Fault prediction system and process based on operation of power distribution equipment

Abstract

The invention relates to the technical field of data analysis, in particular to a fault prediction system and process based on power distribution equipment operation, and the system comprises a prior layer, an acquisition layer and a prediction layer. The historical fault operation state parameters of the power distribution equipment are uploaded through a priori layer, the priori layer synchronously identifies characteristic parameters in the uploaded historical fault operation state parameters of the power distribution equipment, the identified characteristic parameters are copied, and the historical fault operation state parameters of the power distribution equipment and the copied characteristic parameters are stored in the priori layer in a distinguished mode. According to the invention, the operation state parameters of the historical faults of the power distribution equipment are used as the prior data, the real-time operation state collection is carried out on the operating power distribution equipment, and then the fault prediction is carried out on the power distribution equipment through the comparison of the prior data and the real-time operation state parameter data of the power distribution equipment. Meanwhile, through the prediction mode and continuous accumulation of prior data, the system can predict various faults of the power distribution equipment more comprehensively.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and specifically relates to a fault prediction system and process based on the operation of power distribution equipment. Background Art

[0002] Power distribution equipment is a general term for a series of equipment used for power distribution; Power distribution equipment plays a crucial role in the power system. The power distribution equipment steps down high-voltage electric energy and distributes it to each user or electrical equipment, and realizes operations such as switching on and off, regulating, and protecting the power.

[0003] A patent for invention with the application number 202110635238.1 discloses a method for predicting faults in power distribution network equipment. The method is characterized in that the method includes the following steps: calculating the equivalent service life of the power distribution network equipment in the selected area; determining the equipment type of the power distribution network equipment in the selected area; the equipment type is an environment-exposed equipment or an environment-enclosed equipment; when the equipment type is an environment-exposed equipment, a double-condition cloud reasoning model is adopted to determine the influencing factors of weather conditions and external force damage, and according to the influencing factors and the equivalent service life of the power distribution network equipment, determining the failure rate of the power distribution network equipment: when the equipment type is an environment-enclosed equipment, using a proportional failure probability model to calculate the CHI value of the power distribution network equipment, and according to the CHI value and the equivalent service life of the power distribution network equipment, determining the failure rate of the power distribution network equipment; the adopting of the double-condition cloud reasoning model to determine the influencing factors of weather conditions and external force damage specifically includes: collecting the current data of the meteorological factors causing faults in the power distribution network equipment; the weather influencing factors include: lightning, icing, rainfall, wind, temperature, typhoon, hail, snow, and sandstorm.

[0004] This application aims to solve the problems of "the equipment failure rate model considers single factors, cannot fully utilize the operation data, cannot comprehensively consider the influence of many other risk factors that cannot be modeled on the failure rate, and is relatively one-sided when applied to reliability calculation. Moreover, the established model is relatively rough, the defined range is fuzzy, and the accuracy is relatively low."

[0005] However, the fault monitoring and prediction during the daily operation of power distribution equipment often focus on the evaluation of the current operation state parameters of the power distribution equipment, and the historical fault operation state parameters of the power distribution equipment are not effectively applied. As a result, the current fault monitoring and prediction during the daily operation of power distribution equipment are only limited to the real-time operation state parameters of the power distribution equipment, and the fault monitoring and prediction results of the power distribution equipment are less predictable, and the response to power distribution equipment faults is too late to help. Summary of the Invention

[0006] In view of the above-mentioned disadvantages of the prior art, the present invention provides a fault prediction system and process based on the operation of power distribution equipment, which solves the technical problems raised in the above-mentioned background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: In a first aspect, a fault prediction system based on the operation of power distribution equipment includes: a prior layer, a collection layer, and a prediction layer; The operation state parameters of the historical faults of the power distribution equipment are uploaded through the prior layer. The prior layer synchronously identifies the characteristic parameters in the uploaded operation state parameters of the historical faults of the power distribution equipment, copies the identified characteristic parameters, and stores the operation state parameters of the historical faults of the power distribution equipment and the copied characteristic parameters separately in the prior layer. The collection layer real-time collects the operation state parameters of the power distribution equipment, identifies the characteristic parameters in the operation state parameters of the power distribution equipment, retrieves the operation state parameters of the historical faults of the power distribution equipment and the corresponding copied characteristic parameters stored in the prior layer, and feeds back the operation state parameters of the power distribution equipment collected by the collection layer and the identified characteristic parameters to the prediction layer together. The prediction layer predicts whether there is a fault risk of the power distribution equipment based on the four groups of parameters fed back by the collection layer; The collection layer includes a collection module, a forwarding module, and a retrieval module. The collection module is used to collect the real-time operation state parameters of the power distribution equipment. The forwarding module is used to receive the real-time operation state parameters of the power distribution equipment collected in the collection module, forward the received real-time operation state parameters of the power distribution equipment to the prior layer, identify the characteristic parameters in the real-time operation state parameters of the power distribution equipment based on the prior layer, and receive the identified characteristic parameters again. The retrieval module is used to retrieve the operation state parameters of the historical faults of the power distribution equipment and the corresponding copied characteristic parameters, and feed back the retrieved four groups of parameters to the prediction layer together with the real-time operation state parameters and characteristic parameters of the power distribution equipment received by the forwarding module; Among them, when the retrieval module retrieves the operation state parameters of the historical faults of the power distribution equipment and the corresponding copied characteristic parameters, the number of the operation state parameters of the historical faults of the power distribution equipment and the corresponding copied characteristic parameters retrieved each time is one group, and the source of the retrieved copied characteristic parameters is the retrieved operation state parameters of the historical faults of the power distribution equipment.

