CN113283702B - Power flow state evaluation method and device integrating safety and stability characteristics of power system - Google Patents
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Abstract
The invention discloses a tide state evaluation method and device fusing safety and stability characteristics of a power system, and belongs to the technical field of power systems and automation thereof. According to the method, the batch power grid operation mode data generated by machine learning is considered from the aspects of safety, mode rationality and data balance, the power grid operation mode is evaluated to form a safety index, a rationality index and a data balance index, and the rationality of the power grid operation mode data serving as machine learning sample data is evaluated from three aspects.
Description
Technical Field
The invention belongs to the technical field of electric power systems and automation thereof, and particularly relates to a tidal current state evaluation method and device integrating safety and stability characteristics of an electric power system.
Background
The operation mode of the power system is a general technical scheme of power system production and operation compiled by a power operation regulation and control department, is an important guarantee for ensuring the safety, stability and economic operation of a power grid, is an important embodiment of the power grid dispatching management level, and has guidance effect on power grid planning design, daily power generation planning, real-time power grid dispatching, maintenance plan formulation and the like.
The analysis of the operation mode of the power grid needs to fully consider complex factors such as the structure of the power grid, the distribution of a power supply and a load, the bearing capacity of equipment operation and the like, a large amount of special analysis and calculation such as short-circuit capacity, load flow distribution, stability, economic dispatching, reliability analysis and the like are carried out, the power transmission capacity of the power transmission and transformation equipment is fully exerted, the load requirement is met to the maximum extent, the power supply quality of the power grid is ensured to meet the specified standard, the whole safety, stability, reliability, flexibility and economic operation of the power grid are realized, the characteristics of various influencing factors, complex association relation, wide analysis range, large calculation workload and the like are realized, the technology such as deep learning and the like is used for the analysis of the operation mode of the power grid along with the development of the technologies such as large data, artificial intelligence and the like, the neural network model is constructed and trained by using mass sample data, the obtained neural network model can carry out the rapid judgment of the safety analysis of the operation mode of the power grid, the power grid operation analysis efficiency is greatly improved. The training samples have a large influence on the performance of the neural network model, the samples are derived from historical data or manually adjusted typical operation mode data and the like, but the data are limited, and some operation modes are approximately overlapped, so that power grid operation mode data need to be generated based on the data in a generalization mode, and the training samples are expanded. The method is crucial to how to evaluate a large amount of generated operation mode data, an effective means is not available at present, the existing operation mode evaluation method is mainly used for carrying out analysis such as load flow calculation, stability analysis and network loss calculation aiming at single operation mode data, and the reasonability evaluation of a large amount of sample mode data is rarely carried out.
Disclosure of Invention
In order to overcome the defect of evaluating batch power grid operation mode data generated in a generalization mode in the prior art, the invention provides a power flow state evaluation method and device integrating the safety and stability characteristics of a power system, and power grid operation mode data are evaluated from the three aspects of safety, reasonability and data balance.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention provides a tide state evaluation method fusing the safety and stability characteristics of a power system,
performing safety evaluation and rationality evaluation on each power grid operation mode data to be evaluated, and performing balance evaluation on a power grid operation mode data set to be evaluated; the power grid operation mode data to be evaluated is generalized power grid operation mode data generated by machine learning of historical power grid operation mode data and typical power grid operation mode data;
and comprehensively evaluating the tidal current state of the power grid operation mode data based on the evaluation results of safety, rationality and balance.
Further, the safety evaluation of the power grid operation mode data to be evaluated comprises the following steps:
carrying out load flow calculation on the power grid operation mode data to be evaluated, and judging whether convergence occurs or not;
and calculating the static safety level, the power angle stable level, the voltage stable level, the frequency stable level and the short-circuit current level of the converged power grid operation mode data.
Further, the rationality evaluation of the power grid operation mode data to be evaluated includes:
judging whether the key characteristic quantity of the power grid operation mode data to be evaluated is within the operation interval range;
judging whether the standby capacity levels of the thermal power, the conventional hydropower and the storage unit are within a preset standby capacity level range or not in the power grid operation mode data to be evaluated;
and the number of the first and second groups,
and calculating the network loss level of the power grid operation mode data to be evaluated.
Further, the key feature quantity determination method is as follows:
acquiring a historical operation mode data sample of a power grid, and acquiring a simulation result data sample through online safety and stability analysis and offline analysis;
analyzing the influence of the power grid operation state quantity on the safety and stability of the power grid under each examination fault of the data sample, and screening the key characteristic quantity under each examination fault.
