Substation automation equipment monitoring method and device, terminal equipment and storage medium

Abstract

The application discloses a kind of substation automation equipment monitoring method, device, terminal equipment and storage medium.The monitoring method of the present application includes: obtaining the monitoring data of substation automation equipment key chip as sample monitoring data;Sample monitoring data is input into grey prediction model for prediction, to score monitoring value with predicted value;The obtained chip monitoring value score is combined with other state monitoring information of device, input into device evaluation matrix to obtain device evaluation vector, and obtains device disposal opinion with device evaluation vector.The present application can monitor equipment state in real time and whole cycle, and realize effective early warning before equipment failure.

Description

Technical Field

[0001] This invention belongs to the field of substation automation equipment monitoring, and in particular, it relates to a method, device, terminal equipment and storage medium for monitoring substation automation equipment based on key chip monitoring. Background Technology

[0002] The normal operation of substation automation equipment is the foundation for the stable operation of the power system. Currently, the monitoring of the status of substation automation equipment is limited to monitoring equipment faults, combining routine manual and automated inspections, mainly focusing on the timely detection of equipment faults after they occur; it cannot achieve real-time and full-cycle monitoring of equipment status, nor can it provide effective early warning before equipment faults occur. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method, device and terminal equipment for monitoring substation automation equipment based on key chip monitoring, so as to monitor the equipment status in real time and throughout the entire cycle and realize effective early warning before equipment failure.

[0004] In a first aspect, the present invention provides a method for monitoring substation automation equipment, comprising:

[0005] Acquire monitoring data of key chips in substation automation equipment as sample monitoring data;

[0006] The sample monitoring data is input into the grey prediction model for prediction, and the predicted value is used to score the monitoring value.

[0007] The obtained chip monitoring score is combined with other device status monitoring information and input into the device evaluation matrix to obtain the device evaluation vector. The device evaluation vector is then used to obtain the device handling opinion.

[0008] Furthermore, the chip monitoring data is input into a gray prediction model for prediction, specifically including:

[0009] The sample monitoring data is serialized to meet the application requirements of the grey prediction model for predicting data sequences;

[0010] Based on the processed data sequence, the prediction operator of the grey prediction model is calculated.

[0011] The predicted values ​​of chip monitoring data are calculated based on the prediction operator of the grey prediction model.

[0012] Furthermore, the sample monitoring data undergoes serialization processing to meet the data sequence requirements of the grey prediction model, specifically including:

[0013] Acquire time-stamped data from sample monitoring data and form an initial data sequence in time-stamped order;

[0014] Based on the characteristics of the initial data sequence, the preliminary processed data sequence is obtained by accumulating, subtracting, weighted accumulating, and weighted subtracting.

[0015] The data sequence is initially processed and then weighted again to obtain a valid data sequence.

[0016] Furthermore, the sample monitoring data is input into the grey prediction model for prediction, and the predicted values ​​are used to score the monitoring values. Specifically, this includes:

[0017] After powering on and setting the initialization time, the system begins monitoring the consistency between the predicted and monitored values.

[0018] The difference ratio method or the standard deviation ratio method can be used to determine the consistency between the predicted value and the monitored value.

[0019] Once the predicted and monitored values ​​become consistent, the single standard deviation method is used to score the monitored values ​​based on the predicted values.

[0020] Furthermore, the method for constructing the device evaluation matrix includes:

[0021] The severity of the impact of evaluation factors on the operational stability of substation automation equipment is used as the basic row vector of the basic evaluation matrix;

[0022] The evaluation matrix is ​​composed of the scores of the monitoring values ​​of each chip in the substation automation equipment and the device status monitoring data. The corresponding influencing factors are used as the corresponding values ​​to form the basic evaluation matrix.

[0023] The impact of operational aging is taken into account by introducing aging parameters and constructing an aging correction matrix based on these parameters.

[0024] The device evaluation matrix is ​​constructed by combining the aging correction matrix and the basic evaluation matrix.

[0025] Furthermore, obtaining the device disposal opinion specifically includes:

[0026] The chip monitoring value score, combined with other status monitoring information of the device, constitutes the device monitoring vector;

[0027] The device monitoring vector is input into the device evaluation matrix to obtain the device evaluation vector;

[0028] Based on the device evaluation vector, interval judgment is used to obtain device disposal opinions.

