly identification: Based on the operating efficiency anomalies of each specified power device, as well as the output power anomalies and operating status anomalies during the current monitoring period, determine whether there are anomalies in each specified power device. If there are anomalies, perform a stop operation on the specified power device, and record the specified power device without anomalies as a normal power device.

[0010] S5. Equipment addition and subtraction demand identification: Analyze the habitual power demand load of the current power station in the next monitoring period based on the historical demand load records, and then analyze the output power deviation situation of the current power station based on the habitual power demand load in the next monitoring period, and determine whether power equipment addition and subtraction control is required.

[0011] In a preferred embodiment of the present invention, the specific analysis steps of the reference operating efficiency are as follows: Extract the historical data monitoring records of each specified power device, and obtain the monitoring period, reference flow rate, reference head, and reference output power corresponding to the historical data monitoring records of each specified power device.

[0012] Classify the historical data monitoring records of each specified power device according to the monitoring period to obtain the historical data monitoring records of each specified power device in each monitoring period, and then match them with the current monitoring period to obtain the reference historical data monitoring records of each specified power device.

[0013] The reference flow rate , reference head and reference output power corresponding to the reference historical data monitoring records of each specified power device are substituted into the formula to obtain the reference operating efficiency corresponding to each reference historical data monitoring record of each specified power device, where represents the acceleration due to gravity, represents the number of the specified power device, , represents the number corresponding to the reference historical data monitoring record, , and then perform a mean value calculation to obtain the reference operating efficiency of each specified power device.

[0014] In a preferred embodiment of the present invention, during the analysis of the abnormal operation efficiency situation, an abnormal operation efficiency index needs to be constructed, and the specific analysis is as follows: Obtain the rated operation efficiency of the water turbine generator from the operation manual of the water turbine generator, calculate the difference between the rated operation efficiency of each specified power equipment and the corresponding reference operation efficiency to obtain the operation efficiency deviation situation of each specified power equipment, and then calculate the ratio of the operation efficiency deviation situation of each specified power equipment to the corresponding rated operation efficiency to obtain the abnormal operation efficiency index.

[0015] In a preferred embodiment of the present invention, during the analysis of the abnormal output power situation, an abnormal output power index needs to be constructed, and the specific analysis is as follows: Use the power detection equipment to obtain the real-time output power of each specified power equipment in the current monitoring period in real time, draw a real-time output power change curve with time as the abscissa and the real-time output power as the ordinate, and then evenly distribute points on the real-time output power change curve, and calculate the average value of the ordinates of each point on the real-time output power change curve to obtain the collected output power of each specified power equipment in the current monitoring period.

[0016] Obtain the number of specified power equipment in the current monitoring period, and calculate the average value of the power demand load in the current monitoring period and the number of specified power equipment to obtain the unit demand load of the specified power equipment.

[0017] Calculate the difference between the collected output power of each specified power equipment in the current monitoring period and the unit demand load to obtain the collected output power deviation of each specified power equipment in the current monitoring period, and then calculate the ratio of the absolute value of the collected output power deviation to the unit demand load to obtain the abnormal output power index of each specified power equipment in the current monitoring period.

[0018] In a preferred embodiment of the present invention, the specific analysis of the operation state data is as follows: Use the speed sensor to obtain the speed of the runner of each specified power equipment in the current monitoring period, draw a speed change curve with time as the abscissa and the speed as the ordinate, and then evenly distribute points on the speed change curve, and calculate the average value of the corresponding ordinates of each point on the speed change curve to obtain the actual speed of each specified power equipment in the current monitoring period.

[0019] Calculate the difference between the actual speed of each specified power equipment in the current monitoring period and the pre-set speed threshold to obtain the speed deviation value of each specified power equipment in the current monitoring period, and then calculate the ratio to the speed threshold to obtain the speed abnormality degree of each specified power equipment in the current monitoring period.