Further, the judgment of whether the key characteristic quantity is within the operation interval range means that,
through carrying out statistical analysis to power grid historical operation mode data, obtain the operation interval of key characteristic vector in the power grid, include:
determining a power grid output operation interval according to the power grid output and the relevance of the load and time;
determining a hydropower running interval and a wind power running interval according to a four-season distribution rule of hydropower resources, a four-season distribution rule of wind power resources and a complementarity rule of the wind power resources and the hydropower resources;
and determining the operation interval of the electric load according to the relevance of the electric load and the time.
Further, the calculating the grid loss level of the grid operation mode data to be evaluated includes:
wherein L is loss To be evaluatedNetwork loss, n, in the operating mode of the power network Line To the number of lines, L l In order to account for the network loss of the line l,
is the average loss of the line l, n Trans Number of transformers, L m In order to account for the network loss of the transformer m,
is the average loss of the transformer m.
Further, the balance evaluation of the power grid operation mode data to be evaluated comprises the following steps:
calculating the distance between the power grid operation mode data to be evaluated and each operation mode cluster, and dividing the power grid operation modes to be evaluated into each operation mode cluster;
based on the division result, calculating an equilibrium evaluation index:
wherein tau is a balance evaluation index, N is the number of operation mode clusters, m is the number of power grid operation modes to be evaluated, and C i For the number of grid operation modes in the i-th class of operation mode cluster,
and averaging the number of the power grid operation modes for each operation mode cluster.
Further, the operation mode cluster determination mode is as follows:
and clustering the massive historical operation mode data of the power grid by using the extracted key characteristic quantity and adopting a clustering algorithm to obtain an operation mode cluster.
Further, the method also comprises the following steps:
and adding the screened power grid operation mode data to a power grid historical operation mode data sample library.
The invention also provides a tide state evaluation device fusing the safety and stability characteristics of the power system, which comprises the following components:
the evaluation module is used for evaluating the safety and the rationality of each power grid operation mode data to be evaluated and evaluating the balance of the power grid operation mode data set to be evaluated; the power grid operation mode data to be evaluated is generalized power grid operation mode data generated by machine learning of historical power grid operation mode data and typical power grid operation mode data;
and the number of the first and second groups,
and the screening module is used for comprehensively evaluating the tidal current state of the power grid operation mode data based on the evaluation results of safety, rationality and balance.
Furthermore, the evaluation module is specifically configured to,
carrying out load flow calculation on the power grid operation mode data to be evaluated, and judging whether convergence occurs or not;
and calculating the static safety level, the power angle stable level, the voltage stable level, the frequency stable level and the short-circuit current level of the converged power grid operation mode data.
Furthermore, the evaluation module is specifically configured to,
judging whether the key characteristic quantity of the power grid operation mode data to be evaluated is within the operation interval range;
judging whether the standby capacity levels of the thermal power, the conventional hydropower and the storage unit are within a preset standby capacity level range or not in the power grid operation mode data to be evaluated;
and the number of the first and second groups,
and calculating the network loss level of the power grid operation mode data to be evaluated.
Furthermore, the evaluation module is specifically configured to,
calculating the distance between the power grid operation mode data to be evaluated and each operation mode cluster, and dividing the power grid operation modes to be evaluated into each operation mode cluster;
based on the division result, calculating an equilibrium evaluation index:
wherein tau is a balance evaluation index, N is the number of operation mode clusters, m is the number of power grid operation modes to be evaluated, and C i For the number of grid operation modes in the i-th class of operation mode cluster,
and averaging the number of the power grid operation modes for each operation mode cluster.
The invention has the following beneficial effects:
the method aims at batch power grid operation mode data generated by a machine learning or manual adjustment mode, considers the aspects of the tidal current state (safety), mode rationality and data balance, and evaluates the power grid operation mode. The method not only considers the safety and the rationality of a single operation mode, but also analyzes the coverage balance of the generated operation mode set by using historical mode data and evaluates the operation mode set from the overall perspective.
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Fig. 1 is a flow chart of a power flow state evaluation method fusing safety and stability characteristics of a power system according to the present invention.
