New energy monitoring method and system based on medium and low voltage distribution network

Abstract

The invention belongs to the technical field of new energy monitoring, and discloses a new energy monitoring method and system based on a medium and low voltage power distribution network, and the method comprises the following steps: building an integrated model database based on the business architecture of a power distribution master station, collecting medium and low voltage integrated monitoring data, and storing the data in a database; importing the medium and low voltage integrated monitoring data into an integrated model database in an integrated importing mode; based on the medium and low voltage integrated monitoring data in the integrated model database, combining an artificial intelligence algorithm and a deep neural network prediction algorithm model, predicting the power generation condition of the distributed power supply, and analyzing the operation state of the power distribution network according to the prediction result; and on the basis of the operation state of the power distribution network, real-time scheduling control and display are carried out on source-network-load-storage multi-type resources through a power distribution master station service architecture. The electric power and electric quantity balance and the new energy consumption level of the electric power system are improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy monitoring technology, and in particular to a new energy monitoring method, system, equipment and medium based on a medium and low voltage distribution network. Background Art

[0002] Guided by the energy development goals and the grid development goals of building a new power system with new energy as the main body, the energy structure is changing and new energy technologies are developing rapidly. High-penetration distributed new energy power generation and electric energy substitution loads are connected to the distribution network on a large scale, which has brought about tremendous changes in the structure and operation mode of the traditional power grid.

[0003] The access of distributed power sources changes the distribution network from "passive" to "active", and the power flow from "unidirectional" to "multidirectional", which can easily lead to problems such as limited space resources for the development of the distribution network and difficulty in expanding the network scale. At the same time, the large-scale access of electric energy replacement loads often has periodic and seasonal characteristics (such as electric heating loads only appear on a large scale in winter), making it extremely uneconomical to expand the distribution network by relying on the traditional extensive "peak capacity setting" method, and the asset utilization rate is relatively low.

[0004] Therefore, how to provide a new energy monitoring method, system, equipment and medium based on medium and low voltage distribution networks is a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present invention provide a new energy monitoring method, system, device and medium based on a medium and low voltage distribution network to solve the problems in the prior art.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a new energy monitoring method based on a medium and low voltage distribution network is provided.

[0008] In one embodiment, a new energy monitoring method based on a medium and low voltage distribution network includes the following steps:

[0009] Based on the business architecture of the power distribution master station, an integrated model database is created to collect medium and low voltage integrated monitoring data, and the medium and low voltage integrated monitoring data is imported into the integrated model database through integrated import;

[0010] Based on the integrated monitoring data of medium and low voltage in the integrated model database, combined with artificial intelligence algorithms and deep neural network prediction algorithm models, the power generation of distributed power sources is predicted, and the operating status of the distribution network is analyzed according to the prediction results; based on the operating status of the distribution network, the distribution master station business architecture is used to conduct real-time scheduling, control and display of multiple types of resources such as source-grid-load-storage.

[0011] In one embodiment, the creation of an integrated model database based on the power distribution master station business architecture, the collection of medium and low voltage integrated monitoring data, and the importation of the medium and low voltage integrated monitoring data into the integrated model database by an integrated import method also includes:

[0012] Based on the splicing of the medium and low voltage topological relationship on the distribution transformer side, the integrated connection of the medium and low voltage model is constructed;

[0013] Access the central service center to obtain the data required for business and present it to users in an interactive way.

[0014] In one embodiment, the medium and low voltage integrated monitoring data includes: area distributed photovoltaic data, feeder distributed photovoltaic data, grid distributed photovoltaic data, area load data, feeder load data and grid load data.

[0015] In one embodiment, the prediction of the power generation situation of the distributed power source includes: distributed photovoltaic power prediction, feeder load prediction, feeder reverse overload prediction and feeder power generation plan prediction.