[0029] Furthermore, substation equipment condition monitoring devices are deployed in substations. These devices function as data exchange hubs, collecting key chip monitoring information and operational status data of substation automation equipment through communication. They then use a grey prediction model to predict the key chip monitoring information, using the predicted values ​​as a benchmark to score subsequent monitoring information of the key chips. The chip monitoring scores are combined with other status monitoring information of the device to obtain a device evaluation vector through an evaluation matrix. The device's handling opinion is obtained by weighting the device evaluation vector. The substation equipment condition monitoring device then transmits the substation equipment monitoring information, scoring information, evaluation information, and handling opinion to the monitoring master station.

[0030] In a second aspect, the present invention provides a substation automation equipment monitoring device, comprising:

[0031] The data acquisition module is used to acquire monitoring data of substation automation equipment, including monitoring data of key chips, and the monitoring data includes data with different attributes.

[0032] The data processing module processes the key chip monitoring data and inputs it into the gray prediction model, using the model's predicted value as a benchmark to score subsequent monitoring values.

[0033] The device evaluation module combines the monitoring scores of key chips and other monitoring data of substation automation equipment, and uses a device evaluation matrix to evaluate the substation automation equipment to obtain evaluation vectors, ultimately generating device disposal recommendations.

[0034] Thirdly, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described substation automation equipment monitoring method.

[0035] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described substation automation equipment monitoring method.

[0036] The beneficial effects of this invention compared to existing technologies are as follows: This invention acquires monitoring data of key chips in substation equipment, inputs the acquired chip monitoring data into a grey prediction model for prediction, and obtains subsequent predicted values ​​of the monitoring data; using the predicted values ​​of the prediction model as a monitoring benchmark, it scores the subsequent actual measured values; it constructs a device evaluation matrix, and uses the key chip scores combined with the chip monitoring measured values ​​as data input into the evaluation matrix to evaluate the device, obtaining a device evaluation vector; and using the device evaluation vector, it generates device handling suggestions using an interval judgment method. In other words, this invention can acquire monitoring data of key chips in equipment, input the monitoring data into a preset grey model for prediction, use the predicted values ​​as a benchmark to score the monitoring values, and obtain device handling opinions by evaluating the scores. This allows for real-time and full-cycle monitoring of equipment status and provides effective early warning before equipment failure. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the network architecture of a substation automation equipment monitoring method based on key chip status monitoring according to an embodiment of the present invention;

[0039] Figure 2 This is a schematic flowchart of a substation automation equipment monitoring method based on key chip status monitoring, provided by an embodiment of the present invention.

[0040] Figure 3 This is a schematic flowchart of a method for constructing a preset gray prediction model according to an embodiment of the present invention;

[0041] Figure 4 This is a schematic flowchart of monitoring data serialization processing provided in an embodiment of the present invention;

[0042] Figure 5 This is a schematic flowchart of a method for scoring subsequent actual monitoring values ​​using predicted values ​​as monitoring benchmarks, provided by an embodiment of the present invention.

[0043] Figure 6 This is a schematic flowchart of a method for constructing an evaluation matrix of a device according to an embodiment of the present invention;

[0044] Figure 7This is a schematic flowchart of a method for evaluating a device and obtaining disposal opinions by combining key chip scores with chip monitoring and measurement values ​​as data input into an evaluation matrix, according to an embodiment of the present invention.

[0045] Figure 8 This is a schematic diagram of the structure of a substation automation equipment monitoring device based on key chip monitoring according to an embodiment of the present invention;

[0046] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0047] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0048] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0049] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0050] References to "embodiments of the invention" or "some embodiments" in this specification mean that one or more embodiments of the invention include the specific features, structures, or characteristics described in connection with that embodiment. Therefore, phrases such as "in other embodiments," "an embodiment of the invention," and "other embodiments of the invention" appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0051] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0052] Currently, the normal operation of substation automation equipment is the foundation for the stable operation of the power system, and condition monitoring of power equipment in substations is an important measure to ensure reliable power supply. At present, condition monitoring of power equipment in substations mainly focuses on monitoring equipment faults, combining routine manual and automated inspections, primarily targeting the timely detection of equipment failures; it cannot achieve real-time and full-cycle monitoring of equipment status, nor can it provide effective early warnings before equipment failures occur.

