Power distribution system operation state evaluation method based on q-order orthogonal fuzzy number and cloud model

Abstract

The invention relates to a power distribution system operation state evaluation method based on a q-order orthogonal fuzzy number and a cloud model. In operation of a power distribution system, a traditional evaluation method is difficult to process parameter uncertainty and randomness due to influence of factors such as load change and equipment aging, and lacks comprehensive evaluation capability. In order to overcome the defects, a fuzzy set is defined for key parameters, a membership function is constructed, a q-order orthogonal fuzzy number is generated, a cloud model is constructed based on the q-order orthogonal fuzzy number, and then a multi-dimensional evaluation model is established. The model is used for calculating the similarity between an evaluation vector and a cloud model and a comprehensive evaluation index, the cloud model is converted into a membership function to calculate a comprehensive membership, the system state is evaluated, a threshold value is set to judge a normal state, an early warning state or an abnormal state, risks are analyzed according to different states, optimization suggestions are provided, and visual feedback and an optimization closed loop are formed. And the evaluation accuracy and the system reliability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid system operation status monitoring and evaluation, and in particular relates to a distribution system operation status evaluation method based on q-order orthogonal fuzzy numbers and a cloud model. Background Art

[0002] During the operation of the power distribution system, its operating status is affected by many complex factors, including but not limited to random changes in load, aging and failure of equipment, interference from environmental factors, and complexity of the power grid structure. These factors lead to significant uncertainty and randomness in the key parameters of the power distribution system (such as voltage, current, power factor, etc.).

[0003] The traditional distribution system operation status assessment method has the following main defects:

[0004] Difficulty in dealing with uncertainty: Most traditional methods are based on precise mathematical models and definite parameter values ​​for evaluation, which makes it difficult to effectively deal with the parameter uncertainty that is prevalent in distribution systems. For example, when the load changes suddenly, traditional methods find it difficult to accurately describe the fuzzy range of changes in parameters such as voltage and current, which may lead to a large deviation between the evaluation results and the actual operating status.

[0005] Lack of comprehensive evaluation capabilities: Some existing methods only consider a single or a few key parameters and cannot fully reflect the overall operating status of the distribution system. The distribution system is a complex multi-parameter interrelated system. Single parameter evaluation cannot accurately capture the true state of the system and is prone to overlooking potential risks and problems.

[0006] Insufficient description of randomness: Existing technologies often ignore the randomness of parameters during the evaluation process and cannot accurately characterize the random change characteristics of parameters under different operating conditions. This makes the evaluation results lack reliability and accuracy when facing random fluctuations in actual operation. Summary of the invention

[0007] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide an evaluation method based on q-order orthogonal fuzzy numbers and cloud models to accurately characterize parameter characteristics, realize multi-dimensional comprehensive evaluation, and improve evaluation accuracy and system operation reliability, in view of the shortcomings of existing distribution system operation status evaluation technology, such as difficulty in dealing with uncertainty and randomness and lack of comprehensive evaluation capabilities.

[0008] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0009] The distribution system operation status evaluation method based on q-order orthogonal fuzzy number and cloud model includes the following steps:

[0010] S1. Construct q-order orthogonal fuzzy numbers, define fuzzy sets for key parameters in the distribution system, each fuzzy set represents a specific state range, and construct membership functions through the defined fuzzy sets. The membership functions map the actual observed values ​​of key parameters into a specific interval; use the membership functions to generate q-order orthogonal fuzzy numbers, which can represent the membership of parameter values ​​in multiple fuzzy sets, thereby capturing the uncertainty of the parameters;

[0011] S2. Construct a cloud model, based on the q-order orthogonal fuzzy numbers generated in S1, to construct a cloud model to describe the uncertainty and randomness of the operating state of the distribution system;

[0012] S3. Establish a state assessment model, using the cloud model constructed in S2 to establish a multi-dimensional distribution system operation state assessment model;

[0013] S4. Comprehensive evaluation and decision-making. Combined with the status evaluation model established in S3, a comprehensive evaluation of the operating status of the distribution system is conducted. The cloud model constructed in S2 is converted into a membership function to evaluate the operating status of the distribution system. Combined with q-order orthogonal fuzzy numbers and cloud models, a comprehensive evaluation of the operating status of the distribution system is conducted, and the comprehensive membership of each status is calculated. Based on the comprehensive evaluation results, the operating status of the distribution system is analyzed, potential risks and problems are identified, and optimization suggestions are put forward.

