An intelligent American transformer and a combined substation

Abstract

The present invention belongs to the technical field of transformers, and specifically relates to an intelligent American transformer and a combined substation. The transformer includes a transformer body, a data acquisition device, and an intelligent component. The intelligent component includes a plurality of monitoring units. The monitoring units are used to obtain predicted operation data based on historical operation data, historical environmental data, and relevant data, calculate a monitoring index based on the predicted operation data and the relevant data, and calculate an estimated monitoring memory size based on the monitoring index; the intelligent American transformer selects one of the microprocessors in the plurality of monitoring units as the main processor; the main processor is configured to allocate the memory of each monitoring unit according to the estimated monitoring memory size corresponding to each monitoring unit, so as to optimize the memory utilization rate of each monitoring unit. The present invention can allocate the memory of each monitoring unit, optimize the memory utilization rate of each monitoring unit, and ensure the safe and reliable operation of the American transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of transformers, and more particularly, to an intelligent American transformer and a combined substation. Background Art

[0002] The American transformer is applicable to commercial centers, industrial parks, residential communities, streets, airports, docks, subways, etc. in densely populated urban areas. It is also deeply involved in the load center for ring network and two-way power supply. Once installed, it can realize power transmission. It has the characteristics of full insulation, fully sealed structure, maintenance-free, and convenient installation. The American transformer often has multiple monitoring units for monitoring different types of data signals. Each monitoring unit is equipped with an independent microprocessor to analyze the collected data signals, thereby judging whether the transformer fails and feeding back to the monitoring terminal or dispatching center.

[0003] In the prior art, for transformers and / or box-type substations, the memory of multiple monitoring units is often used separately without additional allocation. This easily leads to the problem that due to insufficient memory of the monitoring unit, the relevant data of the transformer body cannot be effectively analyzed, resulting in monitoring errors or omissions. Or it may lead to too much remaining monitoring memory, and thus the monitoring unit memory cannot be effectively utilized, resulting in low memory utilization. Some transformers and / or box-type substations may simply allocate the monitoring memory unit, but do not consider the specific memory size that different monitoring units may use, nor combine the corresponding operation failure index of the transformer body. The intelligent degree of the memory allocation of its monitoring unit is low, and the memory utilization efficiency is also low. Summary of the Invention

[0004] In order to solve the problems existing in the prior art, the present invention provides an intelligent American transformer, which can obtain predicted operation data according to the historical operation data and historical environment data of the target object collected by the monitoring unit, calculate the monitoring index according to the predicted operation data and relevant data, and then calculate the corresponding estimated monitoring memory size. Finally, according to the estimated monitoring memory size corresponding to each monitoring unit, the memory of each monitoring unit is allocated to optimize the memory utilization rate of each monitoring unit. The specific technical solution of this intelligent American transformer is as follows:

[0005] An intelligent American transformer includes a transformer body, a plurality of data acquisition devices installed on the transformer body, and an intelligent component connected to the data acquisition devices. The intelligent component includes a plurality of monitoring units, and the plurality of data acquisition units correspond to the plurality of monitoring units one by one. The data acquisition device is used to collect relevant data of the transformer body and feedback it to the corresponding monitoring unit. The monitoring unit is used to obtain corresponding historical operation data and historical environment data, obtain predicted operation data according to the historical operation data, historical environment data and relevant data, calculate a monitoring index according to the predicted operation data and relevant data, and calculate an estimated monitoring memory size according to the monitoring index.

[0006] The intelligent American transformer selects one of the microprocessors from the microprocessors of the plurality of monitoring units as the main processor;

[0007] The main processor is configured to allocate the memory of each monitoring unit according to the estimated monitoring memory size corresponding to each monitoring unit, so as to optimize the memory utilization rate of each monitoring unit.

[0008] Preferably, the monitoring unit first calculates an operation fault index according to real-time operation data, predicted operation data and real-time environment data, then calculates a monitoring index according to the operation fault index, and finally calculates an estimated monitoring memory size according to the monitoring index and a preset basic monitoring memory.

