Method and device for evaluating running state of power distribution network

Abstract

The embodiment of the invention discloses a power distribution network operation state evaluation method and device, and relates to the technical field of power systems, and the method comprises the steps: obtaining the operation data of each index in a multi-dimensional index system; a combined weighting strategy is adopted to determine the comprehensive weight of each index, a quantitative evaluation model is applied to obtain a quantitative evaluation intermediate result according to the operation data and the comprehensive weight, then two evaluation outputs are generated, the first evaluation output is used for dividing the operation state of the power distribution network to a preset discrete state level, and the second evaluation output is used for dividing the operation state of the power distribution network to a preset discrete state level; the second evaluation output is a continuously changing quantitative score, the change amplitude can reflect the change intensity of the running state of the power distribution network, and the second evaluation output is used for dynamic monitoring and early warning; according to the method and the device, dual evaluation output with complementary functions is generated, so that the static level can be given, timely early warning of the sudden change of the system state can be realized through the quantitative score sensitive to the state change, the problem that the evaluation result is not sensitive to the sudden change of the state is solved, and the evaluation accuracy and the decision support capability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a power distribution network operation state evaluation method and device. BACKGROUND

[0002] With the continuous expansion of the scale of the power distribution network and the wide access of new elements such as distributed power supply and diversified load, the operation environment of the power distribution network is increasingly complex, and the evaluation of the operation state thereof also faces new challenges. The traditional evaluation method usually relies on a single or a few key parameters, such as voltage deviation rate or power loss rate, which is simple but cannot comprehensively and accurately reflect the overall operation health status of the modern power distribution network.

[0003] In order to overcome the limitations of single parameter evaluation, some comprehensive evaluation methods have emerged. One kind of method adopts a subjective weighting method, such as the analytic hierarchy process, which invites experts in the field to judge and score the importance of each evaluation index, so as to determine the weight. However, this kind of method highly depends on the knowledge and experience of experts, and is highly subjective, which may lead to insufficient stability and universality of the evaluation results. Another kind of method adopts an objective weighting method, such as the entropy method, which calculates the weight according to the distribution characteristics and dispersion degree of the operation data itself, which reduces the human subjective interference, but in the case of low data quality or uneven distribution, the calculated weight may be distorted, and cannot truly reflect the actual importance of the index.

[0004] In order to take into account subjective experience and objective data, some technical solutions combine subjective and objective weighting methods, and apply mathematical models such as fuzzy comprehensive evaluation to quantitatively calculate the performance of the power distribution network, and finally obtain a comprehensive score or divide a state grade. However, the evaluation results generated by this kind of method, whether it is a specific grade or a score, is still a static conclusion in essence, which is mainly used for horizontal comparison of performance between different time periods or different power distribution networks. For the dynamic change of the operation state of the power distribution network, the change of this kind of static evaluation result may not be significant enough, thereby lacking the monitoring sensitivity and timely warning ability for state mutation. SUMMARY

[0005] The main purpose of the present application is to provide a power distribution network operation state evaluation method, which can solve the technical problem that the evaluation result is not sensitive enough to the mutation of the operation state of the power distribution network, and it is difficult to realize effective dynamic monitoring and timely warning.

[0006] To achieve the above purpose, the first aspect of the present application provides a power distribution network operation state evaluation method, which comprises:

[0007] acquiring operation data of each index in a preset multi-dimensional index system for evaluating the operation state of the power distribution network;

[0008] The combined weighting strategy combines a subjective weighting method for reflecting expert experience and an objective weighting method for reflecting data distribution characteristics;

[0009] A preset quantitative evaluation model is applied to calculate a quantitative evaluation intermediate result for representing the overall operation state of the power distribution network according to the operation data and the comprehensive weights;

[0010] Based on the quantitative evaluation intermediate result, a first evaluation output and a second evaluation output are generated; the first evaluation output is used to divide the operation state of the power distribution network into one of a plurality of preset discrete state levels, and the second evaluation output is a continuously changing quantitative score; the quantitative score is calculated according to the quantitative evaluation intermediate result and a preset calculation rule, and the preset calculation rule makes the change range of the quantitative score reflect the severity of the change in the operation state of the power distribution network, so as to dynamically monitor and warn the operation state of the power distribution network.

[0011] The second aspect of the present application provides a power distribution network operation state evaluation device, comprising:

[0012] A data acquisition module is configured to acquire operation data of each index in a preset multi-dimensional index system for evaluating the operation state of the power distribution network;

[0013] A weight calculation module is configured to determine the comprehensive weights of each index in the multi-dimensional index system by using a combined weighting strategy, wherein the combined weighting strategy combines a subjective weighting method for reflecting expert experience and an objective weighting method for reflecting data distribution characteristics;

[0014] A state evaluation module is configured to apply a preset quantitative evaluation model to calculate a quantitative evaluation intermediate result for representing the overall operation state of the power distribution network according to the operation data and the comprehensive weights;

[0015] A result generation module is configured to generate a first evaluation output and a second evaluation output based on the quantitative evaluation intermediate result; the first evaluation output is used to divide the operation state of the power distribution network into one of a plurality of preset discrete state levels, and the second evaluation output is a continuously changing quantitative score; the quantitative score is calculated according to the quantitative evaluation intermediate result and a preset calculation rule, and the preset calculation rule makes the change range of the quantitative score reflect the severity of the change in the operation state of the power distribution network, so as to dynamically monitor and warn the operation state of the power distribution network.