[0014] The method for constructing q-order orthogonal fuzzy numbers in S1 includes:

[0015] S11. Fuzzy set definition: For the key parameter set X in the distribution system, X = {x1, x2, ..., x n} (where n is the number of key parameters), for each key parameter x in the key parameter set i According to its practical significance and value range in the operation of the distribution system, m fuzzy sets F are defined. i1 ,F i2 ,…,F im For example, for voltage parameter x1, according to the rated voltage of the system and the allowable voltage fluctuation range, define the normal voltage range fuzzy set F 11 、Voltage high fuzzy set F 12 、Voltage is too low fuzzy set F 13 etc.; the fuzzy sets should fully cover all possible situations of the parameters, and there should be a smooth transition between adjacent fuzzy sets to accurately describe the fuzzy characteristics of the parameters;

[0016] S12. Membership function construction, for each fuzzy set F ij , construct the corresponding membership function μF ij (x i), the actual observed value of the key parameter is mapped to the interval [0,1] to indicate the degree to which the observed value belongs to the corresponding fuzzy set. The membership function μF ij (x i ) is expressed as:

[0017]

[0018] Among them, a ij is a fuzzy set F ij The lower limit value of the membership function defines the starting point of the range represented by the fuzzy set. In the distribution system, for voltage parameters, if a fuzzy set with low voltage is defined, a ij It is the minimum value of this low range, which means that when the voltage is lower than this value, the membership degree in this fuzzy set begins to increase gradually from 0; b ij is a fuzzy set F ij The middle value of is also the parameter value corresponding to the maximum membership degree; c ij is the upper limit of the fuzzy set, which determines the end point of the fuzzy set range. For the fuzzy set with high voltage, c ij It is the maximum value of the high voltage range. When the voltage is higher than this value, the membership degree in the fuzzy set gradually decreases to 0;

[0019] S13.q-order orthogonal fuzzy number generation, for each key parameter x i , get its actual observed value x i o , the actual observed value x will be obtained i o Substitute it into the membership function corresponding to each fuzzy set constructed previously, and calculate the membership value in each fuzzy set; finally, these membership values ​​are combined into a vector to obtain the q-order orthogonal fuzzy number. Each element in the vector represents the degree of membership of the observation value in different fuzzy sets, thereby comprehensively describing the uncertainty of the parameter value from the perspective of multiple fuzzy sets.

[0020] In S2, building a cloud model is represented as:

[0021]

[0022]

[0023]

[0024] in, Represents the cloud model, Ex xi For expectation; xi He xi is the super entropy; ω ij is the weight of the fuzzy set; Cij is an eigenvalue of a fuzzy set; is the jth value in a data sequence, associated with the i-th key parameter; is the jth value in another data sequence, associated with the i-th key parameter.

[0025] In S3, the method for establishing a state assessment model includes:

[0026] S31. Evaluation vector construction, considering multiple key parameters in S1, construct an evaluation vector, and the evaluation vector X′ is expressed as:

[0027] X′=(x′1,x′2,…,x′ n );

[0028] Among them, x′1 is the actual observed value of the corresponding key parameter; the evaluation vector X′ integrates the state information of various key aspects at a certain moment, which is a comprehensive description of the operating state of the distribution system;

[0029] S32. Similarity calculation, calculation of evaluation vector X′ and each key parameter cloud model The similarity is expressed as:

[0030]

[0031]

[0032] S xi Indicates the similarity between the evaluation vector and each key parameter cloud model, S xi ∈[0, 1], S xi The closer it is to 1, the more similar the evaluation vector is to the cloud model, that is, the closer the current operating state of the distribution system is to the state represented by the cloud model; represents the Euclidean distance between the evaluation vector and the cloud model;

[0033] S33. Calculate the comprehensive evaluation index, use the hierarchical analysis method to determine the weight of each key parameter in the evaluation, build a hierarchical model, compare each key parameter pairwise, build a judgment matrix, calculate the eigenvector and perform consistency test to obtain the relative weight of each key parameter;

[0034] Calculate the comprehensive evaluation index E, The evaluation index E combines the similarities between each key parameter and the corresponding cloud model, and takes into account the weight of each parameter. It can comprehensively and comprehensively reflect the degree of closeness between the overall operating status of the distribution system and the ideal state.

