Power failure prediction method and system based on power engineering construction

Abstract

The invention belongs to the technical field of power system engineering construction, and particularly relates to a power failure prediction method and system based on power engineering construction. The method comprises the following steps: establishing a standard operation parameter array of power equipment and a historical operation parameter matrix of the power equipment; a historical operation deviation matrix of the power equipment is calculated based on the historical operation parameter matrix of the power equipment and the standard operation parameter array of the power equipment, a worst historical operation parameter group is screened out, whether the power equipment has hidden risks or not is judged, if yes, an early warning signal is output, and if not, no output is made; in a monitoring period, operation parameters of all power equipment in a power system are collected, a real-time operation parameter matrix of the power equipment is obtained, an operation deviation array of the power equipment in the monitoring period is calculated, whether the power equipment is in a normal working state or not is judged, if yes, no output is made, and if not, an alarm signal is output.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system engineering construction, and specifically relates to a power fault prediction method and system based on power engineering construction. Background Art

[0002] The scale of power engineering construction is constantly expanding. However, due to the influence of various environmental factors and human factors, some faults often occur in power engineering construction. Maintenance work plays an important role in power engineering construction and can maximize the life cycle of power equipment. If the fault of power equipment cannot be discovered in time during the power construction process, it will easily lead to huge cost losses.

[0003] In the prior art, the maintenance method for power equipment usually adopts the mode of planned maintenance and emergency repair. Planned maintenance lacks comprehensive analysis of the operating parameters of the power equipment, and it is difficult to carry out intelligent early warning based on the hidden dangers of the power equipment. It is easy to produce "excessive maintenance", that is, to repair the power equipment without hidden dangers, resulting in a waste of maintenance resources. Emergency repair can usually only identify the power equipment when it fails, which can easily lead to shutdown during emergency repair and cause operational losses. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for predicting power faults based on power engineering construction, so as to solve the problem in the background technology that the maintenance mode of power equipment adopts a mode of planned maintenance and emergency repair. The planned maintenance lacks a comprehensive analysis of the operating parameters of the power equipment, and it is difficult to carry out intelligent early warning based on the hidden dangers of the power equipment. It is easy to produce "excessive maintenance", that is, to repair power equipment that does not have hidden dangers, resulting in a waste of maintenance resources, while emergency repair can usually only be identified when the power equipment fails, which can easily lead to shutdown during emergency repair and cause operating losses.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present invention provides a method for predicting power failures based on power engineering construction, comprising:

[0007] Obtain standard operating parameters of all power equipment in the power system and establish an array of standard operating parameters of the power equipment;

[0008] Retrieve several historical operating parameters of all power equipment in the power system and establish a historical operating parameter matrix of the power equipment;

[0009] Calculating a historical operation deviation matrix of the power equipment based on the historical operation parameter matrix of the power equipment and the standard operation parameter array of the power equipment;

[0010] Based on the historical operation deviation matrix of power equipment, the worst parameter group in historical operation is screened out;

[0011] Based on the worst historical operating parameter group, determine whether the power equipment has hidden risks. If so, output a warning signal; if not, do nothing;

[0012] During the monitoring period, the operating parameters of all power equipment in the power system are collected in real time at preset time intervals to obtain a real-time operating parameter matrix of the power equipment;

[0013] Based on the real-time operation parameter matrix of the electric power equipment, the operation deviation array of the electric power equipment within the monitoring period is calculated to obtain the real-time operation deviation array;

[0014] Based on the operation deviation array of the power equipment, determine whether the power equipment is in a normal working state, if so, do not make any output, if not, output an alarm signal;

[0015] Based on the real-time operating parameter matrix of power equipment, the historical operating parameters of the power system are updated.

[0016] Preferably, the calculation of the historical operation deviation matrix of the electric equipment based on the historical operation parameter matrix of the electric equipment and the standard operation parameter array of the electric equipment specifically includes:

[0017] Based on the standard operating parameter array of the power equipment, the deviation array of each set of historical operating parameters is calculated to obtain several historical operating deviation arrays A i ;

[0018] Combine all historical running deviation arrays into the historical running deviation matrix A.