[0016] The application provides a power distribution network operation state evaluation method and device, which obtains operation data of each index in a preset multi-dimensional index system for evaluating the operation state of the power distribution network; adopts a combined weighting strategy to determine the comprehensive weight of each index in the multi-dimensional index system, the combined weighting strategy combines a subjective weighting method for reflecting expert experience and an objective weighting method for reflecting data distribution characteristics; applies a preset quantitative evaluation model to calculate a quantitative evaluation intermediate result for representing the overall operation state of the power distribution network according to the operation data and the comprehensive weight; generates a first evaluation output and a second evaluation output based on the quantitative evaluation intermediate result; wherein the first evaluation output is used to divide the operation state of the power distribution network into one of a plurality of preset discrete state levels, and the second evaluation output is a continuously changing quantitative score; the quantitative score is calculated according to the quantitative evaluation intermediate result and a preset calculation rule, and the preset calculation rule makes the change range of the quantitative score reflect the severity of the change of the operation state of the power distribution network, so as to dynamically monitor and warn the operation state of the power distribution network.

[0017] The application has the following beneficial effects:

[0018] 1. Accurate evaluation and early warning capability: the application generates two evaluation outputs with complementary functions, which not only gives a static level of the current operation state, but more importantly, introduces a second evaluation output sensitive to state changes. When the system state deteriorates sharply, the continuous quantitative score will change significantly, and the change range can timely and effectively indicate the state mutation, providing early warning for the operation and maintenance personnel, and solving the problem of insensitivity to state mutation. 2. Comprehensive and scientific evaluation system: by constructing a multi-dimensional index system and adopting a combined weighting strategy combining subjective and objective methods, the prior knowledge of expert experience and the objective law of operation data are integrated, making the weight distribution more reasonable, and the evaluation result more objective and comprehensive, avoiding the one-sidedness of a single weighting method. 3. Strong decision support: the combination of the two evaluation outputs provides a complete view from "static classification" to "dynamic monitoring" for the operation and maintenance personnel, which can not only understand the current state, but also predict future risks, providing strong decision support for preventive maintenance and optimization scheduling measures, and helping to improve the overall operation safety, stability and economy of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, brief introductions will be given to the drawings needed to be used in the embodiments or prior art descriptions. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0020] Wherein:

[0021] Figure 1 A power distribution network operation state evaluation method provided by an embodiment of the present application;

[0022] Figure 2 A structure schematic diagram of a power distribution network operation state evaluation system provided by an embodiment of the present application;

[0023] Figure 3 A schematic diagram of a level characteristic value and operation state level conversion relationship provided by an embodiment of the present application;

[0024] Figure 4 A level characteristic value conversion schematic diagram provided by an embodiment of the present application;

[0025] Figure 5 A power distribution network sample operation state evaluation result advantage and disadvantage ordering schematic diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the personnel in the art better understand the present application scheme, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor are within the scope of protection of the present application.

[0027] The terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0028] In this document, reference to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor does it necessarily refer to a particular alternative embodiment. It is explicitly and implicitly understood that the embodiments described herein can be combined with other embodiments.

[0029] The embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0030] Please refer toFigure 1 Fig. 1 shows a flowchart of a method for evaluating the operation state of a power distribution network according to an embodiment of the present application. As shown in Fig. 1, the method comprises the following steps. Figure 1

[0031] 101. Obtain operation data of each index in a preset multi-dimensional index system for evaluating the operation state of the power distribution network.

[0032] The execution subject in the embodiments of the present application can be a power distribution network operation state evaluation device. In actual application, the device can be an electronic device, and the method can be executed by a computer program.

[0033] Figure 2 Fig. 2 shows a structure diagram of a power distribution network operation state evaluation system according to an embodiment of the present application.

[0034] In an embodiment of the present application, step 101 can be executed by a data collection module 110 in an evaluation system 100 as shown in Fig. 2. The data collection module 110 is responsible for obtaining operation data of multi-dimensional indexes related to the operation state from multiple data sources of the power distribution network. The data sources include but are not limited to a power distribution automation system, an energy management system, an advanced measurement system, and various sensors deployed at key nodes (such as the outgoing bus of a 10-kilovolt substation, a power distribution transformer, an important load access point, etc.), for example, high-precision voltage sensors, micro phasor measurement units, etc. Figure 2 In order to comprehensively reflect the operation state of the power distribution network, the embodiment constructs a multi-dimensional evaluation index system. Optionally, the index system contains six criterion layers, namely safety, network, adaptability, reliability, quality, and economy, and each criterion layer contains a plurality of specific index layers.