[0035] In S4, the comprehensive evaluation and decision-making method includes:

[0036] S41. Cloud model to membership function conversion, the constructed cloud model Convert to membership function The conversion process is based on the digital characteristics of the cloud model;

[0037] First, generate an En xi For the expectation, He 2 xi is a normal random number with variance En′ xi ;

[0038] Then, generate an Ex xi For expectation, xi ′) 2 A normal random number x′ with variance i , then it is a cloud droplet, where:

[0039]

[0040] By repeating the above steps many times, a sufficient number of cloud droplets are generated, and a membership function is constructed based on the generated cloud droplets. The membership function can more intuitively reflect the membership of key parameters under the uncertainty state described by the cloud model.

[0041] S42. Comprehensive membership calculation, combining q-order orthogonal fuzzy numbers and the converted membership function to calculate the comprehensive membership of each state;

[0042]

[0043] Where n represents the number of key parameters involved in the evaluation process, m represents the number of fuzzy sets associated with each key parameter; μF ij (x iobs ) represents the membership function value in the jth fuzzy set under the i-th key parameter, x iobs is the observed value of the i-th key parameter, which represents the observed value x iobs The degree of membership in the jth fuzzy set ranges from 0 to 1;

[0044] is the membership function value of the cloud model at, and the cloud model Related to the i-th key parameter, x ij It is a typical value in the cloud model, and its value range is between 0 and 1;

[0045] The process integrates the membership information of the parameters represented by q-order orthogonal fuzzy numbers in multiple fuzzy sets and the uncertainty description provided by the membership function after cloud model conversion, thus obtaining a comprehensive and integrated membership value for accurately evaluating the operating status of the distribution system;

[0046] S43. Status evaluation and decision-making, evaluate the operation status of the distribution system according to the comprehensive membership, set the upper and lower limits of the threshold, when μ 综合 When it is greater than the upper threshold, the power distribution system is judged to be in normal operation, indicating that the actual observed values ​​of the key parameters of the system are close to the ideal state, and the system operation is stable and reliable;

[0047] When μ 综合 When it is less than the upper limit but greater than the lower limit, the system is in a warning state, which means that the system has a tendency to deviate from the ideal state to a certain extent. It is necessary to pay close attention to the changes in key parameters and timely check for potential risks.

[0048] When μ 综合 When it is less than the lower limit, the system is in an abnormal state. At this time, there may be major problems in the system operation, such as equipment failure, serious load imbalance, etc., and immediate measures need to be taken to repair and adjust.

[0049] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0050] Accurately handle uncertainty and randomness: The present invention can effectively handle the uncertainty and randomness of key parameters of the power distribution system by introducing q-order orthogonal fuzzy numbers and cloud models. The q-order orthogonal fuzzy numbers describe the parameter membership from the perspective of multiple fuzzy sets, and the cloud model further integrates uncertainty and randomness through expectation, entropy and hyperentropy. The two complement each other, making the evaluation results closer to the actual operating conditions. For example, in an actual power distribution system, the voltage may fluctuate within a certain range due to various factors. This method can accurately incorporate the uncertainty and randomness of such fluctuations into the evaluation scope, avoiding misjudgment due to simple deterministic evaluation.

[0051] Achieve multi-dimensional comprehensive evaluation: The multi-dimensional evaluation model comprehensively considers multiple key parameters, avoiding the one-sidedness of single parameter evaluation in traditional methods. By constructing evaluation vectors, calculating similarity and comprehensive evaluation indicators, the overall operating status of the distribution system is fully reflected. For example, when evaluating the operating status of the system, not only is the voltage considered normal, but also the information of multiple parameters such as current and power factor is integrated, so as to more accurately judge the overall health status of the system and discover potential risks in a timely manner.