[0019] Preferably, the calculation of the deviation array of each set of historical operating parameters specifically includes:

[0020]

[0021] Among them, a ij is the deviation between the jth parameter of the i-th group of historical operating parameters and the standard operating parameters, y ij is the parameter value of the jth parameter of the i-th group of historical operating parameters, p j is the standard parameter value of the jth parameter, and m is the total number of running parameters.

[0022] Preferably, the historical operation deviation matrix is ​​specifically:

[0023]

[0024] Where n is the total number of historical operating parameter groups.

[0025] Preferably, the screening out the worst historical operation parameter group based on the historical operation deviation matrix of the power equipment specifically includes:

[0026] The maximum deviation value of each parameter is selected from the historical operation deviation matrix as the worst deviation value of the parameter;

[0027] Combine the worst deviation values ​​of all parameters into the worst parameter group A in historical operation - .

[0028] Preferably, the worst historical operating parameter group is specifically:

[0029]

[0030] In the formula, is the worst deviation value of the jth parameter.

[0031] The parameters in the historical worst operating parameter group represent the worst operating state in the historical operating state of the power system. By judging the operating data of the power equipment under this state, it is possible to determine whether the power equipment has hidden risks. If the state is lower than the minimum operating standard of the power equipment, it means that there are hidden risks in the power system. If the state is higher than the minimum operating standard of the power equipment, it means that there are no hidden operating risks in the power system.

[0032] Preferably, judging whether the power equipment has hidden risks based on the worst historical operating parameter group specifically includes:

[0033] Calculate the worst operating index of all power equipment in the power system based on the worst historical operating parameter group;

[0034] Determine whether the worst operating index is higher than the preset value of the operating index, and output a signal based on the judgment result.

[0035] Preferably, the calculation formula of the worst operation index is:

[0036]

[0037] In the formula, Z - The worst operating indicator.

[0038] By comprehensively calculating the sum of the squares of the ratios between the maximum deviation values ​​of various parameters and the standard parameter values, the vector distance between the worst parameter and the standard parameter value can be comprehensively calculated. This vector distance is used as an indicator to measure the worst operating state in the power system. The larger the worst operating index, the farther the worst operating state in the power system deviates from the standard operating state, and the worse the worst operating state of the system. If the worst operating index is greater than the preset minimum operating index, it means that the current possible worst operating state of the power system is still above the minimum operating state of the power system, and the power system is in good operating condition with no hidden operating risks.

[0039] Preferably, according to the judgment result, the output signal is specifically as follows:

[0040] If so, it is determined that there is a hidden risk in the power equipment and an early warning signal is output;

[0041] If not, it is determined that the power equipment is operating normally and no output is made.

[0042] By analyzing the collected real-time operating parameters of all power equipment in the power system, a real-time analysis is performed on the deviation distance between the real-time operating parameter group and the standard operating parameter array of the power equipment. If the deviation distance between the real-time operating parameter group and the standard operating parameter array of the power equipment is greater than the minimum operating standard, it means that the operating state of the power system at this time is lower than the minimum operating state of the power system, and the power system is at a greater operating risk. In order to avoid further operating failures, the power system needs to be overhauled as a whole to avoid power equipment failures and cause greater operating losses.

[0043] Preferably, the operation deviation array of the power equipment within the monitoring period is calculated based on the real-time operation parameter matrix of the power equipment to obtain the real-time operation deviation array as follows:

[0044] All real-time deviation distance values ​​are combined into a real-time running deviation array C;

[0045] C=[c 1 …c k …c l ];

[0046] Where, l is the total number of operating parameters of the power equipment collected during the monitoring period;

[0047] The calculation formula of the deviation distance is as follows:

[0048]

[0049] In the formula, c k is the deviation distance between the kth group of real-time operating parameter groups and the standard operating parameter array of the power equipment, v kjThe parameter value of the jth parameter in the kth real-time running parameter group.