[0035] The safety criterion layer is used to measure the ability of the power grid to resist disturbances and faults, and the subordinate indexes thereof can include: an overload index, reflecting whether the carrying capacity of the line or transformer exceeds the limit; a low voltage index, reflecting whether the user end voltage is lower than the allowed range; and an overvoltage index, reflecting whether the system has transient or steady overvoltage risk.

[0036] The network criterion layer focuses on the information-physical fusion characteristics of the power distribution network in the context of intelligentization, especially the network security and communication ability, and the subordinate indexes thereof can include: an information transmission vulnerability index, used to evaluate the security risk existing in the communication network; and an attacked frequency index, used to statistically analyze the number of times or attempts of the power grid information system subjected to network attacks.

[0037]

[0038] ​​The adaptability criterion layer evaluates the accommodation capacity and flexibility of the power grid to the access of new elements such as distributed energy and electric vehicles. The subordinate indexes thereof can include: a distributed power penetration rate, that is, a ratio of a distributed power installed capacity to a regional load; and a load fluctuation rate, reflecting the intensity of load change.

[0039] The reliability criterion layer, as the core of the traditional power grid evaluation, focuses on the ability to provide continuous and stable power supply to users. The subordinate indexes thereof can include: a user average outage time, that is, an average outage time of users in a statistical region within a certain period; and a power supply reliability rate, that is, a ratio of a power supply time to a statistical time.

[0040] The quality criterion layer mainly measures power quality. The subordinate indexes thereof can include: a voltage qualification rate, that is, a proportion of a time length of a statistical voltage within a qualified range; and a total harmonic distortion rate, used to measure the pollution degree of non-sinusoidal waveforms in the power grid.

[0041] The economic criterion layer considers the operation cost and benefit. The subordinate indexes thereof can include: a network loss rate, that is, a loss proportion of electric energy in the transmission and distribution process; and an operation and maintenance cost, covering related fees such as equipment maintenance and fault handling. Through the above system composed of six criterion layers and more than ten specific indexes, the running state of the distribution network can be comprehensively described from multiple dimensions.

[0042] In an optional implementation, after the step 101, the method further includes:

[0043] The above running data is preprocessed, and the preprocessing includes filling missing values in the running data by using an interpolation method, and removing outliers in the running data by using a 3σ principle.

[0044] Specifically, after obtaining the original running data, the data acquisition module 110 can preprocess the data to improve the data quality and ensure the accuracy of subsequent evaluation.

[0045] The preprocessing process mainly includes two aspects:

[0046] Firstly, for the problem of data missing, an interpolation method is used for filling. Due to sensor failure, communication interruption and other reasons, the collected data sequence may have missing values. In this embodiment, a cubic spline interpolation method is preferably used. This method constructs a segmented cubic polynomial between adjacent known data points, and ensures the continuity of the first and second derivatives at the connection points, so as to generate a smooth curve to fit the data trend. Compared with linear interpolation, cubic spline interpolation can better capture the nonlinear change characteristics of the data, so that the filled values are more accurate.

[0047] In a second aspect, a statistical method is used to identify and eliminate data anomalies. The operational data can include abnormal values or outliers caused by measurement errors, transient interference, etc. In this embodiment, the 3σ principle is used for detection. Specifically, for a time series data sample, the mean μ and standard deviation σ are first calculated, and then the data points outside the interval [μ-3σ, μ+3σ] are determined as abnormal values. It can be understood that according to the normal distribution theory, about 99.7% of the data should be distributed within the interval, so this method can effectively identify the data with extreme deviation. For the identified abnormal values, they can be marked, eliminated, or corrected using an interpolation method.

[0048] After preprocessing, a clean and complete data set is obtained and transmitted by the data acquisition module 110 to the weight calculation module 120 and the state evaluation module 130.

[0049] 102. A combination weighting strategy is used to determine the comprehensive weight of each index in the above multi-dimensional index system. The combination weighting strategy combines a subjective weighting method for reflecting expert experience and an objective weighting method for reflecting data distribution characteristics.

[0050] This step aims to scientifically determine the importance of each evaluation index in the overall evaluation. In this embodiment, the weight calculation module 120 shown in FIG. 1 can be used to execute this step. In order to overcome the one-sidedness of a single weighting method, this embodiment uses a combination weighting strategy to combine a subjective weighting method reflecting expert field knowledge with an objective weighting method revealing the internal law of data. Figure 2

[0051] In an optional implementation, the subjective weighting method includes an analytic hierarchy process or an expert scoring method.

[0052] The objective weighting method includes an entropy method or a CRITIC method.

[0053] Specifically, if the subjective weighting method uses the analytic hierarchy process, the weight calculation module 120 first constructs a hierarchical structure model, with the top layer being the overall goal (distribution network operation state evaluation), the middle layer being the six criterion layers, and the bottom layer being each specific index. Then, the system invites multiple experts in the field of power systems through a human-computer interaction interface to compare the relative importance of elements in the same level with respect to a certain goal in the previous level. When comparing, the 1-9 scale method can be used, for example, 1 indicates that the two are equally important, and 9 indicates that the former is the pair-wise comparison judgment matrix.