[0020] The vibration frequency of each specified power device during the current monitoring period is obtained by using a vibration sensor, a vibration frequency change curve is plotted with time as the abscissa and the vibration frequency as the ordinate, and then the vibration frequency change curve is evenly distributed with points. The mean value of the ordinates corresponding to each point on the vibration frequency change curve is calculated to obtain the actual vibration frequency of each specified power device during the current monitoring period.

[0021] The difference between the actual vibration frequency of each specified power device during the current monitoring period and the pre-set vibration frequency threshold is calculated to obtain the vibration frequency deviation value of each specified power device during the current monitoring period, and then the ratio is calculated with the vibration frequency threshold to obtain the vibration frequency abnormality degree of each specified power device during the current monitoring period.

[0022] In a preferred embodiment of the present invention, during the analysis process of the abnormal operating state, an abnormal operating state index needs to be constructed, and the specific analysis is as follows: The rotational speed abnormality degree and the vibration frequency abnormality degree of each specified power device during the current monitoring period are summed according to weights to obtain the abnormal operating state index of each specified power device during the current monitoring period.

[0023] In a preferred embodiment of the present invention, the judgment of whether there is an abnormality in each specified power device is specifically as follows: The operating efficiency abnormal index of each specified power device, the output power abnormal index during the current monitoring period, and the abnormal operating state index are summed according to weights to obtain the operating abnormal index of each specified power device during the current monitoring period.

[0024] The operating abnormal index of each specified power device during the current monitoring period is compared with the pre-set operating abnormal index threshold to obtain the judgment result of whether there is an abnormality in each specified power device.

[0025] In a preferred embodiment of the present invention, the specific analysis steps of the habitual power demand load are as follows: Extract the historical demand load records, and obtain the monitoring period and load amount corresponding to each historical demand load record.

[0026] Each historical demand load record is classified according to the monitoring period to obtain each historical demand load record corresponding to each monitoring period, and then it is matched with the next monitoring period corresponding to the current monitoring period to obtain each reference historical demand load record of the next monitoring period. The mean value of the load amounts corresponding to each reference historical demand load record of the next monitoring period is calculated to obtain the habitual power demand load of the next monitoring period.

[0027] In a preferred embodiment of the present invention, during the analysis process of the output power deviation situation, an output power deviation index needs to be constructed, and the specific analysis is as follows: Extract the collected output power of each normal power device during the current monitoring period.

[0028] Accumulate the collected output powers of all normal power devices during the current monitoring period to obtain the remaining total output power during the current monitoring period. Calculate the difference between the remaining total output power during the current monitoring period and the habitual power demand load of the next monitoring period to obtain the output power deviation amount of the current power station, and then calculate the ratio with the habitual power demand load of the next monitoring period to obtain the output power deviation index of the current power station.

[0029] In a preferred embodiment of the present invention, the judgment of whether power device addition or subtraction control is required is as follows: Compare the absolute value of the output power deviation index of the current power station with a pre-set output power deviation index threshold to obtain the judgment result of whether power device addition or subtraction control is required.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) When analyzing the anomalies of specified power devices, the present invention analyzes the abnormal operation efficiency, abnormal output power, and abnormal operation status of each specified power device, and then judges the anomalies of each specified power device. This analysis method improves the comprehensiveness and accuracy of the anomaly analysis of specified power devices, can timely detect and handle potential abnormal situations, thereby significantly reducing the probability of accidents. At the same time, it can timely optimize the operation efficiency of power devices, reduce energy waste, and lower operating costs.

