A method for evaluating reliability of relay protection based on Markov model analysis method

Abstract

The application discloses a kind of based on Markov model analysis method's reliability evaluation method of relay protection, comprising the following steps: S1 data acquisition and pre-processing;S2 the construction of initial Markov model, based on the historical data of acquisition, using statistical method determines the initial state transition probability matrix, and constructs the time evolution formula of equipment state transition probability;S3 the introduction of equipment aging and degradation mechanism;S4 using the method based on machine learning to adaptively adjust Markov model, by constantly exploring and using historical data, the state transition probability matrix of Markov model is constantly optimized;S5 based on the feedback of adaptive adjustment algorithm, dynamically adjust the parameter in Markov model.The application can combine the adaptive adjustment algorithm of machine learning, Markov model can be dynamically optimized according to the historical data of equipment, so that it can provide high-precision reliability evaluation under different equipment states.

Description

Technical Field

[0001] This invention relates to the field of relay protection assessment technology, and in particular to a relay protection reliability assessment method based on Markov model analysis. Background Technology

[0002] In recent years, with the continuous expansion and increasing complexity of power systems, relay protection systems, as one of the key technologies for ensuring the stable operation of power networks, have received increasing attention. Relay protection systems detect abnormal conditions in power lines, such as short circuits and overloads, and respond quickly to isolate faults and protect equipment from damage. The reliability assessment of most relay protection systems relies on preset models and fixed parameters. These models often cannot fully reflect the impact of factors such as equipment aging and changes in the operating environment on the performance of relay protection systems. Therefore, how to improve the adaptive capability of relay protection systems and provide more accurate analysis for reliability assessment under complex operating conditions has become a current research hotspot.

[0003] While existing technologies can perform certain reliability assessments of relay protection systems, they typically rely on fixed state transition models and lack the ability to dynamically adjust. Existing assessment methods often fail to fully utilize historical operating data and cannot dynamically optimize and adjust model parameters according to actual operating conditions. Faced with complex operating conditions, they are unable to meet the high requirements of modern power systems for the accuracy and timeliness of assessment results. Therefore, it is necessary to design a relay protection reliability assessment method based on Markov model analysis to solve the above problems. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the problems existing in the current relay protection reliability assessment method based on Markov model analysis, this invention is proposed.

[0006] Therefore, the purpose of this invention is to provide a relay protection reliability assessment method based on Markov model analysis, which is suitable for solving the problems of failing to fully utilize historical operating data, being unable to dynamically optimize and adjust model parameters according to actual operating conditions, and being unable to meet the high requirements of modern power systems for the accuracy and timeliness of assessment results in the face of complex operating conditions.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a relay protection reliability assessment method based on Markov model analysis, comprising the following steps:

[0008] S1 Data Acquisition and Preprocessing;

[0009] The initial Markov model of S2 is constructed based on the collected historical data, using statistical methods to determine the initial state transition probability matrix, and constructing the time evolution formula of the device state transition probability.

[0010] The introduction of the S3 equipment aging and degradation mechanism;

[0011] S4 uses a machine learning-based approach to adaptively adjust the Markov model, continuously exploring and utilizing historical data to optimize the state transition probability matrix of the Markov model.

[0012] S5 dynamically adjusts the parameters in the Markov model based on feedback from an adaptive adjustment algorithm.

[0013] S6 uses collected historical data to train the adaptive adjustment algorithm and validates the optimized Markov model;

[0014] Implementation and application of the S7 system.

[0015] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, the time evolution formula of the equipment state transition probability is as follows:

[0016]

[0017] Where, λ ij (t) represents the transition probability from state i to state j at time t, λ0 is the initial transition rate, representing the basic transition probability from state i to state j under the initial conditions, and α ij f represents the equipment degradation factor between state i and state j, reflecting the impact of equipment aging on the transfer rate. ij (x) represents the influence function of historical data, β ij It represents the decay probability of historical data and controls the degree of influence of historical data in the current state transition.

[0018] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, a first threshold λ is set in the output of the time evolution formula of the equipment state transition probability. max The second threshold is λ min ;

[0019] If λij (t)>λ max This indicates that the system is transitioning too quickly in its current state, posing a potential risk of failure.

