Relay protection system reliability evaluation optimization system

Abstract

The invention relates to the technical field of relay protection system evaluation, and discloses a relay protection system reliability evaluation optimization system, which comprises a data acquisition and preprocessing module, an equipment state analysis module, a region division and score summarization module and an optimization strategy generation module, the acquisition module is used for acquiring equipment characteristic parameters of the relay protection device, representing the equipment characteristic parameters through a first characteristic sequence and preprocessing the equipment characteristic parameters to obtain a second characteristic sequence; the equipment state analysis module is used for analyzing the second feature sequence according to a preset equipment health assessment model to obtain an equipment state score of the relay protection device in a second preset time period; the region division and score summarization module is used for performing region division on the first preset region, summarizing scores and calculating to obtain an overall score; and the optimization strategy generation module is used for generating an optimization strategy according to the score. According to the method, the accuracy, the reliability and the optimization efficiency of state evaluation of the relay protection device are remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of relay protection system evaluation, and in particular relates to a relay protection system reliability evaluation optimization system. Background Art

[0002] With the continuous expansion of the scale and increase in complexity of modern power grids, the safe and stable operation of power systems faces unprecedented challenges. As an important guarantee for the safety of power grids, relay protection devices undertake the key functions of quickly identifying and isolating faults, protecting power equipment and maintaining stable operation of power grids. At present, relay protection devices need to face a variety of abnormal situations in actual operation, including short circuits, overloads, overvoltages and other faults, as well as false operations and refusal to operate. How to improve the real-time, accuracy and intelligence level of relay protection devices is the core technical difficulty to ensure the efficient operation of power grids.

[0003] Existing technologies mainly rely on judgment methods based on rule models or fixed thresholds to evaluate whether a device is in a normal state. However, this technology has obvious limitations. It lacks the ability to adapt to the dynamic characteristics of power grid operation and the ability to sensitively capture the operating status of equipment, and it is difficult to identify the complex modes and long-term trends of equipment operation. In addition, due to the limited level of intelligence, existing methods are difficult to meet the requirements in terms of scalability and real-time performance of large-scale power grids. Summary of the invention

[0004] The present invention provides a reliability evaluation and optimization system for a relay protection system, which solves the technical problems in related technologies that lack dynamic adaptability and sensitivity and cannot meet the real-time, reliability and accuracy requirements of modern complex power grids.

[0005] The present invention provides a reliability evaluation and optimization system for a relay protection system, including a data acquisition and preprocessing module, an equipment status analysis module, a region division and scoring summary module, and an optimization strategy generation module:

[0006] A data collection and preprocessing module, used to collect equipment characteristic parameters of relay protection devices in a first preset area within a first preset time period at a first preset time interval, and represent them through a first characteristic sequence, and preprocess the first characteristic sequence to obtain a second characteristic sequence;

[0007] Among them, the equipment characteristic parameters include: current, voltage, frequency, number of actions, communication delay and load utilization;

[0008] The i-th sequence unit of the second characteristic sequence represents the equipment characteristic parameter of the relay protection device at the i-th time point after preprocessing;

[0009] An equipment status analysis module, used to analyze the second characteristic sequence according to a preset equipment health assessment model to obtain an equipment status score of the relay protection device in a second preset time period;

[0010] The area division and scoring aggregation module is used to divide the first preset area into K first-level sub-areas according to the functional type of the power grid, and divide each first-level sub-area into G second-level sub-areas, generate the corresponding second-level sub-area scores by summarizing the equipment status scores of the relay protection devices in the second-level sub-areas, and then aggregate all the second-level sub-area scores to generate the first-level sub-area scores, and finally calculate the overall score by summarizing the first-level sub-area scores;

[0011] The optimization strategy generation module is used to generate an optimization strategy based on the overall score, the first-level sub-area score, the second-level sub-area score and the equipment status score.