[0008] Furthermore, the prior layer includes an upload module, an identification module, and a storage module. The upload module is used to upload the operation state parameters of the historical faults of the power distribution equipment. The identification module is used to receive the operation state parameters uploaded by the upload module and identify the characteristic parameters in the operation state parameters. The storage module is used to obtain the characteristic parameter identification results in the identification module, copy the characteristic parameters identified by the identification module, and receive the operation state parameters uploaded by the upload module, and store the operation state parameters and the copied characteristic parameters separately; Among them, the operating state parameters of the historical faults of the power distribution equipment uploaded by the uploading module are: the operating state parameters of the power distribution equipment before the occurrence of the fault continuously collected based on a specified period. The operating state parameters of the historical faults of the power distribution equipment include: current, voltage, power factor, harmonic content, vibration frequency, flashover voltage, and phase angle.

[0009] Furthermore, the characteristic parameter recognition logic in the operating state parameters of the historical faults of the power distribution equipment in the recognition module is expressed as: ; In the formula: is the characteristic parameter determination value; is the current value in the i-th group of operating state parameters; is the last group of current values based on time sequence in the operating state parameters of the historical faults of the uploaded power distribution equipment; is the voltage value in the i-th group of operating state parameters; is the last group of voltage values based on time sequence in the operating state parameters of the historical faults of the uploaded power distribution equipment; is the power factor in the i-th group of operating state parameters; is the last group of power factors based on time sequence in the operating state parameters of the historical faults of the uploaded power distribution equipment; is the harmonic content in the i-th group of operating state parameters; is the last group of harmonic contents based on time sequence in the operating state parameters of the historical faults of the uploaded power distribution equipment; is the vibration frequency of the power distribution equipment in the i-th group of operating state parameters; is the last group of vibration frequencies of the power distribution equipment based on time sequence in the operating state parameters of the historical faults of the uploaded power distribution equipment; is the phase angle in the i-th group of operating state parameters; is the last group of phase angles based on time sequence in the operating state parameters of the historical faults of the uploaded power distribution equipment; is the last group of flashover voltages based on time sequence in the operating state parameters of the historical faults of the uploaded power distribution equipment; Among them, table constraint function, the circuit where the power distribution equipment is located is a non-single-phase circuit, and and are both positive or negative numbers, , and are one positive and one negative or the circuit where the power distribution equipment is located is a single-phase circuit, , table constraint function, The smaller the value of , the smaller the value of , the larger the value of , and The value is always not less than 1.

[0010] Furthermore, based on the feature parameter recognition logic, the characteristic parameter determination values of the operating state parameters of the historical faults of each group of power distribution equipment are obtained. The three groups of corresponding operating state parameters with the smallest characteristic parameter determination values, and the last group of operating state parameters based on the time series are denoted as the characteristic parameters in the operating state parameters of the historical faults of the power distribution equipment.

[0011] Furthermore, when the acquisition module acquires the real-time operating state parameters of the power distribution equipment, it uses a period consistent with the acquisition period of the operating state parameters before the occurrence of the power distribution equipment fault to acquire the real-time operating state parameters of the power distribution equipment. After the forwarding module forwards the real-time operating state parameters of the power distribution equipment to the prior layer, they are received by the recognition module in the prior layer. The recognition module processes the real-time operating state parameters of the power distribution equipment based on the recognition logic of the characteristic parameters in the operating state parameters of the historical faults of the power distribution equipment, obtains the characteristic parameters in the real-time operating state parameters of the power distribution equipment, and transmits the recognized characteristic parameters in the real-time operating state parameters of the power distribution equipment back to the forwarding module.

[0012] Furthermore, the prediction layer includes a prediction module, a recording module, and a decision module. The prediction module is used to receive the four groups of parameters retrieved by the retrieval module and predict the power distribution equipment fault risk based on the four groups of parameters. The recording module is used to receive the power distribution equipment fault risk prediction result in the prediction module and record the prediction result. The decision module is used to manually decide whether to send the real-time operating state parameters of the power distribution equipment currently collected by the acquisition module in the acquisition layer to the upload module in the prior layer; Among them, when the prediction result of the prediction module is that there is no fault risk for the power distribution equipment, the decision module is triggered to run. The operation of deciding whether to send the real-time operating state parameters of the power distribution equipment to the upload module in the prior layer in the decision module is executed by the system-side user. When the decision result of the decision module is to send the real-time operating state parameters of the power distribution equipment to the upload module, the real-time operating state parameters of the power distribution equipment collected by the acquisition module are sent to the upload module in real time, and the recognition module recognizes the characteristic parameters. The storage module further stores the recognized characteristic parameters and the real-time operating state parameters of the power distribution equipment.