[0053] To address the aforementioned issues, this invention provides a solution by acquiring monitoring data from key chips within the device, inputting the monitoring data into a preset gray model for prediction, using the predicted values ​​as a benchmark to score the monitoring values, and evaluating the scores to obtain device handling recommendations. This allows for real-time and full-cycle monitoring of the device status and provides effective early warnings before device failures occur.

[0054] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0055] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the network architecture of a substation automation equipment monitoring method based on key chip monitoring, provided in an embodiment of the present invention. This network architecture relies on the existing substation master-slave architecture, deploying automation equipment status monitoring devices in the substation. After completing the status monitoring of the substation automation devices, the monitoring results and device handling suggestions are sent to the control master station. Figure 1 In this network architecture, substation automation equipment 30 (hereinafter referred to as the device), substation equipment status monitoring device 20, and control master station 10 (hereinafter referred to as the master station) are included.

[0056] The substation automation equipment 30 includes substation protection devices, measurement and control devices, gateway devices, switch devices, etc. The method described in this embodiment of the invention is to monitor the status of the above-mentioned equipment.

[0057] The substation equipment status monitoring device 20 has a data interaction hub function. The substation equipment status monitoring device 20 collects the monitoring information of the key core chips of the substation automation equipment 30 and the status data of the substation automation equipment 30 through communication. It uses the gray prediction model to perform prediction processing on the key chip monitoring information, and uses the predicted value as a benchmark to score the subsequent monitoring information of the key chip. The chip monitoring score is combined with the equipment status monitoring data to obtain the device evaluation vector through the evaluation matrix. The device disposal opinion is obtained by weighting the device evaluation vector. The substation equipment status monitoring device 20 sends the substation equipment monitoring information, scoring information, evaluation information and device disposal opinion to the control master station 10.

[0058] In this embodiment of the invention, the substation equipment status monitoring device 20 can collect key chip monitoring information and device monitoring information of the substation automation equipment 30 through communication acquisition or by deploying sensors. The main information collected includes, but is not limited to, chip power supply voltage, chip junction temperature, operating status, working status, communication status, data quality, etc.

[0059] The control master station 10, relying on its powerful analysis and computing capabilities and with the data support provided by the edge computing substation equipment status monitoring device 20, can realize further strategic applications of device status monitoring and provide advanced human-computer interaction services such as operation and maintenance auxiliary decision-making.

[0060] In this network architecture, the communication transmission method between the raw status monitoring data of the substation automation equipment 30 and the substation equipment status monitoring device 20, and between the substation equipment status monitoring device 20 and the control master station 10, is not limited in this embodiment of the invention.

[0061] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating a substation automation equipment monitoring method based on key chip monitoring, provided by an embodiment of the present invention. Figure 2 As shown, the method includes: S201 to S203.

[0062] S201. Obtain monitoring data of key chips in substation automation equipment.

[0063] Specifically, the substation equipment status monitoring device deployed within the substation collects monitoring data from key chips of the substation automation equipment via communication. In this embodiment of the invention, the substation automation equipment collected mainly includes protection devices, measurement and control devices, gateway devices, and switches. Each type of equipment, based on its unique characteristics, collects monitoring data from its relevant key chips. For example, various secondary devices have CPU chips and memory chips, and the device can monitor the CPU chip's power supply voltage and junction temperature, as well as the memory chip's usage rate and core voltage. The measurement and control device can monitor the power supply voltage and sampling status of its internal ADC chip. The gateway device's various communication interface chips can monitor the communication status and link status. The switch device can monitor the power supply voltage, junction temperature, and link operating status of its switching chips.

[0064] S202. Input the chip monitoring data into the gray prediction model for prediction, and score the monitoring value based on the predicted value.

[0065] Specifically, in order to score the chip monitoring data in a timely manner to obtain an instantaneous device status assessment value, this invention predicts the chip monitoring data and uses the predicted value as a benchmark value to score subsequent detection values ​​in order to complete the subsequent device evaluation.

[0066] In this embodiment of the invention, please refer to the method for constructing the preset grey prediction model. Figure 3 , Figure 3 This is a schematic flowchart illustrating a method for constructing a preset gray prediction model according to an embodiment of the present invention. Figure 3 As shown, the method includes: S301 to S303.

