Electrical equipment operation data monitoring management system and method

Abstract

The invention discloses an electrical equipment operation data monitoring and management system and method.The system comprises a data monitoring unit, a scheme design unit, a fault maintenance unit and an optimization processing unit, the electric power resource configuration efficiency and the electric power equipment operation state are monitored and analyzed by collecting the operation data of electrical equipment; therefore, the fault risk degree of the electrical equipment is predicted and evaluated, an electrical equipment maintenance scheme is generated, and the fault risk of the electrical equipment is maintained in advance, so that the fault risk is suppressed in advance. The electrical equipment maintenance scheme is optimized and upgraded by performing optimization maintenance on the electrical equipment and comparing, analyzing and evaluating the maintenance efficiency of the electrical equipment, and the working efficiency of maintenance of the electrical equipment is improved and the stability of power resource configuration is ensured by judging the fault risk factor of the electrical equipment and performing optimization maintenance in a targeted manner. And effective configuration of power resources and equipment maintenance is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment control, and in particular to an electrical equipment operation data monitoring and management system and method. Background Art

[0002] Electrical equipment refers to various equipment used for power generation, transformation, transmission, distribution or utilization of electrical energy. Electrical equipment includes generators, transformers, power lines, circuit breakers, etc., and is an indispensable part of the power system. The faults that electrical equipment may encounter mainly include overheating faults and overload faults. Overheating faults refer to faults caused by excessive load, poor heat dissipation or insulation aging, which may cause equipment to burn out or short-circuit. Overload faults refer to current parameters such as current and voltage exceeding the rated value of the equipment, which may cause component damage.

[0003] Overload of power resource configuration will affect the operation status of power equipment. For example, the operation load exceeding the design capacity of the equipment will cause the motor of the electrical equipment to heat up, vibrate abnormally, and make abnormal noise. Excessive temperature will accelerate the aging of circuit components, motors and insulation materials, and even burn components, reducing the performance of power equipment. Therefore, power resource configuration and equipment operation status are in a relationship of mutual influence and interaction.

[0004] However, the single monitoring of power resource configuration lacks analysis of the actual operating status of power equipment, and may not be able to accurately locate risky power equipment. The single monitoring of equipment operating status lacks analysis of the power resources carried by the equipment, and may not be able to achieve precise regulation of electrical parameters. Therefore, the limitations of equipment operation data processing will lead to insufficient accuracy in determining the fault risk of electrical equipment and insufficient pertinence in fault maintenance processing, making it difficult to effectively curb fault risks in advance, resulting in low efficiency in power equipment maintenance and poor stability in power resource configuration.

[0005] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0006] The purpose of the present invention is to solve the problem that the single limitation of operation data processing in the prior art leads to insufficient accuracy in fault risk determination of electrical equipment and insufficient pertinence in fault maintenance and treatment, as well as the difficulty in effectively curbing fault risks in advance, resulting in low efficiency in power equipment maintenance and poor stability in power resource allocation. The present invention collects operation data of electrical equipment, monitors and analyzes power resource allocation efficiency and power equipment operation status, thereby predicting and evaluating the degree of fault risk of power equipment, and generating an electrical equipment maintenance plan, performing advance maintenance on the fault risk of electrical equipment, and continuously obtaining a balance between power resource allocation and power equipment load, thereby curbing fault risks in advance; then optimizing and maintaining the power equipment, and comparing and analyzing and evaluating the power equipment maintenance efficiency, thereby optimizing and upgrading the power equipment maintenance plan, and improving the work efficiency of power equipment maintenance by determining the fault risk factors of the power equipment and performing targeted optimization and maintenance, ensuring the stability of power resource allocation, and realizing effective configuration of power resources and equipment maintenance.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for monitoring and managing operation data of electrical equipment comprises the following steps:

[0009] Step 1: Collecting operating data of electrical equipment: the operating data includes electrical information and status information, wherein the electrical information includes voltage parameters, current parameters and power parameters, and the status information includes environmental parameters, vibration parameters and noise parameters;

[0010] Step 2: Analyze the operation data of electrical equipment and generate maintenance plans for electrical equipment: evaluate the efficiency of power resource allocation through electrical property information analysis of electrical equipment, evaluate the operation status of electrical equipment through status information analysis of electrical equipment, and then comprehensively predict the degree of failure risk of electrical equipment and generate maintenance plans for electrical equipment;

[0011] Step 3: Monitor the operation data of power equipment after maintenance and evaluate the maintenance efficiency of power equipment: optimize the maintenance of power equipment through the power equipment maintenance plan, and simultaneously monitor and collect the operation data of the power equipment after the optimized maintenance, and then compare and analyze the operation data of the power equipment before and after the optimized maintenance. Through the operation data of the power equipment after the optimized maintenance, calculate and evaluate the power resource allocation efficiency and the operation status of the power equipment after the optimized maintenance, and then evaluate the maintenance efficiency of the power equipment;

[0012] Step 4: Optimize the power equipment maintenance plan: Optimize and upgrade the power equipment maintenance plan through power equipment maintenance efficiency and in-depth analysis of the electrical information of the electrical equipment after maintenance, and carry out targeted power regulation.

