CN113313142B - Hydroelectric generating set operation data trend early warning method, system and storage medium - Google Patents
Hydroelectric generating set operation data trend early warning method, system and storage medium Download PDFInfo
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Abstract
The application relates to a hydroelectric generating set operation data trend early warning method, a system and a storage medium, which relate to the technical field of hydroelectric generating set fault diagnosis and comprise the steps of collecting operation power data of each time node of a hydroelectric generating set; performing trend prediction based on the operation power data information of each time node, and acquiring a prediction result value of a subsequent time node and corresponding prediction time; setting an over-low early warning value of the running power of the hydroelectric generating set; and comparing the prediction result value of the trend prediction with the too low early warning value of the running power of the hydroelectric generating set, if the prediction result value is lower than the too low early warning value, analyzing the fault reason causing the too low running power of the hydroelectric generating set, and outputting the fault reason and the prediction time to the control center and a mobile terminal held by a maintenance worker. The utility model provides an analysis goes out the effect of trouble reason when sending the not enough early warning of water turbine power.
Description
Technical Field
The application relates to the technical field of hydroelectric generating set fault diagnosis, in particular to a hydroelectric generating set operation data trend early warning method, a hydroelectric generating set operation data trend early warning system and a storage medium.
Background
The hydroelectric generation is that the potential energy contained in rivers, lakes and the like which are positioned at high positions and have potential energy is converted into the kinetic energy of a water turbine by utilizing the water turbine as motive power to drive a generator to generate electric energy. The hydraulic turbine is driven to rotate by water power, so that the water power is converted into mechanical energy, and if a generator is connected to the hydraulic turbine and rotates along with the hydraulic turbine, electricity can be generated, so that the mechanical energy is converted into electric energy. The hydraulic turbine, the generator and other equipment are collectively called as a hydroelectric generating set, and as the hydroelectric generating set comprises a plurality of equipment, a large amount of manpower is consumed for regularly checking the equipment operation one by one, so that the hydropower station generally integrates and analyzes historical data of the equipment operation, predicts the change trend of the data and gives corresponding trend early warning.
The insufficient power of the water turbine is a common fault of the hydroelectric generating set, in the trend early warning of the existing hydroelectric generating set, the early warning of the insufficient power of the water turbine is only usually made, and after the early warning is sent out, the equipment needs to be gradually checked manually to determine the reason of the insufficient power of the water turbine.
For the related technologies, the inventor thinks that the existing trend early warning gives an early warning to the power shortage of the water turbine, and at the same time, the inventor is difficult to analyze the fault cause of the power shortage of the water turbine.
Disclosure of Invention
The application provides a hydroelectric generating set operation data trend early warning method, a system and a storage medium, which aims to overcome the defect that the existing trend early warning is difficult to analyze the fault reason causing the insufficient power of the water turbine when the existing trend early warning gives an early warning to the insufficient power of the water turbine.
In a first aspect, the hydroelectric generating set operation data trend early warning method provided by the application adopts the following technical scheme:
a hydroelectric generating set operation data trend early warning method comprises the following steps:
acquiring running power data of each time node of the hydroelectric generating set, wherein the time intervals between every two adjacent time nodes are the same, marking the running power data of each time node of the hydroelectric generating set with different numbers, and uploading the running power data number of each time node and running power data information of each corresponding time node to a preset first database;
predicting the trend of the running power of the subsequent time nodes based on the running power data information of each time node called from a preset first database, and analyzing and obtaining the running power prediction result value of the subsequent time nodes and the prediction time corresponding to the running power prediction result value of the subsequent time nodes;
comparing the operation power prediction result value of the subsequent time node with a preset hydropower unit operation power too-low early warning value;
if the operation power prediction result value is smaller than the operation power too-low early warning value, analyzing a fault reason causing the too-low operation power of the hydroelectric generating set based on the operation power prediction result value, sending the fault reason and the corresponding prediction time when the prediction result value is lower than the operation power too-low early warning value to a control center, and sending the fault reason and the corresponding prediction time when the prediction result value is lower than the operation power too-low early warning value to a mobile terminal held by a maintenance worker;
and if the operation power prediction result value is larger than or equal to the operation power too-low early warning value, continuing to perform trend prediction on the operation power of the subsequent time node.
By adopting the technical scheme, the operation power data of each time node of the hydroelectric generating set is collected, trend prediction is carried out on the subsequent operation power data based on the operation power data information, when the prediction result value of the subsequent operation power trend prediction is lower than the preset operation power too-low early warning value, the fault reason of the too-low operation power of the hydroelectric generating set can be analyzed according to the operation power prediction result value, the fault reason is notified and the predicted fault time is notified to the control center and maintenance personnel, and the manpower for troubleshooting is saved.
Optionally, the step of predicting the trend of the operating power of the subsequent time node based on the operating power data information of each time node called from the preset first database, and analyzing and obtaining the operating power prediction result value of the subsequent time node and the prediction time corresponding to the operating power prediction result value of the subsequent time node is as follows:
taking the number of the operation power data of each time node as a query object, and calling the operation power data information of each corresponding time node from a first database;
calculating the operation power prediction result value of the next time node corresponding to each time node by applying a pre-constructed next time node operation power prediction formula, wherein the pre-constructed next time node operation power prediction formula is specifically as follows:
in which S ist+1Predicting the result value of the running power of the next time node, wherein a is a preset constant and y istFor operating power data of the node at the current time, StSetting the predicted result value of the first time node to be the same as the running power data of the first time node for the predicted result value of the current time node;
and performing secondary trend prediction based on the operation power data information of each time node and the next time node operation power prediction formula, and acquiring the operation power prediction result value of the subsequent time node and corresponding prediction time.
By adopting the technical scheme, the prediction result value of the operation power of the next time node corresponding to the last time node can be predicted through calculation of the prediction formula according to the operation power historical data of each time node, so that early warning can be given to the fault of the hydroelectric generating set with too low operation power.