[0067] S301. Perform serialization processing on the sample monitoring data to meet the data sequence requirements of the gray prediction model.

[0068] Specifically, in order to effectively apply the grey prediction model to predict data sequences, the data sequences themselves need to meet the application requirements of the grey prediction model.

[0069] The application of the grey prediction model to data sequences requires that the ratio range of the data sequence levels meets the interval requirements.

[0070] The original data sequence is defined as follows:

[0071] X 0 ={x 0 (1), x 0 (2), x 0 (3), ..., x 0 (n)}

[0072] Where, x 0 (k)>0, k=1,2,...,n.

[0073] The order of the data sequence is:

[0074] σ(k)=x 0 (k-1) / x 0 (k), k = 2, 3, ..., n

[0075] If the order of the data sequence satisfies: σ(k)∈(e -2 / (n+1) e 2 / (n+1) If the conditions are met, a GM(1,1) prediction model can be established for the data sequence to make data predictions.

[0076] To effectively apply the grey prediction model for data prediction, this invention first performs serialization processing on the collected monitoring data. For the processing method, please refer to [link / reference needed]. Figure 4 , Figure 4 This is a schematic flowchart illustrating the serialization processing of chip monitoring data according to an embodiment of the present invention. Figure 4 As shown, the method includes: S401 to S403.

[0077] S401. Obtain time-stamped data of sample monitoring data and form an initial data sequence in time-stamped order.

[0078] This invention applies a grey prediction model to effectively predict the predicted values ​​of chip monitoring data. It acquires time-stamped data of the target monitoring chip at equal time intervals to form an initial data sequence. The predicted value obtained after processing and predicting this sequence is the predicted value of the future time-stamped data at the same time interval, so as to use this as a benchmark to score and judge the actual predicted value at that time.

[0079] S402. Based on the characteristics of the initial data sequence, use methods such as accumulation, subtraction, weighted accumulation, and weighted subtraction to obtain the preliminary processed sequence.

[0080] Specifically, in order to effectively apply the grey prediction model, the initial data sequence is processed to meet the level ratio requirements of the grey prediction model data sequence. Common preliminary data processing methods include accumulation, subtraction, weighted accumulation, and weighted subtraction. The appropriate preliminary data sequence is selected based on the characteristics of the initial data sequence: first-order single-factor increasing data sequences use accumulation, first-order single-factor decreasing data sequences use subtraction, first-order multi-factor increasing data sequences use weighted accumulation, and first-order multi-factor decreasing data sequences use weighted subtraction. This embodiment of the invention applies to device chip monitoring data, which is characterized by a first-order single-factor increasing data sequence, making accumulation processing suitable. The preliminary processed data sequence is obtained after processing using the accumulation method.

[0081] Original time-scaled sequence X 0 The sequence of first-order accumulating genarating operations (1-AGO)X 1 for:

[0082] X 1 ={x 1 (1), x 1 (2), x 1 (3), ..., x 1 (n)}

[0083] in:

[0084] S403. The data sequence is weighted again after the initial processing to obtain a valid data sequence.

[0085] Specifically, the preliminary processed data sequence obtained after initial data processing is further weighted to obtain a valid data sequence, which is then used for data prediction using a grey prediction model. In this embodiment of the invention, the monitoring data of key chips within the application device is processed directly using the nearest neighbor mean without special weighting, thus obtaining a valid data sequence sequentially.

[0086] Sequence X1 The nearest neighbor mean sequence is:

[0087] Z 1 ={z 1 (1), z 1 (2), z 1 (3), ..., z 1 (n)}

[0088] Where: z 1 (k)=1 / 2(x 1 (k-1)+x 1 (k))k=2,3,...

[0089] S302. Based on the processed data sequence, calculate the prediction operator for the gray prediction model.

[0090] For data sequences that meet the sequence level ratio requirements, a grey prediction model is applied for prediction. First, the grey prediction model prediction operator is calculated based on the data sequence.