[0016] In one embodiment, the analysis of the operating status of the distribution network includes: operation abnormality analysis, power grid analysis and capacity analysis;

[0017] The operation abnormality analysis includes: equipment overload abnormality analysis, voltage over-limit abnormality analysis, power generation abnormality analysis, three-phase imbalance analysis and channel abnormality diagnosis;

[0018] The power grid analysis includes: network topology analysis, state estimation, power flow calculation and static stability analysis;

[0019] The capacity analysis includes: absorption capacity analysis, grid carrying capacity analysis, regulation capacity analysis and load rate analysis.

[0020] In one embodiment, the real-time dispatching and control of multiple types of resources such as source-grid-load-storage through the distribution master station business architecture includes: substation maximum limit control, feeder maximum limit control, grid maximum limit control, auxiliary peak regulation control, feeder voltage over-limit control, feeder reactive power optimization control and grid fault handling.

[0021] In one embodiment, the power distribution master station business architecture includes: a production control area, a management information area and a distribution automation master station area;

[0022] The production control area interacts with the dispatching automation system;

[0023] The management information area interacts with the same source maintenance system, power supply service command platform, marketing system, power consumption information collection system and distribution network cloud master station indirectly or directly through the middle platform;

[0024] The management information area interacts with the auxiliary service market platform, the new load management system and the Internet of Vehicles platform indirectly or directly through the middle platform.

[0025] According to a second aspect of an embodiment of the present invention, a new energy monitoring system based on a medium and low voltage distribution network is provided.

[0026] In one embodiment, a new energy monitoring system based on a medium and low voltage distribution network includes:

[0027] The data acquisition and storage module is used to create an integrated model database based on the business architecture of the power distribution master station, collect medium and low voltage integrated monitoring data, and import the medium and low voltage integrated monitoring data into the integrated model database through an integrated import method;

[0028] The resource scheduling and control module is used to predict the power generation of distributed power sources based on the integrated monitoring data of medium and low voltage in the integrated model database, combined with artificial intelligence algorithms and deep neural network prediction algorithm models, and analyze the operating status of the distribution network according to the prediction results; based on the operating status of the distribution network, the distribution master station business architecture is used to perform real-time scheduling, control and display of multiple types of resources such as source-grid-load-storage.

[0029] In one embodiment, the creation of an integrated model database based on the power distribution master station business architecture, the collection of medium and low voltage integrated monitoring data, and the importation of the medium and low voltage integrated monitoring data into the integrated model database by an integrated import method also includes:

[0030] Based on the splicing of the medium and low voltage topological relationship on the distribution transformer side, the integrated connection of the medium and low voltage model is constructed;

[0031] Access the central service center to obtain the data required for business and present it to users in an interactive way.

[0032] In one embodiment, the medium and low voltage integrated monitoring data includes: area distributed photovoltaic data, feeder distributed photovoltaic data, grid distributed photovoltaic data, area load data, feeder load data and grid load data.

[0033] In one embodiment, the prediction of the power generation situation of the distributed power source includes: distributed photovoltaic power prediction, feeder load prediction, feeder reverse overload prediction and feeder power generation plan prediction.

[0034] In one embodiment, the analysis of the operating status of the distribution network includes: operation abnormality analysis, power grid analysis and capacity analysis;

[0035] The operation abnormality analysis includes: equipment overload abnormality analysis, voltage over-limit abnormality analysis, power generation abnormality analysis, three-phase imbalance analysis and channel abnormality diagnosis;

[0036] The power grid analysis includes: network topology analysis, state estimation, power flow calculation and static stability analysis;

[0037] The capacity analysis includes: absorption capacity analysis, grid carrying capacity analysis, regulation capacity analysis and load rate analysis.

[0038] In one embodiment, the real-time dispatching and control of multiple types of resources such as source-grid-load-storage through the distribution master station business architecture includes: substation maximum limit control, feeder maximum limit control, grid maximum limit control, auxiliary peak regulation control, feeder voltage over-limit control, feeder reactive power optimization control and grid fault handling.