Optionally, the step of performing secondary trend prediction based on the operating power data information of each time node, and obtaining the operating power prediction result value of the subsequent time node and the corresponding prediction time is as follows:
and optimizing the predicted result value of the next time node operating power by applying a pre-constructed next time node operating power predicted result optimization formula, wherein the pre-constructed next time node operating power predicted result optimization formula is specifically as follows:
wherein a is a predetermined constant, St+1Predicting a result value, S, for the operating power of the next time nodet+1' optimizing value for prediction result of next time node operation power, StSetting the optimal value of the prediction result of the first time node to be the same as the running power data of the first time node for the optimal value of the prediction result of the current time node;
sequencing the time nodes in a sequence from far to near, marking the time nodes sequenced from far to near in sequence by taking the sequence of natural integers from small to large as a serial number, calculating the running power of the subsequent time nodes by applying a pre-constructed subsequent time node running power prediction formula, and calculating the prediction result optimized value of the running power of the subsequent time nodes, wherein the pre-constructed subsequent time node running power prediction formula is specifically as follows
Where m is the time node number, atIs a first constant, btIs a second constant, Ft+m' optimizing value for prediction result of m-th time node operation power, n is previous n time nodes of current time node,
is the average of the n time node numbers,
the average value of the running power prediction result optimized values of n time nodes before the current time node is obtained;
calculating the predicted time corresponding to a plurality of subsequent time nodes by using a pre-constructed predicted time calculation formula, wherein the pre-constructed predicted time calculation formula is specifically as follows:
wherein T isnPredicted time, T, corresponding to the m-th subsequent time nodeaIs the time corresponding to the first time node, TbIs the time interval between two adjacent time nodes.
By adopting the technical scheme, the following time node running power prediction formula can be constructed according to the next time node running power prediction result optimization formula and the prediction results of the running power of the plurality of nodes, so that the running power of the next time node corresponding to the current time and the running power of the plurality of following time nodes can be predicted, and the prediction time corresponding to the following time node running power prediction result can be calculated according to the prediction time calculation formula.
Optionally, the step of analyzing the fault cause causing the too low running power of the hydroelectric generating set based on the running power prediction result value is as follows:
collecting flow data in front of a grid plate of a water inlet of a hydroelectric generating set and marking different numbers, collecting flow data behind the grid plate of the water inlet of the hydroelectric generating set and marking different numbers, collecting flow data of flow data behind the grid plate of a water outlet of the hydroelectric generating set and marking different numbers, and uploading the flow data numbers in front of the grid plate of the water inlet of the hydroelectric generating set and flow data behind the grid plate of the water inlet of the corresponding hydroelectric generating set, the flow data numbers behind the grid plate of the water inlet of the hydroelectric generating set and flow data behind the grid plate of the water inlet of the corresponding hydroelectric generating set, the flow data numbers of the water outlet of the hydroelectric generating set and the flow data of the water outlet of the corresponding hydroelectric generating set to a preset second database;
taking the flow data serial number in front of the grating plate of the water inlet of the hydro-power generating unit, the flow data serial number behind the grating plate of the water inlet of the hydro-power generating unit and the flow data serial number of the water outlet of the hydro-power generating unit as query objects from a second database, and calling and acquiring the flow data in front of the grating plate of the water inlet of the hydro-power generating unit, behind the grating plate of the water inlet of the hydro-power generating unit and at the water outlet of the hydro-power generating unit one by one;
if the total flow data is kept unchanged, judging that the hydroelectric generating set is in a normal running state, and continuously acquiring flow data of each part of the hydroelectric generating set;
and if the total flow data is in a descending trend, judging that the hydroelectric generating set is in a state of excessively low operating power, and analyzing and judging the fault reason of excessively low operating power of the hydroelectric generating set according to the flow data.
By adopting the technical scheme, the running power of the hydro-power generating unit is usually influenced by the water flow passing through the hydro-power generating unit when the running power of the hydro-power generating unit is too low, the running power of the hydro-power generating unit is higher when the water flow is larger, and the running power of the hydro-power generating unit is lower when the water flow is smaller, so that the flow data in front of the grating plate at the water inlet of the hydro-power generating unit, behind the grating plate at the water inlet of the hydro-power generating unit and at the water outlet of the hydro-power generating unit can be detected through the arrangement of the flowmeter, and the fault reason of the too low running power of the hydro-power generating unit is analyzed through the flow data.
Optionally, the step of analyzing and judging the fault reason of the too low running power of the hydroelectric generating set according to the flow data comprises the following steps:
if the front flow of the grid plate at the water inlet of the hydroelectric generating set is reduced, analyzing the fault reason of the over-low running power of the hydroelectric generating set because the water inlet pipe is blocked;
if the front flow of the grating plate of the water inlet of the hydro-power generating unit is unchanged and the rear flow of the grating plate of the water inlet of the hydro-power generating unit is reduced, detecting the grating plate and analyzing and judging the specific fault reason of the over-low running power of the hydro-power generating unit; if the front flow of the grating plate of the water inlet of the hydroelectric generating set is unchanged, the rear flow of the grating plate of the water inlet of the hydroelectric generating set is unchanged, and the flow of the water outlet of the hydroelectric generating set is reduced, analyzing the specific fault reason of the over-low running power of the hydroelectric generating set according to the detected water turbine.
By adopting the technical scheme, if the front flow of the grating plate at the water inlet of the hydro-power generating unit is reduced, the water flow entering the hydro-power generating unit subsequently is also reduced, and the reason for the reduction of the front flow of the grating plate at the water inlet of the hydro-power generating unit is that the water inlet pipe of the hydro-power generating unit is blocked, and the blocking object can be sand in the water body; if the front flow of the grating plate of the water inlet of the hydro-power generating unit is unchanged and the rear flow of the grating plate of the water inlet of the hydro-power generating unit is reduced, analyzing and judging the specific fault reason according to the detection grating plate; if the front flow of the grating plate at the water inlet of the hydro-power generating unit and the rear flow of the grating plate at the water inlet of the hydro-power generating unit are not changed, and the flow of the water outlet of the hydro-power generating unit is reduced, specific fault reasons need to be analyzed according to the detection of the water turbine.