[0091] The mean equation of the grey prediction model (where parameter a is called the development coefficient and parameter b is called the grey action quantity):

[0092] x 0 (k)+az 1 (k)=b

[0093] Solving the equation, the mean value equation whitening differential equation (also known as the shadow equation) is:

[0094] dx 1 (t) / dt+ax 1 (t)=b

[0095] The solution to the equation is: x 1 (k)=Ce -ak +b / a;

[0096] When k = 1, take x 1 (1) = x 0 (1); We can obtain:

[0097] x 1 (t)=(x 0 (1)-b / a)e -a(t-1) +b / a;

[0098] Therefore, the time response of the grey prediction model can be calculated as follows, which is the corresponding sequence prediction value (x). ～1 x represents 1 (Predicted value).

[0099] x ～1 (k+1)=(x0 (1)-b / a)e -ak +b / a;

[0100] The values ​​of a and b are updated, and the corresponding predicted values ​​can be obtained by substituting them into the input. By employing vectorization processing, the updated values ​​of a and b in the data sequence can be continuously obtained.

[0101] make:

[0102]

[0103] Then we can obtain: U = (B T .B) -1 B T Y, from which the values ​​of a and b can be continuously updated according to the data sequence, and then substituted into the above prediction value calculation formula, the queue prediction value can be obtained.

[0104] The vector U obtained by continuously updating the data sequence is the prediction operator of the grey prediction model.

[0105] S303. Calculate the predicted value of the chip monitoring data based on the gray prediction operator.

[0106] Specifically, based on the data sequence, the gray prediction model prediction operator is continuously updated to update the obtained a and b values, and the corresponding prediction values ​​are calculated.

[0107] x ～1 (k+1)=(x 0 (1)-b / a)e -ak +b / a;

[0108] This calculation yields the predicted values ​​for the original queue and the corresponding predicted values ​​(x) for the original sequence. ～0 x represents 0 The predicted value is:

[0109] x ～0 (k+1)=x ～1 (k+1)-x ～0 (k), k = 1, 2, ..., n-1

[0110] After obtaining the predicted values ​​of the chip monitoring data, the monitoring values ​​are scored using the predicted values ​​as a standard. For the processing method, please refer to [link / reference needed]. Figure 5 . Figure 5 This is a schematic flowchart illustrating a method for scoring monitored values ​​using predicted values ​​as a benchmark, such as... Figure 5 As shown, the method includes: S501 to S503.

[0111] S501: After powering on and setting the initialization time, it begins monitoring the consistency between the predicted value and the monitored value.

[0112] Specifically, after the device is powered on, some monitoring data (such as temperature) of key chips inside the device gradually enter a relatively stable working state from the initial state over a period of time. During this period, the chip monitoring data will tend to stabilize after experiencing a large range of changes. In this process, the predicted value of the prediction model gradually becomes consistent with the actual monitoring value.

[0113] In order to obtain a more effective prediction operator and to reduce the error between the predicted value and the measured value, a power-on initialization running time is set on the device. During this time, the prediction model collects monitoring data from the key chips of the device and calculates and generates prediction data, but does not check the prediction accuracy of the prediction value, nor does it use the prediction value as a benchmark to score the monitoring value. After the power-on initialization running time has elapsed, the consistency between the prediction value and the monitoring value is checked.

[0114] In this embodiment of the invention, considering the operating characteristics of substation automation devices, the startup initialization time is set to 30 minutes.

[0115] S502. Use the difference ratio method or the standard deviation ratio method to determine the consistency between the predicted value and the monitored value.

[0116] Specifically, after setting the initial startup time, in order to ensure the effectiveness of the prediction model, the consistency between the predicted value and the monitored value is first judged. The judgment method can be the difference ratio method and the standard deviation calculation method.

[0117] Following the aforementioned definition, the difference ratio method is applied to calculate the ratio of the difference between the current predicted value and the monitored value to the average of the most recent m monitored values.

[0118] The average value of the most recent m monitoring values ​​is:

[0119] The ratio of the difference between the current predicted value and the monitored value:

[0120] When the difference ratio shrinks to a certain value, it can be considered that the predicted value has become consistent with the monitored value.

[0121] The standard deviation method is used to calculate the standard error of the most recent m predictions. To obtain the annotation error, the population standard deviation of the most recent m predictions is first calculated:

[0122] Then calculate the standard deviation:

[0123] When the calculated standard deviation shrinks to a certain value, it can be considered that the predicted value has become consistent with the monitored value.

[0124] S503. After the predicted value and the monitored value become consistent, the single standard deviation calculation method is used to score the monitored value with the predicted value as the benchmark.