[0054] Specifically, the judgment matrix G can be normalized by column, and the obtained matrix H can be added by row:

[0055]

[0056] ​The subjective weight W of the evaluation index is calculated i :

[0057]

[0058] As an optional implementation, the subjective weighting method can use the "expert scoring method" instead of the analytic hierarchy process. The specific process is as follows: the weight calculation module 120 presents the preset evaluation index system to multiple field experts. Instead of pairwise comparison, the experts directly score the importance of each index independently, for example, using a 1-10 point scoring system. After collecting the scoring results of all experts, the arithmetic mean of the scores of each index is taken to obtain the average score of the index. Finally, the average scores of all indexes are normalized to obtain the subjective weight of each index. Compared with the analytic hierarchy process, this method is more convenient to operate, reduces the complexity of expert judgment, and can also effectively collect the experience and wisdom of the expert group.

[0059] In an embodiment, the objective weighting method can use the entropy method.

[0060] The index data is dimensionless, and the influence of the evaluation index on the overall state is not single. There may be a case where the larger the index value, the better the state evaluation effect - a maximum index; there may also be a case where the smaller the index value, the better the state evaluation effect - a minimum index; there may also be a case where the closer the index value to the intermediate state, the better the state evaluation effect - a central index. The feeder power factor, the load isolated power supply capability are maximum indexes, the overload, the voltage deviation, the line attachment capacity are central indexes, the overvoltage, the low voltage, the fault probability, the loss of load risk, the network loss rate, the reactive power shortage, the attack frequency, the attack severity, the information transmission vulnerability are minimum indexes. In order to unify the influence of the index on the state evaluation, standardization processing of the sample set is needed to eliminate the influence of the objective weighting result caused by the inconsistent units and inconsistent positive and negative effects of each sample index.

[0061] Specifically, the entropy value and the information utility value of the jth index are calculated as follows:

[0062]

[0063] D j =1-E j ,j=1,2,3......,14

[0064] The objective weight of the evaluation index is calculated according to the entropy value:

[0065]

[0066] Optionally, the objective weighting method can use the CRITIC method to replace the entropy method described above. The CRITIC method is an objective weighting method based on index variability and conflict, and its core idea is that the weight of an index depends on two factors: the variability (contrast intensity) within the index and the conflict between the indexes.

[0067] After obtaining the subjective weight and the objective weight, the weight calculation module 120 can combine the two by using weighted multiplication to obtain the final comprehensive weight. The combination formula can be:

[0068]

[0069] where V 1j is the subjective weight obtained by the analytic hierarchy process, and V 2j is the objective weight obtained by the entropy method. This combination method respects the prior knowledge of experts and considers the distribution characteristics of objective data, making the weight distribution more scientific and reasonable. The calculated comprehensive weight W j will be transmitted to the state evaluation module 130.

[0070] 103. Apply a preset quantitative evaluation model to calculate the quantitative evaluation intermediate result for representing the overall operation state of the power distribution network according to the running data and the comprehensive weight.

[0071] In this embodiment, this step can be performed by the state evaluation module 130 shown in Figure 2 . The state evaluation module 130 applies a preset attribute mathematical model to calculate the quantitative evaluation intermediate result for representing the overall operation state of the power distribution network according to the received running data and the comprehensive weight, i.e., the comprehensive attribute measure.

[0072] Specifically, the operation state level of the power distribution network needs to be defined in advance. Optionally, the operation state can be divided into five discrete state levels, including extreme, deterioration, abnormal, normal, and economic. Let the value of the jth evaluation index be x j , 1≤j≤m, and the set Z1, Z2, Z3, Z4, Z5 composed of the five evaluation levels of the operation state is an ordered partition class, where Z1<Z2<Z3<Z4<Z5. The five levels from poor to good comprehensively cover all possible states of the power distribution network.

[0073] Secondly, the classification standard matrix of each index needs to be established. For each evaluation index, the corresponding value range or threshold under the above five state levels needs to be determined according to the industry standard, operation procedure or historical data statistics. For example, for the voltage qualification rate index, it can be defined that less than 95% is extreme, [95%, 97%) is deteriorating, [97%, 98.5%) is abnormal, [98.5%, 99.9%) is normal, and more than 99.9% is economic. The classification standards of all indexes are integrated, that is, a multi-dimensional attribute classification standard is formed. For example:

[0074] A classification standard matrix Z describing five operation states is established, and there are m evaluation indexes in the matrix, which are I1, I2, I3, …, Im. m Each index is divided into five intervals, the left limit of the interval is a jk , and the right limit is b jk , where 1≤j≤m 1≤k≤5, a jk <b jk , the left limit forms a lower limit standard matrix A=[a jk ] m×5 , and the right limit forms an upper limit standard matrix B=[b jk ] m×5 .

[0075]

[0076] Then, for a sample to be evaluated (for example, the operation data of the power distribution network in a certain period), the state evaluation module 130 calculates the relative membership degree of each index value to each state level. The membership degree is a value between [0, 1], which represents the degree to which a certain index value belongs to a certain state level. The specific form of the membership function can be determined according to the characteristics of the index, which is usually a linear or broken line function.