[0012] Furthermore, the specific steps of preprocessing include:

[0013] Step S201, extracting electrical state features according to the first feature sequence and concatenating them after the first feature sequence. The calculation formula of the electrical state features is: Among them, S ele Represents the electrical state characteristics, α 1 represents the current-voltage product coefficient, j represents the time point index, I j represents the current at the jth time point, k represents the time point index, V k represents the voltage at the kth time point, β 1 and β 2 They represent the current harmonic weight coefficient and the voltage unbalance weight coefficient respectively, I harm Indicates the current harmonic distortion coefficient, which is determined according to the proportion of harmonic components in the current, V imb represents the voltage unbalance coefficient, which is determined according to the three-phase voltage, γ represents the frequency stability coefficient, a and b represent the time point index, and f a and f b Respectively represent the frequencies of the ath time point and the bth time point, and N represents the number of time points;

[0014] Step S202: extract the action and load coupling feature according to the first feature sequence, and concatenate it after the first feature sequence. The calculation formula of the action and load coupling feature is: Among them, A l Indicates the action and load coupling characteristics, A c and A c-1 represents the number of actions at the cth time point and the c-1th time point respectively, t represents the first preset time interval, λ 1 Indicates the network load weight coefficient, Lutil represents the load utilization, λ 2 represents the communication delay weight coefficient, d, e and f represent the time point index, D d , D e and D f denote the communication delays at the d-th time point, the e-th time point, and the f-th time point, respectively;

[0015] Step S203: extracting the action and delay coupling feature according to the first feature sequence and concatenating it after the first feature sequence. The calculation formula of the action and delay coupling feature is:

[0016]

[0017] Among them, A d represents the action and delay coupling characteristics, g, h, q, r and s represent the index of the time point, ξ 1 and 2 Denote the delay mean weight coefficient and delay jitter weight coefficient respectively, D g , D h and D q denote the communication delay at the g-th, h-th, and q-th time points, δ denotes the action performance adjustment coefficient, and T r and T s Respectively represent the action time intervals at the rth and sth time points;

[0018] Step S204, using the maximum and minimum normalization method to normalize the concatenated first feature sequence to obtain a second feature sequence.

[0019] Furthermore, the device health assessment model includes: an input layer, an abnormality accumulation layer, a gating mechanism layer, a hidden state update layer, and an output layer;

[0020] The input layer is used to input the second feature sequence;

[0021] The anomaly accumulation layer is used to calculate the cumulative anomaly weight at each time point in combination with the broken window effect;

[0022] The gating mechanism layer includes N first units, each of which is used to output a hidden state at a current time point according to the accumulated anomaly weight of the anomaly accumulation layer;

[0023] The hidden state update layer is used to update the hidden state according to the gating mechanism layer and the abnormal accumulation layer to obtain a first updated state;

[0024] The output layer includes a first classifier, which is used to input the first updated state output at the Nth time point into the first classifier, and the classification space of the first classifier represents the equipment state score of the relay protection device in the second preset time period.

[0025] Furthermore, the calculation formula of the abnormal accumulation layer is:

[0026]

[0027] in, represents the cumulative anomaly weight at the u-th time point, w 1 The memory coefficient representing the historical anomaly, represents the cumulative anomaly weight at the u-1th time point, τ represents the anomaly growth coefficient, B represents the number of features of the second feature sequence, and b 1 An index indicating the number of features, w b1 Indicates the b 1 The weight coefficient of the feature, represents the bth time point u 1 eigenvalues, Indicates the b 1 The first preset standard value of a characteristic.

[0028] Furthermore, the calculation formulas of the gating mechanism layer and the hidden state update layer include:

[0029]

[0030] in, Indicates the c 1 The first updated state output by the hidden state update layer at the time point is, The cth 1 Sequence units, and Respectively represent the c 1 The unified gate and hidden state at each time point, Indicates the c 1 -The first updated state output by the hidden state update layer at one time point, Indicates the c 1 The cumulative anomaly weight at each time point, W g , U g and b g They represent the first weight parameter, the second weight parameter and the first bias parameter corresponding to the unified gate respectively, represents the abnormal weight adjustment coefficient, W h , U h and b h They respectively represent the first weight parameter, the second weight parameter and the first bias parameter corresponding to the hidden state, ζ represents the abnormality penalty coefficient, sigmoid represents the sigmoid activation function, and tanh represents the tanh activation function.

[0031] Furthermore, the first preset area is divided into K first-level sub-areas according to the functional type of the power grid, and each first-level sub-area corresponds to a functional type. For the relay protection devices in each first-level sub-area, the K-Means clustering algorithm is used to perform detailed division according to their longitude and latitude to obtain G second-level sub-areas.