[0050] In a second aspect, the present invention provides a power fault prediction system for power engineering construction, comprising:

[0051] A storage module, the storage module is used to store historical operating parameters of all power equipment in the power system and an array of standard operating parameters of the power equipment;

[0052] A processor, the processor is coupled with the storage module, and an implicit risk analysis module and an operation monitoring module are integrated in the processor. The implicit risk analysis module is used to determine whether the power equipment has implicit risks based on the worst historical operation parameter group, and the operation monitoring module is used to determine whether the power equipment is in a normal working state based on the operation deviation array of the power equipment;

[0053] A data monitoring module, the data monitoring module is electrically connected to the processor, and is used to collect operating parameters of all power equipment in the power system in real time during a monitoring period and at preset time intervals;

[0054] The data updating module is electrically connected to the data monitoring module and the storage module, and is used to update the historical operating parameters of the power system based on the real-time operating parameter matrix of the power equipment.

[0055] The implicit risk analysis module includes:

[0056] A first calculation unit, the first calculation unit is used to calculate the deviation array of each group of historical operation parameters based on the standard operation parameter array of the power equipment and combine all the historical operation deviation arrays into a historical operation deviation matrix;

[0057] A screening unit, which is used to screen out the maximum value of the deviation value of each parameter from the historical operation deviation matrix as the worst deviation value of the parameter, and combine the worst deviation values ​​of all parameters into a historical operation worst parameter group;

[0058] A second calculation unit, the second calculation unit is used to calculate the worst operation index of all power equipment in the power system based on the worst historical operation parameter group;

[0059] The first judgment unit is used to judge whether the worst operation index is higher than a preset value of the operation index.

[0060] The operation monitoring module includes:

[0061] A data processing unit, the data processing unit is used to process the operating parameters of the power equipment collected by the data monitoring module into a real-time operating parameter matrix of the power equipment;

[0062] A third calculation unit, the third calculation unit is used to calculate the deviation distance between each real-time operation parameter group and the standard operation parameter array of the power equipment based on the real-time operation parameter matrix of the power equipment and the standard operation parameter array of the power equipment through a deviation distance calculation formula, obtain a plurality of real-time deviation distance values ​​and form all the real-time deviation distance values ​​into a real-time operation deviation array;

[0063] The second judgment unit is used to judge whether there is any set of real-time operating parameter groups in the operating deviation array and whether the deviation distance between the standard operating parameter array of the power equipment is greater than the preset value of the operating index.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] The power fault prediction method proposed in the present invention, when performing maintenance warning, comprehensively analyzes the historical operating parameters of the power equipment, and comprehensively analyzes the maximum deviation value between the historical operating data of the power equipment and the standard operating parameter array of the power equipment, so as to judge whether the power equipment has operating hidden dangers, and conducts intelligent warning based on the judgment result. Maintenance is carried out according to the warning result, which can effectively carry out targeted maintenance on the hidden dangers in the power equipment, avoid "excessive maintenance", and effectively save maintenance resources.

[0066] The present invention also collects and processes the real-time operating data of the power equipment, and identifies the abnormal conditions existing in the real-time operation process of the power equipment. The abnormal conditions existing in the real-time operation process of the power equipment can be repaired in time to avoid failures of the power equipment and cause greater operating losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0068] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0069] Figure 2 It is a schematic flow chart of a method for calculating a historical operation deviation matrix of an electric power device in the present invention;

[0070] Figure 3 A schematic diagram of the process flow of the method for screening out the worst parameter group in historical operation in the present invention;

[0071] Figure 4 It is a flow chart of the method for judging whether there is a hidden risk in electric power equipment in the present invention;

[0072] Figure 5It is a flow chart of the method for outputting a signal according to the judgment result in the present invention. DETAILED DESCRIPTION

[0073] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0074] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0075] Please refer to Figure 1-5 As shown, a method for predicting power failures based on power engineering construction includes:

[0076] Obtain standard operating parameters of all power equipment in the power system and establish an array of standard operating parameters of the power equipment;

[0077] Retrieve several historical operating parameters of all power equipment in the power system and establish a historical operating parameter matrix of the power equipment;

[0078] Calculating a historical operation deviation matrix of the power equipment based on the historical operation parameter matrix of the power equipment and the standard operation parameter array of the power equipment;

[0079] Based on the historical operation deviation matrix of power equipment, the worst parameter group in historical operation is screened out;