Detailed Description
The invention is further described below. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1:
one embodiment of the present invention provides a power flow state evaluation method integrating safety and stability characteristics of a power system, which includes the steps shown in fig. 1:
step 1, combining historical operation mode data and typical operation mode data to select key quantity features, performing cluster analysis on the historical operation modes, and establishing a basis for subsequent analysis of rationality, balance and the like, wherein the method comprises the following processes:
1-1) selecting key characteristic quantity, analyzing the influence of the safety and stability of the running state quantity of the power grid under each check fault based on the acquired historical running mode data and simulation result data samples of online safety and stability analysis and offline analysis, and selecting the state quantity with larger influence on the safety and stability of the power grid as the key characteristic quantity under the fault.
1-2) clustering historical operating modes. In consideration of the fact that the actual power grid operation has obvious regularity and repeatability, the historical operation mode data of the massive power grid are clustered by using the extracted key characteristic quantity information and using algorithms such as k-means, hierarchical clustering, SOM clustering and FCM clustering, and the historical operation modes are clustered to the corresponding operation mode clusters for balance analysis of the subsequent operation modes.
Step 2, carrying out rationality evaluation on the power flow from a safety angle, combining with power grid safety and stability guide, firstly carrying out rationality evaluation on the power flow from the safety angle, carrying out power flow calculation, and further carrying out various safety and stability analysis calculations if convergence is carried out, wherein the safety and stability analysis calculations comprise a static safety level, a power angle stability level, a voltage stability level, a frequency stability level, a short-circuit current level and the like; and evaluating by using maturity indexes such as: and (3) observing overload levels of lines, main transformers and key sections and measuring power angle stability levels by using an EEAC quantitative analysis method and the like according to the static safety level.
And 3, performing rationality evaluation on the power grid operation mode data, and evaluating the rationality of the power grid operation mode data according to the aspects of screened power grid key characteristic quantity operation interval judgment, power grid reserve capacity level, power grid loss and the like aiming at the power grid operation mode data generated by machine learning, manual adjustment and the like, wherein the rationality evaluation comprises the following steps:
3-1) judging the operation interval of key characteristic quantity of the power grid,
through statistical analysis of power grid operation data, the operation interval of key equipment (hydroelectric power, thermal power, wind and light, load, section and the like) in a power grid is in a certain range and has a certain association rule, for example, on the time distribution rule, the power grid output and load show strong association with time, the time distribution rule of the hydroelectric resources is more summer and less autumn and less winter, the wind power resources are generally abundant in spring, autumn and winter, the summer is lower, the wind power resources and the hydroelectric resources have certain complementarity, the thermal power generation is generally only influenced by fuel storage and the like, the power load generally also shows strong time distribution rule, for example, a typical city in the south of the Yangtze river, the annual load peak usually appears in 7 and 8 months, and the spring load level is lowest; on daily electricity loads, the lowest load point usually occurs at 4-5 am, the peak in the morning usually occurs at about 10:30, and the early peak in spring, autumn and winter usually is the maximum load value in one day, etc.
The overall level of power generation was evaluated as follows, where
For the full grid generating horizontal range, G 0 And the actual power generation amount is the power grid operation mode data.
And similarly, a key new energy unit, a key conventional unit, a key section and the like are investigated.
3-2) evaluating the level of the reserve capacity of the power grid,
the reserve of thermal power, conventional hydropower and a pumping and storage unit is examined,
wherein
For full network spare interval range, PR 0 To investigate the actual level of redundancy of the mode of operation.
3-3) evaluating the network loss level,
the line loss and the transformer loss are respectively evaluated, and indexes are as follows, wherein n Line To the number of lines, L l In order to account for the network loss of the line l,
the average network loss of the line l; n is Trans Number of transformers, L m In order to account for the network loss of the transformer m,
is the average loss of the transformer m.
Step 4, carrying out balance evaluation on the power grid operation mode, carrying out independent evaluation from the sample balance angle, carrying out operation mode data balance analysis based on the accumulated power grid historical operation mode data and online analysis result historical data obtained from online safety and stability analysis application and the like, wherein the data comprises operation modes, equipment fault information, model parameters, topological structures, expected fault sets, various safety and stability analysis results and the like, firstly calculating the distance between the operation mode data to be evaluated and each historical operation mode cluster to obtain a classification N, and further calculating a data balance evaluation index as follows:
wherein, C i The number of modes for the class i mode of operation,
the number of data in the average mode for each operation mode is as follows:
and m is the number of the generated operation modes, if the generated operation mode data are dispersed and balanced, the value of tau is smaller, and otherwise, the value of tau is larger.
And 5, expanding the historical data sample, evaluating through the three dimensions based on the generated operation mode data, filtering and screening qualified operation mode data, adding the qualified operation mode data to the historical data sample, and expanding the sample information.