[0013] Furthermore, the specific collection process of the operation data of the electrical equipment is as follows:

[0014] Integrate and mark N0 electrical equipment, and mark any electrical equipment as E;

[0015] Sa-1, electrical information includes voltage parameters, current parameters and power parameters;

[0016] Among them, the voltage parameters include input voltage and output voltage; the current parameters include input current and output current; the power parameters include input power and output power;

[0017] Mark the electrical property information of the electrical equipment E, mark the input voltage, output voltage, input current, output current, input power and output power as parameter indicators of the electrical property information, and integrate and construct a set of electrical property information parameter indicators;

[0018] Sa-2, status information includes environmental parameters, vibration parameters and noise parameters;

[0019] Among them, environmental parameters include environmental temperature and environmental humidity; vibration parameters include vibration frequency and vibration amplitude; noise parameters include sound frequency and sound amplitude;

[0020] The state information of the electrical equipment E is marked, and the ambient temperature, ambient humidity, vibration frequency, vibration amplitude, sound frequency and sound amplitude are marked as parameter indicators of the state information, and a state information parameter indicator set is integrated to construct.

[0021] Furthermore, the specific process of analyzing the operating data of electrical equipment is as follows:

[0022] Sb-1, establish an electrical performance index analysis model, substitute the parameter index of the electrical performance information of the electrical equipment E into the electrical performance index analysis model, obtain the corresponding configuration coefficients in turn, and then analyze and obtain the power resource configuration efficiency index XN to evaluate the power resource configuration efficiency;

[0023] Sb-2, establish a state index analysis model, substitute the parameter index of the state information of the electrical equipment E into the state index analysis model, obtain the corresponding stability coefficients in turn, and then analyze and obtain the power equipment operation state index ZT to evaluate the power equipment operation state;

[0024] Sb-3, by combining the power resource allocation efficiency index XN and the power equipment operation status index ZT, the fault prediction risk index YC of the electrical equipment is obtained to evaluate the degree of fault prediction risk of the electrical equipment.

[0025] Furthermore, the specific process of establishing the electrical index analysis model is as follows:

[0026] Sb-101, inputting parameter index No. 1 i into the electrical index analysis model, setting a data acquisition period Ts to perform regular acquisition of parameter index No. 1 i, thereby constructing a waveform diagram Si of parameter index No. 1 i;

[0027] Sb-102, obtaining the signal period Ti of parameter index No. 1 i through the waveform diagram Si;

[0028] Sb-103, marking the number of signal periods Ti of the waveform Si as N1, extracting the waveform segment S0i of the parameter index No. 1 of any signal period Ti, and obtaining the peak value Fi, mean value Ji and deviation value Ci of the parameter index No. 1 of any signal period Ti;

[0029] Sb-104, obtaining the configuration coefficient ZPi of the first parameter index i by combining the peak value Fi, the mean value Ji and the deviation value Ci of the first parameter index i;

[0030] Sb-105, the electrical index analysis model outputs the configuration coefficient ZPi of parameter index No. 1;

[0031] Substitute the parameter indicators of the electrical information into the electrical indicator analysis model in turn, output the configuration coefficients of the corresponding parameter indicators, and mark the configuration coefficients of the input voltage, output voltage, input current, output current, input power and output power as ZP1, ZP2, ZP3, ZP4, ZP5 and ZP6 respectively;

[0032] Then, the power resource allocation efficiency index XN is obtained, and the power resource allocation efficiency is evaluated through the power resource allocation efficiency index XN.

[0033] Furthermore, the specific process of establishing the status indicator analysis model is as follows:

[0034] Sb-201, input the second parameter index j into the state index analysis model, set the data collection period Tm to perform regular collection of the second parameter index j, and thus construct a curve graph Sj of the second parameter index j;

[0035] Sb-202, the number of data collection cycles Tm of the marked curve graph Sj is N2, and the curve increase rate Kj, the fluctuation coefficient Kσ and the deviation value Cj of the second parameter index j are obtained through the curve graph Sj;

[0036] Sb-203, obtain the stability coefficient WDj of the second parameter index j by combining the curve increase rate Kj, the fluctuation coefficient Kσ and the deviation value Cj of the second parameter index j;

[0037] Sb-204, the stability coefficient WDj of the second parameter index j output by the state index analysis model;

[0038] Substitute the parameter indicators of the state information into the state indicator analysis model in turn, output the stability coefficients of the corresponding parameter indicators, and mark the stability coefficients of ambient temperature, ambient humidity, vibration frequency, vibration amplitude, sound frequency and sound amplitude as WD1, WD2, WD3, WD4, WD5 and WD6 respectively;

[0039] Then, the power equipment operation status index ZT is obtained, and the power equipment operation status is analyzed and evaluated through the power equipment operation status index ZT.

[0040] Furthermore, the specific process of evaluating the risk level of fault prediction of electrical equipment is as follows:

[0041] Sb-301, constructing a change curve Sxn of the power resource allocation efficiency index XN, and obtaining a curve floating coefficient σ1 and a curve increase rate ψ1 of the power resource allocation efficiency through the change curve Sxn of the power resource allocation efficiency index XN;

[0042] Sb-302, constructing a variation curve Szt of the power equipment operation status index ZT, and obtaining a curve floating coefficient σ2 and a curve increase rate ψ2 of the power equipment operation status through the variation curve Szt of the power equipment operation status index ZT;

[0043] Sb-303, by combining the curve floating coefficient σ1 and the curve increasing rate ψ1 of the power resource allocation efficiency, and the curve floating coefficient σ2 and the curve increasing rate ψ2 of the power equipment operation status, obtain the fault prediction risk index YC of the electrical equipment;

[0044] Sb-304, sets the evaluation interval of the fault prediction risk index YC of the electrical equipment, evaluates the fault prediction risk level of the electrical equipment by interval comparison, and regulates the resource allocation of the power equipment in descending order of the fault prediction risk level, thereby generating a maintenance plan for the electrical equipment.

