CN106779280B

CN106779280B - Decision-making determination method and system for secondary equipment major repair and technical modification

Info

Publication number: CN106779280B
Application number: CN201610990895.7A
Authority: CN
Inventors: 王星华; 周亚武; 许炫壕; 李壮茂
Original assignee: Guangdong University of Technology
Current assignee: Guangdong University of Technology
Priority date: 2016-11-10
Filing date: 2016-11-10
Publication date: 2021-05-11
Anticipated expiration: 2036-11-10
Also published as: CN106779280A

Abstract

The invention discloses a decision-making determination method and a decision-making determination system for secondary equipment major repair technology modification, wherein the method comprises the following steps: acquiring a preset strategy parameter value from a state information processing and analyzing result and a health state evaluation result of preset secondary equipment in the system; respectively calculating a decision index value under a preset secondary equipment overhaul decision and a decision index value under a technical improvement decision according to preset strategy parameter values; the decision index value comprises an LCC (best-effort carrier) and other annual average cost values, an equipment risk value and an equipment efficiency value; carrying out normalization processing on the decision index value; calculating the decision index value after the normalization processing by using a cost risk benefit model as a parameter to obtain the annual average cost comprehensive benefit values of different strategies at different decision-making ages; determining an optimal decision scheme for secondary equipment to carry out major repair and technical improvement by comparing the comprehensive annual cost benefit values of major repair and technical improvement of the same decision year limit; the method can provide decision basis for project establishment of major improvement and realize scientific establishment.

Description

Decision-making determination method and system for secondary equipment major repair and technical modification

Technical Field

The invention relates to the technical field of electricity, in particular to a decision-making determination method and system for secondary equipment major repair and technical modification.

Background

The power industry is used as an important basic industry related to the national civilization, provides an important basic guarantee for the development of the economic society, and has a reliable power supply relationship to the aspects of the development of the national economy from the daily life of the people to the production and operation activities of various industries. With the further development of the power market system, the monopoly pattern of the power industry is broken, and the traditional power enterprise management mode cannot meet the requirement of the modern development of enterprises. In order to improve the competitiveness of enterprises, the electric power industry gradually puts economic and social comprehensive benefits at the same important position, and the management planning scheme evaluation based on the full Life Cycle Cost (LCC) emphasizes continuous, coordinated and unified management on the development process of the full Life Cycle of the project, comprehensively considers the problems of each stage, ensures the consistency of the front-back connection and decision making of activities of each stage, and achieves the purposes of optimal technology, most reliable quality, lowest Cost, best service, best environmental protection and more conformity with the sustainable development requirement of the project in the full Life Cycle.

However, due to the complexity and the particularity of the secondary equipment, the secondary equipment has great differences from the primary equipment in operation characteristics, cost division, operation and maintenance, and technical improvement strategies and methods. And the secondary equipment major repair technical modification decision in the existing power supply enterprises mainly depends on the mode of regular major repair replacement and on-time technical modification. The method can not effectively implement the secondary system management and the asset full life cycle management of the FailNet province company to deeply create the first working requirement, can not fundamentally improve the health level and the use efficiency of the secondary equipment, and can not lead the secondary equipment to reach the lean management level. Therefore, how to improve the accuracy of the decision of the secondary equipment overhaul technology is a technical problem to be solved by those skilled in the art.

Disclosure of Invention

The invention aims to provide a decision-making determination method and a decision-making determination system for major repair of secondary equipment, which are used for improving the decision-making methods of regular major repair, due major repair and subjective qualitative decision-making of the conventional power grid enterprise based on secondary equipment management and providing quantitative analysis decision-making bases for project establishment of major repair and technical modification.

In order to solve the above technical problem, the present invention provides a decision-making determination method for secondary device major repair technology, including:

acquiring a preset strategy parameter value from a state information processing and analyzing result of preset secondary equipment in a system and a health state evaluation result of the preset secondary equipment;

according to the preset strategy parameter values, calculating a decision index value under the preset secondary equipment overhaul decision and a decision index value under the technical improvement decision respectively; the decision index value comprises an LCC (best-effort carrier) and other annual average cost values, an equipment risk value and an equipment efficiency value;

carrying out normalization processing on the decision index value;

calculating the decision index value after the normalization processing by using a cost risk benefit model as a parameter to obtain an annual average cost comprehensive benefit value of the overhaul decision and an annual average cost comprehensive benefit value of the technical improvement decision under different decision years of the preset secondary equipment;

and determining an optimal decision scheme for the preset secondary equipment to carry out major repair and technical improvement by comparing the annual cost comprehensive benefit values of the major repair decision and the technical improvement decision in the same decision year.

Optionally, calculating an average annual cost value of the LCC under the predetermined secondary equipment overhaul decision and an average annual cost value of the LCC under the technical improvement decision according to the predetermined policy parameter value includes:

using formulas

Calculating LCC (lower control limit) equal annual average cost value NPVA (network redundancy protection value) under preset secondary equipment overhaul decision_dx；

Using formulas

Calculating the LCC equivalent annual average cost value NPVA under the preset secondary equipment technical improvement decision_jg；

Wherein the content of the first and second substances,

K＝C_{original value}-C_{Residual value}-T_{Operation of}×C_{Old age}，T_dx、T_jgRespectively showing the remaining service life of the equipment after the major repair and technical modification; CI_dx、CI_jgRespectively representing the initial total investment of major repair estimation and technical modification of equipment, and respectively representing the running cost, the overhaul and maintenance cost, the fault loss cost and the retirement disposal cost corresponding to the major repair and the technical modification; i is the bank interest rate; r is the inflation rate of the currency; n is the difference annual value of the design annual limit of the calculated annual average cost and the year in which the decision is positioned; (A/F, i, T) is an annual investment cost conversion coefficient; k is the equipment net value.

