Active transformer area risk state identification method, system and device and medium

Abstract

The invention discloses an active transformer area risk state identification method, system, device and medium, and the method comprises the steps: carrying out the simulation of different distributed photovoltaic access conditions through a low-voltage transformer area power grid radiation model, and extracting the voltage and current characteristics at a monitoring point of a low-voltage intelligent circuit breaker based on a simulation result; performing calculation based on voltage and current characteristics at the monitoring point of the low-voltage intelligent circuit breaker to obtain identification characteristics; performing risk state identification based on the identification features to obtain the risk state of the transformer area; wherein the risk state comprises a three-phase imbalance risk state, a voltage out-of-limit risk state and a power reverse transmission risk state; according to the method, the risk state identification is carried out by extracting the characteristics of the monitoring points of the low-voltage intelligent circuit breaker, so that the accuracy of the risk state identification of the distributed photovoltaic access transformer area is greatly improved; through the radiation type model of the low-voltage transformer area power grid, conditions in an actual power grid can be better simulated and analyzed.

Description

Technical Field

[0001] The present application relates to the field of low-voltage distribution station areas, low-voltage circuit breakers and station area risk status identification, and specifically to an active station area risk status identification method, system, equipment and medium. Background Art

[0002] With the large-scale distributed photovoltaic access to the low-voltage distribution network, the volatility and intermittency of its output power will bring a huge impact to the distribution network. The low-voltage distribution substation has gradually transformed from the traditional form of one-way energy transmission and clear source-grid-load positioning to a new distribution system with two-way flow and blurred source-grid-load boundaries. First, with the access of a large number of non-full-phase distributed power sources such as photovoltaic power generation, the inherent three-phase imbalance characteristics of the distribution network are more prominent. Secondly, when the relationship between the output supply of photovoltaics and the power load demand is seriously unbalanced, the voltage limit problem arises, especially the problem of feeder voltage exceeding the upper limit during the peak power generation period. After that, if the active output of DG is greater than the active load, power reverse transmission will occur. During the step-up process, the distribution transformer will not only allow a decrease in the passing capacity, but also greatly increase the loss of transmission power. In this disclosure, the photovoltaic penetration rate is defined as the ratio of photovoltaic output to load power. When the penetration rate of distributed power sources exceeds 100%, it will cause power reverse transmission in the area, which will affect the overcurrent protection of the transformer. In addition, the access cost of distributed photovoltaics is high and the control is difficult. In order to reduce its impact on the power grid, the system usually adopts restriction and isolation to deal with distributed photovoltaics. In order to solve the problem of frequent risk states of low-voltage distribution networks and improve the absorption capacity of renewable energy, the research on risk state identification of low-voltage distribution networks containing a high proportion of renewable energy has become a focus. Summary of the invention

[0003] In order to solve the problem of how to improve the accuracy of risk status identification of large-scale distributed photovoltaic access areas in the prior art, this application proposes a method for identifying risk status of active areas, including:

[0004] Different distributed photovoltaic access conditions are simulated through the low-voltage grid radiation model, and the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point are extracted based on the simulation results;

[0005] Calculate based on the voltage and current characteristics at the monitoring point of the low-voltage intelligent circuit breaker to obtain identification characteristics;

[0006] Perform risk status identification based on the identification features to obtain the risk status of the station area;

[0007] The risk states include: a three-phase unbalance risk state, a voltage over-limit risk state and a power reverse transmission risk state.

[0008] Preferably, simulating different distributed photovoltaic access situations through a low-voltage area power grid radiation model, and extracting voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point based on the simulation results, includes:

[0009] The three-phase unbalanced risk state before and after distributed photovoltaic access is simulated, and the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar are extracted;

[0010] The voltage over-limit risk state simulation is performed for the same user node connected to distributed photovoltaics with different capacities and for different user nodes connected to distributed photovoltaics with the same capacity, and the voltage changes at the low-voltage intelligent circuit breaker monitoring points located at each user node are extracted;

[0011] The power backflow risk state of different user nodes selected under different photovoltaic penetrations and connected to distributed photovoltaics of different capacities is simulated, and the effective values ​​of voltage and current at the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side are extracted.

[0012] Preferably, the three-phase unbalanced risk state before and after the distributed photovoltaic connection is simulated, and the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus are extracted, including:

[0013] Before the distributed photovoltaic system is connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic system is connected, and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar;

[0014] After the distributed photovoltaics are connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaics are connected and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar.

[0015] Preferably, the voltage over-limit risk state simulation is performed on the distributed photovoltaics with different capacities connected to the same user node and the distributed photovoltaics with the same capacity connected to different user nodes, and the voltage change of the low-voltage intelligent circuit breaker monitoring point located at each user node is extracted, including:

[0016] Select any user node to connect to distributed photovoltaics of different capacities for simulation, and obtain the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics of different capacities;

[0017] Different user nodes are selected to connect to distributed photovoltaics of the same capacity for simulation, and the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics at different locations are obtained.