[0080] Based on the worst historical operating parameter group, determine whether the power equipment has hidden risks. If so, output a warning signal; if not, do nothing;

[0081] During the monitoring period, the operating parameters of all power equipment in the power system are collected in real time at preset time intervals to obtain a real-time operating parameter matrix of the power equipment;

[0082] Based on the real-time operation parameter matrix of the electric power equipment, the operation deviation array of the electric power equipment within the monitoring period is calculated to obtain the real-time operation deviation array;

[0083] Based on the operation deviation array of the power equipment, determine whether the power equipment is in a normal working state, if so, do not make any output, if not, output an alarm signal;

[0084] Based on the real-time operating parameter matrix of power equipment, the historical operating parameters of the power system are updated.

[0085] The calculation is based on the historical operation parameter matrix of the power equipment and the standard operation parameter array of the power equipment. The historical operation deviation matrix of the power equipment specifically includes:

[0086] Based on the standard operating parameter array of the power equipment, the deviation array of each set of historical operating parameters is calculated to obtain several historical operating deviation arrays A i ;

[0087] Combine all historical running deviation arrays into the historical running deviation matrix A.

[0088] When conducting maintenance warning, this solution comprehensively analyzes the historical operating parameters of the power equipment and the maximum deviation value between the historical operating data of the power equipment and the standard operating parameter array of the power equipment to determine whether the power equipment has operating hidden dangers. Intelligent early warning is carried out according to the judgment result, and maintenance is carried out according to the early warning result. It can effectively carry out targeted maintenance on the hidden dangers in the power equipment, avoid "excessive maintenance", and effectively save maintenance resources;

[0089] This solution also collects and processes the real-time operating data of the power equipment, and identifies the abnormal conditions that exist during the real-time operation of the power equipment. It can promptly repair the abnormal conditions that exist during the real-time operation of the power equipment to avoid power equipment failures and cause greater operational losses.

[0090] The calculation of the deviation array of each set of historical operating parameters specifically includes:

[0091]

[0092] Among them, a ij is the deviation between the jth parameter of the i-th group of historical operating parameters and the standard operating parameters, y ij is the parameter value of the jth parameter of the i-th group of historical operating parameters, p j is the standard parameter value of the jth parameter, and m is the total number of running parameters.

[0093] The historical operation deviation matrix is ​​as follows:

[0094]

[0095] Where n is the total number of historical operating parameter groups.

[0096] Based on the historical operation deviation matrix of power equipment, the worst historical operation parameter groups are screened out, including:

[0097] The maximum deviation value of each parameter is selected from the historical operation deviation matrix as the worst deviation value of the parameter;

[0098] Combine the worst deviation values ​​of all parameters into the worst parameter group A in historical operation - .

[0099] The worst parameter group in historical operation is as follows:

[0100]

[0101] In the formula, is the worst deviation value of the jth parameter.

[0102] The parameters in the historical worst operating parameter group represent the worst operating state in the historical operating state of the power system. By judging the operating data of the power equipment under this state, it is possible to determine whether the power equipment has hidden risks. If the state is lower than the minimum operating standard of the power equipment, it means that there are hidden risks in the power system. If the state is higher than the minimum operating standard of the power equipment, it means that there are no hidden operating risks in the power system.

[0103] Judging whether there are hidden risks in power equipment based on the worst historical operating parameter group specifically includes:

[0104] Calculate the worst operating index of all power equipment in the power system based on the worst historical operating parameter group;

[0105] Determine whether the worst operating index is higher than the preset value of the operating index, and output a signal based on the judgment result.

[0106] The calculation formula of the worst operation index is:

[0107]

[0108] In the formula, Z - The worst operating indicator.

[0109] By comprehensively calculating the sum of the squares of the ratios between the maximum deviation values ​​of various parameters and the standard parameter values, the vector distance between the worst parameter and the standard parameter value can be comprehensively calculated. This vector distance is used as an indicator to measure the worst operating state in the power system. The larger the worst operating index, the farther the worst operating state in the power system deviates from the standard operating state, and the worse the worst operating state of the system. If the worst operating index is greater than the preset minimum operating index, it means that the current possible worst operating state of the power system is still above the minimum operating state of the power system, and the power system is in good operating condition with no hidden operating risks.