Optionally, calculating an equipment risk value under the predetermined secondary equipment overhaul decision and an equipment risk value under the technical improvement decision according to the predetermined policy parameter value includes:

calculating an equipment risk value under the preset secondary equipment overhaul decision and an equipment risk value under the technical improvement decision by using a formula R (t) ═ LE (t) × P (t);

wherein LE ═ w₁X equipment importance + w₂X possible loss of equipment + w₃X user influence, LE (t) is the risk loss value, P (t) is the risk probability value, w is the weight value, and w is₁+w₂+w₃＝1，w₁、w₂、w₃And R (t) is an equipment risk value according to the classification value of the preset secondary equipment.

Optionally, calculating a device performance value under the predetermined secondary device overhaul decision and a device performance value under the technical improvement decision according to the predetermined policy parameter value includes:

calculating the equipment efficiency value under the scheduled secondary equipment overhaul decision and the equipment efficiency value under the technological improvement decision by using an ADC (analog-to-digital converter) analysis model E (analog-to-digital converter);

wherein E is the equipment efficiency value, A is the availability vector, D is the credibility matrix, and C is the inherent capability vector.

Optionally, the normalizing the decision index value includes:

using formulas

C_N∈[0,1]Normalizing the LCC equivalent annual average cost values;

using formulas

R_N∈[0,1]Normalizing the equipment risk value;

using the formula E_N＝E，E_N∈[0,1]Normalizing the equipment efficiency value;

wherein, C_N、R_N、E_NRespectively normalizing the quantized values, CI, of each decision index value_maxThe maximum value of the initial investment cost in the similar equipment; r_maxIs the risk maximum of the risk assessment model.

Optionally, the calculating, with the decision index value after the normalization processing as a parameter, by using a cost benefit model includes:

the quantized value C of the LCC equivalent annual average cost value after normalization processing_NQuantitative value R of the risk value of the device_NAnd a quantized value E of the performance value of the device_NUtilizing cost risk benefit models as parameters

And calculating to obtain the comprehensive benefit value REC of the annual average cost under different decision-making years.

Optionally, the determining, by comparing the annual average cost comprehensive benefit values of the major repair decision and the technical improvement decision for the same decision-making age to determine an optimal decision-making scheme for the major repair and technical improvement of the predetermined secondary device includes:

when the annual average cost comprehensive benefit value of the major repair decision is not less than the annual average cost comprehensive benefit value of the technical improvement decision, major repair establishment is carried out;

and when the annual average cost comprehensive benefit value of the major repair decision is smaller than the annual average cost comprehensive benefit value of the technical improvement decision, performing technical improvement.

Optionally, before acquiring the predetermined policy parameter value from the state information processing analysis result of the predetermined secondary device in the system and the health state evaluation result of the predetermined secondary device, the method further includes:

processing and analyzing results of state information of preset secondary equipment in a system and evaluation results of the health state of the preset secondary equipment, and judging whether major repair and modification are needed or not according to judgment standards;

if not, the operation is carried out after normal maintenance.

The invention also provides a decision-making determination system for secondary equipment major repair technology improvement, which comprises:

the system comprises a parameter acquisition module, a parameter analysis module and a parameter analysis module, wherein the parameter acquisition module is used for acquiring a preset strategy parameter value from a state information processing analysis result of preset secondary equipment in the system and a health state evaluation result of the preset secondary equipment;

a decision index value calculation module, configured to calculate a decision index value in the scheduled secondary device overhaul decision and a decision index value in the technical improvement decision according to the scheduled policy parameter value; the decision index value comprises an LCC (best-effort carrier) and other annual average cost values, an equipment risk value and an equipment efficiency value;

the normalization processing module is used for performing normalization processing on the decision index value;

the annual average cost comprehensive benefit value calculation module is used for calculating the decision index value after the normalization processing as a parameter by using a cost risk benefit model to obtain annual average cost comprehensive benefit values of the overhaul decision and the technical improvement decision of the preset secondary equipment under different decision years;

and the decision determining module is used for determining an optimal decision scheme for the scheduled secondary equipment to carry out major repair and technical improvement by comparing the annual comprehensive cost benefit values of the major repair decision and the technical improvement decision in the same decision year.

Optionally, the annual average cost comprehensive benefit value calculating module includes:

a calculation unit for calculating the comprehensive benefit value of annual average cost of major repair decision, which is used for normalizing the quantized value C of the LCC and other annual average cost values after the major repair decision_NQuantitative value R of the risk value of the device_NAnd a quantized value E of the performance value of the device_NUtilizing cost risk benefit models as parameters

Calculating to obtain the annual average cost comprehensive benefit value REC of the overhaul decision under different decision-making years_dx；

An annual average cost comprehensive benefit value calculation unit of the technical improvement decision, which is used for calculating the quantized value C of the annual average cost value of LCC and the like after normalization processing under the technical improvement decision_NQuantitative value R of the risk value of the device_NAnd a quantized value E of the performance value of the device_NUtilizing cost risk benefit models as parameters

Calculating to obtain the comprehensive benefit value REC of the annual average cost of technical improvement decisions under different decision-making years_jg。