[0052] Visual feedback and optimization closed loop: Visualized result feedback makes it easier for operation and maintenance personnel to intuitively understand the system status and make decisions quickly. At the same time, through the optimization closed loop formed by result feedback, the system can be adjusted in time according to the evaluation results to continuously improve the reliability and operation efficiency of the system. For example, when the operation and maintenance personnel see that the voltage is in a warning state through the visual interface, they can quickly take measures according to the optimization suggestions, such as adjusting the reactive power compensation device, and re-evaluate after the adjustment to ensure that the system always maintains a good operating state, effectively reduce the probability of failure, and improve the power supply quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a logic block diagram of a distribution system operation status evaluation method based on q-order orthogonal fuzzy numbers and cloud models of the present invention;

[0054] Figure 2 To construct the logic block diagram of q-order orthogonal fuzzy number method;

[0055] Figure 3 A logical block diagram for establishing a state assessment model method;

[0056] Figure 4 This is a logical block diagram of the comprehensive evaluation and decision-making method. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0058] like Figure 1 As shown, the distribution system operation status evaluation method based on q-order orthogonal fuzzy number and cloud model includes the following steps:

[0059] S1. Construct q-order orthogonal fuzzy numbers, define fuzzy sets for key parameters (voltage, current, etc.) in the distribution system, each fuzzy set represents a specific state range, and construct membership functions through the defined fuzzy sets. The membership function maps the actual observed values ​​of the key parameters to a specific interval; use the membership function to generate q-order orthogonal fuzzy numbers, which can represent the membership of parameter values ​​in multiple fuzzy sets, thereby capturing the uncertainty of the parameters;

[0060] like Figure 2 As shown, the method for constructing q-order orthogonal fuzzy numbers in S1 includes:

[0061] S11. Fuzzy set definition: For the key parameter set X in the distribution system, X = {x1, x2, ..., x n} (where n is the number of key parameters), for each key parameter x in the key parameter set i According to its practical significance and value range in the operation of the distribution system, m fuzzy sets F are defined. i1 ,F i2 ,…,F im For example, for voltage parameter x1, according to the rated voltage of the system and the allowable voltage fluctuation range, define the normal voltage range fuzzy set F 11 、Voltage high fuzzy set F 12 、Voltage is too low fuzzy set F 13 etc.; the fuzzy sets should fully cover all possible situations of the parameters, and there should be a smooth transition between adjacent fuzzy sets to accurately describe the fuzzy characteristics of the parameters;

[0062] S12. Membership function construction, for each fuzzy set F ij , construct the corresponding membership function μF ij (x i ), the actual observed value of the key parameter is mapped to the interval [0,1] to indicate the degree to which the observed value belongs to the corresponding fuzzy set. The membership function μF ij (x i ) is expressed as:

[0063]

[0064] Among them, a ij is a fuzzy set F ij The lower limit value of the membership function defines the starting point of the range represented by the fuzzy set. In the distribution system, for voltage parameters, if a fuzzy set with low voltage is defined, a ij It is the minimum value of this low range, which means that when the voltage is lower than this value, the membership degree in this fuzzy set begins to increase gradually from 0; b ij is a fuzzy set F ij The middle value of is also the parameter value corresponding to the maximum membership degree. For example, for the fuzzy set of normal voltage range, b ij Set to the center value of the normal voltage range. At this time, when the voltage is equal to b ij When , its degree of membership in the fuzzy set is 1, indicating that it completely belongs to this normal range; c ij is the upper limit of the fuzzy set, which determines the end point of the fuzzy set range. For the fuzzy set with high voltage, c ij It is the maximum value of the high voltage range. When the voltage is higher than this value, the membership degree in the fuzzy set gradually decreases to 0;

[0065] S13.q-order orthogonal fuzzy number generation, for each key parameter xi , get its actual observed value x i o , the actual observed value x will be obtained i o Substitute it into the membership function corresponding to each fuzzy set constructed previously, and calculate the membership value in each fuzzy set; finally, these membership values ​​are combined into a vector to obtain the q-order orthogonal fuzzy number. Each element in the vector represents the degree of membership of the observation value in different fuzzy sets, thereby comprehensively describing the uncertainty of the parameter value from the perspective of multiple fuzzy sets.