[0013] Furthermore, a power distribution equipment fault risk prediction logic is set in the prediction module. The prediction module predicts whether there is a fault risk for the current power distribution equipment based on the power distribution equipment fault risk prediction logic. The power distribution equipment fault risk prediction logic is expressed as: ; In the formula: is the similarity between the real-time operating state parameter X of the power distribution equipment and the historical fault operating state parameter Y of the power distribution equipment; , , , For the voltage, power factor, harmonic content, and vibration frequency of the power distribution equipment in the real-time operating state parameters of the power distribution equipment; , , , For the voltage, power factor, harmonic content, and vibration frequency of the power distribution equipment in the historical fault operating state parameters of the power distribution equipment; For the similarity between the characteristic parameter x in the real-time operating state parameter X of the power distribution equipment and the characteristic parameter y in the historical fault operating state parameter Y of the power distribution equipment; , , , For the voltage, power factor, harmonic content, and vibration frequency of the power distribution equipment in the characteristic parameter x; , , , For the voltage, power factor, harmonic content, and vibration frequency of the power distribution equipment in the characteristic parameter y; For the power distribution equipment fault risk determination value; , For the weights; Among them, the power distribution equipment fault risk determination value is recorded in the recording module. The prediction module operates on all the characteristic parameters of the historical fault operating state parameters of the power distribution equipment and their corresponding copies, and the operating state parameters of the power distribution equipment and the corresponding characteristic parameters collected by the collection module, and applies the power distribution equipment fault risk prediction logic operation to obtain several groups of power distribution equipment fault risk determination values , and is recorded by the recording module. The prediction module selects the maximum power distribution equipment fault risk determination value in the recording module to predict whether there is a fault risk in the power distribution equipment. The power distribution equipment fault risk determination value The smaller it is, the greater the probability that the power distribution equipment has a fault. On the contrary, it means that the probability that the power distribution equipment has a fault is smaller.

[0014] Furthermore, a power distribution equipment fault risk determination threshold is set in the prediction module. The prediction module compares the power distribution equipment fault risk determination threshold with the selected power distribution equipment fault risk determination value to predict whether there is a fault risk in the power distribution equipment; Among them, , The sum is 1. The smaller the interval between the collection periods corresponding to the characteristic parameters in the historical fault operating state parameters of each power distribution equipment, the The larger the value of, on the contrary, the The smaller the value of, and > .

[0015] Further, the acquisition module is wirelessly interconnected with a forwarding module and a retrieval module, the acquisition module is wirelessly interconnected with a storage module, the storage module is wirelessly interconnected with an identification module and an upload module, the retrieval module is wirelessly interconnected with a prediction module, and the prediction module is wirelessly interconnected with a recording module and a decision-making module.

[0016] In a second aspect, a fault prediction process based on the operation of power distribution equipment includes the following steps: Step 1: Upload the operation state parameters of historical faults of power distribution equipment, and identify the characteristic parameters in the operation state parameters of historical faults of power distribution equipment; Step 11: The setting stage of the identification logic of the characteristic parameters in the operation state parameters of historical faults of power distribution equipment; Step 12: The storage stage of the operation state parameters of historical faults of power distribution equipment and the characteristic parameters therein; Step 2: Real-time collect the operation state parameters of power distribution equipment, and identify the characteristic parameters in the operation state parameters of power distribution equipment; Step 3: Retrieve the operation state parameters of historical faults of power distribution equipment and the identified characteristic parameters therein, and based on four groups of parameters, predict the current fault risk of power distribution equipment together with the operation state parameters of power distribution equipment and the identified characteristic parameters therein; Step 4: If there is a fault risk in the power distribution equipment, feedback the prediction result; Step 5: If there is no fault risk in the power distribution equipment, feedback the prediction result, and manually decide whether to store the currently collected operation state parameters of the power distribution equipment.

[0017] Adopting the technical solution provided by the present invention, compared with the known public technologies, it has the following beneficial effects: The present invention provides a fault prediction system based on the operation of power distribution equipment. During the operation of the system, the operation state parameters of historical faults of power distribution equipment are used as prior data to collect the real-time operation state of the operating power distribution equipment. Then, through the comparison of the prior data and the real-time operation state parameter data of the power distribution equipment, the fault prediction of the power distribution equipment is carried out. At the same time, through this prediction method and the continuous accumulation of prior data, the system can predict various faults of the power distribution equipment more comprehensively, and the fault prediction result of the power distribution equipment based on this system is more predictable and accurate, thus providing a more sufficient reaction time for the fault response of the power distribution equipment and effectively maintaining the daily operation safety and stability of the power distribution equipment; Moreover, the configuration of a fault prediction process based on the operation of power distribution equipment provides further operation logic for the operation of the above system, ensures more stable operation of the system, and brings better fault prediction service effects to the power distribution equipment. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic structural diagram of a fault prediction system based on the operation of power distribution equipment; Figure 2 It is a schematic flow diagram of a fault prediction process based on the operation of power distribution equipment; Figure 3 It is a schematic diagram of the system operation logic in the present invention. Specific embodiments