The invention provides a decision-making determination method for secondary equipment major repair technology improvement, which comprises the following steps: acquiring a preset strategy parameter value from a state information processing and analyzing result and a health state evaluation result of preset secondary equipment in the system; respectively calculating a decision index value under a preset secondary equipment overhaul decision and a decision index value under a technical improvement decision according to preset strategy parameter values; the decision index value comprises an LCC (best-effort carrier) and other annual average cost values, an equipment risk value and an equipment efficiency value; carrying out normalization processing on the decision index value; calculating the decision index value after the normalization processing by using a cost risk benefit model as a parameter to obtain an annual average cost comprehensive benefit value of a major repair decision and an annual average cost comprehensive benefit value of a technical improvement decision under different decision years of preset secondary equipment; comparing the annual average cost comprehensive benefit values of the two decision schemes (major repair decision/technical improvement decision) to determine an optimal decision scheme for the scheduled secondary equipment major repair and technical improvement;

the method comprehensively considers the full life cycle cost of the secondary equipment to construct a multi-azimuth comprehensive cost-benefit model so as to realize comprehensive optimization of the full life cycle cost, risk and efficiency of the assets, reasonably manage the secondary equipment major repair and technical improvement projects and improve the value creation capability; providing scientific and effective decision basis for major repair and technical improvement; the economic benefit of enterprises is indirectly improved, and the safety and the reliability of the operation of the power grid are guaranteed. The decision-making method for regular overhaul, due technical improvement and subjective qualitative decision of the conventional power grid enterprise based on secondary equipment management is improved. The decision-making determination system for secondary equipment major repair technology provided by the invention has the beneficial effects, and is not repeated herein.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a flowchart of a decision-making method for secondary device major repair provided in an embodiment of the present invention;

FIG. 2 is a schematic diagram of a full life cycle life cost tree suitable for use in a major modification decision according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a secondary equipment risk assessment model according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a typical fault rate distribution (bath curve) provided by an embodiment of the present invention;

FIG. 5 is a bar graph of the annual average repair cost for the technical improvement provided by the embodiment of the present invention;

fig. 6 is a block diagram of a decision-making system for secondary device major modification according to an embodiment of the present invention.

Detailed Description

The core of the invention is to provide a decision-making determination method and a decision-making determination system for major repair of secondary equipment, which improve the decision-making method of regular major repair, due major repair and subjective qualitative decision-making of the current power grid enterprise based on secondary equipment management and provide quantitative analysis decision-making basis for project establishment of major repair and technical modification.

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

At present, the conventional methods for major repair and technical modification of secondary equipment of a power grid mainly include: cost-benefit methods, fixed-cost methods, trade-off analysis methods, probabilistic analysis methods and statistical theory, npv (net Present value) and ncf (net case flow) methods, but these methods all consider the major modification project decision as a separate individual, and thus are an iterative optimization management. Each method can only be considered for one of the main aspects of the process. They therefore have the following disadvantages:

first, the narrow range, not considered from the point of view of the whole value chain, is a process that encompasses the entire value chain process of equipment, such as investigatable, development, procurement, installation, operation, maintenance, and later scrapping recovery. The traditional method only considers the current project decision, only focuses on the initial investment of the previous project, and ignores the cost management during the whole project operation and maintenance, so the decision making has certain defects.

Secondly, the decision period is too short to fully reflect the management risk; that is, a large project must be strictly demonstrated in the early stage, and after research and demonstration, the project must be monitored in real time to control the operation of the current project and make new improvement measures in time. The traditional model and method do not fully pay attention to the whole process control, the decision is made on the subsequent project management by the results of the previous exploratory demonstration stage, the decision period is too short, and the possible risks in the later period of the project operation and maintenance are not fully evaluated, which is the fundamental reason why many projects are feasible in the exploratory demonstration stage, but face larger loss pressure once put into production.

Thirdly, the evaluation process is simple, the cost of modern equipment management in the whole life cycle can not be comprehensively and accurately reflected, a complex calculation model and informatization simulation measurement and calculation are mostly adopted, and particularly for large-scale electric equipment projects, the method cannot be suitable for the current electric equipment management completely through a financial analysis method in a research and demonstration stage, so that the equipment management not only needs to pay attention to the early-stage investment cost, the purchase cost and the installation cost, but also needs to consider the later-stage social and economic cost, the environmental protection cost and the like. The traditional method pays more attention to the cost of material, ignores the potential social cost, environmental impact cost and the like of the project, and has the great defect of the traditional model and method.

Fourthly, the power industry without matching the characteristics of the power supply enterprises has unique characteristics of project management due to long time period, large influence range, slow capital turnover and complex assets, so that the inherent characteristics of the model method selection of the project decision and management must be considered, an applicable full-life-cycle cost model is established, each cost branch is finely quantized, and the models and methods such as financial or probability analysis cannot be blindly applied for blind use. The traditional method is mainly evolved based on the development of other industries, and most of decision methods are single and non-dynamic, so that most of decision models look at the current initial investment and do not completely fit the characteristics of the power industry, particularly power supply enterprises, such as the system problem, the concept problem, the social problem, the environmental protection problem and the like.

In order to solve the above problems, on the premise of considering both the safety and reliability of the power grid and the equipment, the asset full-life-cycle analysis method may be used to analyze the full-life-cycle cost of the secondary equipment, and perform comprehensive quantitative solution of the performance risk index on the major repair engineering project, so that the comprehensiveness and reliability of the project decision basis are ensured, the asset full-life-cycle cost is controlled as a whole, and the overall benefit of the company is improved. The major difference between the full-life-cycle cost management and the conventional cost management method and project decision is that the full-life-cycle cost management manages the whole project process with the awareness of comprehensive management, i.e. the major technical improvement project decision is not considered as a single individual, so the full-life-cycle cost management is a procedural optimization management. The method is characterized in that asset full-life-cycle management is carried out on all secondary equipment of the power grid, and comprehensive management is carried out from the full life cycle of equipment planning, design, purchase, construction, operation, maintenance, overhaul, update and retirement, so that the comprehensive optimization of the cost, risk and efficiency of the asset full life cycle is realized, and the value creation capability is improved. Referring to fig. 1 in detail, fig. 1 is a flowchart of a decision-making method for secondary device overhaul improvement according to an embodiment of the present invention; the decision determination method may include:

s100, acquiring a preset strategy parameter value from a state information processing and analyzing result of preset secondary equipment in a system and a health state evaluation result of the preset secondary equipment;

specifically, the state information processing analysis result is obtained by performing information processing analysis on the collected state information. In order to improve the comprehensiveness and reasonableness of subsequent calculation, the acquisition of the state information may include online monitoring information of the predetermined secondary device, historical ledger data information, environmental factor information, and the like.