[0018] Preferably, the power reverse transmission risk state simulation is performed on different user nodes selected under different photovoltaic penetrations and connected to distributed photovoltaics of different capacities, and the voltage effective value and current effective value of the low-voltage intelligent circuit breaker monitoring point located on the transformer outlet side are extracted, including:

[0019] Different user nodes are selected to connect to distributed photovoltaics of different capacities under different photovoltaic penetrations for simulation, and the effective values ​​of voltage and current at the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side under different photovoltaic penetrations are obtained.

[0020] Preferably, the calculation based on the voltage and current characteristics at the monitoring point of the low-voltage intelligent circuit breaker to obtain the identification characteristics includes:

[0021] Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state;

[0022] Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state;

[0023] Based on the voltage changes of each user node after connecting to distributed photovoltaics of different capacities and the voltage changes of each user node after connecting to distributed photovoltaics at different locations, the voltage peak values ​​are selected as the identification features of the voltage over-limit risk state.

[0024] Based on the effective value of the transformer outlet voltage and the effective value of the transformer outlet current under different photovoltaic penetration rates combined with the active power calculation formula, the active power on the transformer outlet side under different photovoltaic penetration rates is obtained as the identification feature of the power backflow risk state.

[0025] Preferably, the step of identifying the risk status based on the identification feature to obtain the risk status of the substation includes:

[0026] Based on whether the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the busbar exceed the set threshold, identification is performed. If so, a three-phase unbalance risk state occurs; otherwise, no three-phase unbalance risk state occurs;

[0027] Determine whether the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance at the side of the line close to the bus are greater than the voltage and current imbalance at the end of the line and the voltage and current imbalance at the side of the line close to the bus before the distributed photovoltaic access. If so, the access of distributed photovoltaics will aggravate the imbalance of the power grid; otherwise, the access of distributed photovoltaics will not aggravate the imbalance of the power grid;

[0028] Based on whether the voltage peak after connecting to distributed photovoltaics of different capacities and different locations exceeds the set voltage value, it is identified that if so, a voltage over-limit risk state occurs; otherwise, no voltage over-limit risk state occurs;

[0029] Based on whether the active power on the transformer outlet side is positive under different photovoltaic penetration rates, identification is performed. If so, there is no power reverse transmission risk state; otherwise, there is a power reverse transmission risk state.

[0030] Based on the same application concept, this application also proposes an active area risk status identification system, including:

[0031] The feature extraction module is used to simulate different distributed photovoltaic access conditions through the low-voltage grid radiation model, and extract the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point based on the simulation results;

[0032] A feature calculation module, used to calculate based on the voltage and current features at the monitoring point of the low-voltage intelligent circuit breaker to obtain identification features;

[0033] A feature identification module, used to identify the risk status based on the identification features to obtain the risk status of the station area;

[0034] The risk states include: a three-phase unbalance risk state, a voltage over-limit risk state and a power reverse transmission risk state.

[0035] Preferably, the feature extraction module comprises:

[0036] The three-phase unbalanced risk state simulation submodule is used to simulate the three-phase unbalanced risk state before and after the distributed photovoltaic access, and extract the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar;

[0037] The voltage over-limit risk state simulation submodule is used to simulate the voltage over-limit risk state of the same user node connected to distributed photovoltaics with different capacities and different user nodes connected to distributed photovoltaics with the same capacity, and extract the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node;

[0038] The power backflow risk state simulation submodule is used to simulate the power backflow risk state of distributed photovoltaics with different capacities connected to different user nodes selected under different photovoltaic penetrations, and to extract the voltage and current effective values ​​of the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side.

[0039] Preferably, the three-phase unbalanced risk state simulation submodule is specifically used for:

[0040] Before the distributed photovoltaic system is connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic system is connected, and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar;

[0041] After the distributed photovoltaics are connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaics are connected and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar.

[0042] Preferably, the voltage over-limit risk state simulation submodule is specifically used for:

[0043] Select any user node to connect to distributed photovoltaics of different capacities for simulation, and obtain the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics of different capacities;

[0044] Different user nodes are selected to connect to distributed photovoltaics of the same capacity for simulation, and the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics at different locations are obtained.

[0045] Preferably, the power reverse transmission risk state simulation submodule is specifically used for:

[0046] Different user nodes are selected to connect to distributed photovoltaics of different capacities under different photovoltaic penetrations for simulation, and the effective values ​​of voltage and current of the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side under different photovoltaic penetration rates are obtained.