[0066] S2. Construct a cloud model, based on the q-order orthogonal fuzzy numbers generated in S1, to construct a cloud model to describe the uncertainty and randomness of the operating state of the distribution system;

[0067] The cloud model is represented as:

[0068]

[0069]

[0070]

[0071] in, Represents the cloud model, Ex xi For expectation, Ex xi The center of the cloud is determined, Ex xi The eigenvalues ​​and weights of each fuzzy set are combined to enable the cloud model to reflect the average tendency of key parameters in multiple fuzzy states; xi Entropy quantifies the uncertainty of parameters from the perspective of the coverage of fuzzy sets. The larger the coverage, the more dispersed the values ​​of the parameters in this state, and the higher the uncertainty. xi is super entropy, He xi Determines the thickness and dispersion of clouds, He xi Further characterizing the uncertainty of entropy, when there are more complex uncertainties in the system, the super entropy value will increase accordingly, resulting in a more dispersed and irregular distribution of cloud droplets in the cloud model, thus more comprehensively describing the uncertainty and randomness in the operating state of the distribution system; ω ij is the weight of the fuzzy set; C ij is an eigenvalue of the fuzzy set; c ij is the jth value in a data sequence, associated with the ith key parameter; a ij is the jth value in another data sequence, associated with the i-th key parameter;

[0072] S3. Establish a state assessment model, using the cloud model constructed in S2 to establish a multi-dimensional distribution system operation state assessment model;

[0073] like Figure 3 As shown, in S3, the method for establishing a state assessment model includes:

[0074] S31. Evaluation vector construction, considering multiple key parameters in S1, construct an evaluation vector, and the evaluation vector X′ is expressed as:

[0075] X′=(x′1,x′2,…,x′ n );

[0076] Among them, x1′ is the actual observed value of the corresponding key parameter; the evaluation vector X′ integrates the state information of various key aspects at a certain moment, which is a comprehensive description of the operating state of the distribution system;

[0077] S32. Similarity calculation, calculation of evaluation vector X′ and each key parameter cloud model The similarity is expressed as:

[0078]

[0079]

[0080] S xi Indicates the similarity between the evaluation vector and each key parameter cloud model, S xi ∈[0, 1], S xi The closer it is to 1, the more similar the evaluation vector is to the cloud model, that is, the closer the current operating state of the distribution system is to the state represented by the cloud model; represents the Euclidean distance between the evaluation vector and the cloud model;

[0081] S33. Calculate the comprehensive evaluation index, use the hierarchical analysis method to determine the weight of each key parameter in the evaluation, build a hierarchical model, compare each key parameter pairwise, build a judgment matrix, calculate the eigenvector and perform consistency test to obtain the relative weight of each key parameter;

[0082] Calculate the comprehensive evaluation index E, The evaluation index E combines the similarity between each key parameter and the corresponding cloud model, and takes into account the weight of each parameter. It can comprehensively and comprehensively reflect the degree of proximity between the overall operating state of the distribution system and the ideal state (combined by the states represented by each cloud model); if the weight of the voltage parameter is large and its similarity with the corresponding cloud model is low, then the value of the comprehensive evaluation index will be greatly affected, indicating that the voltage parameter has a significant impact on the overall operating state of the system; if the value of E is between [0.5, 1], the system is in a sub-healthy state and needs attention; when the value of E is less than, the system has major problems and needs further analysis and processing; through such a state evaluation model, the relationship between the evaluation vector and the cloud model can be used, combined with the weights of each key parameter, to quantitatively evaluate the operating state of the distribution system.