[0045] Furthermore, the specific process of evaluating the maintenance effectiveness of power equipment is as follows:

[0046] Sc-1, compare and analyze the operating data of power equipment before and after optimization and maintenance:

[0047] Sc-101, the power resource allocation efficiency index and power equipment operation status index before optimization maintenance are marked as XN0 and ZT0 respectively;

[0048] Sc-102, the power resource allocation efficiency index and power equipment operation status index after optimized maintenance are marked as XN1 and ZT1 respectively;

[0049] Sc-2, compare and analyze the power resource allocation efficiency index and power equipment operation status index before and after the optimized maintenance to obtain the power equipment maintenance efficiency index WH, thereby evaluating the power equipment maintenance efficiency;

[0050] Furthermore, the specific process of optimizing the power equipment maintenance plan is as follows:

[0051] Sd-1, setting the risk threshold G0 of the power equipment maintenance efficiency index WH, when the power equipment maintenance efficiency index WH is lower than the risk threshold G0, the electrical equipment maintenance plan is optimized;

[0052] Sd-2, deeply analyzes the electrical information of electrical equipment after optimization and maintenance, and analyzes the configuration coefficients of various parameter indicators of the electrical information through the electrical indicator analysis model;

[0053] Sd-3, build a risk factor comparison model to determine the fault risk factor, and then optimize the maintenance according to the fault risk factor, so as to optimize the electrical equipment maintenance plan and generate a new electrical equipment maintenance plan;

[0054] The specific process of establishing the risk factor comparison model is as follows:

[0055] Sd-301, input the risk factor previous value R1 and the risk factor later value R2 into the risk factor comparison model;

[0056] Sd-302, by comparing the risk factor's previous value R1 with the risk factor's later value R2, obtain the risk factor optimization coefficient Hr;

[0057] Sd-303, setting a risk threshold Gr of the risk factor optimization coefficient Hr. When the risk factor optimization coefficient Hr is lower than the risk threshold Gr, the risk factor is determined to be a fault risk factor;

[0058] Sd-304, risk factor comparison model outputs failure risk factor.

[0059] An electrical equipment operation data monitoring and management system, comprising a data monitoring unit, a scheme design unit, a fault maintenance unit and an optimization processing unit, wherein the data monitoring unit, the scheme design unit, the fault maintenance unit and the optimization processing unit are connected in communication; the system applies the above-mentioned electrical equipment operation data monitoring and management method;

[0060] The data monitoring unit is used to collect the operating data of the electrical equipment;

[0061] The scheme design unit is used to analyze the operation data of electrical equipment and generate maintenance schemes for electrical equipment;

[0062] The fault maintenance unit is used to implement the maintenance plan for electrical equipment, and feed back to the data monitoring unit to re-monitor the operating data of the power equipment, and then re-evaluate the maintenance efficiency of the power equipment through the plan design unit;

[0063] The optimization processing unit is used to optimize the electrical equipment maintenance plan by analyzing the maintenance efficiency of the electrical equipment.

[0064] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0065] The present invention collects the operating data of electrical equipment, monitors and analyzes the efficiency of power resource allocation and the operating status of power equipment, thereby predicting and evaluating the degree of failure risk of power equipment, and generating a maintenance plan for electrical equipment, pre-maintaining the failure risk of electrical equipment, and continuously obtaining the balance between power resource allocation and power equipment load, thereby curbing the failure risk in advance;

[0066] The present invention optimizes and upgrades the electrical equipment maintenance plan by optimizing the maintenance of the power equipment and comparing, analyzing and evaluating the maintenance efficiency of the power equipment. By determining the failure risk factors of the power equipment and performing targeted optimization maintenance, the work efficiency of the power equipment maintenance is improved, the stability of the power resource allocation is ensured, and the effective allocation of power resources and equipment maintenance is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram showing the steps of the method flow of the present invention is shown;

[0068] Figure 2 A connection diagram of the system modules of the present invention is shown. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] Embodiment 1:

[0071] like Figure 1-Figure 2 As shown, a method for monitoring and managing operation data of electrical equipment comprises the following steps:

[0072] S1, collect the operation data of electrical equipment: the operation data includes electrical information and status information. The electrical information includes voltage parameters, current parameters and power parameters. The status information includes environmental parameters, vibration parameters and noise parameters. The specific collection process is as follows:

[0073] Integrate and mark N0 electrical devices, and mark any electrical device as E.

[0074] S1-1, electrical information includes voltage parameters, current parameters and power parameters, and the electrical information is collected through an oscilloscope;

[0075] Voltage parameters include input voltage and output voltage;

[0076] Current parameters include input current and output current;

[0077] Power parameters include input power and output power;

[0078] Mark the electrical property information of the electrical equipment E, mark the input voltage, output voltage, input current, output current, input power and output power as parameter indicators of the electrical property information, and integrate and construct a set of electrical property information parameter indicators;

[0079] S1-2, the state information includes environmental parameters, vibration parameters and noise parameters, and the state information is collected through corresponding sensors; for example, temperature sensors, humidity sensors, vibration sensors and sound sensors;

[0080] Environmental parameters include ambient temperature and ambient humidity;

[0081] Vibration parameters include vibration frequency and vibration amplitude;

[0082] Noise parameters include sound frequency and sound amplitude;

[0083] Mark the status information of the electrical equipment E, mark the ambient temperature, ambient humidity, vibration frequency, vibration amplitude, sound frequency and sound amplitude as parameter indicators of the status information, and integrate and construct a set of status information parameter indicators;

