A water turbine generator unit efficiency evaluation system

Abstract

The present invention discloses an efficiency evaluation system for a hydro-generator. The system includes an equipment data acquisition module and an equipment data processing module; the equipment data acquisition module is respectively connected to the unit under test and the equipment data processing module; the unit under test is the entire hydro-generator set; the equipment data processing module includes a data encapsulation module, an algorithm processor and a result display module connected in sequence; the data encapsulation module receives the data collected by the equipment data acquisition module for data collection; the algorithm processor uses the ADC+P model to evaluate the implementation efficiency of the unit under test and outputs the current state of the unit under test, that is, the current efficiency value; the result display module outputs the real-time efficiency of the unit under test, obtains a continuous efficiency change curve of the unit under test over time, and provides an auxiliary reference for maintenance decision-making. The present invention solves the problem that it is difficult to quantitatively calculate the prediction probability D update iteration of the generator state transition in the prior art.

Description

Technical Field

[0001] The present invention belongs to the field of efficiency evaluation of hydro-generator units, and particularly relates to an efficiency evaluation system for hydro-generators. Background Art

[0002] As a key main equipment in a hydropower plant, the safe operation of a hydro-generator unit is the fundamental guarantee for the hydropower plant to ensure safe, high-quality, and economical power generation and supply. With the increasing unit capacity and structural size of hydro-generator units, reducing the failure rate of the units and ensuring the stable operation of the units are of great significance for improving the economic and social benefits of hydropower plants.

[0003] Currently, most hydropower plants use the method of regular maintenance to maintain each equipment. Regular maintenance is convenient for periodic condition monitoring and maintenance of the equipment, but it is difficult to monitor the real-time state of the equipment and quantify the failure rate statistically. At present, the ADC model is mostly used for the efficiency evaluation of weapon equipment and satellite efficiency, and there is no efficiency calculation and evaluation related to power generation equipment. Among them, "The ADC Efficiency Evaluation Method of Equipment Based on Interval Grey Numbers" evaluates the efficiency of weapon equipment through the ADC model, and "The Efficiency Evaluation Technology of Constellation Satellite Communication System" makes a specific efficiency evaluation of the communication efficiency of constellation satellites through the ADC efficiency evaluation method. These above difficulties not only lead to additional maintenance costs for some frequently failing equipment in addition to regular maintenance, but also result in redundancy in the regular maintenance of some equipment with few failures, which restricts the accuracy and scientific nature of the ADC efficiency calculation method. Summary of the Invention

[0004] Aiming at the above defects or improvement requirements of the prior art, the purpose of the present invention is to provide an efficiency evaluation method for hydro-generator units based on the ADC model, realizing the real-time iterative update of the failure rate of hydro-generator units and accurately evaluating the efficiency of hydro-generators.

[0005] The purpose of the present invention is achieved by at least one of the following technical solutions.

[0006] An efficiency evaluation system for hydro-generators includes an equipment data acquisition module and an equipment data processing module; the equipment data acquisition module is respectively connected to the unit under test and the equipment data processing module;

[0007] The unit under test is the entire hydro-generator unit, including the hydro-generator unit body, the air cooling system, the off-line data system, and the upper guide, lower guide, and bearing structures;

[0008] The device data acquisition module includes a rotational speed sensor, a temperature sensor, and a torque sensor, which are used to measure the real-time status data, health score data within the entire life cycle, available device time, device maintenance information, device failure rate, and device setting values of each component of the unit under test. Among them, the device failure rate, available device time, and braking shutdown time are directly input from the offline database;

[0009] The device data processing module includes a data encapsulation module, an algorithm processor, and a result display module that are connected in sequence;

[0010] The data encapsulation module receives the data collected by the device data acquisition module for data collection;

[0011] The algorithm processor uses the ADC+P model to evaluate the implementation efficiency of the unit under test and outputs the current state of the unit under test, that is, the current efficiency value;

[0012] The result display module outputs the real-time efficiency of the unit under test, obtains the continuous efficiency change curve of the unit under test over time, and provides an auxiliary reference for the maintenance decision-making.

[0013] Furthermore, the device data acquisition module is used to provide the characteristic data of the unit under test. Among them, the rotational speed sensor provides the real-time rotational speed of the water turbine generator set body, the temperature sensor provides the real-time temperature of the air cooling system, the Supervisory Control And Data Acquisition provides the real-time status data, health score data, available time, device maintenance information, braking shutdown time, device failure rate, and device setting values of the unit under test, and the torque sensor provides the upper guide, lower guide, and bearing structure torque data;

[0014] Data is collected from each unit under test through the rotational speed sensor, temperature sensor, and torque sensor, and the device data is input into the data encapsulation module for data encapsulation to provide data support for the algorithm processor.