[0032] Furthermore, the steps of the K-Means clustering algorithm include:

[0033] Step S301, using the longitude and latitude of the relay protection device as clustering input features;

[0034] Step S302, iteratively determining cluster centers through a clustering algorithm;

[0035] Step S303: Divide all relay protection devices in the primary sub-area into G secondary sub-areas according to a first preset distance threshold.

[0036] Furthermore, the calculation formula for the overall score is:

[0037]

[0038] Among them, S total represents the overall score in the first preset area, K represents the number of first-level sub-areas, and d 1 Represents the index of the first-level sub-region, represents the weight coefficient of the first-level sub-region, Indicates the d 1 The number of second-level sub-regions in a first-level sub-region, e 1 Indicates the d 1 The index of the second-level sub-region within the first-level sub-region, Indicates the d 1 In the first-level sub-area, the 1 The number of relay protection devices in a secondary sub-area, f 1 Indicates the d 1 In the first-level sub-area, the 1 The index of the relay protection device in the secondary sub-area, Indicates the d 1 In the first-level sub-area, the 1 The fth sub-region of the 1 Equipment condition score of each relay protection device, represents the dynamic correction coefficient, Indicates the dth 1 max(R) represents the maximum value of the failure rates in K first-level sub-regions.

[0039] The beneficial effects of the present invention are as follows: the present invention constructs an equipment health assessment model by introducing a modeling method of the broken window effect, thereby enhancing its dynamic perception capability of equipment anomalies, being able to capture the abnormal cumulative effect in equipment operation, and improving the accuracy and reliability of health status assessment;

[0040] The present invention divides the power grid area into primary sub-areas and secondary sub-areas step by step, and summarizes the scores layer by layer, so as to realize hierarchical management and precise positioning of the equipment operation status, and can quickly find areas or equipment with low scores, providing clear direction for optimization measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a module schematic diagram of a reliability evaluation and optimization system of a relay protection system of the present invention;

[0042] Figure 2 It is a flow chart of the preprocessing of the present invention. DETAILED DESCRIPTION

[0043] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0044] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0045] like Figure 1-Figure 2 As shown, a reliability evaluation and optimization system for a relay protection system includes a data acquisition and preprocessing module 101, an equipment status analysis module 102, a region division and scoring summary module 103, and an optimization strategy generation module 104:

[0046] The data collection and preprocessing module 101 is used to collect equipment characteristic parameters of the relay protection device in the first preset area within the first preset time period according to the first preset time interval, and represent them through a first characteristic sequence, and preprocess the first characteristic sequence to obtain a second characteristic sequence;

[0047] Among them, the equipment characteristic parameters include: current, voltage, frequency, number of actions, communication delay and load utilization;

[0048] The i-th sequence unit of the second characteristic sequence represents the equipment characteristic parameter of the relay protection device at the i-th time point after preprocessing;

[0049] The device status analysis module 102 is used to analyze the second feature sequence according to a preset device health assessment model to obtain a device status score of the relay protection device in the second preset time period;

[0050] The area division and score aggregation module 103 is used to divide the first preset area into K first-level sub-areas according to the functional type of the power grid, and divide each first-level sub-area into G second-level sub-areas, aggregate the equipment status scores of the relay protection devices in the second-level sub-areas to obtain the second-level sub-area scores, aggregate the second-level sub-area scores to obtain the first-level sub-area scores, and then aggregate the first-level sub-area scores to obtain the overall scores;

[0051] The optimization strategy generation module 104 is used to generate an optimization strategy according to the overall score, the first-level sub-area score, the second-level sub-area score and the device status score.

[0052] In one embodiment of the present invention, a sensor installed on a relay protection device is used to collect equipment characteristic parameters. Specifically, a current transformer and a voltage transformer are used to collect current and voltage. The collected voltage is a sine wave that changes with time. The frequency is obtained by solving the frequency-voltage formula. The frequency-voltage formula is: Where V represents voltage, V m represents the voltage amplitude, f represents the frequency, t represents the time point, Indicates phase; the number of actions indicates the number of times the relay protection device performs the predetermined control operation according to the detected abnormal situation. It is obtained through the trigger record of the relay protection device and is used to evaluate the use frequency and wear degree of the relay protection device. The time from sending a data packet to receiving a response packet is recorded and the communication delay is calculated. The network traffic is monitored through the network performance monitoring tool Nagios to calculate the load utilization.