[0084] S2, analyze the operation data of electrical equipment and generate maintenance plans for electrical equipment:

[0085] S2-1, evaluate the efficiency of power resource allocation through electrical equipment electrical information analysis;

[0086] Establish an electrical performance index analysis model, substitute the parameter index of the electrical performance information of the electrical equipment E into the electrical performance index analysis model, obtain the corresponding configuration coefficients in turn, and then analyze and evaluate the efficiency of power resource configuration;

[0087] The specific process of establishing the electrical index analysis model is as follows:

[0088] Input parameter index No. 1 i into the electrical index analysis model, set the data acquisition period Ts to perform regular acquisition of parameter index No. 1 i, take the data acquisition period Ts as the horizontal coordinate and the parameter index No. 1 i as the vertical coordinate, so as to construct the waveform diagram Si of parameter index No. 1 i;

[0089] The signal period Ti of the first parameter index i is obtained through the waveform Si; wherein the signal period Ti refers to the time required for the signal to complete a complete change, and is obtained by measuring the time difference between two adjacent peak points of the waveform Si, then the signal period Ti of the first parameter index i = n0*data acquisition period Ts;

[0090] The number of signal periods Ti of the marked waveform Si is N1, a waveform segment S0i of a parameter index No. 1 of any signal period Ti is extracted, and a peak value Fi, a mean value Ji and a deviation value Ci of a parameter index No. 1 of any signal period Ti are obtained;

[0091] The peak value Fi refers to the maximum value of the first parameter index i of the waveform Si in the signal period Ti, which is obtained by sorting the first parameter index i in descending order;

[0092] The mean value Ji refers to the average value of the No. 1 parameter index i of the waveform Si in the signal period Ti. It is obtained by calculating the mean value of the No. 1 parameter index i corresponding to n0 data acquisition periods Ts. The mean value Ji is:

[0093]

[0094] The deviation value Ci refers to the deviation between the waveform segment of the first parameter index i within the signal period Ti and the preset standard waveform, and is obtained by the difference between the first parameter index i of the two waveforms;

[0095] Set the standard waveform S1i of parameter index No. 1 i of signal period Ti, compare the waveform segment S0i with the standard waveform S1i, obtain the difference of parameter index No. 1 i corresponding to n0 data acquisition periods Ts, and thus obtain the deviation value Ci: Among them, Yi refers to the ordinate value of the waveform segment S0i, and Y0i refers to the ordinate value of the standard waveform S1i;

[0096] By combining the peak value Fi, mean value Ji and deviation value Ci of parameter index No. 1, the configuration coefficient ZPi of parameter index No. 1 is obtained:

[0097] Among them, α1, α2 and α3 are weight coefficients of peak value Fi, mean value Ji and deviation value Ci respectively, and α1, α2 and α3 are all greater than 0. The weight coefficients are preset and obtained according to experience after being measured by a large amount of experimental data. When the peak value Fi and mean value Ji are higher, it means that the overall level of parameter index No. 1 i is higher. When the deviation value Ci is higher, it means that the degree of deviation between parameter index No. 1 i and the standard value is higher, indicating that the load of parameter index No. 1 i of electrical equipment E is higher, which is easy to cause the risk of power equipment operation failure, indicating that the configuration energy of parameter index No. 1 i is too high, the power resources are overloaded and the degree of deviation is high;

[0098] The electrical index analysis model outputs the configuration coefficient ZPi of parameter index No. 1 i;

[0099] Substitute the parameter indicators of the electrical information into the electrical indicator analysis model in turn, output the configuration coefficients of the corresponding parameter indicators, and mark the configuration coefficients of input voltage, output voltage, input current, output current, input power and output power as ZP1, ZP2, ZP3, ZP4, ZP5 and ZP6 respectively, and then obtain the power resource configuration efficiency index XN:

[0100] Among them, βi refers to the weight coefficient of the first parameter index i, and βi is greater than 0; e is a natural constant, and e is 2.718. When the configuration coefficient of the first parameter index i is higher, the power resource configuration efficiency index XN is lower; for example, the input voltage is substituted into the electrical index analysis model as the first parameter index i, and the configuration coefficient ZP1 of the input voltage is obtained;

[0101] The efficiency of power resource allocation is evaluated through the power resource allocation efficiency index XN. The lower the power resource allocation efficiency index XN is, the lower the power resource allocation efficiency is evaluated.

[0102] S2-2, analyzing and evaluating the operating status of power equipment through status information of electrical equipment;

[0103] Establish a state index analysis model, substitute the parameter index of the state information of the electrical equipment E into the state index analysis model, obtain the corresponding stability coefficients in turn, and then analyze and evaluate the operating state of the power equipment;

[0104] The specific process of establishing the status indicator analysis model is as follows:

[0105] Input the second parameter index j into the state index analysis model, set the data collection period Tm to collect the second parameter index j regularly, take the data collection period Tm as the horizontal coordinate and take the second parameter index j as the vertical coordinate, so as to construct the curve graph Sj of the second parameter index j;

[0106] The number of data collection cycles Tm of the marked curve graph Sj is N2, and the curve increase rate Kj, the fluctuation coefficient Kσ and the deviation value Cj of the second parameter index j are obtained through the curve graph Sj;

[0107] The curve increase rate Kj refers to the average value of the slope of the second parameter index j corresponding to N2 data collection cycles Tm, and the slope is obtained by measuring the coordinates of the points of the curve graph Sj corresponding to two adjacent data collection cycles;