[0039] In one embodiment, the power distribution master station business architecture includes: a production control area, a management information area and a distribution automation master station area;

[0040] The production control area interacts with the dispatching automation system;

[0041] The management information area interacts with the same source maintenance system, power supply service command platform, marketing system, power consumption information collection system and distribution network cloud master station indirectly or directly through the middle platform;

[0042] The management information area interacts with the auxiliary service market platform, the new load management system and the Internet of Vehicles platform indirectly or directly through the middle platform.

[0043] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0044] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0045] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0046] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0047] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0048] 1. Based on the existing distribution automation master station business architecture, the present invention expands and realizes panoramic monitoring, integrated prediction, and control decision-making of multiple types of resources such as source-grid-load-storage, and complements the grid dispatching functions such as active coordination and reactive power optimization. It realizes the real-time dispatching functions such as panoramic monitoring, integrated prediction, control decision-making, active coordination, reactive power optimization, and fault recovery of multiple types of resources of the medium and low voltage integrated active distribution network monitoring system, thereby improving the power balance of the power system and the level of new energy consumption.

[0049] 2. The present invention is based on the integration of medium and low voltage, and combined with the artificial intelligence AI expert large model system prediction method, adopts deep neural network and reliable prediction algorithm model, and continuously learns the difference between predicted value and actual value through AI to track the change of trend. At the same time, it continuously optimizes multiple algorithm models, so that the optimized algorithm model has better effect, which greatly improves the accuracy of new energy power generation prediction.

[0050] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0052] Figure 1 is a flow chart of a new energy monitoring method based on a medium and low voltage distribution network according to an exemplary embodiment;

[0053] Figure 2 is a principle block diagram of a new energy monitoring system based on a medium and low voltage distribution network according to an exemplary embodiment;

[0054] Figure 3 is a schematic diagram of the structure of a computer device according to an exemplary embodiment;

[0055] Figure 4 It is a diagram of a medium and low voltage integrated energy monitoring architecture of a new energy monitoring method based on a medium and low voltage distribution network according to an exemplary embodiment;

[0056] Figure 5 It is a flow chart of medium and low voltage integrated energy monitoring data interaction of a new energy monitoring method based on a medium and low voltage distribution network according to an exemplary embodiment;

[0057] Figure 6 It is a block diagram of distributed power supply fault location in a new energy monitoring method based on a medium and low voltage distribution network according to an exemplary embodiment;

[0058] Figure 7 It is a flow chart of distributed power supply fault location in a new energy monitoring method based on a medium and low voltage distribution network according to an exemplary embodiment;

[0059] Figure 8 It is a flow chart of a distributed power supply transfer strategy in a new energy monitoring method based on a medium and low voltage distribution network according to an exemplary embodiment. DETAILED DESCRIPTION

[0060] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0061] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0062] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0063] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0064] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0065] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0066] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0067] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0068] Figure 1An embodiment of the new energy monitoring method based on medium and low voltage distribution networks of the present invention is shown.

[0069] In this optional embodiment, the new energy monitoring method based on the medium and low voltage distribution network includes the following steps:

[0070] Step S101, based on the business architecture of the power distribution master station, an integrated model database is created, medium and low voltage integrated monitoring data is collected, and the medium and low voltage integrated monitoring data is imported into the integrated model database by an integrated import method;

[0071] Step S103, based on the medium and low voltage integrated monitoring data in the integrated model database, combined with artificial intelligence algorithms and deep neural network prediction algorithm models, predict the power generation of distributed power sources, and analyze the operating status of the distribution network according to the prediction results; based on the operating status of the distribution network, the source-grid-load-storage multi-type resources are dispatched, controlled and displayed in real time through the distribution master station business architecture.

[0072] In this optional embodiment, the integrated model database is created based on the business architecture of the distribution master station, the medium and low voltage integrated monitoring data is collected, and the medium and low voltage integrated monitoring data is imported into the integrated model database through an integrated import method, which also includes: splicing the medium and low voltage topological relationships based on the distribution transformer side to construct an integrated medium and low voltage model; obtaining the data required for the business by accessing the centralized service center, and presenting it to the user in an interactive manner.