[0047] Preferably, the feature calculation module is specifically used for:

[0048] Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state;

[0049] Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state;

[0050] Based on the voltage changes of each user node after connecting to distributed photovoltaics of different capacities and the voltage changes of each user node after connecting to distributed photovoltaics at different locations, the voltage peak values ​​are selected as the identification features of the voltage over-limit risk state.

[0051] Based on the effective value of the transformer outlet voltage and the effective value of the transformer outlet current under different photovoltaic penetration rates combined with the active power calculation formula, the active power on the transformer outlet side under different photovoltaic penetration rates is obtained as the identification feature of the power backflow risk state.

[0052] Preferably, the feature recognition module is specifically used for:

[0053] Based on whether the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the busbar exceed the set threshold, identification is performed. If so, a three-phase unbalance risk state occurs; otherwise, no three-phase unbalance risk state occurs;

[0054] Determine whether the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance at the side of the line close to the bus are greater than the voltage and current imbalance at the end of the line and the voltage and current imbalance at the side of the line close to the bus before the distributed photovoltaic access. If so, the access of distributed photovoltaics will aggravate the imbalance of the power grid; otherwise, the access of distributed photovoltaics will not aggravate the imbalance of the power grid;

[0055] Based on whether the voltage peak after connecting to distributed photovoltaics of different capacities and different locations exceeds the set voltage value, it is identified that if so, a voltage over-limit risk state occurs; otherwise, no voltage over-limit risk state occurs;

[0056] Based on whether the active power on the transformer outlet side is positive under different photovoltaic penetration rates, identification is performed. If so, there is no power reverse transmission risk state; otherwise, there is a power reverse transmission risk state.

[0057] On the other hand, the present application also proposes an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0058] The memory is used to store one or more programs;

[0059] When the one or more programs are executed by the at least one processor, an active station area risk status identification method as described above is implemented.

[0060] On the other hand, the present application also proposes a readable storage medium having an execution program stored thereon, and when the execution program is executed, an active substation risk status identification method as described above is implemented.

[0061] Compared with the prior art, the beneficial effects of this application are:

[0062] A method, system, device and medium for identifying the risk status of an active substation, comprising: simulating different distributed photovoltaic access situations through a low-voltage substation power grid radiation model, and extracting the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point based on the simulation results; calculating based on the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point to obtain identification characteristics; identifying the risk status based on the identification characteristics to obtain the risk status of the substation; wherein the risk status includes: three-phase unbalanced risk status, voltage over-limit risk status and power reverse transmission risk status; the present application greatly improves the accuracy of risk status identification of substations with distributed photovoltaic access by extracting the characteristics of low-voltage intelligent circuit breaker monitoring points; the low-voltage substation power grid radiation model can better simulate and analyze the situation in the actual power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of a method for identifying risk status of an active station area in the present application;

[0064] Figure 2 This is a typical topological diagram of the low voltage area of ​​this application;

[0065] Figure 3 This is a schematic diagram of the low-voltage area radial power grid model of this application;

[0066] Figure 4 This is a schematic diagram of the photovoltaic power generation access system for this application;

[0067] Figure 5 This is the three-phase unbalanced simulated voltage waveform diagram of this application;

[0068] Figure 6 This is the three-phase unbalanced simulation current waveform diagram of this application;

[0069] Figure 7 The node voltage diagram after the user of this application is connected to photovoltaic with different capacities;

[0070] Figure 8 The voltage diagram of each node after connecting to photovoltaic power at different locations of this application;

[0071] Fig. 9 The active power diagram at the low-voltage side outlet of the transformer of this application;

[0072] Fig.10 A monitoring layout diagram of the low-voltage intelligent circuit breaker of this application;

[0073] Fig.11 This is a structural diagram of an active station area risk status identification system for this application

[0074] Fig.12 A diagram of an electronic device for the present application. DETAILED DESCRIPTION

[0075] In order to further improve the accuracy of risk state identification in large-scale distributed photovoltaic access areas, this application establishes a refined low-voltage area simulation topology model based on Matlab or Simulink, and conducts simulation analysis of key elements such as low-voltage distribution network, user load, and grid-connected inverter. Secondly, by extracting different time-frequency domain characteristics at the monitoring points of low-voltage circuit breakers in the area when a risk state occurs, the optimal layout of the monitoring points is determined. Finally, based on the constructed simulation model, the multi-dimensional transient and steady-state characteristics are evaluated, and a method for identifying different risk states of active areas is proposed. In order to better understand this application, the content of this application is further explained below in conjunction with the drawings and examples of the specification.