[0083] S4. Comprehensive evaluation and decision-making: Combined with the status evaluation model established in S3, a comprehensive evaluation of the operating status of the distribution system is conducted; the cloud model constructed in S2 is converted into a membership function to evaluate the operating status of the distribution system; combined with q-order orthogonal fuzzy numbers and the cloud model, a comprehensive evaluation of the operating status of the distribution system is conducted, and the comprehensive membership of each status is calculated; based on the comprehensive evaluation results, the operating status of the distribution system is analyzed, potential risks and problems are identified, and optimization suggestions are put forward;

[0084] like Figure 4 As shown, the comprehensive assessment and decision-making methods include:

[0085] S41. Cloud model to membership function conversion, the constructed cloud model Convert to membership function The conversion process is based on the digital characteristics of the cloud model;

[0086] First, generate an En xi For the expectation, He 2 xi is a normal random number with variance En xi ′;

[0087] Then, generate an Ex xi For expectation, xi ′) 2 A normal random number x′ with variance i , then it is a cloud droplet, where:

[0088]

[0089] By repeating the above steps many times, a sufficient number of cloud droplets are generated, and a membership function is constructed based on the generated cloud droplets. The membership function can more intuitively reflect the membership of key parameters under the uncertainty state described by the cloud model.

[0090] S42. Comprehensive membership calculation, combining q-order orthogonal fuzzy numbers and the converted membership function to calculate the comprehensive membership of each state;

[0091]

[0092] Where n represents the number of key parameters involved in the evaluation process, m represents the number of fuzzy sets associated with each key parameter; μF ij (x iobs ) represents the membership function value in the jth fuzzy set under the i-th key parameter, x iobs is the observed value of the i-th key parameter, which represents the observed value x iobs The degree of membership in the jth fuzzy set ranges from 0 to 1;

[0093] is the membership function value of the cloud model at, and the cloud model Related to the i-th key parameter, x ij It is a typical value in the cloud model, and its value range is between 0 and 1;

[0094] The process integrates the membership information of the parameters represented by q-order orthogonal fuzzy numbers in multiple fuzzy sets and the uncertainty description provided by the membership function after cloud model conversion, thus obtaining a comprehensive and integrated membership value for accurately evaluating the operating status of the distribution system;

[0095] S43. Status evaluation and decision-making, evaluate the operation status of the distribution system according to the comprehensive membership, set the upper and lower limits of the threshold, when μ 综合 When it is greater than the upper threshold, the power distribution system is judged to be in normal operation, indicating that the actual observed values ​​of the key parameters of the system are close to the ideal state, and the system operation is stable and reliable;

[0096] When μ 综合 When it is less than the upper limit but greater than the lower limit, the system is in a warning state, which means that the system has a tendency to deviate from the ideal state to a certain extent. It is necessary to pay close attention to the changes in key parameters and timely check for potential risks.

[0097] When μ 综合 When it is less than the lower limit, the system is in an abnormal state. At this time, there may be major problems in the system operation, such as equipment failure, serious load imbalance, etc., and immediate measures need to be taken to repair and adjust.

[0098] According to different operating conditions, in-depth analysis is performed to identify potential risks and problems; in the early warning state, by comparing the q-order orthogonal fuzzy numbers of each key parameter and the cloud model related information, it is determined which key parameter or parameters cause the comprehensive membership to be in this range; for example, if the voltage parameter has a large contribution to the membership and its value deviates from the normal range, it may be due to voltage instability caused by grid load fluctuations, or voltage regulation equipment failures. According to the identified problems, targeted optimization suggestions are put forward. For example, for voltage instability problems, the switching strategy of the reactive compensation device can be adjusted, or the voltage regulation equipment can be checked and repaired. In abnormal conditions, the system must be fully inspected to find the root cause of the fault. It may be necessary to overhaul the equipment, replace the faulty components, or re-plan the load distribution to restore the normal operation of the system.