[0077] Specifically, the relative membership degree of the index is calculated, and the relative membership degree of the lower and upper limit standards a jk (1≤j≤m,1≤k≤5) of the kth level operation state evaluation index j.

[0078]

[0079]

[0080] Finally, the comprehensive attribute measure of the sample is calculated by combining the comprehensive weight W j obtained in the foregoing steps. The comprehensive attribute measure can be understood as a vector (μ1, μ2, μ3, μ4, μ5), which represents the degree to which the sample to be evaluated belongs to the five state levels as a whole, and is a key quantitative intermediate result for generating the final evaluation result.

[0081] In a specific embodiment, the attribute measure interval can be calculated first, and the lower bound generalized weighted distance represents the lower bound difference between the sample set of the power distribution network and the kth operating state, which should minimize the weighted distance between the sample set and the evaluation standard, while satisfying μ k As the lower bound probability of the sample set of the power distribution network belonging to the kth operating state, the entropy is maximized. According to the principles of minimum generalized weighted distance and maximum entropy, the lower bound attribute measure of the sample set of the power distribution network is finally obtained as follows:

[0082]

[0083] In the formula, W j is the weight of the index, the sum of the weights is 1, B is a positive integer, generally taking the value of 10, μ k is the lower bound attribute measure. Similarly, the upper bound attribute measure μ

[0084]

[0085] The level characteristic value is calculated, and the lower and upper bound attribute measures are averaged to obtain the comprehensive attribute measure of the sample set of the power distribution network in the kth operating state:

[0086]

[0087] 104、Based on the above-mentioned quantitative evaluation intermediate result, a first evaluation output and a second evaluation output are generated; wherein the first evaluation output is used to divide the operating state of the power distribution network into one of the preset multiple discrete state levels, and the second evaluation output is a continuous quantitative score; the quantitative score is calculated according to the quantitative evaluation intermediate result and a preset calculation rule, and the preset calculation rule makes the change range of the quantitative score reflect the degree of change of the operating state of the power distribution network, so as to be used for dynamic monitoring and early warning of the operating state of the power distribution network.

[0088] This step aims to convert the quantitative intermediate result into a form friendly to operation and maintenance personnel and with early warning capability. In the present embodiment, this step can be performed by the result generation and early warning module 140 shown in Figure 2 Based on the comprehensive attribute measure calculated by the state evaluation module 130, the result generation and early warning module 140 generates two evaluation outputs that are complementary in function. The details are as follows:

[0089] First evaluation output: discrete state level. This output provides an intuitive and qualitative current state judgment for the operation and maintenance personnel. The result generation and early warning module 140 first calculates a level characteristic value according to the comprehensive attribute measure, and the calculation formula is as follows: The level characteristic value is a continuous numerical value, which comprehensively considers the information of the sample belonging to all levels.

[0090] Please refer to Figure 3 , Figure 3 The conversion relationship between the level characteristic value and the operation state level is shown. By presetting the threshold interval, k* can be divided into the corresponding level. For example, when P is in the interval [1, 1.5), the state is “extreme”; when P is in the interval [1.5, 2.5), the state is “deterioration”, and so on. As a more rigorous implementation, an attribute recognition criterion can be used, for example, to find a level k0 such that the cumulative membership degree from the level to the optimal level first exceeds a confidence threshold (such as 0.6), that is, and The current state level is determined to be Finally, the system outputs a clear level conclusion such as “normal” or “abnormal”.

[0091] The second evaluation output: a quantified score that changes continuously. This output aims to achieve dynamic monitoring and mutation warning of the operation state. The result generation and warning module 140 converts the comprehensive attribute measure into a continuous evaluation score according to a preset calculation rule. The design of the calculation rule aims to make the score value and its change sensitively reflect the state of the state and the change intensity. A feasible calculation rule is to assign a standard score to each of the five state levels, for example, economy = 95 points, normal = 85 points, abnormal = 75 points, deterioration = 65 points, and extreme = 30 points. Then, the evaluation score is calculated by weighted average:

[0092] R = [30 65 75 85 95]

[0093] where R k is the standard score of the kth level. The score calculated in this way is a continuous value between 0 and 100, which can finely depict the small changes of the state.

[0094] Further optionally, the result generation and warning module 140 can also include a warning submodule. The submodule continuously monitors the change rate or change amplitude of the second evaluation output (i.e., the evaluation score). The system can preset a warning threshold, for example, when the score decreases by more than 10 points in an evaluation period, it is determined that the system state has deteriorated significantly. At this time, the module will automatically generate a warning message, notify the operation and maintenance personnel through the monitoring system interface pop-up window, send a short message or an email, and so on, prompting them to pay attention to the possible risks, so as to realize timely warning of state mutation.

[0095] The embodiments of the present application can be executed by the evaluation system 100 as shown in Figure 2 Figure 1 ​The evaluation process shown realizes the whole process from data acquisition, weight calculation, state evaluation to result generation, and finally provides two evaluation results of static grading and dynamic early warning, effectively improving the comprehensiveness, accuracy and timeliness of the distribution network operation state evaluation.