[0076] Embodiment 1:

[0077] A method for identifying the risk status of active substations, the specific process is as follows: Figure 1 As shown, including:

[0078] Step 1: simulate different distributed photovoltaic access conditions through the low-voltage power grid radiation model, and extract the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point based on the simulation results;

[0079] Step 2, calculating based on the voltage and current characteristics at the monitoring point of the low-voltage intelligent circuit breaker to obtain identification characteristics;

[0080] Step 3, identifying the risk status based on the identification features to obtain the risk status of the substation;

[0081] The risk states include: a three-phase unbalance risk state, a voltage over-limit risk state and a power reverse transmission risk state.

[0082] In step 1, different distributed photovoltaic access conditions are simulated through the low-voltage grid radiation model, and the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point are extracted based on the simulation results, including:

[0083] according to Figure 2 The typical topological structure of the low-voltage area shown in the figure was established through field visits and research on the wiring method of the low-voltage area. Figure 3 The low-voltage area radial power grid model shown in the figure has a line voltage level of 380V. Considering the three-phase five-wire low-voltage area topology, the line model is LGJ-25mm2, with three outgoing lines. The active power consumed by each user ranges from 8kW to 12kW. The overall power factor of the area is set to 0.92. The distance between each two households ranges from 0.02 to 0.04km. The line unit length impedance is 1.131+j0.393Ω / km. User 4 is connected to distributed rooftop photovoltaics. G is a motor.

[0084] Intended to adopt Figure 4 The distributed photovoltaic access system scheme shown in the figure. CB1, CB2, CB3, CB4 and CB5 are switches.

[0085] The three-phase unbalanced risk state before and after distributed photovoltaic access is simulated, and the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus are extracted, including:

[0086] Before the distributed photovoltaic system is connected, the first outgoing line is connected to single-phase loads and three-phase loads of different capacities to simulate the voltage and current conditions at this time. The three phases are connected to different single-phase loads. The specific distribution is shown in Table 1:

[0087]

[0088] Table 1 Three-phase access to different single-phase load sizes

[0089] The voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus are extracted respectively, and the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus before the distributed photovoltaic access are obtained; the voltage waveform is as follows Figure 5 As shown, the current waveform is Figure 6 shown.

[0090] Distributed photovoltaics are connected to the middle section of the feeder, and the voltage and current measurement results of the same low-voltage intelligent circuit breaker are extracted at the same line end and the bus end of the line to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaics are connected and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus.

[0091] The voltage over-limit risk state simulation is performed for the distributed photovoltaics with different capacities connected to the same user node and the distributed photovoltaics with the same capacity connected to different user nodes, and the voltage changes at the low-voltage intelligent circuit breaker monitoring points located at each user node are extracted, including:

[0092] Select any user node to connect to distributed photovoltaics of different capacities for simulation, and extract the voltage changes of each user node after connecting to distributed photovoltaics of different capacities; connect distributed photovoltaics of different capacities at user node 4, measure the voltage changes of each node as the photovoltaic capacity increases, and the node voltage after the user connects to photovoltaics of different capacities is as follows: Figure 7 As shown, the following conclusions are drawn: when a single photovoltaic power generation is connected, as the photovoltaic output gradually increases, the line voltage changes in the following trends: 1) gradually decrease; 2) first decrease, then increase, and then decrease; 3) first increase, then decrease;

[0093] Different user nodes are selected to be connected to distributed photovoltaics of the same capacity for simulation, and the voltage changes of each user node after connecting to distributed photovoltaics at different locations are obtained; taking the case of connecting to distributed photovoltaics of the same capacity at user nodes 1, 4, and 8 as an example, the voltage change trend of each node as the photovoltaic position changes is measured. The voltage of each node after connecting to photovoltaics at different locations is as follows: Figure 8 As shown in the figure, the following conclusions are drawn: The same capacity of photovoltaic power generation has different impacts on voltage at different access locations. The closer the access location is to the end of the line, the greater its effect on voltage improvement. Among them, USER NODES is the user node, DP is positive, and Access user is the access user.

[0094] The power reverse transmission risk state of different user nodes selected under different photovoltaic penetrations and connected to distributed photovoltaics of different capacities is simulated, and the voltage and current effective values ​​of the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side are extracted, including:

[0095] Connect distributed photovoltaics of different capacities at different user nodes and measure the changes in the transmission power of transformer nodes under different photovoltaic penetration rates;

[0096] Extract the effective value of the transformer outlet voltage and the effective value of the transformer outlet current under different photovoltaic penetration rates.

[0097] Taking the distributed photovoltaics with different capacities connected at user nodes 2, 3, and 4 as an example, the transmission power changes of the transformer nodes are measured according to the different photovoltaic penetration rates. When the photovoltaic penetration rates are 10%, 30%, and 90%, the effective values ​​of the voltage and current measured at the transformer outlet are extracted.