[0108] Then the curve increase rate Kj: Wherein, kj is the slope of any point of the curve graph Sj, and the coordinates of the points corresponding to any two adjacent data acquisition cycles of the curve graph Sj are marked as (Xj1, Yj1) and (Xj2, Yj2) respectively;

[0109] The fluctuation coefficient Kσ refers to the standard deviation of the slope of the second parameter index j corresponding to the N2 data collection period Tm. The fluctuation coefficient Kσ is:

[0110] The deviation value Cj refers to the standard deviation of the second parameter index j corresponding to N2 data collection cycles Tm. The deviation value Cj is: Pj refers to the mean value of parameter index No. 2 j corresponding to N2 data collection cycles Tm;

[0111] By combining the curve increase rate Kj, fluctuation coefficient Kσ and deviation value Cj of the second parameter index j, the stability coefficient WDj of the second parameter index j is obtained:

[0112] Among them, ε1, ε2 and ε3 are the weight coefficients of the curve increase rate Kj, the fluctuation coefficient Kσ and the deviation value Cj respectively, and ε1, ε2 and ε3 are all greater than 0. When the square value of the curve increase rate Kj is higher, it means that the change trend of the second parameter index j is rising or falling and the overall change trend is more obvious. When the fluctuation coefficient Kσ is higher, it means that the trend change degree of the second parameter index j is higher. When the deviation value Cj is higher, it means that the overall fluctuation degree of the curve is higher. Therefore, the stability coefficient WDj of the second parameter index j is lower, indicating that the stability of the second parameter index j is worse.

[0113] The state index analysis model outputs the stability coefficient WDj of the second parameter index j;

[0114] Substitute the parameter indicators of the state information into the state indicator analysis model in turn, output the stability coefficient of the corresponding parameter indicators, mark the stability coefficients of ambient temperature, ambient humidity, vibration frequency, vibration amplitude, sound frequency and sound amplitude as WD1, WD2, WD3, WD4, WD5 and WD6 respectively, and then obtain the power equipment operation state index ZT:

[0115] Among them, ωj refers to the weight coefficient of the second parameter index j, and ωj is greater than 0; when the stability coefficient of the second parameter index j is higher, the power equipment operation status index ZT is higher; for example, the ambient temperature is substituted into the status index analysis model as the second parameter index j, and the stability coefficient WD1 of the ambient temperature is output;

[0116] The power equipment operation status is evaluated by analyzing the power equipment operation status index ZT. The lower the power equipment operation status index ZT is, the worse the power equipment operation status is.

[0117] S2-3, analyzing the operation status of the power equipment through the power equipment operation status index ZT, when the power equipment operation status index ZT is higher, the evaluation takes the higher operation status of the power equipment;

[0118] By combining the power resource allocation efficiency index XN and the power equipment operation status index ZT, the fault prediction risk index YC of the electrical equipment is obtained;

[0119] Construct a change curve Sxn of the power resource allocation efficiency index XN, and obtain a curve floating coefficient σ1 and a curve increase rate ψ1 of the power resource allocation efficiency through the change curve Sxn of the power resource allocation efficiency index XN;

[0120] Construct a change curve Szt of the power equipment operation status index ZT, and obtain a curve floating coefficient σ2 and a curve increase rate ψ2 of the power equipment operation status through the change curve Szt of the power equipment operation status index ZT;

[0121] By combining the curve floating coefficient σ1 and curve increase rate ψ1 of the power resource allocation efficiency, and the curve floating coefficient σ2 and curve increase rate ψ2 of the power equipment operation status, the fault prediction risk index YC of the electrical equipment is obtained: YC = log μ1 (σ1*ψ1 2 )+log μ2 (σ2*ψ2 2 );

[0122] Among them, μ1 and μ2 are the conversion constants of the power resource allocation efficiency index XN and the power equipment operation status index ZT, respectively, and both μ1 and μ2 are greater than 1; when the curve floating coefficients σ1 and σ2 are higher, and the absolute values ​​of the curve increase rates ψ1 and ψ2 are higher, it means that the stability of the power resource allocation efficiency index XN and the power equipment operation status index ZT is worse, and the increase and decrease trend is higher, then the fault prediction risk index YC of the electrical equipment is higher, indicating that the fault prediction risk degree of the electrical equipment is higher;

[0123] S2-4, and then comprehensively predict the failure risk level of electrical equipment and generate a maintenance plan for electrical equipment;

[0124] Set the evaluation interval of the fault prediction risk index YC of the electrical equipment, evaluate the fault prediction risk degree of the electrical equipment by comparing the intervals, and regulate the resource allocation of the power equipment according to the descending order of the fault prediction risk degree;

[0125] Transfer the power resources of power equipment with higher fault prediction risk to power equipment with lower fault prediction risk, sort the fault prediction risk index YC of power equipment in descending order, obtain the power equipment with the highest PXe% in the sorting, integrate them into equipment with high fault risk, and transfer their power resources to the same type of low-risk power equipment;

[0126] The degree of power resource transfer is preset according to the fault prediction risk index YC. For example, the preset power resource transfer coefficient is ηe: ηe = YC -lnυ ×10000;

[0127] Wherein, v is a preset conversion constant, and 0<v<1; when the fault prediction risk index YC is higher, the power resource transfer coefficient ηe is higher, indicating that the degree of power resource transfer is higher, and the power of the power equipment before and after the power resource transfer is marked as Ua and Ub respectively, then Ub=Ua*(1-ηe);

[0128] This generates an electrical equipment maintenance plan, and through the deployment and transfer of power resources, the power load of all electrical equipment is evenly distributed, the overall operation status of N0 electrical equipment is stabilized, and the balance between power resource allocation and power equipment load is continuously obtained, so as to curb the risk of failure in advance;

[0129] S3, monitoring the operation data of power equipment after maintenance and evaluating the maintenance efficiency of power equipment: optimizing the maintenance of electrical equipment through electrical equipment maintenance plans, and simultaneously monitoring and collecting the operation data of power equipment after optimized maintenance;

[0130] Compare and analyze the operating data of power equipment before and after optimized maintenance. Through the operating data of power equipment after optimized maintenance, calculate and evaluate the power resource allocation efficiency and power equipment operating status after optimized maintenance, and then evaluate the maintenance efficiency of power equipment.