[0015] Furthermore, in the algorithm processor, the specific processing operation steps are as follows:

[0016] S1. Establish the availability matrix A and the inherent ability matrix C of the unit under test;

[0017] S2. Establish the credibility matrix D of the unit under test;

[0018] S3. Combine the state health index of the unit under test and construct a failure rate prediction model according to historical data;

[0019] S4. Combine the current state health index of the device, consider the natural evolution, failure, or maintenance incentive P of the device, calculate the device failure rate, and iterate the credibility matrix D';

[0020] S5. Iterate the iterative credibility matrix D' according to the calculation result of step S4;

[0021] S6. Calculate the effectiveness of the unit under test using the ADC+P model, and evaluate the current state of the unit under test based on the output effectiveness value.

[0022] Furthermore, in step S1, the availability matrix A and the inherent ability matrix C of the unit under test are calculated through the ADC model;

[0023] The calculation of the availability matrix A is as follows:

[0024] Availability is a measure of the working state of each power generation device in a pumped-storage power plant when it starts to work; different elements in the availability matrix A represent the probabilities of the power generation device being in different working states. Therefore, when analyzing the availability of the power generation device, it is necessary to first analyze the composition structure and working states it has as a part of the system level;

[0025] After the system structure is determined, the working state depends on three factors: maintainability, reliability, and delay time of the system;

[0026] According to whether each component of the unit under test is working properly and whether there are spare parts, its availability is divided into normal state, fault state, and shutdown state;

[0027] The availability matrix A of the unit under test can be expressed as:

[0028] A = [a1, a2, a3] (1)

[0029] Where a1 is the normal state, indicating that all components of the unit under test are in a normal working state; a2 is the fault state, indicating that some components of the unit under test are in a fault state, but the unit under test can still operate normally; a3 is the shutdown state, indicating that the unit under test stops operating;

[0030] The working states of the unit under test, namely the normal state, the fault state, and the shutdown state, are specifically as follows:

[0031] Normal state a1:

[0032]

[0033] Fault state a2:

[0034]

[0035] Shutdown state a3:

[0036]

[0037] Among them, λ represents the failure rate; MTBF represents the mean time between failures of the system; MTTR represents the mean time to repair of the system;

[0038] The calculation of the inherent ability matrix C is as follows:

[0039] Under the frequency modulation scenario, the inherent ability index of the unit under test is mainly characterized by the task response ability and the compliance effect, which are specifically reflected in the level of the generator unit system to complete various frequency modulation tasks up to standard. The specific evaluation indicators include the frequency modulation operation rate of the unit under test, the frequency modulation output power, the frequency modulation compliance effect, and the maximum regulation power;

[0040] By using the analytic hierarchy process and the entropy weight method, the importance of each index is divided, and the product of the subjective weight and the objective weight is taken as the combined weight W of each index = [W 投运 ,W 输出 ,W 达标 ,W 调节 ; W 投运 、W 输出 、W 达标 、W 调节 are the weights of the four evaluation indicators of the frequency modulation operation rate, the frequency modulation output power, the frequency modulation compliance effect, and the maximum regulation power of the unit under test respectively;

[0041] Using the fuzzy evaluation method, a three-level evaluation grade of 'excellent, good, poor' is established for the four evaluation indicators of the frequency modulation operation rate, the frequency modulation output power, the frequency modulation compliance effect, and the maximum regulation power of the unit under test. According to the fault warning threshold of each index, a membership function is constructed to obtain the membership matrix R of each index;

[0042] Multiply the combined weight of the index by the membership matrix to obtain the inherent ability matrix of the unit under test:

[0043] C = [C1, C2, C3] = W × R (5)

[0044] Among them, C1 is the inherent ability value of the unit under test in the normal working state, C2 is the inherent ability value of the unit under test in the fault state, and C3 is the inherent ability value of the unit under test in the shutdown state.

[0045] Furthermore, in step S2, the fault events of the hydro-generator under investigation conform to the exponential distribution, and the hydro-generator can be regarded as conforming to the Markov state transition process. Therefore, the Markov state transition matrix can be used to characterize the state change process of the hydro-generator;

[0046] In the natural evolution state, that is, without considering the intervention of maintenance, the working state of the unit under test will only change from the normal working state to the fault state or the shutdown state. That is, the availability changes from a1 to a1, a2, a3 or from a2 to a2, a3 or remains unchanged when a3 changes;

[0047] The expression of the credibility matrix D of the hydrogenerator is as follows:

[0048]

[0049] Among them, λ E 、λ C 、λ B 、λ S 、λ A respectively represent the failure rates of the hydrogenerator unit body, air cooling system, bearing structure, braking and shutdown system, and auxiliary system; substituting the initial failure rates of each component of the unit under test, the credibility D matrix is obtained.

[0050] Furthermore, in step S3, the evaluation indicators of the state health index of the unit under test include the inspection of the main shaft seal and its water supply system, the inspection of the water guide bearing, and the inspection of the guide vane thrust bearing;

[0051] Taking the maintenance test regulations including but not limited to "Enterprise Standard of China Southern Power Grid Co., Ltd.: Power Equipment Maintenance and Test Regulations" and "Guide for the State Evaluation of Distribution Network Equipment" as the scoring basis, based on patrol inspection, routine test, diagnostic test, on-line monitoring, and live detection, the intensity of the phenomenon, the magnitude of the measured value, and the development trend are evaluated to obtain the deduction scores of each component, and then the comprehensive deduction score of the specific equipment is obtained as the equipment health index.