[0053] In one embodiment of the present invention, the specific steps of pre-processing include:

[0054] Step S201, extracting electrical state features according to the first feature sequence and concatenating them after the first feature sequence. The calculation formula of the electrical state features is: Among them, S ele Represents the electrical state characteristics, α 1 represents the current-voltage product coefficient, j represents the time point index, I j represents the current at the jth time point, k represents the time point index, V k represents the voltage at the kth time point, β 1 and β 2 They represent the current harmonic weight coefficient and the voltage unbalance weight coefficient respectively, I harm Indicates the current harmonic distortion coefficient, which is determined according to the proportion of harmonic components in the current, V imb represents the voltage unbalance coefficient, which is determined according to the three-phase voltage, γ represents the frequency stability coefficient, a and b represent the time point index, and f a and f b Respectively represent the frequencies of the ath time point and the bth time point, and N represents the number of time points;

[0055] Step S202: extract the action and load coupling feature according to the first feature sequence, and concatenate it after the first feature sequence. The calculation formula of the action and load coupling feature is: Among them, A l Indicates the action and load coupling characteristics, A c and A c-1 represents the number of actions at the cth time point and the c-1th time point respectively, t represents the first preset time interval, λ 1 Indicates the network load weight coefficient, L util represents the load utilization, λ 2 represents the communication delay weight coefficient, d, e and f represent the time point index, D d , D e and D f denote the communication delays at the d-th time point, the e-th time point, and the f-th time point, respectively;

[0056] Step S203: extracting the action and delay coupling feature according to the first feature sequence and concatenating it after the first feature sequence. The calculation formula of the action and delay coupling feature is:

[0057]

[0058] Among them, A d represents the action and delay coupling characteristics, g, h, q, r and s represent the index of the time point, ξ 1 and 2Denote the delay mean weight coefficient and delay jitter weight coefficient respectively, D g , D h and D q denote the communication delay at the g-th, h-th, and q-th time points, δ denotes the action performance adjustment coefficient, and T r and T s They represent the action time intervals at the rth and sth time points respectively. The action time interval is calculated by the difference between the occurrence times of the two actions.

[0059] Step S204, using the maximum and minimum normalization method to normalize the concatenated first feature sequence to obtain a second feature sequence.

[0060] In one embodiment of the present invention, the above-mentioned preprocessing steps are completed locally in the relay protection device through edge computing technology, thereby reducing dependence on central control and reducing the amount of data transmission.

[0061] In one embodiment of the present invention, the device health assessment model includes: an input layer, an abnormality accumulation layer, a gating mechanism layer, a hidden state update layer, and an output layer;

[0062] The input layer is used to input the second feature sequence;

[0063] The anomaly accumulation layer is used to calculate the cumulative anomaly weight at each time point in combination with the broken window effect;

[0064] The gating mechanism layer includes N first units, each of which is used to output a hidden state at a current time point according to the accumulated anomaly weight of the anomaly accumulation layer;

[0065] The hidden state update layer is used to update the hidden state according to the gating mechanism layer and the abnormal accumulation layer to obtain a first updated state;

[0066] The output layer includes a first classifier, which is used to input the first updated state output at the Nth time point into the first classifier, and the classification space of the first classifier represents the equipment state score of the relay protection device in the second preset time period.

[0067] The broken window effect emphasizes the continuous accumulation and diffusion of small problems. Minor anomalies may occur during the operation of the equipment (such as current fluctuations, voltage imbalance, frequency offset, etc.). If these problems are not detected and processed in time, they will gradually intensify and affect the overall health status. By introducing the broken window effect in the gated recurrent unit, small anomalies in the features can be dynamically tracked through the anomaly accumulation layer, and the long-term impact of anomalies on the health status can be integrated into the state update process of the gated recurrent unit. In addition, the update gate of the gated recurrent unit is merged with the reset gate, and the anomaly weight is introduced to enhance the dynamic characteristics of the gate.