Optionally, the specific fault reasons for detecting the grid plates and analyzing and judging the too low running power of the hydroelectric generating set are as follows:
respectively calling and acquiring flow data in front of the grating plate of the water inlet of the corresponding hydroelectric generating set, behind the grating plate of the water inlet of the hydroelectric generating set and at the water outlet of the hydroelectric generating set from a second database by taking the flow data serial number in front of the grating plate of the water inlet of the hydroelectric generating set, the flow data serial number behind the grating plate of the water inlet of the hydroelectric generating set and the flow data serial number of the water outlet of the hydroelectric generating set as query objects one by one;
if the total flow data do not fluctuate, judging that the hydroelectric generating set is in a normal operation state, acquiring the light transmittance of the grating plates when the hydroelectric generating set normally operates, marking and numbering the light transmittance data of the grating plates when the hydroelectric generating set normally operates, and uploading the light transmittance data to a preset third database;
if the front flow of the grating plate of the water inlet of the hydro-power generating unit is unchanged and the rear flow of the grating plate of the water inlet of the hydro-power generating unit is reduced, judging that the hydro-power generating unit is in a state with too low operating power, and acquiring the light transmittance of the grating plate when the operating power of the hydro-power generating unit is too low;
taking the light transmittance data of the grating plates in normal operation of the corresponding hydroelectric generating set as an object by numbering the light transmittance data of the grating plates in normal operation of the hydroelectric generating set from the third database, and comparing the light transmittance of the grating plates when the running power of the hydroelectric generating set is too low with the light transmittance of the grating plates when the running power of the hydroelectric generating set is normal;
if the light transmittance of the grid plate is lower than that of the grid plate when the running power of the hydroelectric generating set is normal when the running power of the hydroelectric generating set is too low, analyzing and judging that the fault source of the too low running power of the hydroelectric generating set is blocked by the grid plate;
if the light transmittance of the grid plate is the same as that of the grid plate when the running power of the hydroelectric generating set is normal when the running power of the hydroelectric generating set is too low, analyzing and judging that the fault source of the too low running power of the hydroelectric generating set is deformation of a communicating pipe between the grid plate and the water turbine.
By adopting the technical scheme, the flow data of the water outlet of the hydro-power generating unit is collected to obtain the total flow data of the hydro-power generating unit before the grid plate of the water inlet of the hydro-power generating unit, the running state of the hydro-power generating unit is judged according to the total flow data, an operator collects the light transmittance of the grid plate when the hydro-power generating unit runs normally and the running power of the hydro-power generating unit is too low, if more sundries are accumulated at the grid plate, the light transmittance of the grid plate can be reduced, the fault reason is judged to be the blockage of the grid plate, if the light transmittance of the grid plate is not changed obviously, the communicating pipe between the grid plate and the water turbine can be judged to be deformed to reduce the pipe diameter, and the flow is reduced.
Optionally, the step of analyzing the specific fault cause of the too low operating power of the hydroelectric generating set according to the detected water turbine includes:
respectively calling and acquiring flow data in front of the grating plate of the water inlet of the corresponding hydroelectric generating set, behind the grating plate of the water inlet of the hydroelectric generating set and at the water outlet of the hydroelectric generating set from a second database by taking the flow data serial number in front of the grating plate of the water inlet of the hydroelectric generating set, the flow data serial number behind the grating plate of the water inlet of the hydroelectric generating set and the flow data serial number of the water outlet of the hydroelectric generating set as query objects one by one;
if the total flow data is not changed, judging that the hydroelectric generating set is in a normal operation state, acquiring the operating decibel of the water turbine during normal operation of the hydroelectric generating set, marking and numbering the operating decibel data of the water turbine during normal operation of the hydroelectric generating set, and uploading the data to a preset fourth database;
if the front flow of the grid plate of the water inlet of the hydroelectric generating set is not changed and the rear flow of the grid plate of the water inlet of the hydroelectric generating set is not changed, judging that the hydroelectric generating set is in a state with too low running power, and collecting the decibel of the running water turbine when the running power of the hydroelectric generating set is too low;
calling the water turbine running decibel data of the corresponding hydroelectric generating set in normal running by taking the water turbine running decibel data number of the hydroelectric generating set in normal running as an object from a fourth database, and comparing the water turbine running decibel of the hydroelectric generating set in normal running with the water turbine running decibel of the hydroelectric generating set when the power of the hydroelectric generating set is too low;
if the operating decibel of the water turbine when the power of the hydroelectric generating set is too low is larger than that when the hydroelectric generating set operates normally, analyzing and judging that the fault reason of the too low power of the hydroelectric generating set is that the water turbine contains sundries;
if the operating decibel of the water turbine when the power of the hydroelectric generating set is too low is the same as the operating decibel of the water turbine when the hydroelectric generating set operates normally, analyzing and judging that the fault reason of the too low power of the hydroelectric generating set is the aging of the water turbine.
Through adopting above-mentioned technical scheme, through gathering and obtaining the decibel when the hydraulic turbine normally operates, if there is debris jam interference in the hydraulic turbine operation process, the noise when the hydraulic turbine operates will increase, consequently detects out the decibel when the hydraulic turbine operates when hydroelectric generating set operating power is low and is greater than the decibel when the hydraulic turbine normally operates, then can the interior entering debris of failure reason hydraulic turbine, if the decibel is the same then can the analysis failure reason because the hydraulic turbine is ageing.
In a second aspect, the application provides a hydroelectric generating set operation data trend early warning system, adopts following technical scheme:
the hydroelectric generating set operation data trend early warning method comprises a memory, a processor and a program which is stored on the memory and can be operated on the processor, wherein the program can be loaded and executed by the processor to realize the hydroelectric generating set operation data trend early warning method.