[0099] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. A distribution system operation status evaluation method based on q-order orthogonal fuzzy numbers and cloud model, characterized in that: The following steps are involved: S1. Construct q-order orthogonal fuzzy numbers, define fuzzy sets for key parameters in the distribution system, each fuzzy set represents a specific state range, and construct membership functions through the defined fuzzy sets. The membership functions map the actual observed values ​​of key parameters into a specific interval; use the membership functions to generate q-order orthogonal fuzzy numbers, which can represent the membership of parameter values ​​in multiple fuzzy sets, thereby capturing the uncertainty of the parameters; S2. Construct a cloud model, based on the q-order orthogonal fuzzy numbers generated in S1, to construct a cloud model to describe the uncertainty and randomness of the operating state of the distribution system; S3. Establish a state assessment model, using the cloud model constructed in S2 to establish a multi-dimensional distribution system operation state assessment model; S4. Comprehensive evaluation and decision-making. Combined with the status evaluation model established in S3, a comprehensive evaluation of the operating status of the distribution system is conducted. The cloud model constructed in S2 is converted into a membership function to evaluate the operating status of the distribution system. Combined with q-order orthogonal fuzzy numbers and cloud models, a comprehensive evaluation of the operating status of the distribution system is conducted, and the comprehensive membership of each status is calculated. Based on the comprehensive evaluation results, the operating status of the distribution system is analyzed, potential risks and problems are identified, and optimization suggestions are put forward.

2. The method for evaluating the operating status of a power distribution system based on q-order orthogonal fuzzy numbers and cloud model according to claim 1, characterized in that: The method for constructing q-order orthogonal fuzzy numbers in S1 includes: S11. Fuzzy set definition: For the key parameter set X in the distribution system, n }, where n is the number of key parameters, for each key parameter x in the key parameter set i According to its practical significance and value range in the operation of the distribution system, m fuzzy sets F are defined. i1 ,F i2 ,…,F im ; The fuzzy sets should fully cover all possible situations of the parameters, and there should be a smooth transition between adjacent fuzzy sets to accurately describe the fuzzy characteristics of the parameters; S12. Membership function construction, for each fuzzy set F ij , construct the corresponding membership function μF ij (x i ), the actual observed value of the key parameter is mapped to the interval [0,1] to indicate the degree to which the observed value belongs to the corresponding fuzzy set. The membership function μF ij (x i ) is expressed as: Among them, a ij is a fuzzy set F ij The lower limit value of the membership function defines the starting point of the range represented by the fuzzy set. In the distribution system, for voltage parameters, if a fuzzy set with low voltage is defined, a ij It is the minimum value of this low range, which means that when the voltage is lower than this value, the membership degree in this fuzzy set begins to increase gradually from 0; b ij is a fuzzy set F ij The middle value of b is also the parameter value corresponding to the maximum value of membership. For the fuzzy set in the normal voltage range, b ij Set to the center value of the normal voltage range. At this time, when the voltage is equal to b ij When , its degree of membership in the fuzzy set is 1, indicating that it completely belongs to this normal range; c ij is the upper limit of the fuzzy set, which determines the end point of the fuzzy set range. For the fuzzy set with high voltage, c ij It is the maximum value of the high voltage range. When the voltage is higher than this value, the membership degree in the fuzzy set gradually decreases to 0; S13.q-order orthogonal fuzzy number generation, for each key parameter x i , get its actual observed value x i o , the actual observed value x will be obtained i o Substitute it into the membership function corresponding to each fuzzy set constructed previously, and calculate the membership value in each fuzzy set; finally, these membership values ​​are combined into a vector to obtain the q-order orthogonal fuzzy number. Each element in the vector represents the degree of membership of the observation value in different fuzzy sets, thereby comprehensively describing the uncertainty of the parameter value from the perspective of multiple fuzzy sets.

3. The method for evaluating the operating status of a power distribution system based on q-order orthogonal fuzzy numbers and cloud model according to claim 1, characterized in that: In S2, building a cloud model is represented as: in, Represents the cloud model, Ex xi For expectation, Ex xi Determine the center of the cloud; xi is entropy; xi is the super entropy; ω ij is the weight of the fuzzy set; C ij is an eigenvalue of the fuzzy set; c ij is the jth value in a data sequence, associated with the ith key parameter; a ij is the jth value in another data sequence, associated with the ith key parameter.