[0068] In one embodiment of the present invention, the calculation formula of the abnormal accumulation layer is:

[0069]

[0070] in, represents the cumulative anomaly weight at the u-th time point, w 1 The memory coefficient representing the historical anomaly, represents the cumulative anomaly weight at the u-1th time point, τ represents the anomaly growth coefficient, B represents the number of features of the second feature sequence, preferably, in this embodiment, the number of features is 9, b 1 An index indicating the number of features, Indicates the b 1 The weight coefficient of the feature, represents the bth time point u 1 eigenvalues, Indicates the b 1 The first preset standard value of a characteristic.

[0071] In one embodiment of the present invention, the calculation formulas of the gating mechanism layer and the hidden state update layer include:

[0072]

[0073] in, Indicates the c 1 The first updated state output by the hidden state update layer at the time point is, The cth 1 Sequence units, and Respectively represent the c 1 The unified gate and hidden state at each time point, Indicates the c 1 -The first updated state output by the hidden state update layer at one time point, Indicates the c 1 The cumulative anomaly weight at each time point, W g , U g and b g They represent the first weight parameter, the second weight parameter and the first bias parameter corresponding to the unified gate respectively, represents the abnormal weight adjustment coefficient, W h , U h and b h They respectively represent the first weight parameter, the second weight parameter and the first bias parameter corresponding to the hidden state, ζ represents the abnormality penalty coefficient, sigmoid represents the sigmoid activation function, and tanh represents the tanh activation function.

[0074] The gating mechanism layer dynamically adjusts the degree of opening and closing of the gate by combining the cumulative anomaly weights output by the anomaly accumulation layer, so that the model can perceive the abnormal changes at each time point in real time. The time points with large anomaly weights have a more significant impact on the update of the hidden state, which enhances the model's ability to respond to sudden anomalies, can capture the cumulative effects of anomalies, and realize the modeling of the broken window effect.

[0075] In one embodiment of the present invention, the first preset area is divided into K first-level sub-areas according to the functional type of the power grid, and each first-level sub-area corresponds to a functional type. Preferably, the functional types include: main substation protection, high-voltage transmission line protection, regional substation and downstream distribution line protection, industrial user power supply protection, distributed power supply protection and low-voltage distribution network protection; for the relay protection devices in each first-level sub-area, the K-Means clustering algorithm is used to perform detailed division according to its longitude and latitude to obtain G second-level sub-areas.

[0076] In one embodiment of the present invention, the steps of the K-Means clustering algorithm include:

[0077] Step S301, using the longitude and latitude of the relay protection device as clustering input features;

[0078] Step S302, determining the cluster center and device allocation results through algorithm iteration;

[0079] Step S303, divide all relay protection devices in the first-level sub-area into G second-level sub-areas according to the first preset distance threshold. Specifically, for each relay protection device in the first-level sub-area, calculate the Euclidean distance between it and the relay protection devices in other first-level sub-areas. When the Euclidean distance is less than the first preset distance threshold, these devices are divided into the same second-level sub-area.

[0080] By dividing the first preset area into first-level sub-areas and second-level sub-areas step by step, a hierarchical management model is formed. The scores at each level are summarized layer by layer, which helps to accurately locate the source of the problem, quickly identify the areas or equipment with low scores, and provide a clear direction for optimization measures; the layer-by-layer score summary can effectively identify the severity and hierarchical distribution of the problem, and provide a basis for hierarchical optimization and reasonable resource allocation. For example, giving priority to optimizing sub-areas or equipment with low scores can improve resource utilization and reduce overall operation risks.

[0081] The calculation formula for the secondary sub-region score is:

[0082] The first-level sub-region scores are:

[0083] The overall score is calculated as follows:

[0084] Among them, S ss Indicates the d 1 In the first-level sub-area, the 1 The second-level sub-region score of the second-level sub-region, S fls Indicates the d 1 The first-level sub-region score of the first-level sub-region, S total represents the overall score in the first preset area, d 1 Represents the index of the first-level sub-region, Represents the weight coefficient of the first-level sub-region, which is determined according to the importance of the grid function type. Indicates the d 1 The number of second-level sub-regions in a first-level sub-region, e 1 Indicates the d 1 The index of the second-level sub-region within the first-level sub-region, Indicates the d 1 In the first-level sub-area, the 1 The number of relay protection devices in a secondary sub-area, f 1 Indicates the d 1 In the first-level sub-area, the 1 The index of the relay protection device in the secondary sub-area, Indicates the d 1 In the first-level sub-area, the 1 The fth sub-region of the 1 Equipment condition score of each relay protection device, represents the dynamic correction coefficient, Indicates the dth 1 max(R) represents the maximum value of the failure rates in K first-level sub-regions.