[0009] Preferably, the intelligent component is further used to compare and analyze the real-time operation data with the corresponding preset data, and remotely transmit the comparison and analysis result to the dispatching center through a communication module to implement fault alarm, and point out the fault cause and fault point, so that the dispatching center can monitor and repair the operation state of the transformer body and the switch of the load terminal to ensure the stable operation of the power grid.

[0010] Preferably, the plurality of monitoring units include an oil chromatography and micro-water monitoring unit, a partial discharge monitoring unit, a core grounding current monitoring unit, an acoustic fingerprint monitoring unit, a transformer bushing insulation monitoring unit and a on-load tap-changer monitoring unit.

[0011] Preferably, the oil chromatography and micro-water monitoring unit, the partial discharge monitoring unit, the core grounding current monitoring unit, the acoustic fingerprint monitoring unit, the transformer bushing insulation monitoring unit and the on-load tap-changer monitoring unit all include a microprocessor and a memory.

[0012] Preferably, the intelligent component further includes a molded case circuit breaker, an intelligent capacitor, an intelligent molded case circuit breaker and an intelligent electric energy meter located on the low-voltage side.

[0013] Preferably, the molded case circuit breaker, the intelligent capacitor, the intelligent molded case circuit breaker and the intelligent electric energy meter are all connected to the RS485 bus.

[0014] Preferably, the dispatching center includes a communication terminal and a background monitor. The intelligent component is connected to the communication terminal and the background monitor through a GPRS / GSM wireless communication link, and the background monitor is also connected to the RS485 bus.

[0015] The present invention also provides a combined substation, which includes the intelligent American transformer described above.

[0016] Preferably, the combined substation further includes a capacitor chamber, a high-voltage chamber, and a low-voltage chamber.

[0017] The present invention provides an intelligent American transformer and a combined substation, which have the following beneficial effects:

[0018] 1. It can obtain predicted operation data based on the historical operation data and historical environment data collected by the monitoring unit, calculate the monitoring index based on the predicted operation data and relevant data, and then calculate the corresponding estimated monitoring memory size. Finally, according to the estimated monitoring memory size corresponding to each monitoring unit, the memory of each monitoring unit is allocated to optimize the memory utilization rate of each monitoring unit and ensure the safe and reliable operation of the American transformer.

[0019] 2. By selecting one of the microprocessors from the microprocessors of multiple monitoring units as the main processor, the main processor is configured to allocate the memory of each monitoring unit according to the estimated monitoring memory size corresponding to each monitoring unit to optimize the memory utilization rate of each monitoring unit, providing a new idea for optimizing the allocation of each monitoring unit of the American transformer to better realize the intelligent monitoring of the American transformer.

[0020] 3. By optimizing the memory utilization rate of each monitoring unit, it can better monitor the operation of the combined substation, avoid the omission of fault monitoring caused by insufficient memory of the transformer monitoring unit, and improve the working reliability of the American transformer and the combined substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention can be further understood from the following description in conjunction with the drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is placed on showing the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0022] Figure 1 is a schematic diagram of the overall structure of the intelligent American transformer of the present invention;

[0023] Figure 2 is a schematic diagram of the structural relationship between the intelligent component and the dispatching center of the present invention;

[0024] Figure 3 is a top view of the combined substation of the present invention;

[0025] Figure 4 This is the front view of the combined substation of the present invention;

[0026] Figure 5 This is the right view of the combined substation of the present invention.

[0027] In the figure: 1. Transformer; 2. Capacitor chamber; 3. High-voltage chamber; 4. Low-voltage chamber. Specific embodiments

[0028] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with its embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the protection scope of the present invention.

[0029] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration and do not represent the only implementation.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0031] The "first" and "second" in the present invention do not represent specific quantities and orders, but are only used for name distinction.

[0032] Embodiment 1:

[0033] Please refer to Figure 1 , an intelligent American transformer, comprising a transformer body, a plurality of data acquisition devices installed on the transformer body, and an intelligent component connected to the data acquisition devices. The intelligent component includes a plurality of monitoring units, and the plurality of data acquisition units correspond to the plurality of monitoring units one by one. The data acquisition device is configured to collect relevant data of the transformer body and feed it back to the corresponding monitoring unit. Different data acquisition devices are installed at different positions on the transformer body, and are configured to collect different types of relevant data of the transformer body, such as data information such as noise, vibration, temperature and humidity, current and voltage, partial discharge, and bushing insulation performance.