[0110] Based on the real-time operation parameter matrix of the power equipment, the operation deviation array of the power equipment within the monitoring period is calculated to obtain the real-time operation deviation array as follows:

[0111] All real-time deviation distance values ​​are combined into a real-time running deviation array C;

[0112] C=[c 1 …c k …c l ];

[0113] Where, l is the total number of operating parameters of the power equipment collected during the monitoring period;

[0114] The calculation formula of the deviation distance is as follows:

[0115]

[0116] In the formula, c k is the deviation distance between the kth group of real-time operating parameter groups and the standard operating parameter array of the power equipment, v kj The parameter value of the jth parameter in the kth real-time running parameter group.

[0117] According to the judgment results, the output signals are as follows:

[0118] If so, it is determined that there is a hidden risk in the power equipment and an early warning signal is output;

[0119] If not, it is determined that the power equipment is operating normally and no output is made.

[0120] By analyzing the collected real-time operating parameters of all power equipment in the power system, a real-time analysis is performed on the deviation distance between the real-time operating parameter group and the standard operating parameter array of the power equipment. If the deviation distance between the real-time operating parameter group and the standard operating parameter array of the power equipment is greater than the minimum operating standard, it means that the operating state of the power system at this time is lower than the minimum operating state of the power system, and the power system is at a greater operating risk. In order to avoid further operating failures, the power system needs to be overhauled as a whole to avoid power equipment failures and cause greater operating losses.

[0121] This scheme also proposes a power fault prediction system based on power engineering construction, including:

[0122] A storage module, the storage module is used to store historical operating parameters of all power equipment in the power system and an array of standard operating parameters of the power equipment;

[0123] A processor, the processor is coupled with the storage module, and an implicit risk analysis module and an operation monitoring module are integrated in the processor. The implicit risk analysis module is used to determine whether the power equipment has implicit risks based on the worst historical operation parameter group, and the operation monitoring module is used to determine whether the power equipment is in a normal working state based on the operation deviation array of the power equipment;

[0124] A data monitoring module, the data monitoring module is electrically connected to the processor, and is used to collect operating parameters of all power equipment in the power system in real time during a monitoring period and at preset time intervals;

[0125] The data updating module is electrically connected to the data monitoring module and the storage module, and is used to update the historical operating parameters of the power system based on the real-time operating parameter matrix of the power equipment.

[0126] The implicit risk analysis module includes:

[0127] A first calculation unit, the first calculation unit is used to calculate the deviation array of each group of historical operation parameters based on the standard operation parameter array of the power equipment and combine all the historical operation deviation arrays into a historical operation deviation matrix;

[0128] A screening unit, which is used to screen out the maximum value of the deviation value of each parameter from the historical operation deviation matrix as the worst deviation value of the parameter, and combine the worst deviation values ​​of all parameters into a historical operation worst parameter group;

[0129] A second calculation unit, the second calculation unit is used to calculate the worst operation index of all power equipment in the power system based on the worst historical operation parameter group;

[0130] The first judgment unit is used to judge whether the worst operation index is higher than a preset value of the operation index.

[0131] The operation monitoring module includes:

[0132] A data processing unit, the data processing unit is used to process the operating parameters of the power equipment collected by the data monitoring module into a real-time operating parameter matrix of the power equipment;

[0133] A third calculation unit, the third calculation unit is used to calculate the deviation distance between each real-time operation parameter group and the standard operation parameter array of the power equipment based on the real-time operation parameter matrix of the power equipment and the standard operation parameter array of the power equipment through a deviation distance calculation formula, obtain a plurality of real-time deviation distance values ​​and form all the real-time deviation distance values ​​into a real-time operation deviation array;

[0134] The second judgment unit is used to judge whether there is any set of real-time operating parameter groups in the operating deviation array and whether the deviation distance between the standard operating parameter array of the power equipment is greater than the preset value of the operating index.