[0031] (2) When analyzing the output power deviation of each specified device, the present invention analyzes the habitual power demand load of the current power station in the next monitoring period based on historical demand load records, and then analyzes the output power deviation. This analysis method can more accurately predict future power demands, and then reasonably allocate resources according to the expected power demands, reducing unnecessary power generation and energy waste. Accurately predicting demands helps maintain the supply-demand balance of the power system and reduces problems such as voltage fluctuations and frequency changes. Brief Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 It is a flowchart of the method implementation steps of the present invention. Detailed Embodiments

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Please refer to Figure 1 As shown, the present invention provides a warning control method based on power equipment failures. The method includes: S1. Analysis of abnormal operating efficiency: Denote the currently operating hydro-generating equipment in the current power station as the specified power equipment. At the same time, divide a single-day time into several monitoring periods, and analyze the reference operating efficiency of each specified power equipment in the current monitoring period based on the historical data monitoring records of each specified power equipment, so as to obtain the abnormal operating efficiency conditions of each specified power equipment.

[0036] It should be noted that the reason for dividing the monitoring periods: The power demand changes with time. Especially within a day, the load difference between peak and trough periods is obvious. The division of monitoring periods can help the system better adapt to these load changes and ensure appropriate monitoring and control strategies in different time periods.

[0037] It should be noted that the reason for analyzing the abnormal operating efficiency conditions of the specified power equipment: By monitoring the efficiency, it is possible to timely detect the situation of efficiency decline, which is often a precursor to equipment failures. Efficiency decline will lead to additional energy consumption and maintenance costs. By monitoring, measures can be taken in a timely manner to save costs. Efficiency decline is often accompanied by problems such as equipment overheating and increased vibration, which may lead to safety accidents.

[0038] Preferably, the specific analysis steps of the reference operating efficiency are as follows: Extract the historical data monitoring records of each specified power equipment, and obtain the monitoring periods, reference flow rates, reference water heads, and reference output powers corresponding to the historical data monitoring records of each specified power equipment.

[0039] It should be explained that the reference flow rate refers to the volume of fluid passing through the water turbine generator per unit time, the reference water head refers to the height difference of the water level during the hydropower generation process, and the reference output power refers to the electric power generated by the generator.

[0040] Classify the historical data monitoring records of each specified power equipment according to the monitoring periods to obtain the historical data monitoring records of each specified power equipment in each monitoring period, and then match them with the current monitoring period to obtain the reference historical data monitoring records of each specified power equipment.

[0041] The reference flow rates corresponding to the reference historical data monitoring records of each specified power equipment , reference water head and reference output power Substitute into the formula to obtain the reference operating efficiency corresponding to each specified power device for each reference historical data monitoring record , where represents the acceleration due to gravity, represents the number of the specified power device, , represents the number corresponding to the reference historical data monitoring record, , and then perform mean calculation to obtain the reference operating efficiency of each specified power device .

[0042] It should be added that .

[0043] Preferably, during the analysis process of the abnormal operating efficiency situation, an abnormal operating efficiency index needs to be constructed. The specific analysis is as follows: Obtain the rated operating efficiency of the water turbine generator from the operation manual of the water turbine generator, calculate the difference between the rated operating efficiency of each specified power device and the corresponding reference operating efficiency to obtain the operating efficiency deviation situation of each specified power device, and then calculate the ratio of the operating efficiency deviation situation of each specified power device to the corresponding rated operating efficiency to obtain the abnormal operating efficiency index.

[0044] It should be explained that the rated operating efficiency refers to the ratio of the active power output by the water turbine generator to the shaft power input from the water turbine main shaft to the generator shaft power under the rated working conditions (i.e., the operating state under the design conditions). The rated operating efficiency reflects the efficiency level of the water turbine generator under normal operating conditions. Exemplarily, the rated operating efficiency is .

[0045] S2. Output power anomaly analysis: Based on the power demand load during the current monitoring period, analyze the output power anomaly situation of each specified power device during the current monitoring period.

[0046] It should be explained that the power demand load refers to the load demand of the external power grid of the current power station for this power station.

[0047] Preferably, during the analysis process of the output power anomaly situation, an output power anomaly index needs to be constructed. The specific analysis is as follows: Use the power detection device to obtain the real-time output power of each specified power device during the current monitoring period in real time, draw the real-time output power change curve with time as the abscissa and the real-time output power as the ordinate, and then evenly distribute points on the real-time output power change curve, and calculate the mean value of the ordinates of each point on the real-time output power change curve to obtain the collected output power of each specified power device during the current monitoring period.