Example 2:
the embodiment of the invention provides a tidal current state evaluation device integrating the safety and stability characteristics of a power system, which comprises:
the evaluation module is used for sequentially carrying out security evaluation, rationality evaluation and balance evaluation on the power grid operation mode data to be evaluated; the power grid operation mode data to be evaluated is generated in a machine learning or manual adjustment mode;
and the number of the first and second groups,
and the screening module is used for screening qualified power grid operation mode data based on the evaluation results of safety, rationality and balance.
The evaluation module of the embodiment of the invention is specifically used for,
carrying out load flow calculation on the power grid operation mode data to be evaluated, and judging whether convergence occurs or not;
and calculating the static safety level, the power angle stability level, the voltage stability level, the frequency stability level and the short-circuit current level of the converged power grid operation mode data.
The evaluation module of the embodiment of the present invention is further configured to,
judging whether the key characteristic quantity of the power grid operation mode data to be evaluated is within the operation interval range;
judging whether the standby capacity levels of the thermal power, the conventional hydropower and the storage unit are within a preset standby capacity level range or not in the power grid operation mode data to be evaluated;
and the number of the first and second groups,
and calculating the network loss level of the power grid operation mode data to be evaluated.
The evaluation module of the embodiment of the present invention is further configured to,
calculating the distance between the power grid operation mode data to be evaluated and each operation mode cluster, and dividing the power grid operation modes to be evaluated into each operation mode cluster;
based on the division result, calculating an equilibrium evaluation index:
wherein tau is a balance evaluation index, N is the number of operation mode clusters, m is the number of power grid operation modes to be evaluated, and C i For the number of grid operation modes in the i-th class of operation mode cluster,
and averaging the number of the power grid operation modes for each operation mode cluster.
It should be noted that the embodiment of the apparatus corresponds to the embodiment of the method, and the implementation manners of the embodiment of the method are all applicable to the embodiment of the apparatus and can achieve the same or similar technical effects, so that the detailed description is omitted here.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A power flow state evaluation method fusing safety and stability characteristics of a power system is characterized by comprising the following steps:
performing safety evaluation and rationality evaluation on each power grid operation mode data to be evaluated, and performing balance evaluation on a power grid operation mode data set to be evaluated; the power grid operation mode data to be evaluated is generalized power grid operation mode data generated by machine learning of historical power grid operation mode data and typical power grid operation mode data;
the safety evaluation of the power grid operation mode data to be evaluated comprises the following steps:
carrying out load flow calculation on the power grid operation mode data to be evaluated, and judging whether convergence occurs or not;
calculating static safety level, power angle stability level, voltage stability level, frequency stability level and short circuit current level of the converged power grid operation mode data;
carrying out rationality evaluation on the power grid operation mode data to be evaluated, wherein the rationality evaluation comprises the following steps:
judging whether the key characteristic quantity of the power grid operation mode data to be evaluated is within the operation interval range;
judging whether the standby capacity levels of the thermal power, the conventional hydropower and the storage unit are within a preset standby capacity level range or not in the power grid operation mode data to be evaluated;
and the number of the first and second groups,
calculating the network loss level of the power grid operation mode data to be evaluated;
the method for evaluating the balance of the power grid operation mode data set to be evaluated comprises the following steps:
calculating the distance between the power grid operation mode data to be evaluated and each operation mode cluster, and dividing the power grid operation modes to be evaluated into each operation mode cluster;
based on the division result, calculating an equilibrium evaluation index:
wherein tau is a balance evaluation index, N is the number of operation mode clusters, m is the number of power grid operation modes to be evaluated, and C i For the number of grid operation modes in the i-th class of operation mode cluster,
averaging the number of the power grid operation modes for each operation mode cluster;
and comprehensively evaluating the tidal current state of the power grid operation mode data based on the evaluation results of safety, rationality and balance.
2. The method for evaluating the power flow state integrating the safety and stability characteristics of the power system according to claim 1, wherein the key characteristic quantity determination mode is as follows:
acquiring a historical operation mode data sample of a power grid, and acquiring a simulation result data sample through online safety and stability analysis and offline analysis;
analyzing the influence of the power grid operation state quantity on the safety and stability of the power grid under each examination fault of the data sample, and screening the key characteristic quantity under each examination fault.