[0073] In this optional embodiment, the medium and low voltage integrated monitoring data includes: substation distributed photovoltaic data, feeder distributed photovoltaic data, grid distributed photovoltaic data, substation load data, feeder load data and grid load data.

[0074] In this optional embodiment, the prediction of the power generation situation of the distributed power source includes: distributed photovoltaic power prediction, feeder load prediction, feeder reverse overload prediction and feeder power generation plan prediction.

[0075] In this optional embodiment, the analysis of the operating status of the distribution network includes: operation abnormality analysis, power grid analysis and capacity analysis;

[0076] The operation abnormality analysis includes: equipment overload abnormality analysis, voltage over-limit abnormality analysis, power generation abnormality analysis, three-phase imbalance analysis and channel abnormality diagnosis;

[0077] The power grid analysis includes: network topology analysis, state estimation, power flow calculation and static stability analysis;

[0078] The capacity analysis includes: absorption capacity analysis, grid carrying capacity analysis, regulation capacity analysis and load rate analysis.

[0079] In this optional embodiment, the real-time dispatching and control of multiple types of resources such as source-grid-load-storage through the distribution master station business architecture includes: substation maximum limit control, feeder maximum limit control, grid maximum limit control, auxiliary peak regulation control, feeder voltage over-limit control, feeder reactive power optimization control and grid fault handling.

[0080] In this optional embodiment, the power distribution master station business architecture includes: a production control area, a management information area and a distribution automation master station area;

[0081] The production control area interacts with the dispatching automation system;

[0082] The management information area interacts with the same source maintenance system, power supply service command platform, marketing system, power consumption information collection system and distribution network cloud master station indirectly or directly through the middle platform;

[0083] The management information area interacts with the auxiliary service market platform, the new load management system and the Internet of Vehicles platform indirectly or directly through the middle platform.

[0084] Figure 2 An embodiment of the new energy monitoring system based on medium and low voltage distribution networks of the present invention is shown.

[0085] In this optional embodiment, the new energy monitoring system based on the medium and low voltage distribution network includes:

[0086] The data acquisition and storage module 201 is used to create an integrated model database based on the business architecture of the power distribution master station, collect medium and low voltage integrated monitoring data, and import the medium and low voltage integrated monitoring data into the integrated model database through an integrated import method;

[0087] The resource scheduling control module 203 is used to predict the power generation of distributed power sources based on the integrated monitoring data of medium and low voltage in the integrated model database, combined with artificial intelligence algorithms and deep neural network prediction algorithm models, and analyze the operating status of the distribution network according to the prediction results; based on the operating status of the distribution network, the source-grid-load-storage multi-type resources are dispatched, controlled and displayed in real time through the distribution master station business architecture.

[0088] In this optional embodiment, the creation of an integrated model database based on the power distribution master station business architecture, the collection of medium and low voltage integrated monitoring data, and the importation of the medium and low voltage integrated monitoring data into the integrated model database by an integrated import method also includes:

[0089] Based on the splicing of the medium and low voltage topological relationship on the distribution transformer side, the integrated connection of the medium and low voltage model is constructed;

[0090] Access the central service center to obtain the data required for business and present it to users in an interactive way.

[0091] In this optional embodiment, the medium and low voltage integrated monitoring data includes: substation distributed photovoltaic data, feeder distributed photovoltaic data, grid distributed photovoltaic data, substation load data, feeder load data and grid load data.

[0092] In this optional embodiment, the prediction of the power generation situation of the distributed power source includes: distributed photovoltaic power prediction, feeder load prediction, feeder reverse overload prediction and feeder power generation plan prediction.

[0093] In this optional embodiment, the analysis of the operating status of the distribution network includes: operation abnormality analysis, power grid analysis and capacity analysis;

[0094] The operation abnormality analysis includes: equipment overload abnormality analysis, voltage over-limit abnormality analysis, power generation abnormality analysis, three-phase imbalance analysis and channel abnormality diagnosis;

[0095] The power grid analysis includes: network topology analysis, state estimation, power flow calculation and static stability analysis;

[0096] The capacity analysis includes: absorption capacity analysis, grid carrying capacity analysis, regulation capacity analysis and load rate analysis.