[0034] The monitoring unit is used to obtain corresponding historical operation data and historical environment data, obtain predicted operation data based on the historical operation data, historical environment data and relevant data, calculate a monitoring index based on the predicted operation data and relevant data, and calculate an estimated monitoring memory size based on the monitoring index.

[0035] Specifically, another method for obtaining predicted operation data based on historical operation data, historical environment data and relevant data is as follows: construct a neural network model for obtaining predicted operation data, train the neural network model with historical operation data and historical environment data, and input the obtained real-time operation data and real-time environment data of the transformer body into the trained neural network model to obtain predicted operation data. It should be noted that when multiple data acquisition devices respectively acquire real-time operation data and real-time environment data of different types of transformer bodies, the monitoring unit obtains corresponding types of predicted operation data based on historical operation data, historical environment data and relevant data.

[0036] In this embodiment, by obtaining corresponding types of predicted operation data based on historical operation data, historical environment data and relevant data and taking into account the influence of environmental data on the operation state of the transformer body, more accurate predicted operation data can be obtained.

[0037] The intelligent American transformer selects one of the microprocessors of multiple monitoring units as the main processor. Specifically, one of the multiple monitoring units can be randomly selected as the main processor. Preferably, first calculate the difference between the actual memory and the estimated memory, then obtain the priorities of the microprocessors of multiple monitoring units according to the magnitude of the difference. The larger the difference between the actual memory and the estimated memory, the higher the priority of the corresponding microprocessor. Finally, the microprocessor with the highest priority is used as the main processor.

[0038] The larger the difference between the actual memory and the estimated memory, the larger the free memory of the microprocessor. In this way, by using the microprocessor with the highest priority as the main processor, the memory utilization efficiency of the microprocessors of multiple monitoring units can be further optimized.

[0039] The main processor is configured to allocate the memory of each monitoring unit according to the estimated monitoring memory size corresponding to each monitoring unit to optimize the memory utilization rate of each monitoring unit.

[0040] The intelligent American transformer described in the present invention obtains predicted operation data based on the historical operation data and historical environment data of the target object collected by the monitoring unit, calculates a monitoring index based on the predicted operation data and relevant data, and then calculates the corresponding estimated monitoring memory size. Finally, according to the estimated monitoring memory sizes corresponding to each monitoring unit, the memory of each monitoring unit is allocated, which can optimize the memory utilization rate of each monitoring unit and ensure the safe and reliable operation of the American transformer.

[0041] In addition, by selecting one of the microprocessors from the microprocessors of multiple monitoring units as the main processor, the main processor is configured to allocate the memory of each monitoring unit according to the estimated monitoring memory sizes corresponding to each monitoring unit, so as to optimize the memory utilization rate of each monitoring unit, providing a new idea for optimizing the allocation of each monitoring unit of the American transformer to better realize the intelligent monitoring of the American transformer.

[0042] Embodiment 2:

[0043] Please refer to Figure 1 , an intelligent American transformer, including a transformer body, a plurality of data acquisition devices installed on the transformer body, and an intelligent component connected to the data acquisition devices. The intelligent component includes a plurality of monitoring units, and the plurality of data acquisition units correspond to the plurality of monitoring units one by one. The data acquisition devices are used to collect relevant data of the transformer body and feedback it to the corresponding monitoring unit. Different data acquisition devices are installed at different positions on the transformer body, and are configured to collect different types of relevant data of the transformer body, such as data information such as noise, vibration, temperature and humidity, current and voltage, partial discharge, and bushing insulation performance. Specifically, the plurality of data acquisition devices can be different types of sensors and / or monitoring devices.

[0044] The monitoring unit is used to obtain the corresponding historical operation data and historical environment data, obtain predicted operation data based on the historical operation data, historical environment data and relevant data, calculate a monitoring index based on the predicted operation data and relevant data, and calculate the estimated monitoring memory size according to the monitoring index.