By adopting the technical scheme, through the calling of the program, the operation power data of each time node of the hydroelectric generating set are collected, the trend prediction is carried out on the subsequent operation power data based on the operation power data information, when the prediction result value of the subsequent operation power trend prediction is lower than the preset operation power too-low early warning value, the fault reason of the hydroelectric generating set with too-low operation power can be analyzed according to the operation power prediction result value, and the fault reason and the predicted fault time are notified to the control center and maintenance personnel.
In a third aspect, the application provides a hydroelectric generating set operation data trend early warning storage medium, which adopts the following technical scheme:
program comprising instructions capable of being loaded and executed by a processor for implementing a hydroelectric generating set operating data trend warning method as claimed in any one of the preceding claims.
By adopting the technical scheme, through the calling of the program, the operation power data of each time node of the hydroelectric generating set are collected, the trend prediction is carried out on the subsequent operation power data based on the operation power data information, when the prediction result value of the subsequent operation power trend prediction is lower than the preset operation power too-low early warning value, the fault reason of the hydroelectric generating set with too-low operation power can be analyzed according to the operation power prediction result value, and the fault reason and the predicted fault time are notified to the control center and maintenance personnel.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method comprises the steps of collecting operation power data of each time node of the hydroelectric generating set, conducting trend prediction on subsequent operation power data based on operation power data information, when a prediction result value of the subsequent operation power trend prediction is lower than a preset operation power too-low early warning value, analyzing a fault reason of too-low operation power of the hydroelectric generating set according to the operation power prediction result value, and informing a control center and maintenance personnel of the fault reason and predicted fault time.
2. The method comprises the steps of preliminarily analyzing fault reasons of excessively low running power of the hydroelectric generating set according to flow data changes of a water outlet of the hydroelectric generating set, a water inlet of the hydroelectric generating set and the front part of a grid plate of the water inlet of the hydroelectric generating set, the back part of the grid plate of the water inlet of the hydroelectric generating set, and then analyzing specific fault reasons of excessively low running power of the hydroelectric generating set through detection of the grid plate and a water turbine.
Drawings
Fig. 1 is an overall flowchart of a hydroelectric generating set operation data trend early warning method in the embodiment of the present application.
Fig. 2 is a schematic flow chart of substep S200 in fig. 1.
Fig. 3 is a schematic flowchart of sub-step S230 in fig. 2.
Fig. 4 is a schematic flow chart of sub-step S400 in fig. 1.
Fig. 5 is a schematic flowchart of sub-step S430 in fig. 4.
Fig. 6 is a flowchart of sub-step S444 in fig. 5.
Fig. 7 is a flowchart of sub-step S445 in fig. 5.
Detailed Description
The present application is described in further detail below with reference to figures 1-7.
Referring to fig. 1, the hydroelectric generating set operation data trend early warning method disclosed by the application comprises steps S100 to S500, wherein the steps S400 and S500 are parallel.
Step S100, acquiring operation power data of each time node of the hydroelectric generating set, marking the operation power data of each time node of the hydroelectric generating set with different numbers, and uploading the operation power data number of each time node and operation power data information of each corresponding time node to a preset first database, wherein the intervals between every two adjacent time nodes are the same.
Specifically, based on the local time in the control system, the operating power of the hydroelectric generating set is obtained at regular intervals, for example, at every ten minutes, and the obtained operating power of the hydroelectric generating set can be obtained by calculating the operating efficiency of the hydroelectric generating set, the water flow rate in the hydroelectric generating set obtained by using a flowmeter, and the water head of the hydroelectric generating set, and the operating power data of the hydroelectric generating set obtained each time is uploaded to a preset first database for storage.
Step S200, performing subsequent time node operation power trend prediction based on the operation power data information of each time node called from the preset first database, and analyzing and acquiring an operation power prediction result value of the subsequent time node and prediction time corresponding to the operation power prediction result value of the subsequent time node.
Referring to fig. 2, step S200 may be divided into step S210 to step S230.
Step S210, taking the number of the operation power data of each time node as a query object, and calling the operation power data information of each corresponding time node from the first database.
Step S220, calculating a predicted operating power result value of the next time node corresponding to each time node by using a pre-constructed next time node operating power prediction formula.
Specifically, the pre-constructed prediction formula of the operating power of the next time node is specifically as follows:
in which S ist+1Predicting the result value of the running power of the next time node, wherein a is a preset constant and has a value range of (0,1), and ytFor operating power data of the node at the current time, StFor the prediction result value of the current time node, because the prediction formula needs to adopt historical data for calculation, and the first time node has no historical data, the prediction result value of the first time node is set to be the same as the operation power data of the first time node, a preset constant a is preset according to the time sequence change of the operation power data, if the time sequence is in a stable horizontal trend, a can take a smaller value, and if the time sequence has an obvious ascending or descending trend, a can take a larger value.
For example, assuming that the obtained operating power of the first time node is 100, the prediction result of the first time nodeThe value is also 100Kw, assuming that the interval between two time nodes is 10 minutes, the operating power of the second time node acquired after 10 minutes is 90Kw, since the operating power is in a descending trend, a takes a larger value, and therefore a is 0.8, and the substitution is made
And calculating to obtain a running power prediction result value of 92Kw of the third time node, obtaining running power of 80Kw of the third time node after 10 minutes, and substituting the actual running power of the third time node after 10 minutes and the predicted running power of the third time node into a formula to calculate to obtain a running power prediction result value of 82.4Kw of the fourth time node.
Step S230, performing quadratic trend prediction based on the operating power data information of each time node and the operating power prediction formula of the next time node, and obtaining an operating power prediction result value of the subsequent time node and corresponding prediction time.
Referring to fig. 3, step S230 may be divided into steps S231 to S233.
And S231, optimizing the prediction result value of the next time node operation power by applying a pre-constructed next time node operation power prediction result optimization formula.
Specifically, in order to make the prediction result more accurate, and therefore perform secondary prediction on the prediction result value of the next time node operating power, the pre-constructed next time node operating power prediction result optimization formula is specifically as follows:
wherein a is a preset constant and the value range is (0,1), St+1Predicting a result value, S, for the operating power of the next time nodet+1' optimizing value, S, for prediction result of next time node operation powert' setting the optimal value of the prediction result of the first time node to be the same as the operation power data of the first time node for the optimal value of the prediction result of the current time node.