[0098] In step 2, the identification features are obtained by calculating based on the voltage and current features at the monitoring point of the low-voltage intelligent circuit breaker, which specifically includes:

[0099] Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state;

[0100] Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaic access and the voltage values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state;

[0101] Extract the average value of voltage and current and calculate the degree of imbalance as follows:

[0102]

[0103] Where LVUR is the line voltage unbalance; V AB is the line voltage between phases A and B; V BC is the line voltage between phases B and C; V CA is the line voltage between phases C and A; V Lav V AB 、V BC 、V CA The average of the three.

[0104] Based on the voltage change curves of each user node after connecting to distributed photovoltaics of different capacities and the voltage change curves of each user node after connecting to distributed photovoltaics at different locations, the voltage peak values ​​are selected as the identification features of the voltage over-limit risk state.

[0105] The selected features include:

[0106] C t1 =max(x i )-min(x i )

[0107] In the formula, Ct1 is the peak-to-peak value; x i is the sampled value of the voltage signal.

[0108]

[0109] In the formula, C t2 is the rectified average value; N is the number of sampling points; i is the serial number of the sampling point; x i is the sampled value of the voltage signal.

[0110] Based on the effective value of the transformer outlet voltage and the effective value of the transformer outlet current under different photovoltaic penetration rates combined with the active power calculation formula, the active power on the transformer outlet side under different photovoltaic penetration rates is obtained as the identification feature of the power backflow risk state.

[0111] The effective value is also called the root mean square value, which is the arithmetic square root operation of the absolute value of the sum of the squares of the original signal sequence. The formula for calculating the effective value is as follows:

[0112]

[0113] In the formula, C t3 is the effective value; N is the number of sampling points; i is the serial number of the sampling point; x i is the sampled value of the voltage signal.

[0114] The active power at the transformer outlet after photovoltaic access at different penetration rates is as follows: Fig. 9 As shown;

[0115] In step 3, risk status identification is performed based on the identification features to obtain the risk status of the substation, which specifically includes:

[0116] Based on whether the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the busbar exceed the three-phase imbalance allowed during normal operation of the power grid, identification is performed. If so, a three-phase imbalance risk state occurs; otherwise, no three-phase imbalance risk state occurs;

[0117] Determine whether the voltage and current imbalance at the end of the line after the distributed photovoltaic connection and the voltage and current imbalance on the side of the line close to the bus are greater than the voltage and current imbalance at the end of the line and the voltage and current imbalance on the side of the line close to the bus before the distributed photovoltaic connection. If so, it means that the connection of distributed photovoltaics will aggravate the imbalance of the power grid; otherwise, the connection of distributed photovoltaics will have no impact;

[0118] Based on whether the voltage peak after connecting to distributed photovoltaics of different capacities and different locations exceeds the set voltage value, it is identified that if so, a voltage over-limit risk state occurs; otherwise, no voltage over-limit risk state occurs;

[0119] Based on whether the active power on the transformer outlet side is positive under different photovoltaic penetration rates, identification is performed. If so, there is no power reverse transmission risk state; otherwise, there is a power reverse transmission risk state.

[0120] The present application also includes, according to the principle of different risk states and the identification method of the above steps, selecting different locations to arrange low-voltage intelligent circuit breakers and optimizing the point configuration. According to the simulation results, the layout nodes of low-voltage intelligent circuit breakers include three-phase imbalance and voltage over-limit monitoring at the end of the line, voltage over-limit and power reverse transmission monitoring of distributed photovoltaic access in the middle of the line, voltage over-limit and power reverse transmission monitoring at the low-voltage side of the transformer outlet, such as Fig.10 PV is the voltage meter.

[0121] This embodiment investigates and understands the risk status of the active low-voltage distribution network, and analyzes the physical principles of different risk states and the causes of their occurrence;

[0122] Based on the measurement method of low-voltage intelligent circuit breaker, different identification features corresponding to different risk states before and after distributed photovoltaic access are extracted on the low-voltage intelligent circuit breaker side;

[0123] In view of the three-phase unbalanced risk state of the low-voltage area, the present invention extracts the average values ​​of voltage and current and calculates the unbalance degree of voltage and current;

[0124]

[0125] Where LVUR is the line voltage unbalance; V AB is the line voltage between phases A and B; V BC is the line voltage between phases B and C; V CA is the line voltage between phases C and A; V Lav V AB 、V BC 、V CA The average of the three.

[0126] In view of the voltage over-limit risk state, the present invention extracts the voltage variation characteristics along the line in the front-end and back-end areas of distributed photovoltaic access, as well as the voltage variation of typical nodes as the photovoltaic access capacity and access location change, and extracts the voltage peak value to identify the voltage over-limit;

[0127] Among them, the selected feature quantities are:

[0128] C t1 =max(x i )-min(xi )

[0129] In the formula, C t1 is the peak-to-peak value; x i is the sampled value of the voltage signal.