[0048] Obtain the quantity of specified power equipment within the current monitoring period, and calculate the average of the power demand load within the current monitoring period and the quantity of specified power equipment to obtain the unit demand load of the specified power equipment.

[0049] It should be noted that the reason for evenly distributing the demand load of the current power station to each specified power equipment: Evenly distributing the load can reduce the possibility of overusing a single device, thereby reducing the risk of wear and damage. Balanced use can enable all devices to operate within an appropriate load range, which helps to extend the service life of the devices. Evenly distributing the load helps to maintain the frequency stability of the system and avoid frequency fluctuations caused by sudden changes in the load.

[0050] Calculate the difference between the collected output power of each specified power equipment within the current monitoring period and the unit demand load to obtain the collected output power deviation of each specified power equipment within the current monitoring period. Furthermore, calculate the ratio of the absolute value of the collected output power deviation to the unit demand load to obtain the output power anomaly index of each specified power equipment within the current monitoring period.

[0051] It should be noted that the hazards of abnormal output power of specified power equipment: Abnormal output power may cause voltage fluctuations in the system, affecting the power supply quality. Excessive output power may cause equipment overload, leading to overheating and increasing the risk of equipment failure. Abnormal output power may cause the power factor to decrease, increasing the reactive power in the system, and further increasing the line loss. Abnormal output power may cause the line current to increase, further increasing the line loss and resulting in energy waste.

[0052] S3. Analysis of abnormal operating status: Real-time obtain the operating status data of each specified power equipment, where the operating status data includes the abnormal rotation speed degree and the abnormal vibration frequency degree, and then analyze the abnormal operating status of each specified power equipment within the current monitoring period.

[0053] It should be noted that the reasons for selecting the abnormal rotation speed degree and the abnormal vibration frequency degree as the influencing factors for the abnormal operating status of each specified power equipment: 1. The rotation speed is one of the core parameters of rotating mechanical equipment, directly affecting the output power, efficiency, and stability of the equipment. Abnormal rotation speed may cause problems such as equipment overload, increased vibration, elevated temperature, lubrication failure, etc., and may even cause equipment damage in severe cases. 2. Abnormal vibration frequency is usually associated with problems such as equipment imbalance, misalignment, bearing wear, and structural looseness. Abnormal vibration may cause problems such as equipment structural fatigue, accelerated component wear, and loosening of connectors, thereby affecting the reliability and life of the equipment.

[0054] Preferably, the operating state data is specifically analyzed as follows: The rotational speed of the runner of each specified power equipment within the current monitoring period is obtained by using a rotational speed sensor, a rotational speed change curve is plotted with time as the abscissa and rotational speed as the ordinate, and then the rotational speed change curve is evenly sampled, and the mean value of the ordinates corresponding to the points on the rotational speed change curve is calculated to obtain the actual rotational speed of each specified power equipment within the current monitoring period.

[0055] The difference between the actual rotational speed of each specified power equipment within the current monitoring period and the pre-set rotational speed threshold is calculated to obtain the rotational speed deviation value of each specified power equipment within the current monitoring period, and then the ratio is calculated with the rotational speed threshold to obtain the rotational speed abnormality degree of each specified power equipment within the current monitoring period.

[0056] It should be noted that the rotational speed threshold in the present invention refers to the maximum allowable rotational speed when the water turbine drives the generator to operate. Exceeding this rotational speed may cause the generator to overheat, mechanical damage or electrical faults. The rotational speed threshold is usually described in detail in the technical documents or specifications of the water turbine or generator. These documents include but are not limited to operation manuals, maintenance manuals and technical specifications. Taking the Francis turbine as an example, the corresponding rotational speed threshold is 。

[0057] The vibration frequency of each specified power equipment within the current monitoring period is obtained by using a vibration sensor, a vibration frequency change curve is plotted with time as the abscissa and vibration frequency as the ordinate, and then the vibration frequency change curve is evenly sampled, and the mean value of the ordinates corresponding to the points on the vibration frequency change curve is calculated to obtain the actual vibration frequency of each specified power equipment within the current monitoring period.