[0085] In one embodiment of the present invention, when the overall score is less than the first preset score threshold, global optimization is initiated, and the optimization measures at this time are: check the first-level sub-area whose score is lower than the second preset score threshold, prioritize the allocation of maintenance resources to this area, strengthen regional inspections and maintenance, and troubleshoot regional problems; when the second-level sub-area score is less than the third preset score threshold, initiate local optimization, focus on optimizing the problem equipment in the second-level sub-area with the lowest score, and repair or replace the problem equipment.

[0086] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A reliability evaluation and optimization system for a relay protection system, characterized in that: It includes data collection and preprocessing module, equipment status analysis module, area division and scoring summary module and optimization strategy generation module: A data collection and preprocessing module, used to collect equipment characteristic parameters of relay protection devices in a first preset area within a first preset time period at a first preset time interval, and represent them through a first characteristic sequence, and preprocess the first characteristic sequence to obtain a second characteristic sequence; Among them, the equipment characteristic parameters include: current, voltage, frequency, number of actions, communication delay and load utilization; The i-th sequence unit of the second characteristic sequence represents the equipment characteristic parameter of the relay protection device at the i-th time point after preprocessing; An equipment status analysis module, used to analyze the second characteristic sequence according to a preset equipment health assessment model to obtain an equipment status score of the relay protection device in a second preset time period; The area division and scoring aggregation module is used to divide the first preset area into K first-level sub-areas according to the functional type of the power grid, and divide each first-level sub-area into G second-level sub-areas, generate the corresponding second-level sub-area scores by summarizing the equipment status scores of the relay protection devices in the second-level sub-areas, and then aggregate all the second-level sub-area scores to generate the first-level sub-area scores, and finally calculate the overall score by summarizing the first-level sub-area scores; The optimization strategy generation module is used to generate an optimization strategy based on the overall score, the first-level sub-area score, the second-level sub-area score and the equipment status score.

2. A relay protection system reliability evaluation and optimization system according to claim 1, characterized in that: The specific steps of preprocessing include: Step S201, extracting electrical state features according to the first feature sequence and concatenating them after the first feature sequence. The calculation formula of the electrical state features is: Among them, S ele represents the electrical state characteristics, α1 represents the current-voltage product coefficient, j represents the time point index, I j represents the current at the jth time point, k represents the time point index, V k represents the voltage at the kth time point, β1 and β2 represent the current harmonic weight coefficient and the voltage unbalance weight coefficient respectively, I harm Indicates the current harmonic distortion coefficient, which is determined according to the proportion of harmonic components in the current, V imb represents the voltage unbalance coefficient, which is determined according to the three-phase voltage, γ represents the frequency stability coefficient, a and b represent the time point index, and f a and f b Respectively represent the frequencies of the ath time point and the bth time point, and N represents the number of time points; Step S202: extract the action and load coupling feature according to the first feature sequence, and concatenate it after the first feature sequence. The calculation formula of the action and load coupling feature is: Among them, A l Indicates the action and load coupling characteristics, A c and A c-1 represents the number of actions at the cth time point and the c-1th time point respectively, t represents the first preset time interval, λ1 represents the network load weight coefficient, L util represents the load utilization, λ2 represents the communication delay weight coefficient, d, e and f represent the time point index, D d , D e and D f denote the communication delays at the d-th time point, the e-th time point, and the f-th time point, respectively; Step S203: extracting the action and delay coupling feature according to the first feature sequence and concatenating it after the first feature sequence. The calculation formula of the action and delay coupling feature is: Among them, A d represents the action and delay coupling feature, g, h, q, r and s represent the index of the time point, ξ1 and ξ2 represent the delay mean weight coefficient and delay jitter weight coefficient respectively, and D g , D h and D q denote the communication delay at the g-th, h-th, and q-th time points, δ denotes the action performance adjustment coefficient, and T r and T s Respectively represent the action time intervals at the rth and sth time points; Step S204, using the maximum and minimum normalization method to normalize the concatenated first feature sequence to obtain a second feature sequence.