[0097] In this optional embodiment, the real-time dispatching and control of multiple types of resources such as source-grid-load-storage through the distribution master station business architecture includes: substation maximum limit control, feeder maximum limit control, grid maximum limit control, auxiliary peak regulation control, feeder voltage over-limit control, feeder reactive power optimization control and grid fault handling.

[0098] In this optional embodiment, the power distribution master station business architecture includes: a production control area, a management information area and a distribution automation master station area;

[0099] The production control area interacts with the dispatching automation system;

[0100] The management information area interacts with the same source maintenance system, power supply service command platform, marketing system, power consumption information collection system and distribution network cloud master station indirectly or directly through the middle platform;

[0101] The management information area interacts with the auxiliary service market platform, the new load management system and the Internet of Vehicles platform indirectly or directly through the middle platform.

[0102] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are further described below from the perspective of architecture and principle, as follows:

[0103] As the central link of the new power system and energy transformation, the distribution network aggregates multi-level energy and integrates multi-dimensional information. The main control objects of the new distribution network are widely distributed on 10kV lines and substations. The distribution automation master station system plays a pivotal role in connecting the upper and lower levels, and has sufficient application requirements for expanding the smart distribution network. Following the existing distribution master station business architecture, it expands and realizes panoramic monitoring, fusion prediction, and control decision-making of multiple types of resources such as source, network, load, and storage, and complements the grid dispatching functions such as active coordination and reactive power optimization. It realizes transparent perception of medium and low voltage distribution networks, grasps various types of data information in real time, and realizes intelligent coordination of source, network, load, and storage.

[0104] The present invention is based on the integration of medium and low voltage, and combined with the current artificial intelligence AI technology, adopts the core route of "prediction → optimization", relying on the prediction function of the large model of distributed power AI experts to effectively support the transformation of the distribution network from "source follows load" to "source and load interaction". According to the distributed power generation forecast, the power generation plan, energy consumption plan and energy storage plan for the next day can be reasonably arranged to reduce the periodic and seasonal phenomena caused by weather changes, and to improve the new distribution network's ability to regulate and control various types of distributed resources, fault handling capabilities, and improve its safety and stability. Provides a new idea. Supported by the accurate prediction of distributed power generation, typical application scenarios such as active fault handling, power reverse transmission limit, source-grid-load-storage coordination, etc. are realized to build a medium and low voltage integrated active distribution network monitoring system.

[0105] Based on the integration of medium and low voltage, and combined with the artificial intelligence AI expert large model system prediction method, it adopts deep neural networks and reliable prediction algorithm models, and uses AI to continuously learn the difference between predicted values ​​and actual values ​​to track trend changes. At the same time, it continuously optimizes multiple algorithm models to make the optimized algorithm models more effective, greatly improving the accuracy of new energy power generation prediction.

[0106] At the same time, based on the business architecture of the distribution network master station, with the functions of medium and low voltage data collection and processing, distributed power source access and control, comprehensive alarm, feeder automation, topology analysis application, load transfer, etc. in the distribution network, the medium and low voltage integrated active distribution network monitoring system realizes panoramic monitoring of multiple types of resources such as source-grid-load-storage, integrated prediction, control decision-making, active power coordination, reactive power optimization, fault recovery and other real-time dispatching functions, thereby improving the power balance of the power system and the level of new energy consumption.

[0107] like Figure 4As shown in the figure, it is an architecture diagram of the medium and low voltage integrated energy monitoring system. The medium and low voltage integrated energy monitoring system, through the existing distribution master station business architecture, makes full use of the deployed functions of the master station, based on the functions of medium and low voltage data collection and processing of the distribution network, distributed power supply access and control, comprehensive alarm, feeder automation, topology analysis application, load transfer, etc., supports the expansion of panoramic monitoring of multiple types of resources such as source-grid-load-storage, integrated prediction, control decision-making, active coordination, reactive power optimization, fault recovery and other real-time dispatching functions, so as to improve the power balance of the power system and the level of new energy consumption.