[0058] The difference between the actual vibration frequency of each specified power equipment within the current monitoring period and the pre-set vibration frequency threshold is calculated to obtain the vibration frequency deviation value of each specified power equipment within the current monitoring period, and then the ratio is calculated with the vibration frequency threshold to obtain the vibration frequency abnormality degree of each specified power equipment within the current monitoring period.

[0059] It should be noted that the vibration frequency threshold in the present invention refers to the maximum allowable vibration frequency range of the generator and its auxiliary equipment (such as water turbine, bearing seat, etc.) under normal operating conditions. The vibration frequency threshold of the waterwheel generator is usually described in the technical documents or specifications of the equipment. These documents include but are not limited to operation manuals, maintenance manuals and technical specifications. Taking the Francis turbine as an example, the corresponding vibration frequency threshold is 。

[0060] It should be noted that the vibration frequency can be the bearing vibration frequency, the rotor vibration frequency or the water turbine blade vibration frequency.

[0061] Preferably, during the analysis of the abnormal operating conditions, an abnormal operating index needs to be constructed, and the specific analysis is as follows: The abnormal rotation speed degree and abnormal vibration frequency degree of each specified power equipment within the current monitoring period are summed according to weights to obtain the abnormal operating index of each specified power equipment within the current monitoring period.

[0062] Exemplarily, the corresponding weights of the abnormal rotation speed degree and the abnormal vibration frequency degree are .

[0063] S4. Abnormal equipment operation identification: Based on the abnormal operating efficiency of each specified power equipment, as well as the abnormal output power situation and abnormal operating status situation within the current monitoring period, it is judged whether there is an abnormality in each specified power equipment. If there is an abnormality, the specified power equipment is stopped, and the specified power equipment without abnormality is recorded as a normal power equipment.

[0064] Preferably, the judgment of whether there is an abnormality in each specified power equipment is as follows: The abnormal operating efficiency index of each specified power equipment, the abnormal output power index within the current monitoring period, and the abnormal operating status index are summed according to weights to obtain the abnormal operating index of each specified power equipment within the current monitoring period.

[0065] Exemplarily, the weights of the abnormal operating efficiency index, the abnormal output power index, and the abnormal operating status index are .

[0066] It should be noted that when analyzing the abnormality of the specified power equipment in the present invention, by analyzing the abnormal operating efficiency situation, the abnormal output power situation, and the abnormal operating status situation of each specified power equipment, and then judging the abnormality of each specified power equipment. This analysis method improves the comprehensiveness and accuracy of the abnormal analysis of the specified power equipment, can timely detect and handle potential abnormal situations, thereby significantly reducing the probability of accidents, and at the same time can timely optimize the operating efficiency of the power equipment, reduce energy waste, and reduce operating costs.

[0067] The abnormal operating index of each specified power equipment within the current monitoring period is compared with the preset abnormal operating index threshold to obtain the judgment result of whether there is an abnormality in each specified power equipment.

[0068] Exemplarily, the abnormal operating index threshold is .

[0069] It should be added that if the abnormal operating index of a specified power equipment within the current monitoring period is greater than the abnormal operating index threshold, it is judged that the specified power equipment has an abnormality. If the abnormal operating index of a specified power equipment within the current monitoring period is less than or equal to the abnormal operating index threshold, it is judged that the specified power equipment has no abnormality.

[0070] S5. Identification of equipment addition and reduction requirements: Analyze the habitual power demand load of the current power station in the next monitoring period based on historical demand load records, and then analyze the output power deviation of the current power station based on the habitual power demand load in the next monitoring period, and determine whether power equipment addition and reduction control is required.