[0096] In another embodiment, the state evaluation module 130 can use the technique for order preference by similarity to ideal solution (TOPSIS) to replace the attribute mathematical model in the foregoing embodiment.

[0097] TOPSIS is a classic multi-attribute decision-making method, and its core idea is to calculate the distance of each evaluation object from the optimal solution (positive ideal solution) and the worst solution (negative ideal solution) to sort and evaluate the evaluation objects. The specific evaluation process is as follows:

[0098] Construct a weighted decision matrix. The state evaluation module 130 first multiplies the standardized data matrix and the comprehensive weight W j calculated in step S20 to obtain a weighted normalized decision matrix V = (v ij ), where v ij = W j ·x′ ij .

[0099] Determine the positive ideal solution and the negative ideal solution. The positive ideal solution A + is a virtual sample composed of the optimal values of each index in the decision matrix, that is, where Correspondingly, the negative ideal solution A - is a virtual sample composed of the worst values of each index in the decision matrix, that is, where

[0100] Calculate the distance of each sample from the positive and negative ideal solutions. For each sample i to be evaluated, the state evaluation module 130 calculates the Euclidean distance of the sample from the positive ideal solution and the Euclidean distance of the sample from the negative ideal solution

[0101] Calculate the relative closeness. The relative closeness C i is a comprehensive evaluation index of each sample from the ideal solution, and the calculation formula is C i The value range of C is between 0 and 1, and the closer the value is to 1, the farther the sample is from the negative ideal solution and the closer the sample is to the positive ideal solution, and the better the corresponding distribution network operation state.

[0102] In this embodiment, the implementation of the result generation and early warning module 140 can also be adjusted accordingly:

[0103] For the second evaluation output (quantitative score continuously changing), the relative closeness C i calculated by the TOPSIS method can be directly taken as the output. Since C i is a continuous value in the interval [0, 1], its change can finely reflect the dynamic evolution of the operating state. When the power distribution network state changes from good to bad, C i will significantly decrease, and the change amplitude can also be used for dynamic monitoring and early warning.

[0104] For the first evaluation output (discrete state level), the result generation and early warning module 140 is implemented by setting a threshold interval for the closeness. For example, the following mapping rules can be predefined: when C i ∈ [0.8, 1.0], the state level is “economical”; when C i ∈ [0.6, 0.8), the state level is “normal”; when C i ∈ [0.4, 0.6), the state level is “abnormal”; when C i ∈ [0.2, 0.4), the state level is “deterioration”; and when C i ∈ [0, 0.2), the state level is “extreme”. By comparing the calculated continuous relative closeness C i with these preset threshold intervals, it can be divided into the corresponding discrete state level.

[0105] This embodiment successfully generates dual evaluation outputs with complementary functions by using the TOPSIS method as the quantitative evaluation model, and achieves sensitive monitoring of state mutations. This proves that the technical solution proposed in this application has strong model adaptability, and the core idea of generating intermediate results through a quantitative model and then generating “qualitative classification” and “quantitative sensitive monitoring” two outputs based on the intermediate results is not limited to this specific model of attribute mathematics, but can be applied to other multi-attribute decision models, thereby greatly widening the protection scope and application scenarios of this application.

[0106] In order to more clearly illustrate the method of the application and its effects, the following will be introduced in combination with experimental data.

[0107] Table 1 is a power distribution network operating state level division table provided by an embodiment of the application, and the power distribution network operating state calculation results are shown in Table 1:

[0108]

[0109] Table 1

[0110] As can be seen from the above table, each operating state interval is inconsistent, and the intervals of “extreme” and “economical” operating states are obviously smaller, which is consistent with the actual situation, verifying the rationality of the method.

[0111] Figure 4 This is a schematic diagram illustrating the conversion of level feature values ​​provided in an embodiment of this application. To more clearly show the size of the interval occupied by each operating state, the calculated level feature values ​​are converted. The conversion rules are as follows: Figure 4 As shown.

[0112] The level characteristic value calculated for time period 1 of the distribution network is 4.417. Figure 1 After conversion, the value is 5. Therefore, the distribution network is currently in an "economical" operating state with an evaluation score of 89.1739. At this time, the distribution network is operating very well and is working at its most efficient state. It can make full use of its various resources, improve power supply quality and efficiency, and reduce the operating cost of the distribution network. Parameters such as voltage, current, and load are all within the optimal fluctuation range.

[0113] Table 2 shows the results of a power distribution network operation status assessment provided in an embodiment of this application. The sample sets for the other seven time periods of the power distribution network were also calculated using the same formula, and the final operation status of the power distribution network is shown in Table 2.

[0114]

[0115] Table 2

[0116] Combining Table 2 and the radar chart, the assessment result for target layer X1 is "economical," and the assessment result for X6 is "normal." However, the difference between the characteristic values ​​of X6 and X1 is very small, and the difference in the yellow areas of the two is also small in the radar chart, indicating that X6 is very close to the "economical" state. The assessment results for X5 and X6 are both "normal," but the difference in their characteristic values ​​is significant; one is close to "economical," and the other is close to "vulnerable." This difference in the yellow areas is evident in the radar chart. Therefore, based on the above analysis, using the overall criterion layer to assess the operating status of the distribution network is reasonable to a certain extent.