[0020] If λ ij (t)<λ max This indicates that the equipment is severely aging, the probability of relocation is reduced, and the equipment needs to be repaired or replaced.

[0021] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, the dynamic adjustment formula of the state transition matrix is ​​constructed based on the output of the time evolution formula of the equipment state transition probability. The dynamic adjustment formula of the state transition matrix is ​​as follows:

[0022]

[0023] Among them, P ij (t) represents the probability that the system transitions from state i to state j at time t. It represents the total transition rate from state i to all possible states k.

[0024] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, wherein: in the output result of the dynamic adjustment formula of the state transition matrix, a first threshold is set to P. max The second threshold is P min ;

[0025] If P ij (t)>P max This indicates that the system is in a high-risk state and requires early warning and protective measures.

[0026] If P ij (t)<P min This indicates that the system is running stably and will continue to maintain its current state.

[0027] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, the reliability assessment formula is constructed based on the output of the dynamic adjustment formula of the state transition matrix, and the reliability assessment formula is as follows:

[0028]

[0029] Where R(t) represents the system's reliability assessment value at time t, γ ij It indicates the rate of equipment degradation, reflecting the decline in reliability caused by equipment aging.

[0030] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, wherein: a first threshold value of R is set in the output of the reliability assessment formula. max The second threshold is R min ;

[0031] If R(t) < R min This indicates a problem with the overall system performance, requiring maintenance measures.

[0032] If R(t) > R max This indicates that the system is in a safe operating state.

[0033] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, in step S6, the reliability and prediction accuracy of the model are verified using an independent test dataset.

[0034] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, in step S7, the optimized Markov model and adaptive adjustment algorithm are integrated into the monitoring platform of the relay protection system. Based on the model prediction results, the health status of the relay protection equipment is monitored in real time, and an alarm signal is issued when a potential fault is detected.

[0035] As a preferred embodiment of the relay protection reliability assessment method based on Markov model analysis described in this invention, in step S3, by introducing equipment aging and degradation mechanisms into the Markov model, the process of equipment performance gradually declining over time is simulated.

[0036] The beneficial effects of this invention are:

[0037] By introducing equipment aging and degradation mechanisms, the invention can simulate the performance changes of relay protection equipment as it gradually declines over time, thereby more realistically reflecting the actual operating status of the equipment. Combined with the adaptive adjustment algorithm of machine learning, the Markov model can dynamically optimize based on the historical data of the equipment, enabling it to provide high-precision reliability assessment under different equipment conditions.

[0038] By employing an adaptive adjustment algorithm based on machine learning, the state transition probability matrix in the Markov model can be continuously optimized. By utilizing historical data, the model can continuously self-adjust and improve under equipment aging and complex operating conditions, thereby providing more accurate fault prediction and assessment results. The optimized model is validated using an independent test dataset to ensure that the system can exhibit high prediction accuracy and stability in different working environments. Compared with traditional methods, this invention significantly improves the prediction accuracy and adaptive capability of relay protection systems under complex operating conditions. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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. Wherein:

[0040] Figure 1 This is a schematic diagram illustrating the steps of a relay protection reliability assessment method based on Markov model analysis proposed in this invention.

[0041] Figure 2 This is a schematic diagram of the state transition probability matrix of the Markov model for a relay protection reliability assessment method based on Markov model analysis proposed in this invention. Detailed Implementation

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0045] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0046] Example

[0047] Reference Figures 1-2 As an embodiment of the present invention, a method for evaluating the reliability of relay protection based on Markov model analysis is provided, comprising the following steps:

[0048] S1 Data Acquisition and Preprocessing;

[0049] The initial Markov model of S2 is constructed based on the collected historical data, using statistical methods to determine the initial state transition probability matrix, and constructing the time evolution formula of the device state transition probability.

[0050] The introduction of the S3 equipment aging and degradation mechanism simulates the process of equipment performance gradually declining over time by incorporating the equipment aging and degradation mechanism into the Markov model.

[0051] This mechanism, by modeling the aging data of relay protection equipment, can more realistically reflect the actual operating condition of the equipment. Compared with traditional static evaluation methods, models that consider equipment aging can more accurately assess the reliability of relay protection systems in long-term operation, providing a basis for equipment maintenance and replacement.