[0071] It should be noted that the reasons for the inconsistent habitual power demand loads in each monitoring period: The reasons for the inconsistent power demand loads in different monitoring periods mainly include factors such as the electricity consumption habits of electricity customers, weather changes, seasonal factors, differences between weekdays and weekends, and specific events. The combined effect of these factors causes the power demand to show peak-valley changes throughout the day. For example, the electricity consumption of residents increases during the morning and evening rush hours, the electricity consumption of refrigeration equipment increases due to high temperatures in summer, and the industrial and commercial electricity demand is relatively high during weekdays and decreases on weekends.

[0072] It should be noted that the reasons for analyzing the output power deviation of the current power station: If the output power of the power station exceeds the demand load, it may cause the generator to operate overloaded, increasing the risk of equipment failure. Power surplus may also cause the transmission line to be overloaded, increasing line losses and the possibility of faults. The mismatch between the output power and the demand load may cause the equipment to bear abnormal mechanical stress, increasing the risk of damage. Abnormal output power may cause electrical faults such as short circuits and open circuits.

[0073] Preferably, the specific analysis steps of the habitual power demand load are as follows: Extract historical demand load records, and obtain the monitoring periods and load amounts corresponding to each historical demand load record.

[0074] Classify each historical demand load record according to the monitoring period to obtain the historical demand load records corresponding to each monitoring period, and then match them with the next monitoring period corresponding to the current monitoring period to obtain the reference historical demand load records for the next monitoring period. Calculate the average value of the load amounts corresponding to the reference historical demand load records for the next monitoring period to obtain the habitual power demand load for the next monitoring period.

[0075] It should be noted that when analyzing the output power deviation of each specified equipment in the present invention, the habitual power demand load of the current power station in the next monitoring period is analyzed based on historical demand load records, and then the output power deviation is analyzed. This analysis method can more accurately predict future power demands, and then reasonably allocate resources according to the expected power demands, reducing unnecessary power generation and energy waste. Accurately predicting demands helps to maintain the balance between supply and demand in the power system and reduce problems such as voltage fluctuations and frequency changes.

[0076] Preferably, during the analysis of the output power deviation situation, an output power deviation index needs to be constructed, and the specific analysis is as follows: Extract the collected output power of each normal power equipment during the current monitoring period.

[0077] Accumulate and calculate the collected output power of each normal power equipment during the current monitoring period to obtain the remaining total output power during the current monitoring period. Calculate the difference between the remaining total output power during the current monitoring period and the habitual power demand load of the next monitoring period to obtain the output power deviation amount of the current power station, and then calculate the ratio with the habitual power demand load of the next monitoring period to obtain the output power deviation index of the current power station.

[0078] Preferably, the judgment on whether power equipment addition or reduction control is needed is as follows: Compare the absolute value of the output power deviation index of the current power station with the preset output power deviation index threshold to obtain the judgment result on whether power equipment addition or reduction control is needed.

[0079] Exemplarily, the output power deviation index threshold is .

[0080] It should be added that if the absolute value of the output power deviation index of the current power station is greater than the output power deviation index threshold, it is judged that power equipment addition or reduction control is needed. If the absolute value of the output power deviation index of the current power station is less than or equal to the output power deviation index threshold, it is judged that power equipment addition or reduction control is not needed.

[0081] It should be further added that when it is judged that power equipment addition or reduction control is needed, extract the output power deviation index. If the sign of the output power deviation index is positive, the target power equipment group needs to be reduced. If the sign of the output power deviation index is negative, the target power equipment group needs to be increased.

[0082] It should be noted that the adjusted output power deviation index after power equipment addition or reduction control should be less than or equal to the output power deviation index threshold, and the specific analysis method of the adjusted output power deviation index is the same as that of the output power deviation index of the current power station.