For example, assuming that the acquired operating power data of the first time node is 100Kw, therefore, the predicted result value of the first time node and the predicted result optimized value of the first time node are both 100Kw, the time interval between the two time nodes is 10 minutes, the operating power data of the second time node acquired after 10 minutes is 90Kw, assuming that a is 0.9, the predicted result value of the operating power of the third time node is 91Kw calculated by the next time node operating power prediction formula, the optimized predicted result value is 91.9Kw calculated by the next time node operating power prediction formula, the operating power data of the third time node acquired after 10 minutes is 93Kw calculated by the next time node operating power prediction formula, the predicted result value of the operating power of the fourth time node is 92.8Kw calculated by substituting the formula, and the predicted result optimized value of the operating power of the fourth time node is 92.71Kw calculated by the fourth time node.
And step S232, sequencing the time nodes in a sequence from far to near, marking the time nodes sequenced from far to near in sequence by taking the sequence of natural integers from small to large as a serial number, and calculating the optimal prediction result values of the operation power of a plurality of subsequent time nodes by applying a pre-constructed subsequent time node operation power prediction formula.
Specifically, a first time node mark is numbered as 1, a second time node mark after 10 minutes is numbered as 2 assuming that the time interval between two time nodes is 10 minutes, a third time node mark after 10 minutes from the second time node is numbered as 3, and so on, so as to list the running power data of each time node and the time sequence of the running power prediction result optimization value of each time node, and the pre-constructed subsequent time node running power prediction formula is specifically as follows
Where m is the time node number, atIs a first constant, btIs a second constant, Ft+m' optimizing value for prediction result of m-th time node operation power, n is previous n time nodes of current time node,
is the average of the n time node numbers,
and running the average value of the power prediction result optimization values for n time nodes before the current time node.
For example, assuming that the operating powers of the first 5 time nodes are respectively 100Kw, 90Kw, 93Kw, 80Kw, and 72Kw according to the time sequence, and it is desired to calculate the predicted result optimized value of the operating power of the 7 th time node, first, the predicted result optimized values of the operating powers of the first 5 time nodes are respectively calculated according to the next time node operating power prediction formula and the next time node operating power prediction result optimized formula, assuming that a is 0.9, the predicted result optimized values of the operating powers of the first 5 time nodes are respectively 100Kw, 91.9Kw, 92.71Kw, 82.42Kw, and 73.88Kw according to the time sequence, and the predicted result optimized values of the operating powers of the first 5 time nodes are substituted into the predicted result optimized values of the operating powers of the first 5 time nodes
Is calculated to obtain
Is 88.182Kw, and is,
is 3, atIs 106.698, btIs-6.172, thus Ft+m' =106.698-6.172m, and the predicted result optimized value of the calculated 7 th time node running power is 63.494Kw by substituting m = 7.
Step S233, a pre-constructed predicted time calculation formula is applied to calculate the predicted time corresponding to the subsequent time nodes.
Specifically, the pre-constructed predicted time calculation formula is specifically as follows:
wherein T isnPredicted time, T, for the following mth time nodeaIs the time corresponding to the first time node, TbBetween two adjacent time nodesFor example, assume the time interval T between two adjacent time nodesbIs 10min, the time T corresponding to the first time nodenAt 14:00, if the predicted time corresponding to the next 7 th time node is desired to be obtained, the predicted time corresponding to the 7 th time node is calculated by substituting the formula to be 15: 10.
Step S300, judging whether the operation power prediction result value of the subsequent time node is smaller than a preset hydroelectric generating set operation power too-low early warning value or not, if the operation power prediction result value of the subsequent time node is smaller than the preset hydroelectric generating set operation power too-low early warning value, executing step S400, otherwise, executing step S500.
And S400, analyzing a fault reason causing the too low running power of the hydroelectric generating set based on the running power prediction result value, sending the corresponding prediction time when the fault reason and the prediction result value are lower than the running power too low early warning value to a control center, and sending the corresponding prediction time when the fault reason and the prediction result value are lower than the running power too low early warning value to a mobile terminal held by a maintenance worker.
Referring to fig. 4, analyzing the cause of the fault causing the too low operating power of the hydroelectric power generating set based on the predicted result value of the operating power in step S400 may be divided into steps S410 to S440, where step S430 and step S440 are parallel steps.
Step S410, collecting flow data in front of a grating plate of a water inlet of the hydroelectric generating set, flow data behind the grating plate of the water inlet of the hydroelectric generating set and flow data of a water outlet of the hydroelectric generating set, and uploading the three flow data to a preset second database.
Specifically, the hydroelectric generating set is generally formed by a water turbine, a water inlet pipe in front of the water turbine and a water outlet pipe behind the water turbine, a grating plate for filtering impurities in water is arranged in the water inlet pipe, and a flow meter is arranged in front of the grating plate at the water inlet of the hydroelectric generating set, behind the grating plate at the water inlet of the hydroelectric generating set and at the water outlet of the hydroelectric generating set, so that flow data of the water in front of the grating plate at the water inlet of the hydroelectric generating set, behind the grating plate at the water inlet of the hydroelectric generating set and at the water outlet of the hydroelectric generating set are obtained.
Step S420, determining whether the total flow rate is in a descending trend according to the flow data of the front grating plate of the water inlet of the hydro-power generating unit, the rear grating plate of the water inlet of the hydro-power generating unit and the water outlet of the hydro-power generating unit, if the total flow rate is in the descending trend, executing step S430, otherwise, executing step S440.
Specifically, the flow data of the water outlet of the hydroelectric generating set, the flow data of the water inlet of the hydroelectric generating set and the flow data of the water inlet of the hydroelectric generating set are added to obtain total flow data, a time sequence of the total flow data is listed, and whether the total flow is in a descending trend or not is judged according to the time sequence of the total flow data.
And step S430, the hydroelectric generating set is in a state of too low running power, and the fault reason of the too low running power of the hydroelectric generating set is analyzed and judged according to the flow data.