The predetermined secondary device herein refers to a user-selected computing object, which may refer to any secondary device, but is intended for the same secondary device each time a overhaul and a craftsmanship comparison decision is made.

S110, respectively calculating a decision index value under the scheduled secondary equipment overhaul decision and a decision index value under the technical improvement decision according to the scheduled strategy parameter value; the decision index value comprises an LCC (best-effort carrier) and other annual average cost values, an equipment risk value and an equipment efficiency value;

specifically, the decision index is a parameter for performing subsequent major modification decision selection, and the accuracy of the decision index directly affects the accuracy of project quantitative analysis decision of major modification. The purpose of the secondary equipment cost-benefit evaluation in this embodiment is to synthesize all-round information of the secondary equipment full-life-cycle risk, efficiency, and cost, perform multidimensional evaluation on the performance of the secondary equipment, and provide a basis for the current major repair technical modification decision of the secondary equipment. In conclusion, the comprehensive benefit value of the annual average cost can be calculated according to the relationship among the risk cost, the benefit value and the efficiency cost and is used as a decision objective function for selecting the optimal major repair technical improvement project.

Specifically, the quantization process of the average annual cost value of LCC and the like is as follows:

the cost evaluation system of the secondary equipment mainly decomposes the full-life-cycle cost of the secondary equipment, and determines the cost of each stage of the secondary equipment, for example, the cost division of each stage of the full-life cycle of the secondary equipment is determined according to a full-life-cycle cost decomposition model of the secondary equipment as shown in fig. 2 and the dimension of the value information about the secondary equipment in the south power grid PMS system. Among them, the LCC (Life Cycle Cost, LCC for short) is also called Life Cycle Cost. It refers to all costs associated with a product that occur during its useful life, including product design costs, manufacturing costs, procurement costs, usage costs, maintenance costs, decommissioning costs, and the like.

The LCC cost of the secondary equipment is: LCC ═ CI + CO + CM + CF + CD

Wherein, LCC-full lifecycle cost; CI-initial investment cost; CO-operating cost; CM-maintenance cost of overhaul; CF-loss of failure cost; CD-retirement cost.

Because the life cycles of different technological improvement strategies are different, in order to overcome the problem of caliber comparison caused by different life cycles, the annual average cost model of the technological improvement decision is selected as an annual value index. The calculation model is as follows:

using formulas

Using formulas

Wherein the content of the first and second substances,

K＝C_{original value}-C_{Residual value}-T_{Operation of}×C_{Old age}，T_dx、T_jgRespectively showing the remaining service life of the equipment after the major repair and technical modification; CI_dx、CI_jgRespectively representing the initial total investment of the overhaul estimation and the technical improvement of the equipment, and CO, CM, CF and CD respectively representing the running cost, the overhaul and maintenance cost, the fault loss cost and the retirement disposal cost, namely CO_dxjFor major maintenance operating costs, CO_jgjThe operation cost is technically improved; i is the bank interest rate (discount rate 8%); r is the inflation rate (3%) of the currency; n is the difference annual value of the design annual limit of the calculated annual average cost and the year in which the decision is positioned; (A/F, i, T) is annual investment cost conversionA coefficient; k is the equipment net value.

The equipment risk value quantification process specifically includes:

and performing risk assessment before and after the implementation of the secondary equipment major repair strategy by a method for quantifying the risk value. The risk evaluation takes a risk value as an index, and comprehensively considers the functions of secondary equipment risk, social hazard, environmental hazard and equipment risk probability. The method comprises the following specific steps:

Where the composition of the parameters can refer to fig. 3, the risk loss value, i.e. the possible loss asset LE, is related to the equipment importance, e.g. substation level, the social environment impact, i.e. the possible loss of equipment, and the impact object property, i.e. the user impact. The average failure rate may be calculated by collecting the status information and analyzing the collected status information in conjunction with the health status evaluation information of the secondary device (i.e., the predetermined secondary device). And finally, obtaining the equipment risk value R by using the formula.

The above process is illustrated below:

for example, southern power grid company safety production risk management system indicates that southern power grid operation safety risk (hereinafter referred to as "power grid risk") evaluation is to comprehensively consider risks in three aspects of power grid safety, social environment, benefits and the like after equipment state evaluation results, determine the risk degree of equipment operation, and provide a basis for the formulation of maintenance strategies and emergency plans. The solution of the risk loss value is obtained according to indexes such as equipment importance degree division and weight values of various device types in Guangdong power grid company equipment state evaluation and risk evaluation technical guide rules, and the probability value is obtained according to a risk probability value evaluation index system and a bathtub curve: the entire bathtub curve in fig. 4 can be divided into three phases.

The first stage is the early expiry date IM (1nfant Mortality): when a product is used, the quality difference of equipment is mainly shown in high and low failure rate, the failure rate of the equipment with poor quality is generally higher than that of the equipment with good quality, the failure rate is very high, but the failure rate is rapidly reduced along with the increase of the working time of the product, and the failure reason at this stage is mostly caused by defects in the design, raw materials and manufacturing process.