[0131] The power resource allocation efficiency index and power equipment operation status index before optimization maintenance are marked as XN0 and ZT0 respectively;

[0132] The power resource allocation efficiency index and power equipment operation status index after optimized maintenance are marked as XN1 and ZT1 respectively;

[0133] Then compare and analyze the power resource allocation efficiency index and power equipment operation status index before and after the optimized maintenance to obtain the power equipment maintenance efficiency index WH:

[0134] Among them, λ1 and λ2 are the weight coefficients of the power resource allocation efficiency index and the power equipment operation status index, respectively, and both λ1 and λ2 are greater than 0; when the power resource allocation efficiency index XN1 and the power equipment operation status index ZT1 after optimized maintenance are higher, and the power resource allocation efficiency index XN0 and the power equipment operation status index ZT0 before optimized maintenance are lower, the higher the power equipment maintenance efficiency index WH is, indicating that the power equipment maintenance efficiency of the electrical equipment maintenance plan is better;

[0135] S4, optimize the maintenance plan of power equipment: optimize the maintenance plan of electrical equipment through the maintenance efficiency of power equipment, deeply analyze the electrical information of the electrical equipment after optimization and maintenance, and carry out targeted power regulation.

[0136] A risk threshold G0 of the power equipment maintenance efficiency index WH is set, and when the power equipment maintenance efficiency index WH is lower than the risk threshold G0, the electrical equipment maintenance plan is optimized;

[0137] In-depth analysis of the electrical information of electrical equipment after optimization and maintenance. The configuration coefficients of various parameter indicators of the electrical information are analyzed through the electrical indicator analysis model, so as to evaluate and determine the fault risk factors in a targeted manner, and optimize the maintenance according to the fault risk factors, thereby optimizing the electrical equipment maintenance plan and generating a new electrical equipment maintenance plan;

[0138] The risk factor comparison model is established. The specific process is as follows:

[0139] Input the risk factor previous value R1 and the risk factor later value R2 into the risk factor comparison model.

[0140] By comparing the risk factor's previous value R1 and the risk factor's later value R2, we can obtain the risk factor optimization coefficient Hr: Hr = e( R2-R1 );

[0141] Set a risk threshold Gr of the risk factor optimization coefficient Hr. When the risk factor optimization coefficient Hr is lower than the risk threshold Gr, the risk factor is determined to be a fault risk factor.

[0142] The risk factor comparison model outputs the failure risk factor;

[0143] The configuration coefficients of input voltage, output voltage, input current, output current, input power and output power before optimization maintenance are marked as ZP1q, ZP2q, ZP3q, ZP4q, ZP5q and ZP6q respectively;

[0144] The configuration coefficients of the input voltage, output voltage, input current, output current, input power and output power after optimization and maintenance are marked as ZP1h, ZP2h, ZP3h, ZP4h, ZP5h and ZP6h respectively;

[0145] For example, by substituting the input voltage configuration coefficient ZP1q before optimization maintenance and the input voltage configuration coefficient ZP1h after optimization maintenance into the risk factor comparison model, it is determined whether the input voltage is a fault risk factor, and similarly, the fault risk factors of the output voltage, input current, output current, input power and output power are determined;

[0146] Then optimize maintenance for the fault risk factor: When the input voltage is determined to be a fault risk factor, optimize maintenance for the input voltage of the electrical equipment E, for example, by setting a voltage stabilizer or transformer, or adjusting the power of the electrical equipment, to specifically reduce the input voltage load of the electrical equipment, to ensure that the input voltage is within a reasonable range of the equipment load;

[0147] By determining the failure risk factors of power equipment and performing targeted optimization maintenance, the work efficiency of power equipment maintenance can be improved, the stability of power resource allocation can be ensured, and the effective allocation of power resources and equipment maintenance can be achieved.

[0148] An electrical equipment operation data monitoring and management system, comprising a data monitoring unit, a scheme design unit, a fault maintenance unit and an optimization processing unit, wherein the data monitoring unit, the scheme design unit, the fault maintenance unit and the optimization processing unit are connected in communication; the system applies the above-mentioned electrical equipment operation data monitoring and management method;

[0149] Among them, the data monitoring unit is used to collect the operating data of the electrical equipment;

[0150] The scheme design unit is used to analyze the operation data of electrical equipment and generate maintenance schemes for electrical equipment;

[0151] The fault maintenance unit is used to implement the maintenance plan for electrical equipment, and feed back to the data monitoring unit to re-monitor the operating data of the power equipment, and then re-evaluate the maintenance efficiency of the power equipment through the plan design unit;

[0152] The optimization processing unit is used to optimize the electrical equipment maintenance plan by analyzing the maintenance efficiency of the electrical equipment.