Referring to fig. 5, the step S430 of analyzing and determining the fault cause of the too low operating power of the hydroelectric generating set according to the flow data may be divided into steps S441 to S445, where steps S442 and S443 are parallel steps, and steps S444 and S445 are parallel steps.
Step S441, determining whether the flow rate of the grid plate at the water inlet of the hydroelectric generating set is reduced, if the flow rate of the grid plate at the water inlet of the hydroelectric generating set is reduced, executing step S442, otherwise, executing step S443.
And step S442, analyzing the reason that the water inlet pipe is blocked due to the fault that the running power of the hydroelectric generating set is too low.
Specifically, because the flow reduces before the grid plate of hydroelectric generating set water inlet, then can directly judge that the inlet tube in front of the grid plate takes place to block up, and the jam reason probably needs operating personnel to clear up the inlet tube for the gravel and sand in the water or the plant in the water.
Step S443, determining whether the rear flow of the grid plate at the water inlet of the hydro-power generating unit is reduced, if the rear flow of the grid plate at the water inlet of the hydro-power generating unit is reduced, executing step S444, otherwise, executing step S445.
And step S444, detecting the grid plates, analyzing and judging the specific fault reason of the hydroelectric generating set with too low operating power.
Referring to fig. 6, step S444 may be divided into steps S4a1 through S4a5, wherein step S4a4 and step S4a5 are parallel steps.
And S4A1, acquiring the light transmittance of the grid plate when the hydroelectric generating set is in a normal operation state.
Specifically, a light source transmitter is installed on one side of the grid plate, a light source receiver is installed on the other side of the grid plate, when the running power of the hydroelectric generating set is normal, the light source transmitter is used for transmitting a light source to the light source receiver and recording the illumination intensity of the transmitted light source, the light source receiver records the illumination intensity of a received light source after receiving the light source, and the ratio of the illumination intensity of the received light source to the illumination intensity of the transmitted light source is the light transmittance of the grid plate when the running power of the hydroelectric generating set is normal.
And S4A2, acquiring the light transmittance of the grid plate when the running power of the hydroelectric generating set is too low.
Specifically, when the running power of the hydroelectric generating set is too low, the light source emitter is used for emitting a light source to the light source receiver and recording the illumination intensity of the emitting light source, the light source receiver records the illumination intensity of the receiving light source after receiving the light source, and the ratio of the illumination intensity of the receiving light source to the illumination intensity of the emitting light source is the light transmittance of the grid plate when the running power of the hydroelectric generating set is too low.
And S4A3, judging whether the light transmittance of the grid plate is lower than that of the grid plate when the running power of the hydroelectric generating set is normal when the running power of the hydroelectric generating set is too low, executing the step S4A4 if the light transmittance of the grid plate is lower than that of the grid plate when the running power of the hydroelectric generating set is normal when the running power of the hydroelectric generating set is too low, and executing the step S4A5 if the light transmittance of the grid plate is not lower than that of the grid plate when the running power of the hydroelectric generating set is normal.
And S4A4, analyzing and judging the fault reason that the running power of the hydroelectric generating set is too low to be grid plate blockage.
Specifically, because the light transmittance of the grid plate is reduced, the grid plate can be judged to be possibly blocked by plants or sand stones in water, so that the water flow in front of and behind the grid plate is changed, and finally the running power of the hydroelectric generating set is reduced.
And S4A5, analyzing and judging the fault reason of the too low running power of the hydroelectric generating set because the communicating pipe between the grid plate and the water turbine deforms.
Specifically, because grid plate luminousness does not change, then can judge that the grid plate does not take place to block up, and the pipeline that is used for connecting hydraulic turbine and grid plate has taken place deformation and has leaded to the pipe diameter to reduce to lead to the discharge through this pipeline to reduce, and finally lead to hydroelectric generating set operating power to reduce.
And step S445, analyzing the specific fault reason of the too low running power of the hydroelectric generating set according to the detected water turbine.
Referring to FIG. 7, step S445 may be divided into step S4B1 through step S4B5, wherein step S4B4 and step S4B5 are parallel steps.
And S4B1, acquiring the running decibel of the water turbine when the hydroelectric generating set is in a normal running state.
Specifically, a sound detection device is arranged at a water turbine of the hydroelectric generating set, and when the running power of the hydroelectric generating set is normal, the sound detection device collects and collects normal running audio of the running water turbine, and decibels of the normal running audio are obtained through the sound detection device.
And S4B2, acquiring the operating decibel of the water turbine when the operating power of the hydroelectric generating set is too low.
Specifically, when the running power of the hydroelectric generating set is too low, the sound detection device collects and collects fault audio when the water turbine runs, and decibels of the fault audio are obtained through the sound detection device.
And S4B3, judging whether the operating decibel of the water turbine during normal operation of the hydroelectric generating set is larger than that during too low power of the hydroelectric generating set, if so, executing the step S4B4, otherwise, executing the step S4B 5.
And S4B4, analyzing and judging the fault reason of the too low running power of the hydroelectric generating set as that the water turbine contains sundries.
Specifically, because the hydraulic turbine when normally operating, rivers promote the hydraulic turbine's water wheels and rotate, mechanical energy turns into the electric energy among the hydraulic turbine rotation process, other noise can not produce among the hydraulic turbine operation process, the decibel of during operation also is in the stationary state, if the debris such as stone have got into in the hydraulic turbine have disturbed the flow of rivers, lead to the hydroelectric set operating power to reduce, the stone is driven by rivers and collides with the water wheels of the hydraulic turbine, thereby can produce the noise when the collision, lead to the decibel increase of hydraulic turbine during operation, consequently can judge whether contain debris in the hydraulic turbine through the operation decibel of the hydraulic turbine.
And S4B5, analyzing and judging the fault reason of the too low running power of the hydroelectric generating set to be the aging of the water turbine.