4. The method for evaluating the operating status of a power distribution system based on q-order orthogonal fuzzy numbers and cloud model according to claim 1, characterized in that: In S3, the method for establishing a state assessment model includes: S31. Evaluation vector construction, considering multiple key parameters in S1, construct an evaluation vector, and the evaluation vector X′ is expressed as: X′=(x′1,x′2,…,x′ n ); Among them, x′1 is the actual observed value of the corresponding key parameter; the evaluation vector X′ integrates the state information of various key aspects at a certain moment, which is a comprehensive description of the operating state of the distribution system; S32. Similarity calculation, calculation of evaluation vector X′ and each key parameter cloud model The similarity is expressed as: S xi Indicates the similarity between the evaluation vector and each key parameter cloud model, S xi ∈[0, 1], S xi The closer it is to 1, the more similar the evaluation vector is to the cloud model, that is, the closer the current operating state of the distribution system is to the state represented by the cloud model; represents the Euclidean distance between the evaluation vector and the cloud model; S33. Calculate the comprehensive evaluation index, use the hierarchical analysis method to determine the weight of each key parameter in the evaluation, build a hierarchical model, compare each key parameter pairwise, build a judgment matrix, calculate the eigenvector and perform consistency test to obtain the relative weight of each key parameter; Calculate the comprehensive evaluation index E, The evaluation index E combines the similarities between each key parameter and the corresponding cloud model, and takes into account the weight of each parameter. It can comprehensively and comprehensively reflect the degree of closeness between the overall operating state of the distribution system and the ideal state. If the weight of the voltage parameter is large and its similarity with the corresponding cloud model is low, then the value of the comprehensive evaluation index will be greatly affected, indicating that the voltage parameter has a significant impact on the overall operating state of the system.

5. The method for evaluating the operating status of a power distribution system based on q-order orthogonal fuzzy numbers and cloud model according to claim 1, characterized in that: In S4, the comprehensive evaluation and decision-making method includes: S41. Cloud model to membership function conversion, the constructed cloud model Convert to membership function The conversion process is based on the digital characteristics of the cloud model; First, generate an En xi For the expectation, He 2 xi is a normal random number with variance En xi ′; Then, generate an Ex xi For expectation, xi ′) 2 A normal random number x′ with variance i , then it is a cloud droplet, where: By repeating the above steps many times, a sufficient number of cloud droplets are generated, and a membership function is constructed based on the generated cloud droplets. The membership function can more intuitively reflect the membership of key parameters under the uncertainty state described by the cloud model. S42. Comprehensive membership calculation, combining q-order orthogonal fuzzy numbers and the converted membership function to calculate the comprehensive membership of each state; Where n represents the number of key parameters involved in the evaluation process, m represents the number of fuzzy sets associated with each key parameter; μF ij (x iobs ) represents the membership function value in the jth fuzzy set under the i-th key parameter, x iobs is the observed value of the i-th key parameter, which represents the observed value x iobs The degree of membership in the jth fuzzy set ranges from 0 to 1; is the membership function value of the cloud model at, and the cloud model Related to the i-th key parameter, x ij It is a typical value in the cloud model, and its value range is between 0 and 1; The process integrates the membership information of the parameters represented by q-order orthogonal fuzzy numbers in multiple fuzzy sets and the uncertainty description provided by the membership function after cloud model conversion, thus obtaining a comprehensive and integrated membership value for accurately evaluating the operating status of the distribution system; S43. Status evaluation and decision-making, evaluate the operation status of the distribution system according to the comprehensive membership, set the upper and lower limits of the threshold, when μ 综合 When it is greater than the upper threshold, the power distribution system is judged to be in normal operation, indicating that the actual observed values ​​of the key parameters of the system are close to the ideal state, and the system operation is stable and reliable; When μ 综合 When it is less than the upper limit but greater than the lower limit, the system is in a warning state, which means that the system has a tendency to deviate from the ideal state to a certain extent. It is necessary to pay close attention to the changes in key parameters and timely check for potential risks. When μ 综合 When it is less than the lower limit, the system is in an abnormal state and there may be major problems in system operation.

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