[0130]

[0131] In the formula, C t2 is the rectified average value; N is the number of sampling points; i is the serial number of the sampling point; x i is the sampled value of the voltage signal.

[0132] For power reverse transmission, the focus is on extracting the effective values ​​of voltage and current and active power on the outlet side of the distribution transformer, calculating the power flow of the entire system under risk conditions, and identifying the power reverse transmission of the entire system.

[0133] The effective value is also called the root mean square value, which is the arithmetic square root operation of the absolute value of the sum of the squares of the original signal sequence. The calculation formula is as follows:

[0134]

[0135] In the formula, C t3 is the effective value; N is the number of sampling points; i is the serial number of the sampling point; x i is the sampled value of the voltage signal.

[0136] By measuring the positive and negative active power transmitted at the transformer outlet, it is possible to determine whether power reverse transmission has occurred in the entire substation area.

[0137] Embodiment 2:

[0138] An active station area risk state identification system, the structure is as follows Fig.11 As shown, including:

[0139] The feature extraction module is used to simulate different distributed photovoltaic access conditions through the low-voltage grid radiation model, and extract the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point based on the simulation results;

[0140] A feature calculation module, used to calculate based on the voltage and current features at the monitoring point of the low-voltage intelligent circuit breaker to obtain identification features;

[0141] A feature identification module, used to identify the risk status based on the identification features to obtain the risk status of the station area;

[0142] The risk states include: a three-phase unbalance risk state, a voltage over-limit risk state and a power reverse transmission risk state.

[0143] Feature extraction module, including:

[0144] The three-phase unbalanced risk state simulation submodule is used to simulate the three-phase unbalanced risk state before and after the distributed photovoltaic access, and extract the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar;

[0145] The voltage over-limit risk state simulation submodule is used to simulate the voltage over-limit risk state of the same user node connected to distributed photovoltaics with different capacities and different user nodes connected to distributed photovoltaics with the same capacity, and extract the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node;

[0146] The power backflow risk state simulation submodule is used to simulate the power backflow risk state of distributed photovoltaics with different capacities connected to different user nodes selected under different photovoltaic penetrations, and to extract the voltage and current effective values ​​of the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side.

[0147] The three-phase unbalanced risk state simulation submodule is specifically used for:

[0148] Before the distributed photovoltaic system is connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic system is connected, and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar;

[0149] After the distributed photovoltaics are connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaics are connected and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar.

[0150] The voltage over-limit risk state simulation submodule is specifically used for:

[0151] Select any user node to connect to distributed photovoltaics of different capacities for simulation, and obtain the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics of different capacities;

[0152] Different user nodes are selected to connect to distributed photovoltaics of the same capacity for simulation, and the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics at different locations are obtained.

[0153] The power reverse transmission risk state simulation submodule is specifically used for:

[0154] Different user nodes are selected to connect to distributed photovoltaics of different capacities under different photovoltaic penetrations for simulation, and the effective values ​​of voltage and current of the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side under different photovoltaic penetration rates are obtained.

[0155] Feature calculation module, specifically used for:

[0156] Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state;

[0157] Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state;

[0158] Based on the voltage changes of each user node after connecting to distributed photovoltaics of different capacities and the voltage changes of each user node after connecting to distributed photovoltaics at different locations, the voltage peak values ​​are selected as the identification features of the voltage over-limit risk state.

[0159] Based on the effective value of the transformer outlet voltage and the effective value of the transformer outlet current under different photovoltaic penetration rates combined with the active power calculation formula, the active power on the transformer outlet side under different photovoltaic penetration rates is obtained as the identification feature of the power backflow risk state.

[0160] Feature recognition module, specifically used for:

[0161] Based on whether the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the busbar exceed the set threshold, identification is performed. If so, a three-phase unbalance risk state occurs; otherwise, no three-phase unbalance risk state occurs;

[0162] Determine whether the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance at the side of the line close to the bus are greater than the voltage and current imbalance at the end of the line and the voltage and current imbalance at the side of the line close to the bus before the distributed photovoltaic access. If so, the access of distributed photovoltaics will aggravate the imbalance of the power grid; otherwise, the access of distributed photovoltaics will not aggravate the imbalance of the power grid;

[0163] Based on whether the voltage peak after connecting to distributed photovoltaics of different capacities and different locations exceeds the set voltage value, it is identified that if so, a voltage over-limit risk state occurs; otherwise, no voltage over-limit risk state occurs;

[0164] Based on whether the active power on the transformer outlet side is positive under different photovoltaic penetration rates, identification is performed. If so, there is no power reverse transmission risk state; otherwise, there is a power reverse transmission risk state.