[0108] The monitoring system source-grid-load-storage coordinated control module functions include operation monitoring, real-time control, power grid analysis, power forecasting, comprehensive analysis, human-machine interface, etc. The details are as follows:

[0109] (1) Real-time power generation monitoring: distributed photovoltaic monitoring in substations, distributed photovoltaic monitoring on feeders, distributed photovoltaic monitoring on grids, load monitoring in substations, load monitoring on feeders, and load monitoring on grids.

[0110] (2) Operation abnormality analysis: equipment overload abnormality analysis, voltage over-limit abnormality analysis, power generation abnormality analysis, three-phase imbalance analysis, and channel abnormality diagnosis.

[0111] (3) Real-time control: maximum limit control of substation area, maximum limit control of feeder line, maximum limit control of grid, auxiliary peak load regulation control, feeder voltage over-limit control, feeder reactive power optimization control, and grid fault handling.

[0112] (4) Power grid analysis: including network topology analysis, state estimation, power flow calculation, and static stability analysis.

[0113] (5) Forecasting and planning: distributed photovoltaic power forecasting, feeder load forecasting, feeder reverse overload forecasting, feeder power generation planning;

[0114] (6) Analysis and evaluation: absorption capacity analysis, grid carrying capacity analysis, regulation capacity analysis, and load factor analysis;

[0115] (7) Human-computer interface: screen display, screen editing, data interaction, and visualization.

[0116] In addition, the data collection of the medium and low voltage integrated monitoring system is mainly based on the distribution automation master station, creating an integrated model database, and importing and connecting medium and low voltage equipment through integrated import. Based on the splicing of the medium and low voltage topological relationship on the distribution transformer side, the integrated connection of the medium and low voltage model is constructed. The medium voltage, low voltage, and medium and low voltage fusion applications obtain the data required by the business by accessing the central platform service center, completing the business processing and presenting it to the user.

[0117] like Figure 5As shown, in the generation control area I, it interacts with the dispatching automation system to achieve safe and reliable operation of the power grid and support operation strategy regulation;

[0118] In the management information area IV, the middle platform interacts directly or indirectly with the same source maintenance system, power supply service command platform, marketing system, power consumption information collection system, and distribution network cloud master station to support real-time control and operation and maintenance management of the power grid;

[0119] In the management information area IV, it interacts directly or indirectly with the auxiliary service market platform, the new load management system, and the Internet of Vehicles platform through the middle platform to support the dynamic balance operation mode and commercial operation mode of source-load interaction.

[0120] Thirdly, the ownership and control of distributed power sources are generally in the hands of users. Distributed power sources are just a "black box" that power companies cannot see or control. The grid source boundary switch provides a fully controllable monitoring point for power companies.

[0121] Based on the remote data collection of various distributed power sources, the operation monitoring and coordinated control functions are realized, such as source-load characteristic monitoring, abnormal alarm, synchronous grid connection control, and cutting off related distributed power sources through grid-source boundary switches or grid connection point switches in emergency situations.

[0122] Therefore, if Figure 6 As shown, a variety of solutions are used for active distribution network fault location, among which low permeability lines (≤30%, adjustable): do not affect fault location, and use traditional system fault location logic. High permeability lines (>30%, adjustable): the probability of misjudgment interval using traditional location strategies is high, and the circuit breaker reclosing and terminal direction protection functions need to be combined to complete fault location. The details are as follows:

[0123] Incremental terminal: According to the penetration rate of distributed power sources on the line, active fault interval positioning based on directional protection elements is adopted.

[0124] Existing terminals: The non-directional protection master station system cannot complete the interval positioning through telesignaling and telemetering analysis, and adopts the reclosing strategy.