[0153] In summary, the present invention collects the operating data of electrical equipment, monitors and analyzes the efficiency of power resource allocation and the operating status of power equipment, thereby predicting and evaluating the degree of failure risk of power equipment, and generating a maintenance plan for electrical equipment, performing advance maintenance on the failure risk of electrical equipment, and continuously obtaining a balance between power resource allocation and power equipment load, thereby curbing the failure risk in advance;

[0154] The present invention optimizes and upgrades the electrical equipment maintenance plan by optimizing the maintenance of the power equipment and comparing, analyzing and evaluating the maintenance efficiency of the power equipment. By determining the failure risk factors of the power equipment and performing targeted optimization maintenance, the work efficiency of the power equipment maintenance is improved, the stability of the power resource allocation is ensured, and the effective allocation of power resources and equipment maintenance is achieved.

[0155] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0156] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0157] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for monitoring and managing operation data of electrical equipment, characterized in that: The following steps are involved: Step 1: Collecting operating data of electrical equipment: the operating data includes electrical information and status information, wherein the electrical information includes voltage parameters, current parameters and power parameters, and the status information includes environmental parameters, vibration parameters and noise parameters; Step 2: Analyze the operation data of electrical equipment and generate maintenance plans for electrical equipment: evaluate the efficiency of power resource allocation through electrical property information analysis of electrical equipment, evaluate the operation status of electrical equipment through status information analysis of electrical equipment, and then comprehensively predict the degree of failure risk of electrical equipment and generate maintenance plans for electrical equipment; Step 3: Monitor the operation data of power equipment after maintenance and evaluate the maintenance efficiency of power equipment: optimize the maintenance of power equipment through the power equipment maintenance plan, and simultaneously monitor and collect the operation data of the power equipment after the optimized maintenance, and then compare and analyze the operation data of the power equipment before and after the optimized maintenance. Through the operation data of the power equipment after the optimized maintenance, calculate and evaluate the power resource allocation efficiency and the operation status of the power equipment after the optimized maintenance, and then evaluate the maintenance efficiency of the power equipment; Step 4: Optimize the power equipment maintenance plan: Optimize and upgrade the power equipment maintenance plan through power equipment maintenance efficiency and in-depth analysis of the electrical information of the electrical equipment after maintenance, and carry out targeted power regulation.

2. The method for monitoring and managing operation data of electrical equipment according to claim 1, characterized in that: The specific process of collecting the operating data of electrical equipment is as follows: Integrate and mark N0 electrical equipment, and mark any electrical equipment as E; Sa-1, electrical information includes voltage parameters, current parameters and power parameters; Among them, the voltage parameters include input voltage and output voltage; the current parameters include input current and output current; the power parameters include input power and output power; Mark the electrical property information of the electrical equipment E, mark the input voltage, output voltage, input current, output current, input power and output power as parameter indicators of the electrical property information, and integrate and construct a set of electrical property information parameter indicators; Sa-2, status information includes environmental parameters, vibration parameters and noise parameters; Among them, environmental parameters include environmental temperature and environmental humidity; vibration parameters include vibration frequency and vibration amplitude; noise parameters include sound frequency and sound amplitude; The state information of the electrical equipment E is marked, and the ambient temperature, ambient humidity, vibration frequency, vibration amplitude, sound frequency and sound amplitude are marked as parameter indicators of the state information, and a state information parameter indicator set is integrated to construct.

3. The method for monitoring and managing the operation data of electrical equipment according to claim 2, characterized in that: The specific process of analyzing the operating data of electrical equipment is as follows: Sb-1, establish an electrical performance index analysis model, substitute the parameter index of the electrical performance information of the electrical equipment E into the electrical performance index analysis model, obtain the corresponding configuration coefficients in turn, and then analyze and obtain the power resource configuration efficiency index XN to evaluate the power resource configuration efficiency; Sb-2, establish a state index analysis model, substitute the parameter index of the state information of the electrical equipment E into the state index analysis model, obtain the corresponding stability coefficients in turn, and then analyze and obtain the power equipment operation state index ZT to evaluate the power equipment operation state; Sb-3, by combining the power resource allocation efficiency index XN and the power equipment operation status index ZT, the fault prediction risk index YC of the electrical equipment is obtained to evaluate the degree of fault prediction risk of the electrical equipment.

4. The method for monitoring and managing the operation data of electrical equipment according to claim 3, characterized in that: The specific process of establishing the electrical index analysis model is as follows: Sb-101, inputting parameter index No. 1 i into the electrical index analysis model, setting a data acquisition period Ts to perform regular acquisition of parameter index No. 1 i, thereby constructing a waveform diagram Si of parameter index No. 1 i; Sb-102, obtaining the signal period Ti of parameter index No. 1 i through the waveform diagram Si; Sb-103, marking the number of signal periods Ti of the waveform Si as N1, extracting the waveform segment S0i of the parameter index No. 1 of any signal period Ti, and obtaining the peak value Fi, mean value Ji and deviation value Ci of the parameter index No. 1 of any signal period Ti; Sb-104, obtain the configuration coefficient ZPi of the first parameter index i by combining the peak value Fi, the mean value Ji and the deviation value Ci of the first parameter index i; Sb-105, the electrical index analysis model outputs the configuration coefficient ZPi of parameter index No. 1; Substitute the parameter indicators of the electrical information into the electrical indicator analysis model in turn, output the configuration coefficients of the corresponding parameter indicators, and mark the configuration coefficients of the input voltage, output voltage, input current, output current, input power and output power as ZP1, ZP2, ZP3, ZP4, ZP5 and ZP6 respectively; Then, the power resource allocation efficiency index XN is obtained, and the power resource allocation efficiency is evaluated through the power resource allocation efficiency index XN.