[0052] This step is applicable to the long-term operational status assessment of various relay protection devices, and is especially suitable for relay protection devices that are frequently affected by environmental factors, loads, etc.

[0053] S4 uses a machine learning-based approach to adaptively adjust the Markov model, continuously exploring and utilizing historical data to optimize the state transition probability matrix of the Markov model.

[0054] Compared to traditional fixed models, machine learning-based adaptive adjustment algorithms enable Markov models to learn and adjust dynamically, allowing the model to continuously optimize based on actual operating data, thereby improving the accuracy, flexibility, and real-time performance of the evaluation.

[0055] This step can be used for equipment health monitoring under complex operating conditions, and is especially suitable for the variable operating environment in power systems. It ensures that the system can reflect the health status of equipment in real time and dynamically adjust the evaluation model according to the actual situation.

[0056] S5 dynamically adjusts the parameters in the Markov model based on feedback from an adaptive adjustment algorithm.

[0057] By leveraging the feedback from the adaptive adjustment algorithm, key parameters in the Markov model (such as state transition probabilities and time evolution formulas) are dynamically adjusted in real time. This allows the model to adapt to different equipment operating conditions and states, making it more accurate and avoiding evaluation errors caused by fixed parameters in traditional methods. Through real-time adjustment, the model can quickly respond to changes in equipment status, providing more timely fault warnings.

[0058] S6 uses collected historical data to train the adaptive adjustment algorithm and validates the optimized Markov model, using an independent test dataset to verify the model's reliability and prediction accuracy.

[0059] Implementation and application of the S7 system;

[0060] The optimized Markov model and adaptive adjustment algorithm are integrated into the monitoring platform of the relay protection system. Based on the model prediction results, the health status of the relay protection equipment is monitored in real time, and an alarm signal is issued when a potential fault is detected. Through real-time monitoring and model prediction results, the system can issue an alarm signal in a timely manner when a potential fault is detected, which greatly improves the accuracy and response speed of fault warning.

[0061] The time evolution formula for the device state transition probability is as follows:

[0062]

[0063] Where, λ ij (t) represents the transition probability from state i to state j at time t, λ0 is the initial transition rate, representing the basic transition probability from state i to state j under the initial conditions, and α ij f represents the equipment degradation factor between state i and state j, reflecting the impact of equipment aging on the transfer rate. ij (x) represents the influence function of historical data, β ij It represents the decay probability of historical data and controls the degree of influence of historical data in the current state transition.

[0064] A first threshold λ is set in the output of the time evolution formula for the device state transition probability. max The second threshold is λ min ;

[0065] If λ ij (t)>λ max This indicates that the system is transitioning too quickly in its current state, posing a potential risk of failure.

[0066] If λ ij(t)<λ max This indicates that the equipment is severely aging, the probability of relocation is reduced, and the equipment needs to be repaired or replaced.

[0067] The dynamic adjustment formula for the state transition matrix is ​​constructed based on the output of the time evolution formula for the device state transition probability. The dynamic adjustment formula for the state transition matrix is ​​as follows:

[0068]

[0069] Among them, P ij (t) represents the probability that the system transitions from state i to state j at time t. It represents the total transition rate from state i to all possible states k.

[0070] In the output of the dynamic adjustment formula for the state transition matrix, the first threshold is set to P. max The second threshold is P min ;

[0071] If P ij (t)>P max This indicates that the system is in a high-risk state and requires early warning and protective measures.

[0072] If P ij (t)<P min This indicates that the system is running stably and will continue to maintain its current state.

[0073] A reliability assessment formula is constructed based on the output of the dynamic adjustment formula for the state transition matrix, and the reliability assessment formula is as follows:

[0074]

[0075] Where R(t) represents the system's reliability assessment value at time t, γ ij It indicates the rate of equipment degradation, reflecting the decline in reliability caused by equipment aging.

[0076] In the output of the reliability assessment formula, a first threshold is set as R. max The second threshold is R min ;

[0077] If R(t) < R min This indicates a problem with the overall system performance, requiring maintenance measures.

[0078] If R(t) > R max This indicates that the system is in a safe operating state.