[0165] Embodiment 3:

[0166] like Fig.12 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0167] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of an active area risk status identification method in the above-mentioned embodiment.

[0168] Example 4

[0169] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of an active station area risk status identification method in the above embodiment.

[0170] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0171] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0172] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0174] The above are merely embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application are included in the scope of the claims of the present application to be approved.

Claims

1. A method for identifying the risk status of an active station area, characterized in that: include: Different distributed photovoltaic access conditions are simulated through the low-voltage grid radiation model, and the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point are extracted based on the simulation results; Calculate based on the voltage and current characteristics at the monitoring point of the low-voltage intelligent circuit breaker to obtain identification characteristics; Perform risk status identification based on the identification features to obtain the risk status of the station area; The risk states include: a three-phase unbalance risk state, a voltage over-limit risk state and a power reverse transmission risk state.

2. The method according to claim 1, characterized in that The method simulates different distributed photovoltaic access conditions through the low-voltage grid radiation model, and extracts the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point based on the simulation results, including: The three-phase unbalanced risk state before and after distributed photovoltaic access is simulated, and the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar are extracted; The voltage over-limit risk state simulation is performed for the same user node connected to distributed photovoltaics with different capacities and for different user nodes connected to distributed photovoltaics with the same capacity, and the voltage changes at the low-voltage intelligent circuit breaker monitoring points located at each user node are extracted; The power backflow risk state of different user nodes selected under different photovoltaic penetrations and connected to distributed photovoltaics of different capacities is simulated, and the effective values ​​of voltage and current at the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side are extracted.

3. The method according to claim 2, characterized in that The three-phase unbalanced risk state before and after the distributed photovoltaic connection is simulated, and the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus are extracted, including: Before the distributed photovoltaic system is connected, the single-phase load and three-phase load with different capacities are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar before the distributed photovoltaic system is connected. After the distributed photovoltaics are connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaics are connected and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar.

4. The method according to claim 2, characterized in that: The voltage over-limit risk state simulation is performed on the distributed photovoltaics with different capacities connected to the same user node and the distributed photovoltaics with the same capacity connected to different user nodes, and the voltage change of the low-voltage intelligent circuit breaker monitoring point located at each user node is extracted, including: Select any user node to connect to distributed photovoltaics of different capacities for simulation, and obtain the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics of different capacities; Different user nodes are selected to connect to distributed photovoltaics of the same capacity for simulation, and the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics at different locations are obtained.

5. The method according to claim 2, characterized in that: The power reverse transmission risk state simulation is performed on different user nodes selected under different photovoltaic penetrations and connected to distributed photovoltaics of different capacities, and the voltage effective value and current effective value of the low-voltage intelligent circuit breaker monitoring point located on the transformer outlet side are extracted, including: Different user nodes are selected to connect to distributed photovoltaics of different capacities under different photovoltaic penetrations for simulation, and the effective values ​​of voltage and current of the low-voltage intelligent circuit breaker monitoring point located on the transformer outlet side under different photovoltaic penetration rates are obtained.

6. The method according to claim 2, characterized in that The calculation based on the voltage and current characteristics at the monitoring point of the low-voltage intelligent circuit breaker to obtain the identification characteristics includes: Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state; Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state; Based on the voltage changes of each user node after connecting to distributed photovoltaics of different capacities and the voltage changes of each user node after connecting to distributed photovoltaics at different locations, the voltage peak values ​​are selected as the identification features of the voltage over-limit risk state. Based on the effective value of the transformer outlet voltage and the effective value of the transformer outlet current under different photovoltaic penetration rates combined with the active power calculation formula, the active power on the transformer outlet side under different photovoltaic penetration rates is obtained as the identification feature of the power backflow risk state.

7. The method according to claim 1, characterized in that The step of identifying the risk status based on the identification features to obtain the risk status of the station area includes: Based on whether the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the busbar exceed the set threshold, identification is performed. If so, a three-phase unbalance risk state occurs; otherwise, no three-phase unbalance risk state occurs; Determine whether the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance at the side of the line close to the bus are greater than the voltage and current imbalance at the end of the line and the voltage and current imbalance at the side of the line close to the bus before the distributed photovoltaic access. If so, the access of distributed photovoltaics will aggravate the imbalance of the power grid; otherwise, the access of distributed photovoltaics will not aggravate the imbalance of the power grid; Based on whether the voltage peak after connecting to distributed photovoltaics of different capacities and different locations exceeds the set voltage value, it is identified that if so, a voltage over-limit risk state occurs; otherwise, no voltage over-limit risk state occurs; Based on whether the active power on the transformer outlet side is positive under different photovoltaic penetration rates, identification is performed. If so, there is no power reverse transmission risk state; otherwise, there is a power reverse transmission risk state.