The second stage is the occasional expiration period rf (random failures): during this period, the failure occurs randomly, the failure rate is the lowest, and the failure rate is approximately in a steady state and can be approximately regarded as a constant, and this period is a good use period of the product, and what is expressed by the product reliability index is this period. During the period of equipment occasional failure, the quality difference of the equipment is mainly reflected in two aspects: firstly, the length of the accidental fault period of the equipment and secondly, the fault rate is high and low. The equipment with better quality has long accidental fault period and low fault rate. During the period of equipment loss failure, the quality difference of the equipment is mainly reflected in whether the period of equipment loss failure is entered in advance.

The third stage is the wear-out period wo (wear out): in the later period of using the equipment, the failure rate is rapidly increased along with the prolonging of time due to the reasons of abrasion, aging, corrosion and the like of the parts of the equipment, and the failure rate is continuously increased. The devices of better quality enter the device wear-out period only at the last stage of the device life cycle, while the devices of poorer quality often enter the device wear-out period at the middle stage of the device life cycle. When finding out that the device is selected to be replaced or continuously used after being overhauled, namely whether to implement technical improvement or not, at the beginning of the wear-and-tear period, namely point P in the graph, in the practical application process of the secondary device, the optimal time point of the major improvement strategy can be researched by utilizing the LCC management theory.

The risk assessment method can be distinguished according to the quantitative assessment technical specification of the southern power grid operation safety risk aiming at the influence and the hazard degree of the power grid risk of the secondary equipment according to the risk value, and is divided into 6 risk levels shown in the following table:

TABLE 1 Secondary Equipment Risk assessment criteria

Wherein, grade i risk (red): the risk value is more than or equal to 5; class ii risk (orange): the risk value is more than or equal to 3 and less than 5; grade iii risk (pink): the risk value is more than or equal to 1 and less than 3; grade iv risk (yellow): the risk value is more than or equal to 0.5 and less than 1; risk class v (green): the risk value is more than or equal to 0.1 and less than 0.5; risk class vi (blue): the risk value is more than or equal to 0 and less than 0.1. I.e. the device risk level can be known from the device risk value.

The quantization process of the device efficiency value specifically includes:

an efficiency evaluation index system is established according to an efficiency ADC analysis method, namely an ADC analysis model. At present, secondary equipment of an electric power system mostly has a single function such as a protection device or two functions such as a measurement and control device. According to the above characteristics of the secondary device, the secondary device performance evaluation conforms to the characteristics of the ADC analysis method application. The expression of the model is as follows:

E＝ADC

The final performance results from the ADC analysis method are presented in table 2:

TABLE 2 Performance grading Table

Potency values	0-0.6	0.6-0.8	0.8-0.95	0.95-1
					Level of performance	Difference (D)	Is lower than	Equalization	Efficient

In this embodiment, the specific calculation process of the specific decision index value is not limited, and only the accurate decision index value is obtained by using the calculation model.

S120, normalizing the decision index value;

specifically, when the predetermined secondary equipment is evaluated, the equipment can be comprehensively evaluated from three dimensions of cost, risk and efficiency of the equipment. In order to avoid the comparison problem of different dimensions and different calibers, the three indexes can be unified to the same dimension of a numerical value for comparison through normalization processing. Optionally, the normalizing the decision index value may include:

using formulas

C_N∈[0,1]Normalizing the LCC equivalent annual average cost values;

using formulas

R_N∈[0,1]Normalizing the equipment risk value;

S130, calculating the decision index value after normalization processing as a parameter by using a cost risk benefit model to obtain an annual average cost comprehensive benefit value of the overhaul decision and an annual average cost comprehensive benefit value of the technical improvement decision under different decision years of the preset secondary equipment; here, the major repair decision is the major repair plan, and the technical change decision is the technical change plan.

Specifically, the normalized quantized value C of the annual average cost value such as LCC is used_NQuantitative value R of the risk value of the device_NAnd a quantized value E of the performance value of the device_NUtilizing cost risk benefit models as parameters

Wherein, each decision index value has a certain range, and each decision index has a range of normal operation or forbidden operation in the process of engineering implementation, so that the allowable range of each decision index value can be set. If the decision index value exceeds the set allowable range, the engineering is indicated to have a great hidden danger or is not necessary to be continued. I.e. preferably, the cost-risk benefit model may also be:

wherein: REC (Risk Effect cost) is the annual average cost comprehensive benefit value which is obtained by the risk benefit model; c_KA proportionality coefficient which is the maximum value of the secondary equipment input cost acceptable by a power grid company and takes the initial input cost as a basic value; r_tIs the highest risk value tolerable; e_tThe lowest tolerable efficacy value. The numerical values in the above formula are only specific examples, and this embodiment does not limit C_K，R_tAnd E_tThe specific numerical value of (1). For example, when in the projectThe value is calculated before execution to obtain E_NAt 0.3, the item may be discarded.

S140, determining an optimal decision scheme for the scheduled secondary equipment to carry out major repair and technical improvement by comparing the annual average cost comprehensive benefit values of the major repair decision and the technical improvement decision in the same decision year.

Specifically, when the annual average cost comprehensive benefit value of the overhaul scheme (i.e. the overhaul decision) is REC_dxNot less than the annual average cost composite benefit value (REC) of the improvement plan (i.e., improvement decision)_jgIf the numerical value is middle, performing major repair; and when the annual average cost comprehensive benefit value of the major repair scheme is smaller than the annual average cost comprehensive benefit value of the technical improvement scheme, performing technical improvement. Namely according to the principle of the annual average cost comprehensive benefit value: when REC_jg＞REC_dxWhen the equipment is used, equipment technical improvement items are considered preferentially; when REC_jg≤REC_dxAnd the equipment major repair is preferably considered.