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] The following further describes the present invention with reference to the embodiments. Embodiment

[0022] A fault prediction system based on the operation of power distribution equipment in this embodiment, as Figure 1 shown, includes: a prior layer, a collection layer, and a prediction layer; The operation state parameters of the historical faults of the power distribution equipment are uploaded through the prior layer. The prior layer synchronously identifies the characteristic parameters in the uploaded operation state parameters of the historical faults of the power distribution equipment, copies the identified characteristic parameters, and stores the operation state parameters of the historical faults of the power distribution equipment and the copied characteristic parameters separately in the prior layer. The collection layer real-time collects the operation state parameters of the power distribution equipment, identifies the characteristic parameters in the operation state parameters of the power distribution equipment, retrieves the operation state parameters of the historical faults of the power distribution equipment stored in the prior layer and their corresponding copied characteristic parameters, and feeds them back to the prediction layer together with the operation state parameters of the power distribution equipment collected by the collection layer and the identified characteristic parameters. The prediction layer predicts whether there is a fault risk of the power distribution equipment based on the four groups of parameters fed back by the collection layer; The prior layer includes an upload module, an identification module, and a storage module. The upload module is used to upload the operating state parameters of the historical faults of the power distribution equipment. The identification module is used to receive the operating state parameters uploaded by the upload module and identify the characteristic parameters in the operating state parameters. The storage module is used to obtain the identification results of the characteristic parameters in the identification module, copy the identified characteristic parameters, and receive the operating state parameters uploaded by the upload module, and store the operating state parameters and the copied characteristic parameters separately; Among them, the operating state parameters of the historical faults of the power distribution equipment uploaded by the upload module are: the operating state parameters before the occurrence of the faults of the power distribution equipment continuously collected based on a specified period. The operating state parameters of the historical faults of the power distribution equipment include: current, voltage, power factor, harmonic content, vibration frequency, flashover voltage, and phase angle; The acquisition layer includes an acquisition module, a forwarding module, and a retrieval module. The acquisition module is used to acquire the real-time operating state parameters of the power distribution equipment. The forwarding module is used to receive the real-time operating state parameters of the power distribution equipment acquired by the acquisition module, forward the received real-time operating state parameters of the power distribution equipment to the prior layer, identify the characteristic parameters in the real-time operating state parameters of the power distribution equipment based on the prior layer, and receive the identified characteristic parameters again. The retrieval module is used to retrieve the historical fault operating state parameters of the power distribution equipment and their corresponding copied characteristic parameters, and feedback the four groups of parameters retrieved, together with the real-time operating state parameters and characteristic parameters received by the forwarding module, to the prediction layer; The prediction layer includes a prediction module, a recording module, and a decision-making module. The prediction module is used to receive the four groups of parameters retrieved by the retrieval module and predict the fault risk of the power distribution equipment based on the four groups of parameters. The recording module is used to receive the prediction results of the fault risk of the power distribution equipment in the prediction module and record the prediction results. The decision-making module is used to manually decide whether to send the real-time operating state parameters of the power distribution equipment currently acquired by the acquisition module in the acquisition layer to the upload module in the prior layer; Among them, when the prediction result of the prediction module is that there is no fault risk for the power distribution equipment, the decision-making module is triggered to operate. The operation of deciding whether to send the real-time operating state parameters of the power distribution equipment to the upload module in the prior layer in the decision-making module is performed by the user at the system end. When the decision result of the decision-making module is to send the real-time operating state parameters of the power distribution equipment to the upload module, the real-time operating state parameters of the power distribution equipment acquired by the acquisition module are sent to the upload module in real time, and the identification module identifies the characteristic parameters, and the storage module further stores the identified characteristic parameters and the real-time operating state parameters of the power distribution equipment; Among them, when the retrieval module retrieves the historical fault operating state parameters of the power distribution equipment and their corresponding copied characteristic parameters, the number of the historical fault operating state parameters of the power distribution equipment and their corresponding copied characteristic parameters retrieved each time is one group, and the source of the retrieved copied characteristic parameters is from the retrieved historical fault operating state parameters of the power distribution equipment; The acquisition module is wirelessly interconnected with a forwarding module and a retrieval module. The acquisition module is wirelessly interconnected with a storage module. The storage module is wirelessly interconnected with an identification module and an upload module. The retrieval module is wirelessly interconnected with a prediction module. The prediction module is wirelessly interconnected with a recording module and a decision-making module.