[0079] By introducing equipment aging and degradation mechanisms, the invention can simulate the performance changes of relay protection equipment as it gradually declines over time, thereby more realistically reflecting the actual operating status of the equipment. Combined with the adaptive adjustment algorithm of machine learning, the Markov model can dynamically optimize based on the historical data of the equipment, enabling it to provide high-precision reliability assessment under different equipment conditions.

[0080] By employing an adaptive adjustment algorithm based on machine learning, the state transition probability matrix in the Markov model can be continuously optimized. By utilizing historical data, the model can continuously self-adjust and improve under equipment aging and complex operating conditions, thereby providing more accurate fault prediction and assessment results. The optimized model is validated using an independent test dataset to ensure that the system can exhibit high prediction accuracy and stability in different working environments. Compared with traditional methods, this invention significantly improves the prediction accuracy and adaptive capability of relay protection systems under complex operating conditions.

[0081] It should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A relay protection reliability assessment method based on Markov model analysis, characterized in that, Includes the following steps: S1 Data Acquisition and Preprocessing; The initial Markov model of S2 is constructed based on the collected historical data, using statistical methods to determine the initial state transition probability matrix, and constructing the time evolution formula of the device state transition probability. The introduction of the S3 equipment aging and degradation mechanism; S4 uses a machine learning-based approach to adaptively adjust the Markov model, continuously exploring and utilizing historical data to optimize the state transition probability matrix of the Markov model. S5 dynamically adjusts the parameters in the Markov model based on feedback from an adaptive adjustment algorithm. S6 uses collected historical data to train the adaptive adjustment algorithm and validates the optimized Markov model; Implementation and application of the S7 system; The time evolution formula for the device state transition probability is as follows: ； in, This represents the probability of transitioning from state i to state j at time t. The initial transition rate represents the basic transition probability from state i to state j under the initial conditions. This represents the equipment degradation factor between state i and state j, reflecting the impact of equipment aging on the transfer rate. The influence function representing historical data, It represents the decay probability of historical data and controls the degree of influence of historical data in the current state transition; A first threshold is set in the output of the time evolution formula for the device state transition probability. The second threshold is ; like > This indicates that the system is transitioning too quickly in its current state, posing a potential risk of failure. like < This indicates that the equipment is severely aged, the probability of relocation is reduced, and the equipment needs to be repaired or replaced. A dynamic adjustment formula for the state transition matrix is ​​constructed based on the output of the time evolution formula for the device state transition probability. The dynamic adjustment formula for the state transition matrix is ​​as follows: ； in, This represents the probability that the system transitions from state i to state j at time t. It represents the total transition rate from state i to all possible states k.

2. The relay protection reliability assessment method based on Markov model analysis according to claim 1, characterized in that: In the output of the dynamic adjustment formula for the state transition matrix, the first threshold is set as... The second threshold is ; like > This indicates that the system is in a high-risk state and requires early warning and protective measures. like < This indicates that the system is running stably and will continue to maintain its current state.

3. The relay protection reliability assessment method based on Markov model analysis according to claim 2, characterized in that: A reliability assessment formula is constructed based on the output of the dynamic adjustment formula for the state transition matrix, and the reliability assessment formula is as follows: ； in, This represents the system's reliability assessment value at time t. It indicates the rate of equipment degradation, reflecting the decline in reliability caused by equipment aging.

4. The relay protection reliability assessment method based on Markov model analysis according to claim 3, characterized in that: In the output of the reliability assessment formula, a first threshold is set as... The second threshold is ; like < This indicates a problem with the overall system performance, requiring maintenance measures. like > This indicates that the system is in a safe operating state.

5. The relay protection reliability assessment method based on Markov model analysis according to claim 1, characterized in that: In S6, the reliability and prediction accuracy of the model are verified using an independent test dataset.

6. The relay protection reliability assessment method based on Markov model analysis according to claim 1, characterized in that: In step S7, the optimized Markov model and adaptive adjustment algorithm are integrated into the monitoring platform of the relay protection system. Based on the model prediction results, the health status of the relay protection equipment is monitored in real time, and an alarm signal is issued when a potential fault is detected.

7. The relay protection reliability assessment method based on Markov model analysis according to claim 1, characterized in that: In S3, by introducing equipment aging and degradation mechanisms into the Markov model, the process of equipment performance gradually declining over time is simulated.

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