[0045] Specifically, another method for obtaining predicted operation data based on historical operation data, historical environment data and relevant data is: constructing a neural network model for obtaining predicted operation data, training the neural network model with historical operation data and historical environment data, and inputting the obtained real-time operation data and real-time environment data of the transformer body into the trained neural network model to obtain predicted operation data. It should be noted that when multiple data acquisition devices respectively collect real-time operation data of different types of transformer bodies, the monitoring unit obtains corresponding types of predicted operation data based on historical operation data, historical environment data and relevant data.

[0046] In this embodiment, corresponding types of predicted operation data are obtained through historical operation data, historical environment data, and relevant data. By taking into account the impact of environmental data on the operation state of the transformer body, more accurate predicted operation data can be obtained.

[0047] The intelligent American transformer selects one microprocessor from the microprocessors of multiple monitoring units as the main processor. Specifically, one of the multiple monitoring units can be randomly selected as the main processor. Preferably, first calculate the difference between the actual memory and the estimated memory, then obtain the priorities of the microprocessors of the multiple monitoring units according to the magnitude of the difference. The larger the difference between the actual memory and the estimated memory, the higher the priority of the corresponding microprocessor. Finally, the microprocessor with the highest priority is used as the main processor. The larger the difference between the actual memory and the estimated memory means the larger the free memory of the microprocessor. In this way, by using the microprocessor with the highest priority as the main processor, the memory utilization efficiency of the microprocessors of multiple monitoring units can be further optimized.

[0048] The main processor is configured to allocate the memory of each monitoring unit according to the size of the estimated monitoring memory corresponding to each monitoring unit, so as to optimize the memory utilization rate of each monitoring unit.

[0049] The intelligent American transformer according to the present invention obtains predicted operation data based on the historical operation data and historical environment data of the target object collected by the monitoring unit, calculates the monitoring index according to the predicted operation data and relevant data, and then calculates the corresponding size of the estimated monitoring memory. Finally, according to the size of the estimated monitoring memory corresponding to each monitoring unit, the memory of each monitoring unit is allocated, which can optimize the memory utilization rate of each monitoring unit, improve the intelligent level of the transformer operation state monitoring, and ensure the safe and reliable operation of the American transformer.

[0050] Here, the real-time target object refers to the type of data collected, such as temperature and humidity, vibration value, voiceprint value, partial discharge parameter, etc.

[0051] In addition, by selecting one microprocessor from the microprocessors of multiple monitoring units as the main processor, and configuring the main processor to allocate the memory of each monitoring unit according to the size of the estimated monitoring memory corresponding to each monitoring unit to optimize the memory utilization rate of each monitoring unit, it provides a new idea for allocating and optimizing each monitoring unit of the American transformer to better realize the intelligent monitoring of the American transformer.

[0052] Preferably, the monitoring unit first calculates the operation fault index based on the real-time operation data, predicted operation data, and real-time environment data, then calculates the monitoring index based on the operation fault index, and finally calculates the estimated monitoring memory size based on the monitoring index and the preset basic monitoring memory.

[0053] Generally speaking, the operation fault index is proportional to the monitoring index, and the estimated monitoring memory size is proportional to the monitoring index.

[0054] In the prior art, for transformers and / or box-type substations, the memories of multiple monitoring units are often used separately without additional allocation, which easily leads to the inability to effectively analyze the data related to the transformer body due to insufficient memory of the monitoring unit, and then problems such as monitoring errors and omissions may occur. Or it may lead to excessive remaining monitoring memory, and then the memory of the monitoring unit cannot be effectively utilized, resulting in low memory utilization. Some transformers and / or box-type substations may simply allocate the monitoring memory unit, but do not consider the specific memory sizes that different monitoring units may use, nor combine the corresponding operation fault index of the transformer body, and the intelligence level of the monitoring unit memory allocation is relatively low.

[0055] Compared with the prior art, the present application calculates the monitoring index through the operation fault index, and finally calculates the estimated monitoring memory size based on the monitoring index and the preset basic monitoring memory. It combines the corresponding operation fault index of the transformer body, can calculate the specific memory size that the monitoring unit may use more specifically, improves the intelligence level of the monitoring unit memory allocation, and can better avoid problems such as monitoring errors and omissions and low memory utilization.