8. An active station area risk status identification system, characterized in that: include: The feature extraction module is used to simulate different distributed photovoltaic access conditions through the low-voltage grid radiation model, and extract the voltage and current characteristics at the low-voltage intelligent circuit breaker monitoring point based on the simulation results; A feature calculation module, used to calculate based on the voltage and current features at the monitoring point of the low-voltage intelligent circuit breaker to obtain identification features; A feature identification module, used to identify the risk status based on the identification features to obtain the risk status of the station area; The risk states include: a three-phase unbalance risk state, a voltage over-limit risk state and a power reverse transmission risk state.

9. The system according to claim 8, characterized in that The feature extraction module comprises: The three-phase unbalanced risk state simulation submodule is used to simulate the three-phase unbalanced risk state before and after the distributed photovoltaic access, and extract the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar; The voltage over-limit risk state simulation submodule is used to simulate the voltage over-limit risk state of the same user node connected to distributed photovoltaics with different capacities and different user nodes connected to distributed photovoltaics with the same capacity, and extract the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node; The power backflow risk state simulation submodule is used to simulate the power backflow risk state of distributed photovoltaics with different capacities connected to different user nodes selected under different photovoltaic penetrations, and to extract the voltage and current effective values ​​of the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side.

10. The system according to claim 9, characterized in that The three-phase unbalanced risk state simulation submodule is specifically used for: Before the distributed photovoltaic system is connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic system is connected, and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar; After the distributed photovoltaics are connected, the single-phase load and three-phase load with different capacities connected to the outgoing line are simulated to obtain the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaics are connected and the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the busbar.

11. The system according to claim 9, characterized in that The voltage over-limit risk state simulation submodule is specifically used for: Select any user node to connect to distributed photovoltaics of different capacities for simulation, and obtain the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics of different capacities; Different user nodes are selected to connect to distributed photovoltaics of the same capacity for simulation, and the voltage changes of the low-voltage intelligent circuit breaker monitoring points located at each user node after connecting to distributed photovoltaics at different locations are obtained.

12. The system according to claim 9, characterized in that The power reverse transmission risk state simulation submodule is specifically used for: Different user nodes are selected to connect to distributed photovoltaics of different capacities under different photovoltaic penetrations for simulation, and the effective values ​​of voltage and current of the low-voltage intelligent circuit breaker monitoring point on the transformer outlet side under different photovoltaic penetration rates are obtained.

13. The system according to claim 9, characterized in that The feature calculation module is specifically used for: Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line before the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state; Based on the voltage and current waveforms of the low-voltage intelligent circuit breaker at the end of the line after the distributed photovoltaic access and the voltage and current values ​​in the voltage and current waveforms of the low-voltage intelligent circuit breaker on the side of the line close to the bus, the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the bus are calculated in combination with the imbalance calculation formula to obtain the identification characteristics of the three-phase imbalance risk state; Based on the voltage changes of each user node after connecting to distributed photovoltaics of different capacities and the voltage changes of each user node after connecting to distributed photovoltaics at different locations, the voltage peak values ​​are selected as the identification features of the voltage over-limit risk state. Based on the effective value of the transformer outlet voltage and the effective value of the transformer outlet current under different photovoltaic penetration rates combined with the active power calculation formula, the active power on the transformer outlet side under different photovoltaic penetration rates is obtained as the identification feature of the power backflow risk state.

14. The system according to claim 8, characterized in that The feature recognition module is specifically used for: Based on whether the voltage and current imbalance at the end of the line before the distributed photovoltaic access and the voltage and current imbalance on the side of the line close to the busbar exceed the set threshold, identification is performed. If so, a three-phase unbalance risk state occurs; Otherwise, there is no three-phase unbalance risk state; Determine whether the voltage and current imbalance at the end of the line after the distributed photovoltaic access and the voltage and current imbalance at the side of the line close to the bus are greater than the voltage and current imbalance at the end of the line and the voltage and current imbalance at the side of the line close to the bus before the distributed photovoltaic access. If so, the access of distributed photovoltaics will aggravate the imbalance of the power grid; otherwise, the access of distributed photovoltaics will not aggravate the imbalance of the power grid; Based on whether the voltage peak after connecting to distributed photovoltaics of different capacities and different locations exceeds the set voltage value, it is identified. If so, a voltage over-limit risk state occurs; Otherwise, there is no voltage over-limit risk state; Based on different photovoltaic penetration rates, it is identified whether the active power at the transformer outlet is positive. If so, there is no power reverse transmission risk state; Otherwise, there is a risk of power reverse transmission.

15. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an active station area risk status identification method as described in any one of claims 1 to 7 is implemented.

16. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, an active area risk status identification method as described in any one of claims 1 to 7 is implemented.

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