[0052] Furthermore, in step S3, the equipment failure rate is positively correlated with the equipment health index, and the exponential relationship formula is calculated as follows:

[0053] λ = K × e C×HI (7)

[0054] Among them, λ is the equipment failure rate; K is the proportionality coefficient; C is the curvature coefficient; HI is the equipment health index, and the numerical range is 0 - 100;

[0055] Using the known historical data of the unit under test in the past two years, including the health score index HI, the total number of equipment N, the number of faulty equipment n, and the overall annual failure probability P of the unit under test year , through inversion calculation, the proportionality coefficient K and the curvature coefficient C are obtained, and the inversion calculation formula is as follows:

[0056]

[0057] Among them, i is the classification of the equipment, I is the number of equipment types, N i is the number of equipment of the i-th category, HI i is the average value of the upper and lower limits of the corresponding HI score according to the i-th category of equipment.

[0058] Further, in step S4, the health index of the unit under test within a fixed time scale can be used as the input quantity to obtain the failure rate of the unit under test during this maintenance cycle. The elements of the credibility matrix are functions of the failure rates of the main body of the hydro-generator unit, the air cooling system, the off-line data system, and the upper guide, lower guide, and bearing structures at a certain moment t during frequency regulation and conform to the form of exponential distribution. According to the ADC model, the iterative credibility matrix D’ is obtained.

[0059] Further, in step S5, the iterative process of the iterative credibility matrix D’ includes the equipment state transition under the natural evolution process of the equipment and the equipment state transition caused by the equipment being subjected to the failure or maintenance incentive P. Under the natural evolution process, the monthly is used as the minimum time scale to evaluate the health of the equipment state. After obtaining the equipment failure rate of that month according to the monthly health index, the iterative credibility matrix D’ is iteratively calculated;

[0060] After the equipment is subjected to the failure or maintenance incentive P, the health of the equipment state is evaluated. The failure rate of the equipment after maintenance is obtained according to the health index, and the iterative credibility matrix D’ is iteratively calculated to obtain the iterated iterative credibility matrix D’. The number of iterations is consistent with the number of incentives. The expression is as follows:

[0061]

[0062] Among them, λ′ E 、λ′ C 、λ′ B 、λ′ S 、λ′ A respectively represent the failure rates of the main body of the hydro-generator unit, the air cooling system, the bearing structure, the braking and stopping system, and the auxiliary system after iteration.

[0063] Further, in step S6, the expression for calculating the effectiveness of the unit under test by the ADC+P model is:

[0064] E = A × D × C;

[0065] Among them, the availability matrix A obtained according to step S1 represents the equipment health state of the unit under test. The iterative credibility matrix D’ finally obtained according to step S5 represents the probability of the unit under test transferring from the normal state to the failure state. The C matrix obtained according to step S1 represents the level of the unit under test achieving the standard for various frequency regulation tasks. The overall effectiveness E obtained by multiplication represents the probability distribution value of the unit under test being in the equipment health state;

[0066] Outputting the magnitude value of the overall effectiveness E to evaluate the current state of the unit under test is specifically manifested as:

[0067] The calculated overall effectiveness E is a time-domain change result, and the change of the overall effectiveness E is determined by the change of the curve.

[0068] Compared with the prior art, the advantages of the present invention are as follows:

[0069] During the performance evaluation process, by combining the equipment health score coefficient, the real-time update of the equipment failure rate is realized, and a quantitative basis is provided for the iterative calculation of the prediction probability D of the generator state transition. The present invention solves the problem that it is difficult to quantitatively calculate the iterative update of the prediction probability D of the generator state transition in the prior art, improves the accuracy and scientificity of the ADC+P matrix method calculation, and provides a reference for the maintenance decision-making through the real-time change curve of the performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic diagram of the change curve of the performance value over time in Embodiment 1 of the present invention;

[0071] Figure 2 It is a schematic diagram of the change curve of the performance value over time in Embodiment 2 of the present invention;

[0072] Figure 3 It is a schematic diagram of the change curve of the performance value over time in Embodiment 3 of the present invention;

[0073] Figure 4 It is a technical analysis roadmap of the performance of a hydro-generator unit in an embodiment of the present invention.