[0135] The use process of the above-mentioned power equipment early warning system is as follows:

[0136] Step 1: The first calculation unit retrieves the historical operating parameters of the power equipment and the standard operating parameter array of the power equipment from the memory, and based on the standard operating parameter array of the power equipment, calculates the deviation array of each group of historical operating parameters and combines all the historical operating deviation arrays into a historical operating deviation matrix;

[0137] Step 2: The screening unit screens out the maximum deviation value of each parameter from the historical operation deviation matrix as the worst deviation value of the parameter, and combines the worst deviation values ​​of all parameters into a historical operation worst parameter group;

[0138] Step 3: The second calculation unit calculates the worst operation index of all power equipment in the power system based on the worst historical operation parameter group;

[0139] Step 4: The first judgment unit judges whether the worst operation index is higher than the preset value of the operation index. If so, it is judged that the power equipment has an implicit risk and outputs a warning signal. If not, it is judged that the power equipment is operating normally and no output is made.

[0140] Step 5: During the monitoring period, the data monitoring module collects the operating parameters of all power equipment in the power system in real time at preset time intervals, and transmits the collected operating parameters to the processor and the data update module. The data update module updates the historical operating parameters of the power system based on the real-time operating parameter matrix of the power equipment and stores them in the memory;

[0141] Step 6: The data processing unit processes the operating parameters of the power equipment collected by the data monitoring module into a real-time operating parameter matrix of the power equipment;

[0142] Step 7: The third calculation unit calculates the deviation distance between each real-time operation parameter group and the standard operation parameter array of the power equipment based on the real-time operation parameter matrix of the power equipment and the standard operation parameter array of the power equipment through the deviation distance calculation formula, obtains a plurality of real-time deviation distance values ​​and forms all the real-time deviation distance values ​​into a real-time operation deviation array;

[0143] Step 8: The second judgment unit judges whether there is any set of real-time operating parameter groups in the operating deviation array and whether the deviation distance between the standard operating parameter array of the power equipment is greater than the preset value of the operating index. If so, it is judged that the working state of the power equipment is abnormal; if not, it is judged that the working state of the power equipment is normal.

[0144] Analyze some real-life cases:

[0145] The whole process of power construction includes three stages: early stage of infrastructure construction, installation and commissioning, and production preparation. From planning and design, selection and matching, equipment supervision, installation and commissioning, production preparation to the whole process of production and compliance, a problem in one link may leave hidden dangers for future production and operation, and eventually cause quality and safety accidents, which will directly affect the production period of the unit and the economic benefits and repayment of the owner. Therefore, it is very important to strengthen the safety and quality management of every link in the whole process of power infrastructure construction, and the lessons learned in this regard are also very painful.

[0146] During the construction of a power plant in a certain region, a short-circuit fault occurred at the excitation end when the entire system was started for a short-circuit test, causing severe burning. The plant was forced to return to a foreign country for restorative maintenance, which lasted nearly 10 months and has not yet been completed. The losses were severe, and the subsequent construction period was extended. The reason was that a generator burned out due to excessive temperature during operation.

[0147] If the system designed by the present invention is used for monitoring, the failure will not occur, and the staff will repair it in time, thereby improving the efficiency of engineering construction.

[0148] In summary, the advantages of the present invention are: through comprehensive analysis of the operating parameters of the power equipment, it is possible to effectively carry out intelligent early warning and monitoring of the hidden dangers of the power equipment, thereby realizing timely troubleshooting of power system faults and ensuring the stable operation of the power system.

[0149] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

[0150] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0154] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting power failures based on power engineering construction, characterized in that: include: Obtain standard operating parameters of all power equipment in the power system and establish an array of standard operating parameters of the power equipment; Retrieve several historical operating parameters of all power equipment in the power system and establish a historical operating parameter matrix of the power equipment; Calculating a historical operation deviation matrix of the power equipment based on a historical operation parameter matrix of the power equipment and a standard operation parameter array of the power equipment; Based on the historical operation deviation matrix of power equipment, the worst parameter group in historical operation is screened out; Based on the worst historical operating parameter group, determine whether the power equipment has hidden risks. If so, output a warning signal; if not, do nothing; During the monitoring period, the operating parameters of all power equipment in the power system are collected in real time at preset time intervals to obtain a real-time operating parameter matrix of the power equipment; Based on the real-time operation parameter matrix of the electric power equipment, the operation deviation array of the electric power equipment within the monitoring period is calculated to obtain the real-time operation deviation array; Based on the operation deviation array of the power equipment, determine whether the power equipment is in a normal working state, if so, do not make any output, if not, output an alarm signal; Based on the real-time operating parameter matrix of power equipment, the historical operating parameters of the power system are updated.