[0125] Specifically, once the predicted value and the monitored value are consistent, it indicates that the prediction model has a good fit to the chip monitoring value curve; if a large error occurs between the chip monitoring value and the predicted value, it is very likely that the chip's working state is abnormal.

[0126] The method of scoring monitored values ​​using predicted values ​​as a benchmark employs the single standard deviation calculation method, which calculates the standard deviation of the most recent monitored value relative to the predicted value:

[0127] The score is obtained by comparing the calculated standard deviation with the model standard deviation: D = σ new / σ n .

[0128] S203. Combine the chip monitoring value score with other status monitoring information of the device, input it into the device evaluation matrix to obtain the device evaluation vector, and obtain the device disposal opinion based on the device evaluation.

[0129] Specifically, after scoring the monitoring information of key chips within the device, this information, combined with other status monitoring information, forms a device monitoring vector. This vector is then input into the device evaluation matrix to obtain the final device evaluation vector, from which the device processing recommendations are derived. For the method of constructing the device evaluation matrix, please refer to [link to documentation]. Figure 6 . Figure 6 This is a schematic flowchart for constructing a device evaluation matrix, such as... Figure 6 As shown, the method includes: S601 to S604.

[0130] S601. The severity of the impact of evaluation factors on the operational stability of the device is used as the basic row vector of the basic evaluation matrix.

[0131] Specifically, different key chips in the device exhibit varying degrees of anomalies, resulting in different impacts on device operation. To comprehensively integrate chip scores and device monitoring information, the severity of the impact of evaluation factors on device operational stability is categorized into different levels as the basic row vectors of the basic evaluation matrix. In this embodiment, the impact on device operational stability is uniformly defined as follows: significant functional anomaly, partial functional anomaly, potential functional anomaly, and stability at risk. An impact coefficient is entered at the corresponding position for each risk level; the impact coefficient represents the influence on stable device operation, with a larger coefficient indicating a greater impact on device operational stability. The basic row vectors of the basic evaluation matrix are constructed accordingly.

[0132] w = (w1, w2, ... w n )

[0133] S602. The evaluation matrix is ​​formed by combining the scores of each chip monitoring value in the device with the device status monitoring data, and the corresponding influencing factors are used as the corresponding values ​​to form the basic evaluation matrix.

[0134] Specifically, based on the different situations of each device, the scores of each key chip in the device are combined with the device status monitoring information, and the influence coefficient of each quantity corresponding to each risk level is arranged as a row to form a basic evaluation matrix.

[0135]

[0136] S603. To add consideration to the impact of operational aging, aging parameters are introduced, and an aging correction matrix is ​​constructed using these aging parameters.

[0137] Specifically, the probability of failure of substation automation equipment changes over time with increasing years of operation, exhibiting inherent characteristics and patterns. Generally speaking, failures are more likely to occur in the initial period after the equipment is put into operation and after a longer period of operation. The effects of aging are incorporated into the equipment evaluation matrix, and an aging correction matrix is ​​constructed using aging parameters. The aging correction matrix is ​​updated monthly based on the equipment's operating time; the aging correction matrix parameters are updated once every month of operation.

[0138]

[0139] S604. The device evaluation matrix is ​​constructed by combining the aging correction matrix and the basic evaluation matrix.

[0140] Specifically, an aging correction matrix and a basic evaluation matrix are used to construct a device evaluation matrix. The basic evaluation matrix is ​​set according to the monitoring status of key chips and the device monitoring information. The aging correction matrix corresponds to the aging status of the device operation.

[0141]

[0142] The method for obtaining device evaluation vectors by inputting device monitoring vectors into the device evaluation matrix, and then obtaining device disposal recommendations based on these vectors, can be found in [reference needed]. Figure 7 . Figure 7 This is a schematic flowchart illustrating a method for obtaining device evaluation vectors by inputting device monitoring vectors into a device evaluation matrix, and then using this matrix to derive device handling recommendations. Figure 7 As shown, the method includes: S701 to S703.

[0143] S701, The device monitoring vector is composed of the device key chip score and the device status monitoring information.

[0144] Specifically, the scores of key chips in the device are combined with the device status monitoring information and arranged sequentially to form the device monitoring vector.