[0083] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for early warning control based on power equipment failure, characterized in that: include: S1. Operation efficiency abnormality analysis: the hydro-turbine power generation equipment in operation at the current power station is recorded as the designated power equipment, and a single day is divided into several monitoring periods. The reference operation efficiency of each designated power equipment in the current monitoring period is analyzed based on the historical data monitoring records of each designated power equipment, thereby obtaining the abnormal operation efficiency of each designated power equipment; S2. Output power abnormality analysis: Analyze the output power abnormality of each designated power equipment in the current monitoring period based on the power demand load in the current monitoring period; S3, abnormal operation status analysis: real-time acquisition of operation status data of each designated power device, the operation status data including rotation speed abnormality and vibration frequency abnormality, and then analysis of abnormal operation status of each designated power device in the current monitoring period; S4. Equipment operation abnormality identification: based on the abnormal operation efficiency of each designated power equipment, the abnormal output power and the abnormal operation status during the current monitoring period, determine whether each designated power equipment has an abnormality. If an abnormality exists, stop the operation of the designated power equipment, and record the designated power equipment without abnormality as a normal power equipment; S5. Identification of equipment increase and decrease demand: Analyze the customary power demand load of the current power station in the next monitoring period based on the historical demand load records, and then analyze the output power deviation of the current power station based on the customary power demand load in the next monitoring period, and determine whether power equipment increase and decrease control is needed.

2. The early warning control method based on power equipment failure according to claim 1, characterized in that: The specific analysis steps of the reference operation efficiency are as follows: Extract the historical data monitoring records of each designated power equipment, and obtain the monitoring period, reference flow, reference water head and reference output power corresponding to the historical data monitoring records of each designated power equipment; Classify the historical data monitoring records of each designated power equipment according to the monitoring period to obtain each historical data monitoring record of each designated power equipment in each monitoring period, and then match them with the current monitoring period to obtain each reference historical data monitoring record of each designated power equipment; The reference flow corresponding to each reference historical data monitoring record of each designated power equipment , Reference head and reference output power Substitute into the formula Get the reference operating efficiency corresponding to each reference historical data monitoring record of each designated power equipment ,in represents the acceleration due to gravity, Indicates the number of the specified power equipment. , Indicates the number corresponding to the reference historical data monitoring record. , and then the mean value is calculated to obtain the reference operating efficiency of each specified power equipment .

3. The early warning control method based on power equipment failure according to claim 1, characterized in that: In the process of analyzing the abnormal operation efficiency, it is necessary to construct an abnormal operation efficiency index, and the specific analysis is as follows: The rated operating efficiency of the hydraulic generator is obtained from the hydraulic generator operating manual, and the rated operating efficiency of each designated power equipment is calculated by difference with the corresponding reference operating efficiency to obtain the operating efficiency deviation of each designated power equipment. Then, the operating efficiency deviation of each designated power equipment is calculated by ratio with the corresponding rated operating efficiency to obtain the operating efficiency abnormality index.

4. The early warning control method based on power equipment failure according to claim 3, characterized in that: During the analysis of the output power abnormality, an output power abnormality index needs to be constructed. The specific analysis is as follows: The real-time output power of each designated power device in the current monitoring period is obtained in real time by using the power detection device, and the real-time output power change curve is drawn with time as the horizontal coordinate and the real-time output power as the vertical coordinate, and then the real-time output power change curve is evenly distributed, and the vertical coordinates of each point on the real-time output power change curve are averaged to obtain the collected output power of each designated power device in the current monitoring period; Obtain the number of designated power equipment in the current monitoring period, and calculate the average of the power demand load in the current monitoring period and the number of designated power equipment to obtain the unit demand load of the designated power equipment; The collected output power of each designated power device in the current monitoring period is calculated by difference with the unit demand load to obtain the collected output power deviation of each designated power device in the current monitoring period, and then the absolute value of the collected output power deviation is calculated by ratio with the unit demand load to obtain the output power anomaly index of each designated power device in the current monitoring period.