Based on the technical scheme, the decision-making determination method for secondary equipment major repair technology improvement provided by the embodiment of the invention can realize more comprehensive evaluation on the equipment from three dimensions of cost, risk and efficiency of the secondary equipment when the method is used for evaluating the secondary equipment and collecting data. Through normalization processing, the three indexes are unified to the same dimension of the numerical value for comparison, and the comparison problem of different dimensions and different calibers is avoided. The comprehensive information of the whole life cycle risk, the efficiency and the cost of the secondary equipment is integrated, the performance of the secondary equipment is evaluated in multiple dimensions, and a decision basis is provided for the current major repair technical modification decision of the secondary equipment.

Based on the above embodiment, the decision-making method for secondary device major repair may be performed whenever a project exists, or may be performed when it is determined that the system needs major repair. To save labor and reduce the number of calculations that are performed when a major repair decision is needed, it can be determined whether the system needs major repair before the decision is made. Specifically, the process of determining whether the system needs to be modified by major repair may include:

wherein, the judgment standard can be 'national grid major repair technical improvement admission guide rule';

and if so, carrying out decision-making determination calculation process of secondary equipment major repair technology modification.

If not, the operation is carried out after normal maintenance.

The status information source of the secondary device may include historical ledger data, online monitoring information, and environmental factor information.

Furthermore, the method can directly calculate the year with balanced comprehensive cost benefit value of the annual average major repair improvement period, can predict the optimal year for carrying out the technical improvement project, and can easily find out that the P point of the bathtub curve appears at about the moment, namely the optimal time point of equipment application for technical improvement, namely REC_jg＝REC_dx. When prediction is performed, the acquisition mode of the fixed parameters or the fixed and variable parameters refers to the calculation process, and the acquisition of the real-time parameters can be obtained by prediction according to historical data. For example, the fault rate in the formula is obtained, a fault rate trend graph can be obtained by directly performing data fitting according to historical fault rate data recorded in historical ledger data, and the system fault rate at each time can be obtained according to the graph.

Based on the above technical solution, the decision determining method for secondary device major repair technical modification provided by the embodiment of the present invention has the following advantages:

firstly, aiming at the problem that the cost range selected by the traditional method is narrow, the method makes up for the deficiency, takes the advantages of the benefit cost method into consideration of the full life cycle cost of the secondary equipment from the perspective of the whole value chain, so that the full life cycle cost life tree for the major repair technology is the analysis of the full-dimensional whole process of the secondary equipment, not only takes the whole life cycle from the planning design to the scrapping into consideration, avoids the temporary thought, but also requires the application of the LCC method to be ensured from the system.

Secondly, for the problems of short decision period and simple evaluation process of the traditional method, the LCC cost model is selected to show more characteristics of a whole system, the boundary of functional departments is broken, the cost of different stages of planning, design, infrastructure, operation and the like is considered comprehensively, and the best scheme is searched by taking the overall benefits of enterprises as the starting point. The method fully evaluates the possible risks in the later period of project operation and maintenance, and greatly reduces the risk of loss pressure of the secondary equipment after production.

And thirdly, constructing a full-dimensional life cycle cost model suitable for secondary equipment major repair technology improvement from the aspect of the characteristics of secondary equipment through analyzing and researching the full life cycle cost management concept. The electric power industry which truly fits the characteristics of power supply enterprises has unique characteristics of project management due to long time period, large influence range, slow capital turnover and complex assets, and comprehensive and long-term tracking evaluation is carried out on the process based on the whole life cycle cost, so that scientific and reasonable target functions and constraint conditions are made.

Fourthly, combining the NPV method and the NCF method, the time value of the equipment is considered in all directions, but the equipment overhaul needs to cost large people, property and materials, and the operation time of the secondary equipment after overhaul is limited by the remaining years of the secondary equipment. The technology is improved and new secondary equipment is replaced, one-time investment is huge, and the new equipment can safely run for a long time. The service life of the equipment after the major repair and the technical improvement are different, and the difference between the current value and the final value is large, so that the current value and the final value cannot be directly compared, the method comprehensively combines a time value theory, incorporates the currency expansion rate and the bank discount rate to obtain a full life cycle annual average value model of the major repair and the technical improvement items, and calculates the annual average value of the major repair and the technical improvement as a cost index basis for decision selection of the major repair and the technical improvement.

The above calculation process is illustrated below by a specific example of a certain power supply company:

according to the embodiment, a single-bus protection device (1) of 220kV of a certain power supply bureau is selected to be improved, the whole batch protection device is produced and put into use in 2007, the unit price of each equipment plug-in unit is 10000 yuan, and the equipment can be directly replaced by a standby plug-in unit under the condition of failure. The overhaul period is 8 years according to the maintenance guide rules and the requirements of the manufacturer maintenance specifications. According to the device state and the operation table account data of 07-15 years, modeling calculation is carried out by adopting the full life cycle cost, the comprehensive cost-benefit improvement amount of the LCC (lower control computer) of major repair and technical improvement of the protective device is simulated, then the LCC and the protective device are compared, the technical improvement strategy is determined, and reference opinions are provided for similar major repair and technical improvement decisions.

The first scheme is as follows: carrying out major repair on the protection device in 2015, replacing a fault plug-in unit, continuing to operate for 4 years, and then carrying out technical improvement and updating, wherein the operating life of the protection device reaches the design operating life;

scheme II: the novel protection device is directly technically changed and replaced in 2015, and the device runs for 12 years according to the designed service life.