[0023] In this embodiment, the upload module operates to upload the operating state parameters of the historical faults of the power distribution equipment. The identification module receives in real time the operating state parameters uploaded by the upload module, identifies the characteristic parameters in the operating state parameters. The storage module synchronously obtains the identification results of the characteristic parameters in the identification module, copies the identified characteristic parameters, and receives the operating state parameters uploaded by the upload module, and stores the operating state parameters and the copied characteristic parameters separately. The acquisition module further acquires the real-time operating state parameters of the power distribution equipment. The forwarding module runs later to receive the real-time operating state parameters of the power distribution equipment acquired by the acquisition module, and forwards the received real-time operating state parameters of the power distribution equipment to the prior layer. Based on the prior layer, the characteristic parameters in the real-time operating state parameters of the power distribution equipment are identified, and the identified characteristic parameters are received again. Then, the retrieval module retrieves the historical fault operating state parameters of the power distribution equipment and their corresponding copied characteristic parameters, and compares them with the real-time operating state parameters and characteristic parameters received by the forwarding module. The four groups of parameters retrieved are fed back to the prediction layer. Finally, the prediction module receives the four groups of parameters retrieved by the retrieval module, predicts the fault risk of the power distribution equipment based on the four groups of parameters. The recording module runs to receive the prediction results of the fault risk of the power distribution equipment in the prediction module, records the prediction results, and manually makes a decision through the decision-making module on whether to send the current real-time operating state parameters of the power distribution equipment collected by the acquisition module in the acquisition layer to the upload module in the prior layer.

[0024] Through the operation of the system in the above embodiment, a better fault prediction effect is brought to the power distribution equipment, ensuring that the impending fault problems during the operation of the power distribution equipment can be monitored and predicted in time, so as to achieve the purpose of maintaining the stable daily operation of the power distribution equipment. Embodiment

[0025] At the specific implementation level, on the basis of Embodiment 1, this embodiment refers to Figure 1 to further specifically describe a fault prediction system based on the operation of power distribution equipment in Embodiment 1: The identification logic of the characteristic parameters in the operating state parameters of the historical faults of the power distribution equipment in the identification module is expressed as: ; In the formula: is the characteristic parameter determination value; is the current value in the i-th group of operating state parameters; is the last set of current values based on time sequence among the operation status parameters of the uploaded historical faults of the power distribution equipment; is the voltage value in the i-th set of operation status parameters; is the last set of voltage values based on time sequence among the operation status parameters of the uploaded historical faults of the power distribution equipment; is the power factor in the i-th set of operation status parameters; is the last set of power factors based on time sequence among the operation status parameters of the uploaded historical faults of the power distribution equipment; is the harmonic content in the i-th set of operation status parameters; is the last set of harmonic contents based on time sequence among the operation status parameters of the uploaded historical faults of the power distribution equipment; is the vibration frequency of the power distribution equipment in the i-th set of operation status parameters; is the last set of vibration frequencies of the power distribution equipment based on time sequence among the operation status parameters of the uploaded historical faults of the power distribution equipment; is the phase angle in the i-th set of operation status parameters; is the last set of phase angles based on time sequence among the operation status parameters of the uploaded historical faults of the power distribution equipment; is the last set of flashover voltages based on time sequence among the operation status parameters of the uploaded historical faults of the power distribution equipment; wherein, represents a constraint function. When the circuit where the power distribution equipment is located is not a single-phase circuit, and and are both positive or negative numbers, , and are one positive and one negative or when the circuit where the power distribution equipment is located is a single-phase circuit, , represents a constraint function, the smaller the value of , the smaller the value of , the larger the value of , and the value of is always not less than 1;

[0026] Through the above-set feature parameter recognition logic, feature parameters are screened out from the operation status parameters of the historical faults of the power distribution equipment, providing necessary operation data support for the operation of subsequent modules in the system, and making the fault prediction results of the power distribution equipment output by the system more accurate through the recognition and selection of feature parameters; It should be noted that the constraint function can be expressed as , where is a constant

[0027] For example Figure 1 As shown, when the acquisition module acquires the real-time operation state parameters of the power distribution equipment, it uses a cycle consistent with the acquisition cycle of the operation state parameters before the occurrence of the power distribution equipment failure to acquire the real-time operation state parameters of the power distribution equipment. After the forwarding module forwards the real-time operation state parameters of the power distribution equipment to the prior layer, they are received by the identification module in the prior layer. The identification module processes the real-time operation state parameters of the power distribution equipment based on the identification logic of the characteristic parameters in the operation state parameters of the historical faults of the power distribution equipment, obtains the characteristic parameters in the real-time operation state parameters of the power distribution equipment, and transmits the identified characteristic parameters in the real-time operation state parameters of the power distribution equipment back to the forwarding module

[0028] Through the above settings, the operation logic of the forwarding module is further defined