Specifically, if the running power of the hydro-power generating unit is reduced and the decibel of the running of the water turbine is normal, the fact that impurities are not contained in the water turbine can be judged, and the reason that the running power of the hydro-power generating unit is reduced is that the water turbine is aged, for example, a guide vane of the water turbine is aged, and a worker is required to overhaul and replace a water turbine assembly.
And step S440, the hydroelectric generating set is in a normal running state, and flow data of all parts of the hydroelectric generating set are continuously acquired.
Specifically, when the hydro-power generating unit is in a normal operation state, flow data of the front part of a grid plate of a water inlet of the hydro-power generating unit, the rear part of the grid plate of the water inlet of the hydro-power generating unit and a water outlet of the hydro-power generating unit are continuously collected, meanwhile, the light transmittance of the grid plate can be collected through a light source transmitter and a light source receiver, and the operating decibel of the water turbine is detected through a sound detection device.
And step S500, continuing to perform trend prediction on the running power of the nodes at the subsequent time.
An embodiment of the present invention provides a computer-readable storage medium, which includes a program capable of being loaded and executed by a processor to implement any one of the methods shown in fig. 1-7.
The computer-readable storage medium includes, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Based on the same inventive concept, an embodiment of the present invention provides an intelligent environmental sanitation management system based on the internet of things, which includes a memory and a processor, wherein the memory stores a program that can be executed on the processor to implement any one of the methods shown in fig. 1 to 7.
It will be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic disk or optical disk, etc. for storing program codes.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: equivalent changes in structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (7)
1. A hydroelectric generating set operation data trend early warning method is characterized by comprising the following steps:
acquiring running power data of each time node of the hydroelectric generating set, wherein the time intervals between every two adjacent time nodes are the same, marking the running power data of each time node of the hydroelectric generating set with different numbers, and uploading the running power data number of each time node and running power data information of each corresponding time node to a preset first database;
taking the number of the operation power data of each time node as a query object, and calling the operation power data information of each corresponding time node from a first database;
calculating the operation power prediction result value of the next time node corresponding to each time node by applying a pre-constructed next time node operation power prediction formula, wherein the pre-constructed next time node operation power prediction formula is specifically as follows:
in which S ist+1Predicting the result value of the running power of the next time node, wherein a is a preset constant, ytFor operating power data of the node at the current time, StSetting the predicted result value of the first time node to be the same as the running power data of the first time node for the predicted result value of the current time node;
and optimizing the predicted result value of the next time node operating power by applying a pre-constructed next time node operating power predicted result optimization formula, wherein the pre-constructed next time node operating power predicted result optimization formula is specifically as follows:
wherein a is a predetermined constant, St+1Predicting a result value, S, for the operating power of the next time nodet+1' optimizing value, S, for prediction result of next time node operation powertSetting the optimal value of the prediction result of the first time node to be the same as the operation power data of the first time node for the optimal value of the prediction result of the current time node;
sequencing the time nodes in a sequence from far to near, marking the time nodes sequenced from far to near by using the sequence of natural integers from small to large as a serial number, and calculating the optimal prediction value of the operation power of a plurality of subsequent time nodes by applying a pre-constructed subsequent time node operation power prediction formula which is specifically as follows
Where m is the time node number, Ft+m' optimizing value for prediction result of m-th time node operation power, n is previous n time nodes of current time node,
is the average of the n time node numbers,
the average value of the running power prediction result optimized values of n time nodes before the current time node is obtained;
calculating the predicted time corresponding to a plurality of subsequent time nodes by using a pre-constructed predicted time calculation formula, wherein the pre-constructed predicted time calculation formula is specifically as follows:
wherein T isnPredicted time, T, corresponding to the m-th subsequent time nodeaTime, T, corresponding to the first time nodebThe time interval between two adjacent time nodes is defined;
comparing the operation power prediction result value of the subsequent time node with a preset hydroelectric generating set operation power too-low early warning value;
if the operation power prediction result value is smaller than the operation power too-low early warning value, analyzing a fault reason causing the too-low operation power of the hydroelectric generating set based on the operation power prediction result value, sending the fault reason and the corresponding prediction time when the prediction result value is lower than the operation power too-low early warning value to a control center, and sending the fault reason and the corresponding prediction time when the prediction result value is lower than the operation power too-low early warning value to a mobile terminal held by a maintenance worker;
and if the operation power prediction result value is larger than or equal to the operation power too-low early warning value, continuing to perform trend prediction on the operation power of the subsequent time node.
2. The hydroelectric generating set operation data trend early warning method according to claim 1, wherein the step of analyzing the fault cause of the hydroelectric generating set with too low operation power based on the operation power prediction result value comprises the following steps:
acquiring flow data in front of a grating plate of a water inlet of a hydroelectric generating set and marking different numbers, acquiring flow data behind the grating plate of the water inlet of the hydroelectric generating set and marking different numbers, acquiring flow data of a water outlet of the hydroelectric generating set and marking different numbers, and uploading the flow data in front of the grating plate of the water inlet of the hydroelectric generating set and flow data behind the grating plate of the corresponding water inlet of the hydroelectric generating set, the flow data in behind the grating plate of the water inlet of the hydroelectric generating set and flow data behind the grating plate of the corresponding water inlet of the hydroelectric generating set, the flow data number of the water outlet of the hydroelectric generating set and the flow data of the water outlet of the corresponding hydroelectric generating set to a preset second database;
taking the flow data serial number in front of the grating plate of the water inlet of the hydro-power generating unit, the flow data serial number behind the grating plate of the water inlet of the hydro-power generating unit and the flow data serial number of the water outlet of the hydro-power generating unit as query objects from a second database, and calling and acquiring the flow data in front of the grating plate of the water inlet of the hydro-power generating unit, behind the grating plate of the water inlet of the hydro-power generating unit and at the water outlet of the hydro-power generating unit one by one;
if the total flow data is kept unchanged, judging that the hydroelectric generating set is in a normal running state, and continuously acquiring flow data of each part of the hydroelectric generating set;
and if the total flow data is in a descending trend, judging that the hydroelectric generating set is in a state of excessively low operating power, and analyzing and judging the fault reason of excessively low operating power of the hydroelectric generating set according to the flow data.