[0056] The intelligent component is also used to compare and analyze the real-time operation data with the corresponding preset data, and remotely transmit the comparison and analysis result to the dispatching center through the communication module to implement fault alarm, and point out the fault cause and fault point, so that the dispatching center can monitor and repair the operation state of the transformer body and the switch of the load terminal to ensure the stable operation of the power grid.

[0057] Specifically, the intelligent component includes a transformer monitoring main control unit, a comprehensive measurement unit, an oil chromatogram and micro-water monitoring unit, a partial discharge monitoring unit, an iron core grounding current monitoring unit, an acoustic fingerprint monitoring unit, a transformer bushing insulation monitoring unit, and a on-load tap-changer monitoring unit.

[0058] The monitoring main control unit, comprehensive measurement unit, oil chromatogram and micro water monitoring unit, partial discharge monitoring unit, iron core grounding current monitoring unit, acoustic fingerprint monitoring unit, transformer bushing insulation monitoring unit, and on-load tap-changer monitoring unit of the transformer all adopt monitoring units with the same structure. The monitoring unit includes a microprocessor, synchronous dynamic random access memory, memory, serial port driver circuit, and Ethernet control. The microprocessor communicates bidirectionally with the synchronous dynamic random access memory, the microprocessor communicates bidirectionally with the memory, the microprocessor communicates bidirectionally with the serial port driver circuit, the microprocessor communicates bidirectionally with the Ethernet controller, and the Ethernet controller is used to communicate bidirectionally with the communication module.

[0059] Regarding the specific installation positions of the monitoring units such as the monitoring main control unit, comprehensive measurement unit, oil chromatogram and micro water monitoring unit, partial discharge monitoring unit, iron core grounding current monitoring unit, acoustic fingerprint monitoring unit, transformer bushing insulation monitoring unit, and on-load tap-changer monitoring unit of the transformer, as well as the data acquisition device on the transformer body, since they are conventional technical means in this field, they will not be elaborated here.

[0060] Since the American-style transformer integrates multiple data acquisition devices and multiple monitoring units, it does not need to be combined with other detection devices and can independently detect the operation data of the transformer through the data acquisition device and the monitoring unit.

[0061] By setting multiple different types of data acquisition devices to obtain multiple different types of measurement parameters, the operation status of the transformer is monitored in real time, unnecessary power outage tests and maintenance are reduced, the maintenance workload is reduced, the maintenance cost is reduced, the pertinence of maintenance is improved, the automation of on-line monitoring information processing is realized, the reliability of the power system is significantly improved, and the work intensity of personnel is greatly reduced.

[0062] In addition, referring to Figure 2 , the intelligent component further includes a universal circuit breaker, intelligent capacitor, intelligent molded case circuit breaker, and intelligent watt-hour meter located on the low-voltage side. The intelligent component, universal circuit breaker, intelligent capacitor, intelligent molded case circuit breaker, and intelligent watt-hour meter are all connected to the RS485 bus. The dispatching center includes a communication terminal and a background monitor. The intelligent component is connected to the communication terminal and the background monitor through a GPRS / GSM wireless communication link, and the background monitor is also connected to the RS485 bus.

[0063] Communication networking is achieved through RS485 bus technology. Staff can monitor the operating status of this product at different locations in real time through the background monitor. In addition, since the intelligent component is connected to the communication terminal and the background monitor through the GPRS / GSM wireless communication link, the real-time alarm function of the American-style box substation can be realized through GPRS / GSM technology, ensuring the safe operation of this product in the unattended state, realizing the automation of on-line monitoring information processing, significantly improving the reliability of the power system, and greatly reducing the work intensity of personnel.

[0064] Please refer to Figures 3 - 5 , this embodiment also provides a combined substation, which includes the intelligent American transformer 1 described above. Specifically, the combined substation further includes a capacitor chamber 2, a high-voltage chamber 3, and a low-voltage chamber 4. In the low-voltage chamber, a low-voltage switch is installed. Since the specific installation layout of the capacitor chamber, high-voltage chamber, low-voltage chamber, and transformer belongs to conventional technical means, it will not be elaborated here.