2. The method for predicting power failures based on power engineering construction according to claim 1, characterized in that: The calculation of the historical operation deviation matrix of the power equipment based on the historical operation parameter matrix of the power equipment and the standard operation parameter array of the power equipment specifically includes: Based on the standard operating parameter array of the power equipment, the deviation array of each set of historical operating parameters is calculated to obtain several historical operating deviation arrays A i ; Combine all historical running deviation arrays into the historical running deviation matrix A.

3. The method for predicting power failures based on power engineering construction according to claim 2, characterized in that: The calculation of the deviation array of each set of historical operating parameters specifically includes: Among them, a ij is the deviation between the jth parameter of the i-th group of historical operating parameters and the standard operating parameters, y ij is the parameter value of the jth parameter of the i-th group of historical operating parameters, p j is the standard parameter value of the jth parameter, and m is the total number of running parameters.

4. The method for predicting power failures based on power engineering construction according to claim 1, characterized in that: The historical operation deviation matrix is ​​specifically: Where n is the total number of historical operating parameter groups.

5. The method for predicting power failures based on power engineering construction according to claim 1, characterized in that: The method of screening out the worst parameter group based on the historical operation deviation matrix of the power equipment specifically includes: The maximum deviation value of each parameter is selected from the historical operation deviation matrix as the worst deviation value of the parameter; Combine the worst deviation values ​​of all parameters into the worst parameter group A in historical operation - .

6. The method for predicting power failures based on power engineering construction according to claim 5, characterized in that: The worst parameter group in historical operation is specifically: In the formula, is the worst deviation value of the jth parameter.

7. The method for predicting power failures based on power engineering construction according to claim 1, characterized in that: The method of judging whether there is a hidden risk in the power equipment based on the worst historical operating parameter group specifically includes: Calculate the worst operating index of all power equipment in the power system based on the worst historical operating parameter group; Determine whether the worst operating index is higher than the preset value of the operating index, and output a signal based on the judgment result.

8. The method for predicting power failures based on power engineering construction according to claim 7, characterized in that: The calculation formula of the worst operation index is: In the formula, Z - The worst operating indicator.

9. The method for predicting power failures based on power engineering construction according to claim 1, characterized in that: The operation deviation array of the power equipment within the monitoring period is calculated based on the real-time operation parameter matrix of the power equipment to obtain the real-time operation deviation array as follows: All real-time deviation distance values ​​are combined into a real-time running deviation array C; C=[c1…c k …c l ]; Where, l is the total number of operating parameters of the power equipment collected during the monitoring period; The calculation formula of the deviation distance is as follows: In the formula, c k is the deviation distance between the kth group of real-time operating parameter groups and the standard operating parameter array of the power equipment, v kj The parameter value of the jth parameter in the kth real-time running parameter group.

10. A power failure prediction system based on power engineering construction, characterized in that: include: A storage module, the storage module is used to store historical operating parameters of all power equipment in the power system and an array of standard operating parameters of the power equipment; A processor, the processor is coupled with the storage module, and an implicit risk analysis module and an operation monitoring module are integrated in the processor. The implicit risk analysis module is used to determine whether the power equipment has implicit risks based on the worst historical operation parameter group, and the operation monitoring module is used to determine whether the power equipment is in a normal working state based on the operation deviation array of the power equipment; A data monitoring module, the data monitoring module is electrically connected to the processor, and is used to collect operating parameters of all power equipment in the power system in real time during a monitoring period and at preset time intervals; The data updating module is electrically connected to the data monitoring module and the storage module, and is used to update the historical operating parameters of the power system based on the real-time operating parameter matrix of the power equipment.

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