[0029] For example Figure 1 As shown, a power distribution equipment fault risk prediction logic is set in the prediction module. The prediction module predicts whether there is a fault risk in the current power distribution equipment based on the power distribution equipment fault risk prediction logic. The power distribution equipment fault risk prediction logic is expressed as ; In the formula is the similarity between the real-time operation state parameter X of the power distribution equipment and the historical fault operation state parameter Y of the power distribution equipment , , , are the voltage, power factor, harmonic content, and vibration frequency of the power distribution equipment in the real-time operation state parameters of the power distribution equipment , , , are the voltage, power factor, harmonic content, and vibration frequency of the power distribution equipment in the historical fault operation state parameters of the power distribution equipment is the similarity between the characteristic parameter x in the real-time operation state parameter X of the power distribution equipment and the characteristic parameter y in the historical fault operation state parameter Y of the power distribution equipment , , , are the voltage, power factor, harmonic content, and vibration frequency of the power distribution equipment in the characteristic parameter x , , , are the voltage, power factor, harmonic content, and vibration frequency of the power distribution equipment in the characteristic parameter y is the fault risk determination value of the power distribution equipment; and is the weight; Among them, the fault risk determination value of the power distribution equipment is recorded in the recording module. The prediction module operates on the historical fault operation state parameters of all power distribution equipment and their corresponding copy characteristic parameters, and the operation state parameters and corresponding characteristic parameters of the power distribution equipment collected by the collection module, and applies the fault risk prediction logic operation of the power distribution equipment to obtain several groups of fault risk determination values of the power distribution equipment and is recorded by the recording module. The prediction module selects the maximum fault risk determination value of the power distribution equipment in the recording module to predict whether there is a fault risk in the power distribution equipment. The smaller the fault risk determination value of the power distribution equipment , the greater the probability that the power distribution equipment has a fault. On the contrary, it means that the probability that the power distribution equipment has a fault is smaller; There is a set fault risk determination threshold in the prediction module. The prediction module compares based on the fault risk determination threshold of the power distribution equipment and the selected fault risk determination value of the power distribution equipment to predict whether there is a fault risk in the power distribution equipment; Among them, and The sum is 1. The smaller the interval between the collection cycles corresponding to the characteristic parameters in the historical fault operation state parameters of each power distribution equipment, the The larger the value, on the contrary, the The smaller the value, and > .

[0030] Through the setting of the fault risk prediction logic of the power distribution equipment, the fault prediction result of the power distribution equipment is further output in a digital form to ensure that the system can stably make a reliable prediction of the operation fault risk of the power distribution equipment. Embodiment

[0031] At the specific implementation level, on the basis of Embodiment 1, this embodiment further specifically describes a fault prediction system based on the operation of power distribution equipment in Embodiment 1 with reference to Figure 2 : A fault prediction process based on the operation of power distribution equipment includes the following steps: Step 1: Upload the operation state parameters of the historical faults of the power distribution equipment and identify the characteristic parameters in the historical fault operation state parameters of the power distribution equipment; Step 11: The setting stage of the identification logic of the characteristic parameters in the historical fault operation state parameters of the power distribution equipment; Step 12: The storage stage of the historical fault operation state parameters of the power distribution equipment and the characteristic parameters therein; Step 2: Collect the operation status parameters of the power distribution equipment in real time and identify the characteristic parameters among the operation status parameters of the power distribution equipment; Step 3: Retrieve the historical fault operation status parameters of the power distribution equipment and the identified characteristic parameters therein, compare them with the operation status parameters of the power distribution equipment and the identified characteristic parameters therein, and predict the current fault risk of the power distribution equipment based on the four groups of parameters; Step 4: If there is a fault risk in the power distribution equipment, feedback the prediction result; Step 5: If there is no fault risk in the power distribution equipment, feedback the prediction result and manually decide whether to store the currently collected operation status parameters of the power distribution equipment.

[0032] In summary, in the above embodiments, the system uses the historical fault operation status parameters of the power distribution equipment as prior data during operation, collects the real-time operation status of the operating power distribution equipment, and then predicts the faults of the power distribution equipment by comparing the prior data with the real-time operation status parameter data of the power distribution equipment. At the same time, through the continuous accumulation of this prediction method and prior data, the system can more comprehensively predict various faults of the power distribution equipment, and the fault prediction results of the power distribution equipment based on this system are more predictable and accurate, thus providing a more sufficient response time for the fault response of the power distribution equipment and effectively maintaining the daily operation safety and stability of the power distribution equipment.