[0074] Figure 5 It is a schematic diagram of the structure of a performance evaluation system for a hydro-generator in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those of ordinary skill in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0076] Embodiment 1:

[0077] A performance evaluation system for a hydro-generator includes an equipment data acquisition module and an equipment data processing module; the equipment data acquisition module is respectively connected to the unit under test and the equipment data processing module;

[0078] The unit under test is the entire hydro-generator unit, including the hydro-generator unit body, the air cooling system, the off-line data system, and the upper guide, lower guide, and bearing structures;

[0079] The device data acquisition module includes a rotational speed sensor, a temperature sensor, and a torque sensor, which are used to measure the real-time status data of each component of the unit under test, the health score data during the entire life cycle, the available time of the device, the device maintenance information, the device failure rate, and the device setting value. Among them, the device failure rate, the available time of the device, and the braking shutdown time are directly input from the offline database;

[0080] The device data acquisition module is used to provide the characteristic data of the hydro-generator set. Among them, the rotational speed sensor provides the real-time rotational speed of the hydro-generator set body, the temperature sensor provides the real-time temperature of the air cooling system, the Supervisory Control And Data Acquisition provides the real-time status data, health score data, available time, device maintenance information, braking shutdown time, device failure rate, and device setting value of the unit under test, and the torque sensor provides the upper guide, lower guide, and bearing structure torque data;

[0081] Data is collected from each unit under test through the rotational speed sensor, temperature sensor, and torque sensor, and the device data is input into the data encapsulation module for data encapsulation to provide data support for the algorithm processor.

[0082] The device data processing module includes a data encapsulation module, an algorithm processor, and a result display module that are connected in sequence;

[0083] The data encapsulation module receives the data collected by the device data acquisition module for data collection;

[0084] The algorithm processor uses the ADC+P model to evaluate the implementation efficiency of the hydro-generator set and outputs the current state of the hydro-generator set, that is, the current efficiency value;

[0085] In the algorithm processor, the specific processing operation steps are as follows:

[0086] S1. Establish the availability matrix A and the inherent ability matrix C of the unit under test;

[0087] The availability matrix A and the inherent ability matrix C of the unit under test are calculated through the ADC model;

[0088] The calculation of the availability matrix A is as follows:

[0089] Availability is a measure of the working state of each power generation device in the pumped storage power plant when it starts to work; different elements in the availability matrix A represent the probabilities when the power generation device is in different working states. Therefore, when analyzing the availability of the power generation device, it is necessary to first analyze the composition structure and working states owned by the power generation device as a system level;

[0090] After the system structure is determined, the working state depends on three factors: maintainability, reliability, and delay time of the system;

[0091] According to whether each component of the hydro-generator unit is working properly and whether there are spare parts, its availability is divided into normal state, fault state, and shutdown state;

[0092] The availability matrix A of the hydro-generator unit can be expressed as:

[0093] A = [a1, a2, a3] (1)

[0094] Among them, a1 is the normal state, indicating that all components of the hydro-generator unit are in a normal working state; a2 is the fault state, indicating that some components of the hydro-generator unit are in a fault state, but the unit under test can still operate normally; a3 is the shutdown state, indicating that the hydro-generator unit has stopped operating;

[0095] The working states of the hydro-generator unit, namely the normal state, fault state, and shutdown state, are specifically as follows:

[0096] Normal state a1:

[0097]

[0098] Fault state a2:

[0099]

[0100] Shutdown state a3:

[0101]

[0102] Among them, λ represents the failure rate; MTBF represents the mean time between failures of the system; MTTR represents the mean time to repair of the system;

[0103] The calculation of the inherent ability matrix C is as follows:

[0104] The inherent ability index of the hydro-generator mainly characterizes the response ability and compliance effect of the task in the frequency modulation scenario, specifically reflected in the level of the hydro-generator unit system to complete and meet various frequency modulation tasks. The specific evaluation indicators include the frequency modulation operation rate of the hydro-generator unit, the frequency modulation output power, the frequency modulation compliance effect, and the maximum regulation power;

[0105] Through the analytic hierarchy process and entropy weight method, the importance degree of each index is divided, and the product of the subjective weight and the objective weight is taken as the combined weight W = [W 投运 , W 输出 , W 达标 , W 调节 ; W 投运 , W 输出 , W达标 and W 调节 are the weights of the four evaluation indexes of the frequency regulation operation rate, frequency regulation output power, frequency regulation compliance effect, and maximum regulation power of the hydro-generating unit respectively;

[0106] Using the fuzzy evaluation method, establish a three-level evaluation grade of 'good, medium, poor' for the four evaluation indexes of the frequency regulation operation rate, frequency regulation output power, frequency regulation compliance effect, and maximum regulation power of the hydro-generating unit. Construct a membership function according to the fault warning lower limit of each index, and obtain the membership matrix R of each index;

[0107] Multiply the combined weight of the indexes by the membership matrix of the inherent ability matrix of the hydro-generating unit:

[0108] C = [C1, C2, C3] = W × R (5)

[0109] Among them, C1 is the inherent ability value of the hydro-generating unit in the normal working state, C2 is the inherent ability value of the hydro-generating unit in the fault state, and C3 is the inherent ability value of the hydro-generating unit in the shutdown state.

[0110] S2. Establish the credibility matrix D of the hydro-generating unit;

[0111] The fault events of the hydro-generator under investigation conform to the exponential distribution, and the hydro-generator can be regarded as conforming to the Markov state transition process. Therefore, the Markov state transition matrix can be used to characterize the state change process of the hydro-generator;

[0112] In the natural evolution state, that is, without considering the intervention of maintenance, the working state of the hydro-generating unit will only change from the normal working state to the fault state or the shutdown state. That is, the availability changes from a1 to a1, a2, a3 or from a2 to a2, a3 or a3 remains unchanged;

[0113] The expression of the credibility matrix D of the hydro-generator is as follows:

[0114]

[0115] Among them, λ E and λ C and λ B and λ S and λ A represent the failure rates of the hydro-generating unit body, air cooling system, bearing structure, braking shutdown system, and auxiliary system respectively; Substitute the initial failure rates of each component of the unit under test to obtain the credibility D matrix.