[0065] Embodiment Three:

[0066] It should be understood that this embodiment includes at least all the technical features of the above embodiments, and on the basis of the above embodiments, the American transformer and the combined substation described in this application are further specifically elaborated.

[0067] The specific method for obtaining predicted operation data according to historical operation data, historical environment data, and relevant data includes: constructing a prediction neural network model, training the prediction neural network model through historical operation data and historical environment data, and the trained prediction neural network model is used to obtain predicted operation data according to real-time operation data and real-time environment data. The predicted operation data includes, but is not limited to, the operating current, voltage, vibration sound pattern, partial discharge, and bushing insulation performance parameters of the transformer body within a future preset time period, as well as the environmental parameters around the transformer body within a future preset time period.

[0068] The specific method for calculating the monitoring index according to the predicted operation data and relevant data includes the following steps:

[0069] The first step is to calculate the predicted failure probability of the target monitoring object within a future preset time period according to the predicted operation data.

[0070] Specifically, the failure data set of the target monitoring object can be obtained first, and the predicted failure probability is calculated according to the similarity between the calculated predicted operation data and the failure data set.

[0071] The second step is to calculate the real-time failure probability of the target monitoring object according to the relevant data (i.e., real-time operation data and real-time environment data).

[0072] Specifically, the fault dataset of the target monitoring object can be obtained first, and the real-time fault probability can be calculated according to the similarity between the calculated relevant data and the fault dataset.

[0073] In the third step, according to the predicted fault probability P of the target monitoring object p , the real-time fault probability P r , the fault occurrence time Ft and the number of times n within the past preset time period, the minimum time interval Ft between two consecutive fault occurrences min , the designed service life Lc of the transformer body, and the operation start time Rt of the transformer body to calculate the monitoring index.

[0074] Specifically, the monitoring index where Ft i represents the fault occurrence time of the i-th time within the past preset time period.

[0075] It can be seen from the formula that the monitoring index is proportional to the ratio of the predicted fault probability, the real-time fault probability, the fault occurrence time and the minimum interval time, the designed service life, and the difference between the designed service life and the operation start time. The higher the preset fault probability and the real-time fault probability, the relatively higher the monitoring index; at the same time, the longer the operation start time of the monitoring object, the relatively higher its monitoring index. The longer the fault occurrence time, to a certain extent, indicates that the fault is more serious, and the smaller the time interval between two consecutive fault occurrences, the higher the fault occurrence frequency of the target monitoring object. By calculating the ratio of the fault occurrence time and the minimum interval time, the fault occurrence degree or the comprehensive health degree of the target monitoring object within the past preset time period can be obtained.

[0076] It should be noted that the fault occurrence time Ft of the target monitoring object within the past preset time period refers to the time when the data monitored by the monitoring unit exceeds the preset threshold, and the number of fault occurrences n of the target monitoring object within the past preset time period refers to the number of times the data monitored by the monitoring unit exceeds the preset threshold. That is to say, when the data monitored by the monitoring unit exceeds the preset threshold, it is determined that a fault has occurred.

[0077] By comprehensively considering the predicted fault probability P of the target monitoring object p , the real-time fault probability P r , the fault occurrence time Ft and the number of times n within the past preset time period, the minimum time interval Ft between two consecutive fault occurrences min , the designed service life Lc of the transformer body, and the operation start time Rt of the transformer body to calculate the monitoring index, various different factors affecting the memory size of the monitoring unit can be considered more comprehensively, making the monitoring index more in line with the actual situation of the monitoring unit.

[0078] Step 4: Calculate the estimated monitoring memory size according to the monitoring index. Specifically, according to the monitoring index and the preset basic monitoring memory PMR', calculate the estimated monitoring memory size PMR = α * Mi + PMR'. Here, α is the memory allocation ratio value corresponding to the monitoring index, which can be set by technicians and will not be elaborated here. The preset basic monitoring memory can be set according to the average value of the actual memory used by the monitoring unit in the past preset time period.

[0079] After calculating the estimated monitoring memory sizes of each monitoring unit, the main processor allocates the memory of each monitoring unit to optimize the memory utilization rate of each monitoring unit, making the working memory required by each monitoring unit more reasonable. Specifically, for a monitoring unit whose actual memory is less than the corresponding estimated monitoring memory, it can borrow the data analysis work from other monitoring units whose actual memory is greater than the corresponding estimated monitoring memory.