Calculating the average annual cost: the annual average cost of the protection device obtained by the method of obtaining the annual average cost value is shown in table 3: an annual average major repair cost bar graph is plotted according to the data in table 3 and is shown in fig. 5:

TABLE 3 annual average cost table for each case after decision-making and maintenance

Calculation of risk quantification value:

the 220kV single bus protection device is evaluated according to the risk probability value, and the risk probability of the protection device is known as follows:

(scheme one) overhaul in 9 th year: the overall risk probability and risk value of the protection device are:

P＝0.06032×[1+0.03×(90-85)]＝0.069368

R(t)_dx＝(0.6×6.2+0.4×7)×0.069368＝0.452

(scheme II) technical improvement in the 9 th year, wherein the evaluation fault rate and the risk value of the whole protection device are as follows:

P＝0.0058×[0.9+0.02×(100-95)]＝0.058

R(t)_jg＝(0.6×6.2+0.4×7)×0.058＝0.37816

according to the secondary equipment risk assessment standard, the equipment risk values after major repair and technical improvement decision are controlled within 0.5 green risk, and the actual situation is met.

And (3) calculating a performance quantification value:

the guideline of the secondary equipment major repair technology is that the health level, reliability and availability of the equipment are improved as targets, and the failure rate of the equipment at the moment is known as follows according to risk calculation: lambda [ alpha ]_dx＝0.06032、λ_jgThe single-bus protection device of 0.058 and 220kV can know μ 0.8 from the secondary equipment repair rate value table, and can know by the performance quantization formula ADC analysis method:

as the final result of calculation is compared with the efficiency grading table, the state of the measurement and control device after overhaul belongs to a balanced state, and the measurement and control device conforms to the reality.

And (3) major modification technical scheme decision:

the risk and efficiency cost indexes are normalized by combining the cost comparison of the two models through the major repair technology and the efficiency risk cost indexes. According to the south network risk level evaluation standard, the maximum risk value is 10, the maximum initial investment cost value of the same type of equipment of the measurement and control device is 94366.6 yuan, and the maximum initial investment cost value can be known by using a normalization formula:

normalization of risk values:

overhaul R_dx＝0.452/10＝0.0452

Technical improvement R_jg＝0.37816/10＝0.037816

Normalizing the effect values:

overhaul E_dx＝0.8688

Technical improvement E_dx＝0.8999

Normalization of cost values:

major repair C_dx＝35777.98/94366.6＝0.379

Technical improvement C_jg＝33922.06/94366.6＝0.359

And balancing the cost risk and efficiency constraint condition coefficients of the whole device by combining the equipment characteristics of the protection device in the whole station integrated system and under the environment of whether the capital sum of the project plan library is surplus or not, and finding out that all index factors meet the constraint conditions easily. Through a technical method decision formula, according to the principle of high and low annual average cost comprehensive benefit value:

the REC can be known through comparison_jg＞REC_dxIt can be known that the annual average cost comprehensive benefit value of the protection device after the technical improvement decision is larger than that of the major repair decision, namely, the technical improvement item of the second scheme should be prioritized in the 9 th year. By calculation, the annual average benefit improvement amount of the device at year 8 is calculated as:

through analysis and comparison, although the annual average consumption cost of major repair in the 8 th year is 34327.04 yuan less than the annual average cost of the technical improvement decision of 34499.8 yuan, the annual average cost comprehensive benefit value of the technical improvement decision of the whole protection device is still greater than that of the major repair decision, namely, the technical improvement item should be prioritized over the second scheme in the 8 th year. To enhance the persuasiveness of the technical approach, the annual average benefit improvement amount of the device at year 7 can be obtained by the same:

through analysis and comparison, the annual average consumption cost of protecting the whole overhaul of the equipment in the 7 th year is lower than the annual average cost of the technical improvement decision, and the annual average benefit improvement amount of the equipment is larger than the annual average benefit improvement amount of the technical improvement decision of the equipment, namely, the first plan for overhaul should be prioritized in the 7 th year.

In summary, it can be predicted that the 8 th year is the best year for the improvement project, and it is easy to find that the P point of the bathtub curve occurs at about the time point, which is the best time point for the equipment to apply for improvement. Practical suggestions may be made that the annual average cost of replacing the plug-in overhaul of the batch of 220kV protection devices increases with the operating age, but the improvement in efficiency becomes smaller and smaller, as opposed to the technical improvement strategy. Through calculation and analysis, the comprehensive annual average cost benefit value of equipment replacement in overhaul is superior to that of technical improvement when the equipment fails in 7 th year and before operation, and the comprehensive annual average cost benefit value of technical improvement strategy replacement is superior to that of the major repair plug-in replacement strategy when the protection device fails in 8 th year and after operation.

Therefore, in the verification of the present example, the technical method in the solution is obviously more practical, persuasive and operable than other conventional methods.

The decision-making system for secondary device major repair modification provided in the embodiments of the present invention is described below, and the decision-making system for secondary device major repair modification described below and the decision-making method for secondary device major repair modification described above may be referred to correspondingly.

Referring to fig. 6, fig. 6 is a block diagram of a decision-making system for secondary device overhaul improvement according to an embodiment of the present invention; the system may include:

a parameter obtaining module 100, configured to obtain a predetermined policy parameter value from a state information processing analysis result of a predetermined secondary device in a system and a health state evaluation result of the predetermined secondary device;

a decision index value calculation module 200, configured to calculate a decision index value in the scheduled overhaul decision and a decision index value in the technical improvement decision of the secondary device according to the scheduled policy parameter value; the decision index value comprises an LCC (best-effort carrier) and other annual average cost values, an equipment risk value and an equipment efficiency value;

a normalization processing module 300, configured to perform normalization processing on the decision index value;

an annual average cost comprehensive benefit value calculation module 400, configured to calculate, by using the cost risk benefit model, the decision index value after the normalization processing as a parameter, and obtain an annual average cost comprehensive benefit value of the overhaul decision and an annual average cost comprehensive benefit value of the technical improvement decision under different decision years of the predetermined secondary device;

and the decision determining module 500 is configured to determine an optimal decision scheme for performing major repair and technical modification on the predetermined secondary device by comparing the annual comprehensive cost benefit values of the major repair decision and the technical modification decision in the same decision-making age.