3. The hydroelectric generating set operation data trend early warning method according to claim 2, wherein the step of analyzing and judging the fault reason of the too low operation power of the hydroelectric generating set according to the flow data is as follows:
if the front flow of the grid plate at the water inlet of the hydroelectric generating set is reduced, analyzing the fault reason of the over-low running power of the hydroelectric generating set because the water inlet pipe is blocked;
if the front flow of the grating plate of the water inlet of the hydro-power generating unit is unchanged and the rear flow of the grating plate of the water inlet of the hydro-power generating unit is reduced, detecting the grating plate and analyzing and judging the specific fault reason of the over-low running power of the hydro-power generating unit; and if the front flow of the grating plate at the water inlet of the hydro-power generating unit is unchanged, the rear flow of the grating plate at the water inlet of the hydro-power generating unit is unchanged, and the flow of the water outlet of the hydro-power generating unit is reduced, analyzing the specific fault reason of the over-low running power of the hydro-power generating unit according to the detected water turbine.
4. The hydroelectric generating set operation data trend early warning method according to claim 3, wherein the steps of detecting the grating plates and analyzing and judging the specific fault reasons of the running power of the hydroelectric generating set which is too low are as follows:
respectively calling and acquiring flow data in front of the grating plate of the water inlet of the corresponding hydroelectric generating set, behind the grating plate of the water inlet of the hydroelectric generating set and at the water outlet of the hydroelectric generating set from a second database by taking the flow data serial number in front of the grating plate of the water inlet of the hydroelectric generating set, the flow data serial number behind the grating plate of the water inlet of the hydroelectric generating set and the flow data serial number of the water outlet of the hydroelectric generating set as query objects one by one;
if the total flow data do not fluctuate, judging that the hydroelectric generating set is in a normal operation state, acquiring the light transmittance of the grating plates when the hydroelectric generating set normally operates, marking and numbering the light transmittance data of the grating plates when the hydroelectric generating set normally operates, and uploading the light transmittance data to a preset third database;
if the front flow of the grating plate of the water inlet of the hydro-power generating unit is unchanged and the rear flow of the grating plate of the water inlet of the hydro-power generating unit is reduced, judging that the hydro-power generating unit is in a state with too low operating power, and acquiring the light transmittance of the grating plate when the operating power of the hydro-power generating unit is too low;
taking the light transmittance data of the grating plates in normal operation of the corresponding hydroelectric generating set as an object by numbering the light transmittance data of the grating plates in normal operation of the hydroelectric generating set from the third database, and comparing the light transmittance of the grating plates when the running power of the hydroelectric generating set is too low with the light transmittance of the grating plates when the running power of the hydroelectric generating set is normal;
if the light transmittance of the grid plate is lower than that of the grid plate when the running power of the hydro-power generating unit is normal when the running power of the hydro-power generating unit is too low, analyzing and judging that the fault reason of the too low running power of the hydro-power generating unit is the blockage of the grid plate;
if the light transmittance of the grid plate is the same as that of the grid plate when the running power of the hydroelectric generating set is normal when the running power of the hydroelectric generating set is too low, analyzing and judging that the fault source of the too low running power of the hydroelectric generating set is deformation of a communicating pipe between the grid plate and the water turbine.
5. The hydroelectric generating set operation data trend early warning method according to claim 3, wherein the step of analyzing the specific fault reason of the too low operation power of the hydroelectric generating set according to the detected water turbine comprises the following steps:
respectively calling and acquiring flow data in front of the grating plate of the water inlet of the corresponding hydroelectric generating set, behind the grating plate of the water inlet of the hydroelectric generating set and at the water outlet of the hydroelectric generating set from a second database by taking the flow data serial number in front of the grating plate of the water inlet of the hydroelectric generating set, the flow data serial number behind the grating plate of the water inlet of the hydroelectric generating set and the flow data serial number of the water outlet of the hydroelectric generating set as query objects one by one;
if the total flow data do not fluctuate, judging that the hydroelectric generating set is in a normal operation state, acquiring the operating decibel of the water turbine during normal operation of the hydroelectric generating set, marking and numbering the operating decibel data of the water turbine during normal operation of the hydroelectric generating set, and uploading the data to a preset fourth database;
if the front flow of the grid plate of the water inlet of the hydroelectric generating set is not changed and the rear flow of the grid plate of the water inlet of the hydroelectric generating set is not changed, judging that the hydroelectric generating set is in a state with too low running power, and collecting the decibel of the running water turbine when the running power of the hydroelectric generating set is too low;
taking the operating decibel data of the water turbine during normal operation of the hydroelectric generating set as a target to call the operating decibel data of the water turbine during normal operation of the corresponding hydroelectric generating set from a fourth database, and comparing the operating decibel of the water turbine during normal operation of the hydroelectric generating set with the operating decibel of the water turbine during over-low power of the hydroelectric generating set;
if the operating decibel of the water turbine when the power of the hydroelectric generating set is too low is larger than that when the hydroelectric generating set operates normally, analyzing and judging that the fault reason of the too low power of the hydroelectric generating set is that the water turbine contains sundries;
if the operating decibel of the water turbine when the power of the hydroelectric generating set is too low is the same as the operating decibel of the water turbine when the hydroelectric generating set operates normally, analyzing and judging that the fault reason of the too low power of the hydroelectric generating set is the aging of the water turbine.
6. The utility model provides a hydroelectric generating set operation data trend early warning system which characterized in that: the hydroelectric generating set operation data trend early warning method comprises a memory, a processor and a program which is stored on the memory and can be operated on the processor, wherein the program can be loaded and executed by the processor to realize the hydroelectric generating set operation data trend early warning method according to any one of claims 1 to 5.
7. A computer storage medium, characterized in that: the hydroelectric generating set operation data trend early warning method comprises a program which can be loaded and executed by a processor to realize the hydroelectric generating set operation data trend early warning method according to any one of claims 1 to 5.
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