3. A relay protection system reliability evaluation and optimization system according to claim 1, characterized in that: The equipment health assessment model includes: input layer, abnormality accumulation layer, gating mechanism layer, hidden state update layer and output layer; The input layer is used to input the second feature sequence; The anomaly accumulation layer is used to calculate the cumulative anomaly weight at each time point in combination with the broken window effect; The gating mechanism layer includes N first units, each of which is used to output a hidden state at a current time point according to the accumulated anomaly weight of the anomaly accumulation layer; The hidden state update layer is used to update the hidden state according to the gating mechanism layer and the abnormal accumulation layer to obtain a first updated state; The output layer includes a first classifier, which is used to input the first updated state output at the Nth time point into the first classifier, and the classification space of the first classifier represents the equipment state score of the relay protection device in the second preset time period.

4. A relay protection system reliability evaluation and optimization system according to claim 3, characterized in that: The calculation formula of the abnormal accumulation layer is: in, represents the cumulative anomaly weight at the u-th time point, w1 represents the memory coefficient of historical anomalies, represents the cumulative anomaly weight at the u-1th time point, τ represents the anomaly growth coefficient, B represents the number of features in the second feature sequence, b1 represents the index of the number of features, represents the weight coefficient of the b1th feature, represents the b1th eigenvalue at the uth time point, Indicates the first preset standard value of the b1th feature.

5. A relay protection system reliability evaluation and optimization system according to claim 3, characterized in that: Calculation formula for the gating mechanism layer and hidden state update layer include: in, represents the first updated state output by the hidden state update layer at the c1th time point, represents the c1th sequence unit of the second characteristic sequence, and Respectively represent the unified gate and hidden state at the c1th time point, represents the first updated state output by the hidden state update layer at the c1-1th time point, represents the cumulative anomaly weight at the c1th time point, W g , U g and b g They represent the first weight parameter, the second weight parameter and the first bias parameter corresponding to the unified gate respectively, represents the abnormal weight adjustment coefficient, W h , U h and b h They respectively represent the first weight parameter, the second weight parameter and the first bias parameter corresponding to the hidden state, ζ represents the abnormality penalty coefficient, sigmoid represents the sigmoid activation function, and tanh represents the tanh activation function.

6. A relay protection system reliability evaluation and optimization system according to claim 1, characterized in that: According to the functional type of the power grid, the first preset area is divided into K first-level sub-areas, each first-level sub-area corresponds to a functional type, and for the relay protection devices in each first-level sub-area, the K-Means clustering algorithm is used to perform detailed division according to its longitude and latitude to obtain G second-level sub-areas.

7. A relay protection system reliability evaluation and optimization system according to claim 6, characterized in that: The steps of K-Means clustering algorithm include: Step S301, using the longitude and latitude of the relay protection device as clustering input features; Step S302, iteratively determining cluster centers through a clustering algorithm; Step S303: Divide all relay protection devices in the primary sub-area into G secondary sub-areas according to a first preset distance threshold.

8. A relay protection system reliability evaluation and optimization system according to claim 1, characterized in that: The overall score is calculated as follows: Among them, S total represents the overall score in the first preset area, K represents the number of first-level sub-areas, d1 represents the index of the first-level sub-area, represents the weight coefficient of the first-level sub-region, represents the number of secondary sub-regions in the d1th primary sub-region, e1 represents the index of the secondary sub-region in the d1th primary sub-region, represents the number of relay protection devices in the e1th secondary sub-area in the d1th primary sub-area, f1 represents the index of the relay protection device in the e1th secondary sub-area in the d1th primary sub-area, represents the equipment status score of the f1th relay protection device in the e1th secondary sub-area in the d1th primary sub-area, represents the dynamic correction coefficient, represents the failure rate of the d1th first-level sub-region obtained according to historical data, and max(R) represents the maximum failure rate among the K first-level sub-regions.

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