[0116] S3. Combine the state health index of the unit under test and construct a failure rate prediction model according to historical data;

[0117] The evaluation indicators for the health index of the unit under test include the inspection of the main shaft seal and its water supply system, the inspection of the water guide bearing, and the inspection of the guide vane thrust bearing;

[0118] Taking the maintenance test regulations including but not limited to "Enterprise Standard of China Southern Power Grid Co., Ltd.: Regulations for Maintenance and Test of Power Equipment" and "Guide for Condition Assessment of Distribution Network Equipment" as the scoring basis, based on patrol inspection, routine test, diagnostic test, online monitoring, and live detection, evaluate the phenomenon intensity, quantity value, and development trend to obtain the deduction scores of each component, and then calculate the comprehensive deduction score of the specific equipment as the equipment health index.

[0119] There is a positive correlation between the equipment failure rate and the equipment health index, and the exponential relationship formula is calculated as follows:

[0120] λ = K × e C×HI (7)

[0121] Among them, λ is the equipment failure rate; K is the proportionality coefficient; C is the curvature coefficient; HI is the equipment health index, and the numerical range is 0 - 100;

[0122] Using the known equipment historical data of the unit under test in the past two years, including the health score index HI, the total number of equipment N, the number of faulty equipment n, and the overall annual failure probability P of the unit under test year , through inverse calculation, obtain the proportionality coefficient K and the curvature coefficient C. The inverse calculation formula is as follows:

[0123]

[0124] Among them, i is the classification of the equipment, I is the number of equipment types, N i is the number of equipment of the i-th category, HI i is the average value of the upper and lower limits of the corresponding HI scores according to the i-th category of equipment.

[0125] S4. Combining the current state health index of the equipment, considering the natural evolution of the equipment, the failure or maintenance incentive P, calculate the equipment failure rate and the iterative credibility matrix D';

[0126] The health index of the unit under test within a fixed time scale can be used as the input quantity to obtain the failure rate of the unit under test during this maintenance cycle. The elements of the credibility matrix are functions of the failure rates of the water turbine generator set body, the air cooling system, the offline data system, and the upper guide, lower guide, and bearing structures at a certain moment t during frequency modulation and conform to the form of exponential distribution. According to the ADC model, obtain the iterative credibility matrix D'.

[0127] S5. Iterate the iterative credibility matrix D' according to the calculation results of step S4;

[0128] The iterative process of the iterative credibility matrix D' includes the equipment state transition under the natural evolution process of the equipment and the equipment state transition caused by the equipment being subjected to a fault or maintenance incentive P. Under the natural evolution process, the monthly scale is used as the minimum time scale to evaluate the health of the equipment state. After obtaining the equipment failure rate for that month based on the monthly health index, iterative calculations are performed on the iterative credibility matrix D'.

[0129] After the equipment is subjected to a fault or maintenance incentive P, the health of the equipment state is evaluated. Based on the health index, the equipment failure rate after maintenance is obtained, and iterative calculations are performed on the iterative credibility matrix D' to obtain the iterated iterative credibility matrix D'. The number of iterations is consistent with the number of incentives. The expression is as follows:

[0130]

[0131] Among them, λ′ E 、λ′ C 、λ′ B 、λ′ S 、λ′ A respectively represent the failure rates of the water turbine generator set body, air cooling system, bearing structure, braking and shutdown system, and auxiliary system after iteration.

[0132] S6. Use the ADC+P model to calculate the efficiency of the water turbine generator set, and evaluate the current state of the generator set according to the output efficiency value;

[0133] The expression for calculating the efficiency of the water turbine generator set using the ADC+P model is:

[0134] E = A×D×C;

[0135] Among them, the availability matrix A obtained according to step S1 represents the equipment health state of the unit under test, the iterative credibility matrix D' finally obtained according to step S5 represents the probability of the unit under test transitioning from the normal state to the fault state, the C matrix obtained according to step S1 represents the level of compliance of the unit under test with various frequency modulation tasks, and the overall efficiency E obtained by multiplication represents the probability distribution value of the unit under test being in the equipment health state;

[0136] Evaluating the current state of the water turbine generator set by outputting the magnitude value of the overall efficiency E is specifically manifested as:

[0137] The calculated overall efficiency E is a time-domain change result, and the change of the overall efficiency E is determined by the change of the curve.

[0138] The result display module outputs the real-time efficiency of the water turbine generator set, obtains the continuous efficiency change curve of the water turbine generator set over time, and provides an auxiliary reference for the maintenance decision-making.