[0033] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fault prediction system based on the operation of power distribution equipment, characterized in that: include: Prior layer, collection layer and prediction layer; The operating status parameters of the historical faults of the distribution equipment are uploaded through the a priori layer, and the a priori layer synchronously identifies the characteristic parameters in the uploaded operating status parameters of the historical faults of the distribution equipment, and copies the identified characteristic parameters, so that the operating status parameters of the historical faults of the distribution equipment and the copied characteristic parameters are distinguished and stored in the a priori layer. The collection layer collects the operating status parameters of the distribution equipment in real time, identifies the characteristic parameters in the operating status parameters of the distribution equipment, retrieves the operating status parameters of the historical faults of the distribution equipment stored in the a priori layer and the corresponding copied characteristic parameters, and feeds them back to the prediction layer together with the operating status parameters of the distribution equipment collected by the collection layer and the identified characteristic parameters. The prediction layer predicts whether there is a failure risk of the distribution equipment based on the four groups of parameters fed back by the collection layer; The collection layer includes a collection module, a forwarding module and a retrieval module. The collection module is used to collect the real-time operating status parameters of the power distribution equipment. The forwarding module is used to receive the real-time operating status parameters of the power distribution equipment collected in the collection module, forward the received real-time operating status parameters of the power distribution equipment to the priori layer, identify the characteristic parameters in the real-time operating status parameters of the power distribution equipment based on the priori layer, and receive the identified characteristic parameters again. The retrieval module is used to retrieve the historical fault operating status parameters of the power distribution equipment and the characteristic parameters of the corresponding copies thereof, and the real-time operating status parameters and characteristic parameters of the power distribution equipment received by the forwarding module, and feed back the retrieved four sets of parameters to the prediction layer; Among them, when the calling module calls the historical fault operating status parameters of the distribution equipment and the characteristic parameters of their corresponding copies, the number of historical fault operating status parameters of the distribution equipment and the characteristic parameters of their corresponding copies called each time is a group, and the source of the called copy characteristic parameters is the called historical fault operating status parameters of the distribution equipment.

2. A fault prediction system based on power distribution equipment operation according to claim 1, characterized in that: The priori layer includes an upload module, an identification module and a storage module. The upload module is used to upload the operating status parameters of the historical faults of the power distribution equipment. The identification module is used to receive the operating status parameters uploaded in the upload module and identify the characteristic parameters in the operating status parameters. The storage module is used to obtain the characteristic parameter identification result in the identification module, copy the characteristic parameters identified by the identification module, and receive the operating status parameters uploaded in the upload module, and distinguish and store the operating status parameters and the copied characteristic parameters. Among them, the operating status parameters of the historical faults of the distribution equipment uploaded in the upload module are: the operating status parameters of the distribution equipment before the fault occurs based on continuous collection in a specified period, and the operating status parameters of the historical faults of the distribution equipment include: current, voltage, power factor, harmonic content, vibration frequency, flashover voltage and phase angle.

3. A fault prediction system based on power distribution equipment operation according to claim 2, characterized in that: The characteristic parameter identification logic in the operating state parameters of the historical faults of the power distribution equipment in the identification module is expressed as follows: ； Where: is the characteristic parameter determination value; is the current value in the i-th group of operating state parameters; The last set of current values ​​based on time sequence in the uploaded operating status parameters of the historical faults of the power distribution equipment; is the voltage value in the i-th group of operating state parameters; The last set of voltage values ​​based on time sequence in the uploaded operating status parameters of the historical faults of the power distribution equipment; is the power factor in the i-th group of operating status parameters; The last set of power factors based on time sequence in the uploaded operating status parameters of the historical faults of the power distribution equipment; is the harmonic content in the i-th group of operating status parameters; The last group of harmonic contents based on time sequence in the uploaded operating status parameters of historical faults of power distribution equipment; is the vibration frequency of the power distribution equipment in the i-th group of operating status parameters; The last group of power distribution equipment vibration frequencies based on time sequence in the uploaded operating status parameters of historical faults of power distribution equipment; is the phase angle of the i-th group of operating state parameters; The last set of phase angles based on time sequence in the uploaded operating status parameters of historical faults of power distribution equipment; The last set of flashover voltages based on time sequence in the uploaded operating status parameters of historical faults of power distribution equipment; in, Table constraint function, the circuit where the power distribution equipment is located is not a single-phase circuit, and and When both are positive or negative, , and When one is positive and the other is negative or the circuit where the power distribution equipment is located is a single-phase circuit, , Table constraint functions, The smaller the value of The smaller the value of The larger the value of The larger the value of The value of is always not less than 1.

4. A fault prediction system based on the operation of power distribution equipment according to claim 3, characterized in that: Based on the characteristic parameter identification logic, the characteristic parameter judgment values ​​of the operating status parameters of each group of historical faults of the distribution equipment are obtained. The three groups of corresponding operating status parameters with the smallest characteristic parameter judgment values ​​and the last group of operating status parameters based on the timing are recorded as the characteristic parameters in the operating status parameters of the historical faults of the distribution equipment.

5. A fault prediction system based on power distribution equipment operation according to claim 1, characterized in that: When collecting the real-time operating status parameters of the power distribution equipment, the acquisition module applies a cycle that is consistent with the operating status parameter acquisition cycle before the power distribution equipment fault occurs, and collects the real-time operating status parameters of the power distribution equipment. After the forwarding module forwards the real-time operating status parameters of the power distribution equipment to the priori layer, the recognition module in the priori layer receives the parameters. The recognition module processes the real-time operating status parameters of the power distribution equipment based on the recognition logic of the characteristic parameters in the operating status parameters of the historical faults of the power distribution equipment, obtains the characteristic parameters in the real-time operating status parameters of the power distribution equipment, and transmits the identified characteristic parameters in the real-time operating status parameters of the power distribution equipment back to the forwarding module.