5. The early warning control method based on power equipment failure according to claim 4, characterized in that: The operating status data is specifically analyzed as follows: The speed sensor is used to obtain the speed of the rotor of each designated power equipment in the current monitoring period, and a speed change curve is drawn with time as the horizontal coordinate and the speed as the vertical coordinate. Then, the speed change curve is evenly distributed, and the vertical coordinates corresponding to each point on the speed change curve are averaged to obtain the actual speed of each designated power equipment in the current monitoring period; The actual speed of each designated power device in the current monitoring period is calculated by difference with the preset speed threshold to obtain the speed deviation value of each designated power device in the current monitoring period, and then the speed abnormality of each designated power device in the current monitoring period is calculated by ratio calculation with the speed threshold; The vibration sensor is used to obtain the vibration frequency of each designated power device in the current monitoring period, and a vibration frequency change curve is drawn with time as the horizontal coordinate and vibration frequency as the vertical coordinate. Then, points are evenly distributed on the vibration frequency change curve, and the vertical coordinates corresponding to each point on the vibration frequency change curve are averaged to obtain the actual vibration frequency of each designated power device in the current monitoring period. The actual vibration frequency of each designated power device in the current monitoring period is calculated by difference with the preset vibration frequency threshold to obtain the vibration frequency deviation value of each designated power device in the current monitoring period, and then the ratio with the vibration frequency threshold is calculated to obtain the vibration frequency abnormality of each designated power device in the current monitoring period.

6. The early warning control method based on power equipment failure according to claim 5, characterized in that: During the analysis of the abnormal operation status, it is necessary to construct an abnormal operation status index, and the specific analysis is as follows: The speed abnormality and vibration frequency abnormality of each designated power equipment in the current monitoring period are summed up according to the weights to obtain the operation status abnormality index of each designated power equipment in the current monitoring period.

7. The early warning control method based on power equipment failure according to claim 6, characterized in that: The specific details of determining whether each designated electrical equipment has an abnormality are as follows: The operation abnormality index of each designated power device in the current monitoring period is calculated by summing the operation efficiency abnormality index of each designated power device and the output power abnormality index and the operation state abnormality index in the current monitoring period according to the weights; The operation abnormality index of each designated power device in the current monitoring period is compared with the preset operation abnormality index threshold to obtain a judgment result of whether each designated power device has an abnormality.

8. The early warning control method based on power equipment failure according to claim 1, characterized in that: The specific analysis steps of the customary power demand load are as follows: Extract historical demand load records, and obtain the monitoring period and load amount corresponding to each historical demand load record; The historical demand load records are classified according to the monitoring period to obtain the historical demand load records corresponding to each monitoring period, and then matched with the next monitoring period corresponding to the current monitoring period to obtain the reference historical demand load records of the next monitoring period, and the load corresponding to the reference historical demand load records of the next monitoring period is averaged to obtain the customary power demand load of the next monitoring period.

9. The early warning control method based on power equipment failure according to claim 8, characterized in that: During the analysis of the output power deviation, an output power deviation index needs to be constructed. The specific analysis is as follows: Extract the collected output power of each normal power equipment during the current monitoring period; The collected output power of each normal power equipment in the current monitoring period is accumulated and calculated to obtain the remaining total output power in the current monitoring period, and the difference between the remaining total output power in the current monitoring period and the customary power demand load in the next monitoring period is calculated to obtain the output power deviation of the current power station, and then the ratio is calculated with the customary power demand load in the next monitoring period to obtain the output power deviation index of the current power station.

10. The early warning control method based on power equipment failure according to claim 9, characterized in that: The determination of whether to perform power equipment increase / decrease control is as follows: The absolute value of the output power deviation index of the current power station is compared with the preset output power deviation index threshold to obtain a judgment result on whether it is necessary to increase or decrease the power equipment.

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