5. The method for monitoring and managing the operation data of electrical equipment according to claim 4, characterized in that: The specific process of establishing the status indicator analysis model is as follows: Sb-201, input the second parameter index j into the state index analysis model, set the data collection period Tm to perform regular collection of the second parameter index j, and thus construct a curve graph Sj of the second parameter index j; Sb-202, the number of data collection cycles Tm of the marked curve graph Sj is N2, and the curve increase rate Kj, the fluctuation coefficient Kσ and the deviation value Cj of the second parameter index j are obtained through the curve graph Sj; Sb-203, obtain the stability coefficient WDj of the second parameter index j by combining the curve increase rate Kj, the fluctuation coefficient Kσ and the deviation value Cj of the second parameter index j; Sb-204, the stability coefficient WDj of the second parameter index j output by the state index analysis model; Substitute the parameter indicators of the state information into the state indicator analysis model in turn, output the stability coefficients of the corresponding parameter indicators, and mark the stability coefficients of ambient temperature, ambient humidity, vibration frequency, vibration amplitude, sound frequency and sound amplitude as WD1, WD2, WD3, WD4, WD5 and WD6 respectively; Then, the power equipment operation status index ZT is obtained, and the power equipment operation status is analyzed and evaluated through the power equipment operation status index ZT.

6. The method for monitoring and managing the operation data of electrical equipment according to claim 5, characterized in that: The specific process of evaluating the risk level of fault prediction of electrical equipment is as follows: Sb-301, constructing a change curve Sxn of the power resource allocation efficiency index XN, and obtaining a curve floating coefficient σ1 and a curve increase rate ψ1 of the power resource allocation efficiency through the change curve Sxn of the power resource allocation efficiency index XN; Sb-302, constructing a variation curve Szt of the power equipment operation status index ZT, and obtaining a curve floating coefficient σ2 and a curve increase rate ψ2 of the power equipment operation status through the variation curve Szt of the power equipment operation status index ZT; Sb-303, by combining the curve floating coefficient σ1 and the curve increasing rate ψ1 of the power resource allocation efficiency, and the curve floating coefficient σ2 and the curve increasing rate ψ2 of the power equipment operation status, obtain the fault prediction risk index YC of the electrical equipment; Sb-304, sets the evaluation interval of the fault prediction risk index YC of the electrical equipment, evaluates the fault prediction risk level of the electrical equipment by interval comparison, and regulates the resource allocation of the power equipment in descending order of the fault prediction risk level, thereby generating a maintenance plan for the electrical equipment.

7. The method for monitoring and managing operation data of electrical equipment according to claim 6, characterized in that: The specific process of evaluating the effectiveness of power equipment maintenance is as follows: Sc-1, compare and analyze the operating data of power equipment before and after optimization and maintenance: Sc-101, the power resource allocation efficiency index and power equipment operation status index before optimization maintenance are marked as XN0 and ZT0 respectively; Sc-102, the power resource allocation efficiency index and power equipment operation status index after optimized maintenance are marked as XN1 and ZT1 respectively; Sc-2, then compare and analyze the power resource allocation efficiency index and power equipment operation status index before and after the optimized maintenance to obtain the power equipment maintenance efficiency index WH, so as to evaluate the power equipment maintenance efficiency.

8. The method for monitoring and managing the operation data of electrical equipment according to claim 7, characterized in that: The specific process of optimizing the power equipment maintenance plan is as follows: Sd-1, setting the risk threshold G0 of the power equipment maintenance efficiency index WH, when the power equipment maintenance efficiency index WH is lower than the risk threshold G0, the electrical equipment maintenance plan is optimized; Sd-2, deeply analyzes the electrical information of the electrical equipment after optimization and maintenance, and analyzes the configuration coefficients of various parameter indicators of the electrical information through the electrical indicator analysis model; Sd-3, build a risk factor comparison model to determine the fault risk factor, and then optimize the maintenance according to the fault risk factor, so as to optimize the electrical equipment maintenance plan and generate a new electrical equipment maintenance plan; The specific process of establishing the risk factor comparison model is as follows: Sd-301, input the risk factor previous value R1 and the risk factor later value R2 into the risk factor comparison model; Sd-302, by comparing the risk factor's previous value R1 with the risk factor's later value R2, obtain the risk factor optimization coefficient Hr; Sd-303, setting a risk threshold Gr of the risk factor optimization coefficient Hr. When the risk factor optimization coefficient Hr is lower than the risk threshold Gr, the risk factor is determined to be a fault risk factor; Sd-304, risk factor comparison model outputs failure risk factor.

9. An electrical equipment operation data monitoring and management system, characterized in that: The system comprises a data monitoring unit, a scheme design unit, a fault maintenance unit and an optimization processing unit, wherein the data monitoring unit, the scheme design unit, the fault maintenance unit and the optimization processing unit are communicatively connected with each other, and the system applies an electrical equipment operation data monitoring and management method as described in any one of claims 1 to 8 above; The data monitoring unit is used to collect the operating data of the electrical equipment; The scheme design unit is used to analyze the operation data of electrical equipment and generate maintenance schemes for electrical equipment; The fault maintenance unit is used to implement the maintenance plan for electrical equipment, and feed back to the data monitoring unit to re-monitor the operating data of the power equipment, and then re-evaluate the maintenance efficiency of the power equipment through the plan design unit; The optimization processing unit is used to optimize the electrical equipment maintenance plan by analyzing the maintenance efficiency of the electrical equipment.

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