6. A fault prediction system based on power distribution equipment operation according to claim 1, characterized in that: The prediction layer includes a prediction module, a recording module and a decision module. The prediction module is used to receive the four sets of parameters retrieved by the retrieval module and predict the failure risk of the power distribution equipment based on the four sets of parameters. The recording module is used to receive the prediction results of the failure risk of the power distribution equipment in the prediction module and record the prediction results. The decision module is used to manually decide whether to send the real-time operating status parameters of the power distribution equipment currently collected by the collection module in the collection layer to the upload module in the priori layer. Among them, when the prediction result of the prediction module is that there is no failure risk of the distribution equipment, the decision module is triggered to run. The decision module decides whether to send the real-time operating status parameters of the distribution equipment to the upload module in the priori layer, which is executed by the system end user. When the decision result of the decision module is to send the real-time operating status parameters of the distribution equipment to the upload module, the operating status parameters of the distribution equipment collected by the collection module are sent to the upload module in real time, and the characteristic parameters are identified by the identification module. The storage module further stores the identified characteristic parameters and the real-time operating status parameters of the distribution equipment.

7. A fault prediction system based on the operation of power distribution equipment according to claim 6, characterized in that: The prediction module is provided with a power distribution equipment failure risk prediction logic. The prediction module predicts whether the current power distribution equipment has a failure risk based on the power distribution equipment failure risk prediction logic. The power distribution equipment failure risk prediction logic is expressed as: ； Where: is the similarity between the real-time operating state parameter X of the power distribution equipment and the historical fault operating state parameter Y of the power distribution equipment; , , , The voltage, power factor, harmonic content and vibration frequency of the power distribution equipment in the real-time operating status parameters of the power distribution equipment; , , , The voltage, power factor, harmonic content and vibration frequency of the power distribution equipment in the historical fault operation status parameters of the power distribution equipment; is the similarity between the characteristic parameter x in the real-time operating state parameter X of the power distribution equipment and the characteristic parameter y in the historical fault operating state parameter Y of the power distribution equipment; , , , The voltage, power factor, harmonic content and vibration frequency of the power distribution equipment in the characteristic parameter x; , , , The voltage, power factor, harmonic content and vibration frequency of the power distribution equipment in the characteristic parameter y; is the risk determination value of power distribution equipment failure; , is the weight; Among them, the distribution equipment failure risk judgment value The prediction module runs the historical fault operation status parameters of all distribution equipment and their corresponding copied characteristic parameters, and the operation status parameters and corresponding characteristic parameters of the distribution equipment collected by the collection module, and applies the distribution equipment fault risk prediction logic operation to obtain several groups of distribution equipment fault risk judgment values. , and is recorded by the recording module. The prediction module selects the maximum distribution equipment failure risk judgment value in the recording module. , predict whether there is a failure risk in the power distribution equipment, and the failure risk judgment value of the power distribution equipment The smaller it is, the greater the probability that the power distribution equipment is faulty. Conversely, the smaller the probability that the power distribution equipment is faulty.

8. A fault prediction system based on the operation of power distribution equipment according to claim 7, characterized in that: The prediction module is set with a distribution equipment failure risk determination threshold value, and the prediction module is based on the distribution equipment failure risk determination threshold value and the selected distribution equipment failure risk determination value. Compare and predict whether there is a risk of failure in the power distribution equipment; in, , The sum is 1. The smaller the corresponding collection period interval of the characteristic parameters in the historical fault operation status parameters of each distribution equipment is, the The larger the value, the smaller the The smaller the value, and > .

9. A fault prediction system based on the operation of power distribution equipment according to claim 1, characterized in that: The acquisition module is interactively connected to a forwarding module and a retrieval module via a wireless network, the acquisition module is interactively connected to a storage module via a wireless network, the storage module is interactively connected to an identification module and an upload module via a wireless network, the retrieval module is interactively connected to a prediction module via a wireless network, and the prediction module is interactively connected to a recording module and a decision-making module via a wireless network.

10. A fault prediction process based on the operation of power distribution equipment, the process is an implementation process of a fault prediction system based on the operation of power distribution equipment as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Upload the operating status parameters of the historical faults of the power distribution equipment and identify the characteristic parameters in the operating status parameters of the historical faults of the power distribution equipment; Step 11: setting stage of identification logic of characteristic parameters in historical fault operation status parameters of power distribution equipment; Step 12: Storage of historical fault operation status parameters of power distribution equipment and characteristic parameters thereof; Step 2: Collect the operating status parameters of the power distribution equipment in real time and identify the characteristic parameters in the operating status parameters of the power distribution equipment; Step 3: retrieve the historical fault operation status parameters of the power distribution equipment and the characteristic parameters identified therein, and the operation status parameters of the power distribution equipment and the characteristic parameters identified therein, and predict the current fault risk of the power distribution equipment based on the four groups of parameters; Step 4: If there is a risk of failure in the power distribution equipment, the prediction results are fed back; Step 5: If there is no risk of failure of the power distribution equipment, the prediction results are fed back and a manual decision is made as to whether the currently collected operating status parameters of the power distribution equipment should be stored.

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