[0125] like Figure 7 As shown, assuming Figure 6 Fault between K2 and K3;

[0126] Case 1: No directional protection element, CB1 trips;

[0127] DG anti-islanding protection action, if the protection action fails, the network source boundary switch starts the protection tripping delay 3s, the master station side judges and starts the anti-islanding tripping;

[0128] CB1 recloses and the permanent fault trips again;

[0129] Determine the fault interval based on the fault action signal;

[0130] Case 2: There is a directional protection element and CB1 trips;

[0131] The DG anti-islanding protection is activated, and the master station locates and determines the fault section based on the fault signal of the directional protection element.

[0132] In addition, if Figure 8 As shown, in the case of active fault transfer, the grid power supply is used first to restore the power supply in the non-fault area, and then the distributed power supply is used as the transfer source to calculate the output. The distributed power supply and the grid jointly / independently transfer to restore the power supply in the non-fault area. For fault recovery, the power supply side is used to complete the line power restoration, and then the distributed power supply is connected to the grid. The specific strategies are as follows:

[0133] Strategy 1: The power reserve capacity of the power grid is greater than the transfer area;

[0134] 1. Isolation of K1 and K2;

[0135] 2. CB1, contact K6 remotely;

[0136] 3. KM2 is closed and distributed power supply is connected to the grid;

[0137] Strategy 2: The power reserve capacity of the power grid is smaller than that of the transfer area;

[0138] 1. Isolation of K1, K2 and K5;

[0139] 2. CB1 and K6 are closed, and partial power supply is restored through the grid power supply;

[0140] 3. Based on the predicted output of distributed power generation, KM2 is controlled to restore the remaining power outage areas.

[0141] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0142] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0143] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0144] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0145] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0146] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A new energy monitoring method based on medium and low voltage distribution networks, characterized in that: The method comprises the following steps: Based on the business architecture of the power distribution master station, an integrated model database is created to collect medium and low voltage integrated monitoring data, and the medium and low voltage integrated monitoring data is imported into the integrated model database through integrated import; Based on the integrated monitoring data of medium and low voltage in the integrated model database, combined with artificial intelligence algorithms and deep neural network prediction algorithm models, the power generation of distributed power sources is predicted, and the operating status of the distribution network is analyzed according to the prediction results; based on the operating status of the distribution network, the distribution master station business architecture is used to conduct real-time scheduling, control and display of multiple types of resources such as source-grid-load-storage.

2. The new energy monitoring method based on medium and low voltage distribution network according to claim 1 is characterized in that: The method of creating an integrated model database based on the power distribution master station business architecture, collecting medium and low voltage integrated monitoring data, and importing the medium and low voltage integrated monitoring data into the integrated model database by means of integrated import also includes: Based on the splicing of the medium and low voltage topological relationship on the distribution transformer side, the integrated connection of the medium and low voltage model is constructed; Access the central service center to obtain the data required for business and present it to users in an interactive way.

3. The new energy monitoring method based on medium and low voltage distribution network according to claim 1 is characterized in that: The medium and low voltage integrated monitoring data includes: distributed photovoltaic data in the substation, distributed photovoltaic data in the feeder, distributed photovoltaic data in the grid, substation load data, feeder load data and grid load data.

4. The new energy monitoring method based on medium and low voltage distribution network according to claim 2 is characterized in that: The prediction of the power generation situation of the distributed power source includes: distributed photovoltaic power prediction, feeder load prediction, feeder reverse overload prediction and feeder power generation plan prediction.

5. The new energy monitoring method based on medium and low voltage distribution network according to claim 1 is characterized in that: The analysis of the operating status of the distribution network includes: abnormal operation analysis, power grid analysis and capacity analysis; The operation abnormality analysis includes: equipment overload abnormality analysis, voltage over-limit abnormality analysis, power generation abnormality analysis, three-phase imbalance analysis and channel abnormality diagnosis; The power grid analysis includes: network topology analysis, state estimation, power flow calculation and static stability analysis; The capacity analysis includes: absorption capacity analysis, grid carrying capacity analysis, regulation capacity analysis and load rate analysis.

6. The new energy monitoring method based on medium and low voltage distribution network according to claim 5 is characterized in that: The real-time dispatching and control of multiple types of resources such as source-grid-load-storage through the distribution master station business architecture includes: maximum limit control of the substation area, maximum limit control of the feeder line, maximum limit control of the grid, auxiliary peak regulation control, feeder voltage over-limit control, feeder reactive power optimization control and grid fault handling.