3. The method for evaluating the power flow state fusing the safety and stability characteristics of the power system according to claim 1, wherein the judgment of whether the key characteristic quantity of the power grid operation mode data to be evaluated is in the operation interval range means that,
through carrying out statistical analysis to power grid historical operation mode data, obtain the operation interval of key characteristic vector in the power grid, include:
determining a power grid output operation interval according to the power grid output and the relevance of the load and time;
determining the running interval of the water and the electricity and the running interval of the wind power according to the distribution rule of the water and electricity resources all year round, the distribution rule of the wind power resources all year round and the complementarity rule of the wind power resources and the water and electricity resources;
and determining the operation interval of the electric load according to the relevance of the electric load and the time.
4. The method for evaluating the power flow state fusing the safety and stability characteristics of the power system according to claim 1, wherein the calculating of the network loss level of the to-be-evaluated power grid operation mode data comprises:
wherein L is loss For the network loss, n, in the operating mode of the network to be evaluated Line To the number of lines, L l In order to account for the network loss of the line l,
is the average loss of the line l, n Trans Number of transformers, L m In order to account for the network loss of the transformer m,
is the average loss of the transformer m.
5. The method for evaluating the power flow state fusing the safety and stability characteristics of the power system as claimed in claim 1, wherein the operation mode cluster determination mode is as follows:
and clustering the massive historical operation mode data of the power grid by using the extracted key characteristic quantity and adopting a clustering algorithm to obtain an operation mode cluster.
6. The method for evaluating the power flow state of the power system with the safety and stability characteristics integrated according to claim 1, further comprising: and adding the screened power grid operation mode data to a power grid historical operation mode data sample library.
7. A power flow state evaluation device for integrating the safety and stability characteristics of a power system, characterized in that the power flow state evaluation is carried out by the power flow state evaluation method for integrating the safety and stability characteristics of a power system according to any one of claims 1 to 6,
the device comprises:
the evaluation module is used for evaluating the safety and the rationality of each power grid operation mode data to be evaluated and evaluating the balance of the power grid operation mode data set to be evaluated; the power grid operation mode data to be evaluated is generalized power grid operation mode data generated by machine learning of historical power grid operation mode data and typical power grid operation mode data;
and the number of the first and second groups,
and the screening module is used for comprehensively evaluating the tidal current state of the power grid operation mode data based on the evaluation results of safety, rationality and balance.
8. The power flow state evaluation device fused with the safety and stability characteristics of the power system as claimed in claim 7, wherein the evaluation module is specifically configured to,
carrying out load flow calculation on the power grid operation mode data to be evaluated, and judging whether convergence occurs or not;
and calculating the static safety level, the power angle stable level, the voltage stable level, the frequency stable level and the short-circuit current level of the converged power grid operation mode data.
9. The power flow state evaluation device fused with the safety and stability characteristics of the power system according to claim 7, wherein the evaluation module is specifically configured to,
judging whether the key characteristic quantity of the power grid operation mode data to be evaluated is within the operation interval range;
judging whether the standby capacity levels of the thermal power, the conventional hydropower and the storage unit are within a preset standby capacity level range or not in the power grid operation mode data to be evaluated;
and the number of the first and second groups,
and calculating the network loss level of the power grid operation mode data to be evaluated.
10. The power flow state evaluation device fused with the safety and stability characteristics of the power system as claimed in claim 7, wherein the evaluation module is specifically configured to,
calculating the distance between the power grid operation mode data to be evaluated and each operation mode cluster, and dividing the power grid operation modes to be evaluated into each operation mode cluster; based on the division result, calculating an equilibrium evaluation index:
wherein tau is a balance evaluation index, N is the number of operation mode clusters, m is the number of power grid operation modes to be evaluated, and C i For the number of grid operation modes in the i-th class of operation mode cluster,
and averaging the number of the power grid operation modes for each operation mode cluster.
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| CN108039737A (en) * | 2017-12-29 | 2018-05-15 | 国网能源研究院有限公司 | One introduces a collection net lotus coordinated operation simulation system |
| CN108133322A (en) * | 2017-12-21 | 2018-06-08 | 清华大学 | It is a kind of based on when sort run simulation balance of electric power and ener index calculating method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108133322A (en) * | 2017-12-21 | 2018-06-08 | 清华大学 | It is a kind of based on when sort run simulation balance of electric power and ener index calculating method |
| CN108039737A (en) * | 2017-12-29 | 2018-05-15 | 国网能源研究院有限公司 | One introduces a collection net lotus coordinated operation simulation system |
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