[0145] M = (c1, c2, ... c m ) T

[0146] S702. Input the device monitoring vector into the device evaluation matrix to obtain the device evaluation vector.

[0147] Specifically, the device monitoring vector obtained from real-time monitoring is input into the device evaluation matrix, and the device evaluation vector is obtained through calculation.

[0148]

[0149] The resulting evaluation vector is an n-dimensional vector, specifically corresponding to the severity of the impact of the evaluation factors on the operational stability of the device in the basic row vectors of the basic evaluation matrix. It can be written as: E = (e1, e2, ... e... n ) T .

[0150] S703. Based on the device evaluation vector, interval judgment is used to obtain device disposal opinions.

[0151] Specifically, after obtaining the device evaluation vector, the device handling recommendations can be obtained based on the final evaluation values ​​of different degrees of impact on device operational stability within the device evaluation vector. In this embodiment of the invention, an interval judgment method is used for processing: the degree of impact on the operational stability of the device at this level is judged based on the numerical range of the evaluation values, and a corresponding handling recommendation for this level is given; after obtaining the handling recommendations at each level, they are combined into a device handling recommendation.

[0152] In summary, the substation automation equipment monitoring method based on key chip monitoring provided in this invention acquires key chip monitoring data of substation equipment, inputs the acquired chip monitoring data into a grey prediction model for prediction, and obtains subsequent predicted values ​​of the monitoring data; uses the predicted values ​​of the prediction model as a monitoring benchmark to score subsequent actual measurement values; constructs a device evaluation matrix, and uses the key chip score combined with the chip monitoring measurement values ​​as data input into the evaluation matrix to evaluate the device, obtaining a device evaluation vector; and uses the device evaluation vector to generate device handling opinions using an interval judgment method. This method can achieve real-time and full-cycle status monitoring of substation automation devices and provide effective early warnings before equipment failures occur.

[0153] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0154] Please refer to Figure 8 , Figure 8This is a schematic diagram of a substation automation equipment monitoring device based on key chip monitoring according to an embodiment of the present invention. The device includes:

[0155] Data acquisition module 81 is used to acquire monitoring data of substation automation equipment, including monitoring data of key chips, and the monitoring data includes data with different attributes;

[0156] The data processing module 82 processes the key chip monitoring data and inputs it into the gray prediction model, using the model's predicted value as a benchmark to score subsequent monitoring values.

[0157] The device evaluation module 83 combines the monitoring scores of key chips and other monitoring data of the device, uses the device evaluation matrix to evaluate the device and obtain evaluation vectors, and finally generates device disposal recommendations.

[0158] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the embodiment of the substation automation equipment monitoring method based on key chip monitoring of the present invention. For details on their specific functions and technical effects, please refer to the method embodiment section, which will not be repeated here.

[0159] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the aforementioned embodiment of the substation automation equipment monitoring method based on key chip monitoring, and will not be repeated here.

[0160] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of the present invention. Figure 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 (Only one is shown in the image), memory 101, and computer program 92 stored in the memory 91 and executable on the at least one processor 90, wherein the processor 90 executes the computer program 92 to implement the steps in any of the above embodiments of the substation automation equipment monitoring method based on key chip monitoring.

[0161] The terminal device 9 can be a desktop computer, laptop, handheld computer, or cloud server, etc. This terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will understand that... Figure 9 This is merely an example of terminal device 9 and does not constitute a limitation on terminal device 9. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0162] The processor 90 can be a central processing unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0163] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. In other embodiments, the memory 91 may be an external storage device of the terminal device 9, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device 9. Furthermore, the memory 91 may include both internal and external storage units of the terminal device 9. The memory 91 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 91 can also be used to temporarily store data that has been output or will be output.

[0164] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the substation automation equipment monitoring method based on key chip monitoring described in the above embodiments.

[0165] This invention provides a computer program product that, when run on a terminal device, enables the terminal device to implement the substation automation equipment monitoring method based on key chip monitoring as described in the above embodiments.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable storage media cannot be electrical carrier signals or telecommunication signals.

[0167] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0168] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0169] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0170] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do 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, and should all be included within the protection scope of the present invention.