[0080] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0081] The above-described embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. An intelligent American transformer, comprising a transformer body, a plurality of data acquisition devices mounted on the transformer body, and an intelligent component connected to the data acquisition devices. The intelligent component includes a plurality of monitoring units, and the plurality of data acquisition units correspond to the plurality of monitoring units one by one. The data acquisition device is configured to collect relevant data of the transformer body and feedback it to the corresponding monitoring unit, and is characterized in that: The monitoring unit is used to obtain corresponding historical operation data and historical environment data, obtain predicted operation data according to the historical operation data, historical environment data and relevant data, calculate a monitoring index according to the predicted operation data and relevant data, and calculate an estimated monitoring memory size according to the monitoring index; The intelligent American transformer selects one of the microprocessors from the microprocessors of multiple monitoring units as the main processor; The main processor is configured to allocate the memory of each monitoring unit according to the estimated monitoring memory size corresponding to each monitoring unit, so as to optimize the memory utilization rate of each monitoring unit; Wherein, the relevant data includes real-time operation data and real-time environment data; The specific method for calculating the monitoring index according to the predicted operation data and relevant data includes the following steps: The first step is to calculate the predicted failure probability of the target monitoring object within a preset future time period according to the predicted operation data; The second step is to calculate the real-time failure probability of the target monitoring object according to the relevant data; Step 3: Calculate the monitoring index based on the predicted failure probability P of the target monitoring object p , the real-time failure probability P r , the failure occurrence time Ft and the number n within the past preset time period, and the minimum time interval Ft between two consecutive failure occurrences min , the designed service life Lc of the transformer body, and the operation start time Rt of the transformer body Specifically, the monitoring index wherein, a1, a2, a3, and a4 represent preset weight coefficients.

2. The intelligent American transformer according to claim 1, characterized in that: The monitoring unit first calculates an operation failure index according to the real-time operation data, predicted operation data and real-time environment data, then calculates a monitoring index according to the operation failure index, and finally calculates an estimated monitoring memory size according to the monitoring index and a preset basic monitoring memory.

3. An intelligent American transformer according to claim 2, characterized in that: The intelligent component is also used to compare and analyze the real-time operation data with the corresponding preset data, and remotely transmit the comparison and analysis result to the dispatching center through the communication module, implement fault alarm, and point out the fault cause and fault point, so that the dispatching center can monitor and repair the operation state of the transformer body and the switch of the load terminal, and ensure the stable operation of the power grid.

4. An intelligent American transformer according to claim 3, characterized in that: The multiple monitoring units include an oil chromatogram and micro-water monitoring unit, a partial discharge monitoring unit, a core grounding current monitoring unit, an acoustic fingerprint monitoring unit, a transformer bushing insulation monitoring unit and a on-load tap-changer monitoring unit.

5. An intelligent American transformer according to claim 4, characterized in that: The oil chromatogram and micro-water monitoring unit, partial discharge monitoring unit, core grounding current monitoring unit, acoustic fingerprint monitoring unit, transformer bushing insulation monitoring unit and on-load tap-changer monitoring unit all include a microprocessor and a memory.

6. An intelligent American transformer according to claim 5, characterized in that: The intelligent component further includes a molded case circuit breaker, intelligent capacitors, a miniature circuit breaker and an intelligent electric energy meter located on the low-voltage side.

7. An intelligent American transformer according to claim 6, characterized in that: The molded case circuit breaker, intelligent capacitors, miniature circuit breaker and intelligent electric energy meter are all connected to the RS485 bus.

8. An intelligent American transformer according to claim 7, characterized in that: The dispatching center includes a communication terminal and a background monitor. The intelligent component is connected to the communication terminal and the background monitor through a GPRS / GSM wireless communication link, and the background monitor is also connected to the RS485 bus.

9. A combined substation, characterized in that: Including the intelligent American transformer according to any one of claims 1-8.

10. A combined substation according to claim 9, characterized in that: The combined substation further includes a capacitor chamber, a high-voltage chamber and a low-voltage chamber.

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