[0117] In the sample X1, the failure probability and feeder power factor are in a fragile operating state, according to the traditional power distribution network operating state evaluation method, the power distribution network is also in a "fragile" state at this time, but in the method of the application, since the failure probability and feeder power factor account for a small proportion of the total, and other index values are basically in the "economic" state, the index value is very large, therefore, this period is evaluated as an "economic" operating state, it can be considered that although the feeder power factor and failure probability deviate from the normal value, it has little effect on the power distribution network. The new type of power distribution system gradually becomes a huge and complex system due to the large-scale access of distributed power and large-scale loads, which is composed of thousands of power equipment, including transformers, switch cabinets, cables, transmission lines, etc. The working state of each of these devices is very complicated, and only the cooperation and coordination between them can ensure the normal operation of the power distribution network. Similarly, in actual operation, many indicators of the power distribution network are interrelated. In many cases, due to weather or load fluctuations, one or more indicators may be abnormal, but the time is very short. At this time, comprehensive analysis is needed to correctly judge whether the power distribution network is in a normal state. The method of the application considers the comprehensive influence of each indicator of the power distribution network and analyzes it in the form of weight, which improves the accuracy of the power distribution network operating state evaluation.

[0118] Figure 5 A power distribution network sample operating state evaluation result advantage and disadvantage ordering schematic diagram is provided for the embodiments of the application.

[0119] The data set samples of 8 different time periods (T1-T8) in the actual power distribution network are displayed in order of the advantages and disadvantages of the operating state evaluation results, as shown in Figure 5 The horizontal coordinate represents the samples sorted in order of the advantages and disadvantages of the operating state, from left to right, they are {X1, X6, X4, X3, X8, X2, X5, X7}, and the corresponding vertical coordinate operating state evaluation levels are {5 levels, 4 levels, 4 levels, 4 levels, 4 levels, 4 levels, 4 levels, 3 levels} respectively. The evaluation sample X1 is in the "economic" state, at this time, the operating performance of the power distribution network is excellent, and the utilization rate of electric energy is maximized; the evaluation samples X2, X3, X4, X5, X6, X8 are in the "normal" state (and X6>X4>X3>X8>X2>X5), at this time, the parameters of the power distribution network are within the normal range, the power supply is reliable, the stability is good, and the safety is high. As the operating state gradually deteriorates, the parameters of the power distribution network will deviate from the normal value, and the stability will deteriorate; the evaluation sample X7 is in the suboptimal "fragile" state, at this time, the power distribution network may be unstable and out of control to some extent, and the operating and control personnel need to pay close attention and strive for reasonable optimization.

[0120] As shown in Figure 5It can also be seen that the level characteristic value and the evaluation score are in the same trend change, when the power distribution network is in the economic state (X1), the values of both are high, and from the image it can be seen that the difference between the two is small, when the operation state of the power distribution network gradually deteriorates, for example, from X5 to X7, that is, the power distribution network changes from a normal state to a fragile state, the level characteristic value and the evaluation score will decrease, but the degree of decrease starts to change, the level characteristic value decreases by a small margin, and the evaluation score decreases by a higher degree, so when the power distribution network jumps from a certain state to another state, the change of the level characteristic value may not be obvious, which may cause disturbance to the power grid personnel in evaluating the operation state of the power distribution network, and the addition of the evaluation score can avoid this situation, when the power distribution network suddenly jumps to a fragile or deteriorating operation state, the evaluation score will suddenly decrease, and the staff will also realize the sudden change of the operation state, so that certain measures can be taken, which will greatly improve the accuracy of the evaluation of the power distribution network.

[0121] The voltage sensor adopted in the embodiment of the application can monitor the voltage change in the power distribution network in real time and accurately, including the key parameters such as voltage amplitude, frequency and phase, and the high-precision monitoring capability provides a solid data basis for the stable operation of the power distribution network.

[0122] The method in the embodiment of the application constructs the power distribution network operation state evaluation system from six levels of safety, reliability and the like, covering all key aspects of the operation of the power distribution network, and such a comprehensive evaluation system can avoid the one-sidedness caused by single factor evaluation and accurately grasp the overall operation situation of the power distribution network.

[0123] In the embodiment of the application, the analytic hierarchy process and the entropy method are combined to calculate the evaluation index weight, the analytic hierarchy process fully considers the experience and judgment of the evaluator to determine the subjective weight, which reflects the importance of the human decision factor of the evaluation system, and the entropy method calculates the objective weight based on the objective information of the data to reduce the interference of human factors, the combination of the two weighting methods guarantees the scientificity of the evaluation and takes into account the flexibility in practical application, so that the evaluation result is more persuasive and reliable.