[0139] In this embodiment, taking the data of a certain hydro-generating unit in 2010 as an example, the monitoring data and off-line data of each component of the actual hydro-generating unit are used as data input.

[0140] Availability matrix A: From the historical data of a certain hydro-generating unit, it can be obtained that the failure rate P of the hydro-generating unit is 0.02, the mean time between failures MTBF of the system is 0.97, and the mean time to repair MTTR of the system is 0.03:

[0141]

[0142]

[0143]

[0144] Establish the availability matrix A:

[0145] A = [0.9506, 0.0194, 0.03]

[0146] Initial credibility matrix D: From the historical health assessment index of a certain hydro-generating unit, the proportionality coefficient K = 0.0112 and the curvature coefficient C = 0.0451 can be obtained; from the historical data of the hydro-generating unit, the failure rate λ of each component E = 0.01, λ C = 0.01, λ B = 0.02, λ S = 0.01, λ A = 0.02. From Equation (13), the initial credibility matrix D is as follows:

[0147]

[0148] Taking 12 months in a year as an example, in the first 4 months, the hydro-generating unit is in a natural evolution state. In May, it is subjected to a fault excitation P1, and the failure rate of each component suddenly changes to λ E = 0.05, λ C = 0.13, λ B = 0.07, λ S = 0.04, λ A = 0.04; in July, it is subjected to a maintenance excitation P2, and the failure rate of each component suddenly changes to λ E = 0.01, λ C = 0.01, λ B = 0.01, λ S = 0.01, λ A = 0.01. The iterative credibility matrix D' for 12 months is shown in the following table:

[0149]

[0150] Intrinsic ability matrix C: The evaluation value of the effectiveness index can be obtained from the real-time monitoring data of a certain hydro-generating unit. The index weights are determined according to the analytic hierarchy process and the entropy weight method:

[0151]

[0152] Effectiveness value E of the hydro-generating unit:

[0153] Month 1 2 3 4 Overall efficiency value E 0.8022 0.7496 0.7006 0.6548 Month 5 6 7 8 Overall efficiency value E 0.6309 0.4654 0.8181 0.7797 Month 9 10 11 12 Overall efficiency value E 0.7432 0.7084 0.6753 0.6437

[0154] The overall effectiveness value E can be obtained Figure 1 as shown.

[0155] Example 2:

[0156] In this example, taking the data of a certain hydro-generating unit in 2011 as an example:

[0157] Failure rate P of the hydro-generating unit = 0.02, mean time between failures MTBF of the system = 0.97, mean time to repair MTTR of the system = 0.03:

[0158]

[0159]

[0160]

[0161] The failure rate λ of each component can be obtained from the historical data of the hydro-generating unit E = 0.01, λ C = 0.01, λ B = 0.01, λ S = 0.01, λ A = 0.01; Taking 12 months in a year as an example, the hydro-generating unit is in the natural evolution state in the first 4 months. In May, it is affected by the failure incentive P1, and the failure rate of each component suddenly changes to λ E = 0.03, λ C = 0.05, λ B = 0.07, λ S = 0.04, λ A = 0.04; In July, it is affected by the maintenance incentive P2, and the failure rate of each component suddenly changes to λ E = 0.03, λ C = 0.02, λ B = 0.01, λ S = 0.01, λ A = 0.01, and the 12-month iterative credibility matrix D’ is shown in the following table:

[0162]

[0163]

[0164] Overall efficiency value E of the hydro-generating unit:

[0165] Month 1 2 3 4 Overall efficiency value E 0.8195 0.7807 0.7438 0.7086 Month 5 6 7 8 Overall efficiency value E 0.6886 0.5516 0.7963 0.7371 Month 9 10 11 12 Overall efficiency value E 0.6824 0.6319 0.5851 0.5419

[0166] The overall efficiency value E can be obtained Figure 2 as shown below.

[0167] Example 3:

[0168] In this example, taking the data of a certain hydro-generating unit in 2012 as an example:

[0169] Failure rate P of the hydro-generating unit = 0.02, mean time between failures MTBF of the system = 0.97, mean time to repair MTTR of the system = 0.03:

[0170]

[0171]

[0172]

[0173] The failure rates λ of each component can be obtained from the historical data of the hydro-generating unit E = 0.02, λ C = 0.01, λ B = 0.01, λ S = 0.03, λ A = 0.01; Taking 12 months in a year as an example, the hydro-generating unit is in a natural evolution state in the first 4 months. In May, it is affected by the failure incentive P1, and the failure rates of each component suddenly change to λ E = 0.19, λ C = 0.06, λ B = 0.04, λ S = 0.08, λ A = 0.04; In July, it is affected by the maintenance incentive P2, and the failure rates of each component suddenly change to λ E = 0.04, λ C = 0.03, λ B = 0.01, λ S = 0.02, λ A = 0.01, and the 12-month iteration credibility matrix D’ is shown in the following table:

[0174]

[0175]

[0176] Overall efficiency value E of the hydro-generating unit:

[0177] Month 1 2 3 4 Overall efficiency value E 0.7701 0.7125 0.6593 0.6101 Month 5 6 7 8 Overall efficiency value E 0.5599 0.3778 0.7495 0.6751 Month 9 10 11 12 Overall efficiency value E 0.6082 0.5481 0.4940 0.4456

[0178] The overall efficiency value E can be obtained Figure 3 as shown

Claims

1. An efficiency evaluation system for a hydro-generator, characterized in that It includes a device data acquisition module and a device data processing module; the device data acquisition module is respectively connected to the unit under test and the device data processing module; The unit under test is the overall hydro-generator set; the device data acquisition module includes a speed sensor, a temperature sensor and a torque sensor; The device data processing module includes a data encapsulation module, an algorithm processor and a result display module connected in sequence; The data encapsulation module receives the data collected by the device data acquisition module for data collection; The algorithm processor uses the ADC+P model to evaluate the implementation efficiency of the unit under test and outputs the current state of the unit under test, that is, the current efficiency value; in the algorithm processor, the specific processing operation steps are as follows: S1. Calculate the availability matrix A and the inherent ability matrix C of the unit under test through the ADC model; The availability matrix A of the unit under test can be expressed as: A = [a1, a2, a3] Where a1 is the normal state, indicating that all components of the unit under test are in a normal working state; a2 is the fault state, indicating that some components of the unit under test are in a fault state, but the unit under test can still operate normally; a3 is the shutdown state, indicating that the unit under test stops running; The working states of the unit under test, the normal state, the fault state and the shutdown state are specifically as follows: Normal state a1: Fault state a2: Shutdown state a3: Where, λ represents the device failure rate; MTBF represents the mean time between failures of the system; MTTR represents the mean time to repair of the system; The calculation of the inherent ability matrix C is as follows: The inherent ability index of the unit under test mainly characterizes the response ability and compliance effect of the task in the frequency modulation scenario, specifically reflected in the level of the generator unit system to complete various frequency modulation tasks up to standard. The specific evaluation indicators include the frequency modulation operation rate of the unit under test, the frequency modulation output power, the frequency modulation compliance effect and the maximum regulation power; By using the analytic hierarchy process and the entropy weight method, the importance degrees of each index are divided, and the product of the subjective weight and the objective weight is taken as the combined weight of each index W = [W 投运 , W 输出 , W 达标 , W 调节 ; W 投运 , W 输出 , W 达标 , W 调节 are the weights of the four evaluation indexes of the frequency modulation operation rate, the frequency modulation output power, the frequency modulation compliance effect, and the maximum regulation power of the unit under test, respectively; Using the fuzzy evaluation method, establish a three-level evaluation grade of 'good, medium, poor' for the four evaluation indicators of the frequency modulation operation rate, frequency modulation output power, frequency modulation compliance effect and maximum regulation power of the unit under test, construct a membership function according to the fault warning bottom limit of each indicator, and obtain the membership matrix R of each indicator; Multiply the index combination weight by the membership matrix of the inherent ability matrix of the unit under test: C = [C1, C2, C3] = W × R Where, C1 is the inherent ability value of the unit under test in the normal working state, C2 is the inherent ability value of the unit under test in the fault state, and C3 is the inherent ability value of the unit under test in the shutdown state; S2. Establish the credibility matrix D of the unit under test; S3. Combine the state health index of the unit under test and construct a failure rate estimation model according to historical data; S4. Combine the current state health index of the device, consider the natural evolution, fault or maintenance incentive P of the device, and calculate the device failure rate and the iterative credibility matrix D'; S5. Iterate the iterative credibility matrix D' according to the calculation result of step S4; S6. Use the ADC+P model to calculate the efficiency of the unit under test and evaluate the current state of the unit under test according to the output efficiency value; The result display module outputs the real-time efficiency of the unit under test, obtains the continuous efficiency change curve of the unit under test over time, and provides an auxiliary reference for the maintenance decision-making.

2. The effectiveness evaluation system for a hydro-generator according to claim 1, characterized in that The overall hydro-generating unit includes the hydro-generating unit body, the air cooling system, the off-line data system, and the upper guide, lower guide, and bearing structures; the equipment data acquisition module is used to measure the real-time status data of each component of the unit under test, the health score data during the entire life cycle, the equipment available time, the equipment maintenance information, the equipment failure rate, and the equipment setting value, among which the equipment failure rate, the equipment available time, and the braking shutdown time are directly input from the off-line database.

3. The effectiveness evaluation system for a hydro-generator according to claim 1, characterized in that The equipment data acquisition module is used to provide the characteristic data of the unit under test. Among them, the speed sensor provides the real-time speed of the hydro-generating unit body, the temperature sensor provides the real-time temperature of the air cooling system, the data acquisition and monitoring control system provides the real-time status data, health score data, available time, equipment maintenance information, braking shutdown time, equipment failure rate, and equipment setting value of the unit under test, and the torque sensor provides the torque data of the upper guide, lower guide, and bearing structures; Data is collected from each unit under test through the speed sensor, temperature sensor, and torque sensor, and the equipment data is input into the data encapsulation module for data encapsulation to provide data support for the algorithm processor.