7. The new energy monitoring method based on medium and low voltage distribution network according to claim 1 is characterized in that: The business architecture of the power distribution master station includes: a production control area, a management information area and a power distribution automation master station area; The production control area interacts with the dispatching automation system; The management information area interacts with the same source maintenance system, power supply service command platform, marketing system, power consumption information collection system and distribution network cloud master station indirectly or directly through the middle platform; The management information area interacts with the auxiliary service market platform, the new load management system and the Internet of Vehicles platform indirectly or directly through the middle platform.

8. A new energy monitoring system based on medium and low voltage distribution networks, characterized in that: The system includes: The data acquisition and storage module is used to create an integrated model database based on the business architecture of the power distribution master station, collect medium and low voltage integrated monitoring data, and import the medium and low voltage integrated monitoring data into the integrated model database through an integrated import method; The resource scheduling and control module is used to predict the power generation of distributed power sources based on the integrated monitoring data of medium and low voltage in the integrated model database, combined with artificial intelligence algorithms and deep neural network prediction algorithm models, and analyze the operating status of the distribution network according to the prediction results; based on the operating status of the distribution network, the distribution master station business architecture is used to perform real-time scheduling, control and display of multiple types of resources such as source-grid-load-storage.

9. The new energy monitoring system based on medium and low voltage distribution network according to claim 8 is characterized in that: The method of creating an integrated model database based on the power distribution master station business architecture, collecting medium and low voltage integrated monitoring data, and importing the medium and low voltage integrated monitoring data into the integrated model database by means of integrated import also includes: Based on the splicing of the medium and low voltage topological relationship on the distribution transformer side, the integrated connection of the medium and low voltage model is constructed; Access the central service center to obtain the data required for business and present it to users in an interactive way.

10. The new energy monitoring system based on medium and low voltage distribution network according to claim 8 is characterized in that: The medium and low voltage integrated monitoring data includes: distributed photovoltaic data in the substation, distributed photovoltaic data in the feeder, distributed photovoltaic data in the grid, substation load data, feeder load data and grid load data.

11. The new energy monitoring system based on medium and low voltage distribution network according to claim 10 is characterized in that: The prediction of the power generation situation of the distributed power source includes: distributed photovoltaic power prediction, feeder load prediction, feeder reverse overload prediction and feeder power generation plan prediction.

12. The new energy monitoring system based on medium and low voltage distribution network according to claim 8, characterized in that: The analysis of the operating status of the distribution network includes: abnormal operation analysis, power grid analysis and capacity analysis; The operation abnormality analysis includes: equipment overload abnormality analysis, voltage over-limit abnormality analysis, power generation abnormality analysis, three-phase imbalance analysis and channel abnormality diagnosis; The power grid analysis includes: network topology analysis, state estimation, power flow calculation and static stability analysis; The capacity analysis includes: absorption capacity analysis, grid carrying capacity analysis, regulation capacity analysis and load rate analysis.

13. The new energy monitoring system based on medium and low voltage distribution network according to claim 8 is characterized in that: The real-time dispatching and control of multiple types of resources such as source-grid-load-storage through the distribution master station business architecture includes: maximum limit control of the substation area, maximum limit control of the feeder line, maximum limit control of the grid, auxiliary peak regulation control, feeder voltage over-limit control, feeder reactive power optimization control and grid fault handling.

14. The new energy monitoring system based on medium and low voltage distribution network according to claim 8, characterized in that: The business architecture of the power distribution master station includes: a production control area, a management information area and a power distribution automation master station area; The production control area interacts with the dispatching automation system; The management information area interacts with the same source maintenance system, power supply service command platform, marketing system, power consumption information collection system and distribution network cloud master station indirectly or directly through the middle platform; The management information area interacts with the auxiliary service market platform, the new load management system and the Internet of Vehicles platform indirectly or directly through the middle platform.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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