[0124] The method in the embodiment of the application establishes a power distribution network operation state evaluation model based on attribute mathematics, and through calculation of the comprehensive attribute measure and the evaluation score, the operation state of the power distribution network can be quantitatively evaluated, the operation health degree of the power distribution network can be intuitively reflected, and the problems and potential risks in the operation of the power distribution network can be accurately identified, through analysis of the indexes at each level by the evaluation model, the specific factors affecting the safety, reliability and the like of the power distribution network can be deeply mined to help take targeted measures for prevention and improvement in advance, and the overall operation quality of the power distribution network is improved.

[0125] In an embodiment, a computer readable storage medium storing a computer program is also provided. The computer program, when executed by a processor, causes the processor to perform any of the steps of the above method embodiments.

[0126] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database, or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0127]

[0128] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not contradict, they should be considered within the scope of the present disclosure.

[0129] The above embodiments only express several implementation manners of the present application, and the description is specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.​

Claims

1. A power distribution network operating state assessment method, characterized by, The method comprises: acquiring operation data of each index in a preset multi-dimensional index system for evaluating the operation state of the power distribution network; determining comprehensive weights of each index in the multi-dimensional index system by using a combined weighting strategy, the combined weighting strategy combining a subjective weighting method for reflecting expert experience and an objective weighting method for reflecting data distribution characteristics; applying a preset quantitative evaluation model to calculate a quantitative evaluation intermediate result for representing the overall operation state of the power distribution network according to the operation data and the comprehensive weights; generating a first evaluation output and a second evaluation output based on the quantitative evaluation intermediate result, wherein the first evaluation output is used to divide the operation state of the power distribution network into one of a plurality of preset discrete state levels, and the second evaluation output is a continuously changing quantitative score, the quantitative score being calculated according to the quantitative evaluation intermediate result and a preset calculation rule, the preset calculation rule making the change range of the quantitative score reflect the severity of the change in the operation state of the power distribution network, so as to dynamically monitor and warn the operation state of the power distribution network.

2. The power grid operating state evaluation method according to claim 1, characterized in that, The subjective weighting method comprises an analytic hierarchy process or an expert scoring method. The objective weighting method comprises an entropy method or a CRITIC method.

3. The power grid operating state evaluation method according to claim 1, characterized by, The quantitative evaluation model comprises an attribute mathematical model or a technique for order preference by similarity to ideal solution.

4. The power grid operating state assessment method according to claim 3, characterized in that, When the quantitative evaluation model is the attribute mathematical model, the quantitative evaluation intermediate result is a comprehensive attribute measure, the first evaluation output is a level characteristic value determined based on the comprehensive attribute measure, and the second evaluation output is an evaluation score determined based on the comprehensive attribute measure.

5. The power grid operating state assessment method according to claim 3, characterized by, When the quantitative evaluation model is the technique for order preference by similarity to ideal solution, the second evaluation output is a relative closeness calculated by the technique for order preference by similarity to ideal solution, and the first evaluation output is a state level obtained by dividing the relative closeness according to a preset closeness threshold interval.

6. The power grid operating state assessment method according to claim 1, wherein After the operation data of each index in the preset multi-dimensional index system for evaluating the operation state of the power distribution network is acquired, the method further comprises: preprocessing the operation data, the preprocessing comprising filling missing values in the operation data by using an interpolation method and removing abnormal values in the operation data by using a 3σ principle.

7. The power grid operating state assessment method according to claim 1, characterized by, The multi-dimensional index system comprises safety, network, adaptability, reliability, quality and economy criterion layers.

8. The power grid operating state assessment method according to claim 7, characterized by, The indexes of the safety are overload, low voltage and overvoltage. The indexes of the network are information transmission vulnerability, attack frequency and attack severity. The indexes of the adaptability are load isolated power supply capacity, reactive power deficiency and line connection capacity. The indexes of the reliability are loss of load risk and fault probability. The indexes of the quality are voltage deviation and feeder power factor. The indexes of the economy are network loss rate.

9. The power grid operating state assessment method according to any one of claims 1-8, characterized in that, The discrete state levels comprise five levels of extreme, deterioration, anomaly, normal and economy.

10. A power distribution network operation status assessment device, characterized in that, The method comprises: a data acquisition module configured to acquire operation data of each index in a preset multi-dimensional index system for evaluating the operation state of the power distribution network; The weight calculation module is configured to determine comprehensive weights of indexes in the multi-dimensional index system by using a combination weighting strategy, which combines a subjective weighting method for reflecting expert experience and an objective weighting method for reflecting data distribution characteristics. The state evaluation module is configured to calculate a quantitative evaluation intermediate result for representing the overall operation state of the power distribution network according to the operation data and the comprehensive weights by applying a preset quantitative evaluation model. The result generation module is configured to generate a first evaluation output and a second evaluation output based on the quantitative evaluation intermediate result. The first evaluation output is used to divide the operation state of the power distribution network into one of a plurality of discrete state levels, and the second evaluation output is a continuous quantitative score. The quantitative score is calculated according to the quantitative evaluation intermediate result and a preset calculation rule. The preset calculation rule makes the change range of the quantitative score reflect the severity of the change in the operation state of the power distribution network, so as to dynamically monitor and warn the operation state of the power distribution network.

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