4. The effectiveness evaluation system for a hydrogenerator according to claim 1, characterized in that In step S2, the fault events of the hydro-generator under investigation conform to the exponential distribution, and the hydro-generator can be regarded as conforming to the Markov state transition process. Therefore, the Markov state transition matrix can be used to characterize the state change process of the hydro-generator; Under the natural evolution state, that is, without considering the intervention of maintenance, the working state of the unit under test will only change from the normal working state to the fault state or the shutdown state. That is, the availability changes from a1 to a1, a2, a3 or from a2 to a2, a3, or remains unchanged when a3 changes; The expression of the credibility matrix D of the hydro-generator is as follows: Among them, λ E , λ C , λ B , λ S , λ A respectively represent the failure rates of the water turbine generator unit body, air cooling system, bearing structure, braking and shutdown system, and auxiliary system; substituting the initial failure rates of each component of the unit under test, the credibility D matrix is obtained.

5. The effectiveness evaluation system for a hydrogenerator according to claim 1, characterized in that In step S3, the evaluation indicators of the state health index of the unit under test include the inspection of the main shaft seal and its water supply system, the inspection of the water guide bearing, and the inspection of the guide vane thrust bearing; Based on the inspection and routine tests, diagnostic tests, on-line monitoring, and live detection, and taking the inspection and test procedures including but not limited to the "Enterprise Standard of China Southern Power Grid Co., Ltd.: Power Equipment Maintenance Test Procedures" and the "Guide for the State Evaluation of Distribution Network Equipment" as the scoring basis, evaluate the phenomenon intensity, quantity value, and development trend to obtain the deduction scores of each component, and then calculate the comprehensive deduction score of the specific equipment as the equipment health index.

6. The effectiveness evaluation system for a hydrogenerator according to claim 5, characterized in that In step S3, there is a positive correlation between the equipment failure rate and the equipment health index, and the exponential relationship formula is calculated as follows: λ = K × e C'×HI Among them, λ is the equipment failure rate; K is the proportionality coefficient; C' is the curvature coefficient; HI is the equipment health index, and the numerical range is 0 - 100; Using the known historical data of the equipment of the unit under test in the past two years, including the health score index HI, the total number of equipment N, the number of faulty equipment n, and the overall annual failure probability P of the unit under test year , the proportionality coefficient K and the curvature coefficient C' are obtained through inversion calculation. The inversion calculation formula is as follows: Among them, i is the classification of the device, I is the number of device types, and N i is the number of devices of the i-th category, and HI i is the average of the upper and lower limits of the corresponding HI score according to the i-th category of devices.

7. The effectiveness evaluation system for a hydrogenerator according to claim 1, wherein In step S4, the health index of the unit under test within a fixed time scale can be used as the input quantity to obtain the failure rate of the unit under test during this maintenance cycle. The elements of the credibility matrix are functions of the failure rates of the main body of the hydro-generator set, the air-cooling system, the off-line data system, and the upper guide, lower guide, and bearing structures at a certain moment t during frequency modulation and conform to the form of an exponential distribution. The iterative credibility matrix D’ is obtained according to the ADC model.

8. An effectiveness evaluation system for a hydrogenerator according to claim 7, characterized in that In step S5, the iterative process of the iterative credibility matrix D’ includes the equipment state transition under the natural evolution process of the equipment and the equipment state transition caused by the equipment being subjected to the failure or maintenance incentive P. Under the natural evolution process, the minimum time scale is taken as a month to evaluate the health degree of the equipment state. After obtaining the equipment failure rate of this month according to the monthly health index, the iterative credibility matrix D’ is iteratively calculated; After the equipment is subjected to the failure or maintenance incentive P, the health degree of the equipment state is evaluated, the equipment failure rate after maintenance is obtained according to the health index, and the iterative credibility matrix D’ is iteratively calculated to obtain the iterated iterative credibility matrix D’. The number of iterations is consistent with the number of incentives. The expression is as follows: Among them, λ′ E , λ′ C , λ′ B , λ′ S , λ′ A respectively represent the failure rates of the main body of the hydro-generating unit, the air-cooling system, the bearing structure, the braking and shutdown system, and the auxiliary system after iteration.

9. A performance evaluation system for a hydrogenerator according to any one of claims 1 to 8, characterized in that In step S6, the expression for calculating the effectiveness of the unit under test by the ADC+P model is: E = A × D × C; Among them, the availability matrix A obtained according to step S1 represents the equipment health state of the unit under test. The iterative credibility matrix D’ finally obtained according to step S5 represents the probability of the unit under test transferring from the normal state to the failure state. The C matrix obtained according to step S1 represents the level of the unit under test achieving the standard for various frequency modulation tasks. The overall effectiveness E obtained by multiplication represents the probability distribution value of the unit under test being in the equipment health state; Outputting the magnitude value of the overall effectiveness E to evaluate the current state of the unit under test is specifically manifested as: The calculated overall effectiveness E is a time-domain change result, and the change of the overall effectiveness E is determined by the change of the curve.

Images