Claims

1. A method for monitoring substation automation equipment, characterized in that, include: Acquire monitoring data of key chips in substation automation equipment. The monitoring data includes one or more of the following: chip power supply voltage, chip junction temperature, operating status, working status, communication status, and data quality, which are used as sample monitoring data. The sample monitoring data is input into the grey prediction model for prediction, and the predicted value is used to score the monitoring value. The obtained chip monitoring value score is combined with other status monitoring information of the device and input into the device evaluation matrix to obtain the device evaluation vector. The device evaluation vector is then used to obtain the device disposal opinion. Methods for constructing the device evaluation matrix include: The severity of the impact of evaluation factors on the operational stability of substation automation equipment is used as the basic row vector of the basic evaluation matrix; The evaluation matrix is ​​composed of the scores of the monitoring values ​​of each chip in the substation automation equipment and the device status monitoring data. The corresponding influencing factors are used as the corresponding values ​​to form the basic evaluation matrix. The impact of operational aging is taken into account by introducing aging parameters and constructing an aging correction matrix based on these parameters. The device evaluation matrix is ​​constructed by combining the aging correction matrix and the basic evaluation matrix; Obtaining the device disposal opinion specifically includes: The chip monitoring value score, combined with other status monitoring information of the device, constitutes the device monitoring vector; The device monitoring vector is input into the device evaluation matrix to obtain the device evaluation vector; Based on the device evaluation vector, interval judgment is used to obtain device disposal opinions.

2. The substation automation equipment monitoring method according to claim 1, characterized in that, The step of inputting sample monitoring data into the grey prediction model for prediction specifically includes: The sample monitoring data is serialized to meet the application requirements of the grey prediction model for predicting data sequences. Based on the processed data sequence, the prediction operator of the grey prediction model is calculated. The predicted values ​​of chip monitoring data are calculated based on the prediction operator of the grey prediction model.

3. The substation automation equipment monitoring method of claim 2, wherein, The sample monitoring data undergoes serialization processing to meet the data sequence requirements of the grey prediction model, specifically including: Acquire time-stamped data from sample monitoring data and form an initial data sequence in time-stamped order; Based on the characteristics of the initial data sequence, the preliminary processed data sequence is obtained by accumulating, subtracting, weighted accumulating, and weighted subtracting. The data sequence is initially processed and then weighted again to obtain a valid data sequence.

4. The substation automation equipment monitoring method of claim 2, wherein, The sample monitoring data is input into the grey prediction model for prediction, and the predicted values ​​are used to score the monitoring values. Specifically, this includes: After powering on and setting the initialization time, the system begins monitoring the consistency between the predicted and monitored values. The difference ratio method or the standard deviation ratio method can be used to determine the consistency between the predicted value and the monitored value. Once the predicted and monitored values ​​become consistent, the single standard deviation method is used to score the monitored values ​​based on the predicted values.

5. The substation automation equipment monitoring method according to claim 1, characterized in that, A substation equipment status monitoring device (20) is deployed in the substation. The substation equipment status monitoring device (20) has a data interaction hub function. The substation equipment status monitoring device (20) collects key chip monitoring information and operating status data of substation automation equipment (30) through communication. It uses a gray prediction model to predict the key chip monitoring information and scores the subsequent monitoring information of the key chip based on the predicted value. The chip monitoring score is combined with other status monitoring information of the device to obtain the device evaluation vector through the evaluation matrix. The device disposal opinion is obtained by weighting the device evaluation vector. The substation equipment status monitoring device (20) sends the substation equipment monitoring information, scoring information, evaluation information and device disposal opinion to the monitoring master station (10).

6. A substation automation equipment monitoring device, used to implement the substation automation equipment monitoring method according to any one of claims 1-5, characterized in that, include: The data acquisition module is used to acquire monitoring data of substation automation equipment, including monitoring data of key chips, and the monitoring data includes data with different attributes. The data processing module processes the key chip monitoring data and inputs it into the gray prediction model, using the model's predicted value as a benchmark to score subsequent monitoring values. The device evaluation module combines the monitoring scores of key chips and other monitoring data of substation automation equipment, and uses a device evaluation matrix to evaluate the substation automation equipment to obtain evaluation vectors, ultimately generating device disposal recommendations.

7. A terminal device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the substation automation equipment monitoring method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. When the computer program is executed by the processor, it implements the substation automation equipment monitoring method as described in any one of claims 1 to 5.

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