Optionally, the annual average cost comprehensive benefit value calculating module 400 may include:

An annual average cost comprehensive benefit value calculation unit of the technical improvement decision, which is used for calculating the quantized value C of the annual average cost value of LCC and the like after normalization processing under the technical improvement decision_NIs provided withQuantitative value R of risk value_NAnd a quantized value E of the performance value of the device_NUtilizing cost risk benefit models as parameters

Based on the above embodiment, the system may further include:

the judging module is used for judging whether major repair technology modification is needed or not from the state information processing and analyzing result of the preset secondary equipment in the system and the health state evaluation result of the preset secondary equipment; if not, the operation is carried out after normal maintenance.

The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The decision-making determining method and system for secondary equipment major repair technical improvement provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims

1. A decision-making method for secondary equipment major modification is characterized by comprising the following steps:

carrying out normalization processing on the decision index value;

determining an optimal decision scheme for the preset secondary equipment to carry out major repair and technical improvement by comparing the annual cost comprehensive benefit values of the major repair decision and the technical improvement decision of the same decision year limit;

calculating the LCC equivalent annual average cost value under the preset secondary equipment overhaul decision and the LCC equivalent annual average cost value under the technical improvement decision according to the preset strategy parameter values, wherein the method comprises the following steps:

using formulas

Using formulas

Wherein the content of the first and second substances,

K＝C_{original value}-C_{Residual value}-T_{Operation of}×C_{Old age}，T_dx、T_jgRespectively showing the remaining service life of the equipment after the major repair and technical modification; CI_dx、CI_jgRespectively representing the initial total investment of major repair estimation and technical modification of equipment, and respectively representing the running cost, the overhaul and maintenance cost, the fault loss cost and the retirement disposal cost corresponding to the major repair and the technical modification; i is the bank interest rate; r is the inflation rate of the currency; n is the difference annual value of the design annual limit of the calculated annual average cost and the year in which the decision is positioned; (A/F, i, T) is a general formula for expressing an annual investment cost conversion coefficient according to annual investment cost, wherein T refers to T_dxOr T_jgK is the equipment net value;

calculating the equipment risk value under the scheduled secondary equipment overhaul decision and the equipment risk value under the scheduled technical improvement decision according to the scheduled strategy parameter value, wherein the method comprises the following steps:

wherein LE ═ w₁X equipment importance + w₂X possible loss of equipment + w₃X user influence, LE (t) is the risk loss value, P (t) is the risk probability value, w is the weight value, and w is₁+w₂+w₃＝1，w₁、w₂、w₃According to the classification value of the preset secondary equipment, R (t) is an equipment risk value;

calculating the equipment efficiency value under the preset secondary equipment overhaul decision and the equipment efficiency value under the technological improvement decision according to the preset strategy parameter values, wherein the method comprises the following steps:

wherein E is the equipment effect value, A is the availability vector, D is the credibility matrix, and C is the inherent capability vector;

carrying out normalization processing on the decision index value, wherein the normalization processing comprises the following steps:

using formulas

C_N∈[0,1]Normalizing the LCC equivalent annual average cost values;

using formulas

R_N∈[0,1]Normalizing the equipment risk value;

2. The decision determination method according to claim 1, wherein calculating the decision index value after the normalization processing as a parameter using a cost-risk benefit model includes:

3. The decision-making method according to claim 2, wherein determining the optimal decision-making scheme for the scheduled secondary device to perform overhaul improvement by comparing the annual combined cost performance value of the overhaul decision and the overhaul decision of the same decision-making year with the combined cost performance value of the overhaul decision comprises:

4. The decision-making determination method according to claim 3, wherein before obtaining the predetermined policy parameter value from the state information processing analysis result of the predetermined secondary device in the system and the health state evaluation result of the predetermined secondary device, the method further comprises:

if not, the operation is carried out after normal maintenance.

5. A decision-making system for secondary device overhaul engineering, comprising:

the decision determining module is used for determining an optimal decision scheme for the scheduled secondary equipment to carry out major repair and technical improvement by comparing the annual average cost comprehensive benefit values of the major repair decision and the technical improvement decision in the same decision year;

using formulas

Using formulas

Wherein the content of the first and second substances,

K＝C_{original value}-C_{Residual value}-T_{Operation of}×C_{Old age}，T_dx、T_jgRespectively showing the remaining service life of the equipment after the major repair and technical modification; CI_dx、CI_jgRespectively show the overhaul and the technical modification of the equipmentThe initial total investment, CO, CM, CF, CD respectively represent the operation cost, overhaul and maintenance cost, fault loss cost, retirement disposal cost corresponding to major repair and technical improvement; i is the bank interest rate; r is the inflation rate of the currency; n is the difference annual value of the design annual limit of the calculated annual average cost and the year in which the decision is positioned; (A/F, i, T) is a general formula for expressing an annual investment cost conversion coefficient according to annual investment cost, wherein T refers to T_dxOr T_jgK is the equipment net value;

using formulas

C_N∈[0,1]Normalizing the LCC equivalent annual average cost values;

using formulas

R_N∈[0,1]Normalizing the equipment risk value;

6. The decision determination system of claim 5, wherein the annual average cost composite benefit value calculation module comprises: