Intelligent high-voltage power distribution method and system, and storage medium
By constructing thermal state parameters and calculating thermal risk index, the load distribution of the high-voltage power distribution system is dynamically adjusted, solving the problem of difficulty in identifying potential hot spots in existing technologies and improving the safety and stability of the high-voltage power distribution system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHENGDU GUODIAN SWITCHGEAR EQUIP CO LTD
- Filing Date
- 2026-03-13
- Publication Date
- 2026-06-09
Smart Images
Figure CN122178319A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power distribution control technology, and more specifically to an intelligent high-voltage power distribution method, system, and storage medium. Background Technology
[0002] In industrial parks, power hubs, and large public buildings, high-voltage power distribution systems typically handle multiple power supply circuits. To ensure power supply safety, temperature sensors and current acquisition devices are usually installed at critical locations such as distribution cabinets, busbar connection points, and cable joints to monitor equipment operating status. When an abnormal increase in local temperature occurs, maintenance personnel generally conduct on-site inspections based on alarm information issued by the monitoring system. If necessary, they manually adjust the load or switch power supply circuits to reduce the risk of equipment overheating.
[0003] From an engineering operation perspective, temperature rise issues in power distribution systems are often not caused by a single load overload. Over long-term operation, some nodes may gradually develop localized hotspots due to factors such as changes in contact resistance, differences in heat dissipation conditions, or heat conduction from nearby equipment. These hotspots typically do not immediately trigger over-temperature protection in the initial stages, but if they accumulate, they can accelerate insulation aging and even affect the operational reliability of switchgear and busbar connections. Therefore, relying solely on simple temperature threshold alarm mechanisms is often insufficient to identify potential risks in a timely manner and to proactively adjust the load.
[0004] On the other hand, existing high-voltage power distribution operation and management mostly rely on current or power indicators as the basis for load dispatching, paying insufficient attention to the thermal status of equipment. Even if some systems have temperature monitoring capabilities, they mostly remain at the level of condition monitoring, lacking a dispatching mechanism that comprehensively assesses temperature distribution, load changes, and the thermal impact relationships between nodes. As a result, when local hotspots gradually form, the system may still operate according to the predetermined load allocation method, unable to proactively optimize and adjust the load of the distribution circuit.
[0005] Therefore, in the operation of high-voltage power distribution systems, how to comprehensively analyze the thermal state of each node based on the temperature and current information of the distribution nodes, and dynamically adjust the load distribution relationship of the distribution circuits on this basis to suppress the continuous development of local hot spots, has become an urgent technical problem to be solved in the current power distribution operation and management. Summary of the Invention
[0006] The purpose of this invention is to provide an intelligent high-voltage power distribution method, system, and storage medium, so as to at least solve the problem that existing high-voltage power distribution systems are unable to dynamically adjust the load of the power distribution circuit based on the thermal state of the power distribution nodes.
[0007] To achieve the above objectives, a first aspect of the present invention provides an intelligent high-voltage power distribution method, the method comprising: acquiring temperature data and current data of each distribution node in a high-voltage power distribution system, and constructing thermal state parameters characterizing the thermal state of each distribution node based on the temperature data and the current data; calculating a thermal risk index corresponding to each distribution node based on the thermal state parameters to obtain a risk assessment result characterizing the heat load level of each distribution node; performing power distribution load reconfiguration analysis based on the thermal risk index of each distribution node to generate a power distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit; and performing power distribution control of the high-voltage power distribution system based on the power distribution load reconfiguration strategy to achieve dynamic adjustment of the load distribution state of each distribution circuit.
[0008] Optionally, constructing thermal state parameters to characterize the thermal state of each distribution node based on the temperature data and the current data includes: calculating the average temperature of each distribution node based on the temperature data of each distribution node, and determining the temperature gradient parameter of each distribution node based on the difference between the temperature data of each distribution node and the average temperature; calculating the current change rate of each distribution node within a preset monitoring period based on the current data of each distribution node to obtain the current change parameter of each distribution node; performing node thermal coupling analysis processing based on the temperature data, the temperature gradient parameter, and the current change parameter of each distribution node to generate a thermal coupling coefficient to characterize the thermal diffusion influence relationship between each distribution node and adjacent distribution nodes; and constructing thermal state parameters for each distribution node based on the temperature data, the temperature gradient parameter, the current change parameter, and the thermal coupling coefficient.
[0009] Optionally, the thermal risk index for each distribution node is calculated based on the thermal state parameters to obtain a risk assessment result characterizing the heat load level of each distribution node. This includes: extracting temperature parameters, temperature gradient parameters, current change parameters, and thermal coupling coefficients based on the thermal state parameters for each distribution node; constructing thermal risk calculation factors for each distribution node based on the temperature parameters, temperature gradient parameters, current change parameters, and thermal coupling coefficients; performing weighted calculation on the thermal risk calculation factors for each distribution node to generate a thermal risk index for each distribution node; and performing node risk ranking processing based on the thermal risk index for each distribution node to generate a risk assessment result.
[0010] Optionally, node risk ranking processing is performed based on the thermal risk index of each distribution node to generate risk assessment results, including: constructing a node risk sequence based on the thermal risk index of each distribution node to describe the thermal risk distribution relationship of each distribution node; calculating the risk difference between adjacent distribution nodes based on the node risk sequence, and constructing a risk gradient parameter based on the risk difference to characterize the degree of spatial variation of thermal risk; performing node risk partitioning processing based on the node risk sequence and the risk gradient parameter to obtain risk partitioning results for each distribution node; and generating the risk assessment results for each distribution node based on the risk partitioning results.
[0011] Optionally, a distribution load reconfiguration analysis is performed based on the thermal risk index of each distribution node to generate a distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit. This includes: constructing risk weight parameters to describe the thermal risk distribution state of each corresponding distribution circuit based on the thermal risk index of each distribution node; calculating target load parameters for each corresponding distribution circuit based on the risk weight parameters and the current load parameters of each distribution circuit; generating load adjustment parameters for each distribution circuit based on the difference between the current load parameters and the target load parameters; and generating the distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit based on the load adjustment parameters of each distribution circuit.
[0012] Optionally, the target load parameters for each distribution circuit are calculated based on the risk weight parameters of each distribution circuit and the current load parameters of each distribution circuit. This includes: constructing a load allocation coefficient based on the risk weight parameters of each distribution circuit to describe the load allocation ratio of each distribution circuit; calculating the total load parameters of the high-voltage distribution system based on the current load parameters of each distribution circuit; calculating the initial target load parameters for each distribution circuit based on the load allocation coefficient and the total load parameters; and performing load correction processing based on the difference between the initial target load parameters and the current load parameters of each distribution circuit to obtain the target load parameters for each distribution circuit.
[0013] Optionally, generating the distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit based on the load adjustment parameters of each distribution circuit includes: calculating the load change rate of each distribution circuit within a preset scheduling period based on the load adjustment parameters of each distribution circuit, and constructing a load adjustment sequence for each distribution circuit based on the load change rate; calculating the load difference parameter between adjacent distribution circuits based on the load adjustment sequence of each distribution circuit, and constructing a load gradient parameter to characterize the load balance degree of each distribution circuit based on the load difference parameter; performing load balance correction processing based on the load adjustment sequence and the load gradient parameter of each distribution circuit to obtain the corrected load adjustment parameter for each distribution circuit; and generating the distribution load reconfiguration strategy for each distribution circuit based on the corrected load adjustment parameter of each distribution circuit.
[0014] Optionally, power distribution control of the high-voltage power distribution system is performed based on the power distribution load reconfiguration strategy to dynamically adjust the load allocation status of each power distribution circuit. This includes: extracting target load parameters for each power distribution circuit based on the power distribution load reconfiguration strategy, and generating load dispatching instructions for each power distribution circuit based on the target load parameters; controlling the power distribution switchgear of each power distribution circuit to perform switch adjustment operations based on the load dispatching instructions for each power distribution circuit, so as to adjust the load allocation status of each power distribution circuit; after performing the switch adjustment operations, re-collecting temperature and current data of each power distribution node, and calculating the updated thermal risk index of each power distribution node based on the re-collected temperature and current data; and performing feedback correction processing on the power distribution load reconfiguration strategy based on the updated thermal risk index to form the power distribution control result for the next dispatching cycle.
[0015] A second aspect of the present invention provides an intelligent high-voltage power distribution system, the system comprising: a data acquisition unit, configured to acquire temperature data and current data of each distribution node in the high-voltage power distribution system, and construct thermal state parameters characterizing the thermal state of each distribution node based on the temperature data and the current data; a risk index determination unit, configured to calculate the thermal risk index corresponding to each distribution node based on the thermal state parameters, so as to obtain a risk assessment result characterizing the heat load level of each distribution node; a reconfiguration unit, configured to perform power distribution load reconfiguration analysis processing based on the thermal risk index of each distribution node, and generate a power distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit; and a control unit, configured to perform power distribution control of the high-voltage power distribution system based on the power distribution load reconfiguration strategy, so as to complete the dynamic adjustment of the load distribution state of each distribution circuit.
[0016] On the other hand, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described intelligent high-voltage power distribution method.
[0017] Through the above technical solution, the present invention constructs thermal state parameters by acquiring temperature and current data of each distribution node, and calculates the thermal risk index of each distribution node based on the thermal state parameters to assess the thermal load level of the distribution node, thereby forming a risk assessment result. Based on this, a distribution load reconfiguration analysis is performed according to the thermal risk index of each distribution node to generate a distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit. Furthermore, based on the distribution load reconfiguration strategy, the distribution control of the high-voltage distribution system is executed, enabling the distribution system to dynamically adjust the load of each distribution circuit according to the node thermal state. This avoids the problem of the distribution system relying solely on temperature thresholds for passive alarms, achieving a comprehensive assessment of the thermal state of distribution nodes and proactive optimization of load allocation, thereby improving the rationality of load allocation and operational stability during the operation of the high-voltage distribution system.
[0018] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description
[0019] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the steps of an intelligent high-voltage power distribution method provided in one embodiment of the present invention; Figure 2 This is a system structure diagram of an intelligent high-voltage power distribution system provided in one embodiment of the present invention; Figure 3 This is an internal structural diagram of a computer device provided in one embodiment of the present invention. Detailed Implementation
[0020] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0021] like Figure 1 As shown, embodiments of the present invention provide an intelligent high-voltage power distribution method, the method comprising: Step S1: Obtain temperature and current data of each distribution node in the high-voltage power distribution system, and construct thermal state parameters to characterize the thermal state of each distribution node based on the temperature and current data.
[0022] Specifically, constructing thermal state parameters to characterize the thermal state of each distribution node based on the temperature data and the current data includes: calculating the average temperature of each distribution node based on the temperature data of each distribution node, and determining the temperature gradient parameter of each distribution node based on the difference between the temperature data of each distribution node and the average temperature; calculating the rate of change of current of each distribution node within a preset monitoring period based on the current data of each distribution node to obtain the current change parameter of each distribution node; performing node thermal coupling analysis based on the temperature data, the temperature gradient parameter, and the current change parameter of each distribution node to generate a thermal coupling coefficient to characterize the thermal diffusion influence relationship between each distribution node and adjacent distribution nodes; and constructing thermal state parameters for each distribution node based on the temperature data, the temperature gradient parameter, the current change parameter, and the thermal coupling coefficient.
[0023] In this embodiment of the invention, during the operation of a high-voltage power distribution system, the temperature state and load state of the distribution nodes are usually closely related. To achieve a more refined characterization of the thermal state of each distribution node, after acquiring the temperature and current data of each distribution node in the high-voltage power distribution system, the raw monitoring data is further processed to construct thermal state parameters characterizing the thermal state of each distribution node. In practical engineering applications, each distribution node corresponds to key heat-sensitive areas such as the busbar connection point of the distribution cabinet, the circuit breaker contact position, the cable joint position, or the busbar transition point. Temperature data can be acquired by temperature sensors deployed at these locations, while current data is collected by loop current transformers.
[0024] After acquiring the temperature data, the temperature data of each distribution node within the same monitoring area are first statistically processed to obtain the average temperature of the monitoring area. This average temperature is expressed as: ; in, This represents the average temperature within the monitored area; Indicates the first The real-time temperature values of each distribution node are used; N represents the number of distribution nodes participating in the statistical calculation. Based on the difference between the temperature data of each distribution node and the average temperature, the temperature gradient parameter for each distribution node is determined. The temperature gradient parameter reflects the degree of deviation of a node from the overall temperature distribution. When the temperature of a node is significantly higher than the average level, its temperature gradient parameter will increase significantly, thus enabling early detection of potential local hotspot trends.
[0025] Simultaneously, the current data of each distribution node within a preset monitoring period is analyzed to obtain current change parameters. In engineering operation scenarios, the preset monitoring period can be set according to the operating characteristics of the power distribution system, for example, ranging from 10 seconds to 60 seconds. By calculating the rate of change of the continuously collected current data sequence, the dynamic characteristics of load changes at the distribution nodes are obtained, thereby distinguishing between stable load states and rapidly rising load states.
[0026] After obtaining the temperature gradient parameters and current variation parameters, node thermal coupling analysis is performed on each distribution node. Due to the significant spatial adjacency of the internal structure of high-voltage power distribution equipment, thermal diffusion often exists between adjacent nodes. To describe this influence, a node thermal coupling coefficient can be constructed, expressed as: ; in, This represents the thermal coupling coefficient between node i and node j; and These represent the temperature values of node i and node j, respectively. This represents the heat conduction weighting coefficient between nodes, which can be determined based on the physical distance or structural connection relationship between the nodes. and This represents the equivalent thermal resistance of the device structure corresponding to the node. This thermal coupling coefficient describes the degree of thermal diffusion influence between adjacent nodes, thus avoiding local misjudgments caused by relying solely on single-point temperature.
[0027] After obtaining temperature data, temperature gradient parameters, current variation parameters, and thermal coupling coefficients, these multi-dimensional parameters are combined to construct the thermal state parameters for each distribution node. These thermal state parameters comprehensively reflect the node's temperature level, load variation trends, and spatial heat diffusion relationships, enabling subsequent calculations of the thermal risk index to more accurately reflect the actual operating state of the distribution system. In practical applications, when the temperature of a distribution node gradually increases due to changes in contact resistance or differences in local heat dissipation conditions, the combined effect of the temperature gradient parameters and the thermal coupling coefficients allows for earlier identification of the node's abnormal thermal state, providing a reliable data foundation for subsequent distribution load reconfiguration analysis.
[0028] Step S2: Calculate the thermal risk index of each distribution node based on the thermal state parameters to obtain risk assessment results that characterize the heat load level of each distribution node.
[0029] Specifically, temperature parameters, temperature gradient parameters, current change parameters, and thermal coupling coefficients are extracted based on the thermal state parameters of each distribution node. Thermal risk calculation factors for each distribution node are then constructed based on these parameters. A weighted calculation process is performed on the thermal risk calculation factors for each distribution node to generate a thermal risk index for each node. Finally, node risk ranking is performed based on the thermal risk indices for each distribution node to generate a risk assessment result.
[0030] Specifically, node risk ranking processing is performed based on the thermal risk index of each distribution node to generate risk assessment results. This includes: constructing a node risk sequence based on the thermal risk index of each distribution node to describe the thermal risk distribution relationship of each distribution node; calculating the risk difference between adjacent distribution nodes based on the node risk sequence, and constructing a risk gradient parameter based on the risk difference to characterize the degree of spatial variation of thermal risk; performing node risk partitioning processing based on the node risk sequence and the risk gradient parameter to obtain risk partitioning results for each distribution node; and generating the risk assessment results for each distribution node based on the risk partitioning results.
[0031] In this embodiment of the invention, in engineering applications, a single temperature value is often insufficient to accurately reflect the true risk status of a node. For example, some nodes may have high temperatures but be under stable load, while other nodes may not have significantly increased temperatures but are experiencing rapid current growth or be affected by heat diffusion from neighboring nodes, thus posing a higher potential risk. Therefore, in this embodiment, a thermal risk calculation factor is constructed by integrating temperature parameters, temperature gradient parameters, current change parameters, and thermal coupling coefficients to comprehensively assess the thermal risk of distribution nodes.
[0032] Specifically, temperature parameters are first extracted from the thermal state parameters of each power distribution node. Temperature gradient parameters Current variation parameters and thermal coupling coefficient .in, Represents a node The real-time temperature value; This indicates the degree of deviation of the node temperature from the average temperature of the monitored area; This indicates the rate of change of current within a preset monitoring period; This represents the relationship between node i and its neighboring node j in terms of thermal diffusion. These parameters together constitute the thermal risk calculation factor for a power distribution node, reflecting the node's current thermal state and its future thermal development trend.
[0033] Based on this, a weighted calculation process is performed on the thermal risk calculation factors of each distribution node to generate the corresponding thermal risk index for each distribution node. The thermal risk index can be expressed as: ; in, This represents the heat risk index of node i; , , and Represents the weighting coefficients of different calculation factors; Represents nodes The set of adjacent nodes. This calculation method simultaneously considers node temperature levels, temperature deviations, load change trends, and the impact of heat diffusion from neighboring nodes, enabling the thermal risk index to more accurately reflect the comprehensive thermal load status of distribution nodes. In actual engineering environments, the weighting coefficients are adjusted based on equipment type, distribution cabinet structure, and historical operating data. For example, the weight of the temperature gradient parameter is appropriately increased at bus connection points to enhance the ability to identify local hotspots.
[0034] After obtaining the thermal risk index of each distribution node, a risk ranking process is performed on the nodes. By arranging the thermal risk indices of each node according to their numerical values, a node risk sequence is constructed to describe the distribution of thermal risk in the system. For example, in an industrial power distribution room, if the thermal risk index of a circuit breaker contact node is significantly higher than that of other nodes, then that node will be ranked higher in the risk sequence, thus being prioritized as a key focus.
[0035] After obtaining the node risk sequence, the spatial distribution characteristics of risks within the power distribution system are further analyzed. Specifically, risk difference parameters are obtained by calculating the risk difference between adjacent distribution nodes in the risk sequence, and risk gradient parameters are constructed based on these parameters. The risk gradient parameters reflect the degree of spatial variation in the distribution of thermal risks within the system. When the risk gradient in a certain area increases significantly, it usually means that there may be concentrated heat load or abnormal local heat dissipation conditions in that area.
[0036] Based on node risk sequences and risk gradient parameters, further node risk zoning is performed to divide nodes in the power distribution system into different risk zones. For example, nodes can be divided into high-risk, medium-risk, and low-risk zones according to changes in risk gradients. High-risk zones typically contain node areas with high thermal risk indices and large risk gradients. In this way, the thermal risk distribution pattern within the power distribution system can be identified at an overall level, rather than being limited to the monitoring of individual nodes.
[0037] Finally, risk assessment results for each distribution node are generated based on the node risk zoning results. These risk assessment results not only reflect the thermal risk level of individual nodes but also demonstrate the heat load distribution characteristics of different areas within the distribution system, providing crucial information for subsequent distribution load reconfiguration analysis and distribution control. In actual operation scenarios, when a region is identified as a high-risk area, load adjustments are prioritized for the relevant distribution circuits in that region to prevent the continued development of local hotspots, thereby improving the operational safety and stability of the high-voltage distribution system.
[0038] Step S3: Perform power distribution load reconfiguration analysis based on the thermal risk index of each power distribution node to generate a power distribution load reconfiguration strategy for adjusting the load distribution relationship of each power distribution circuit.
[0039] Specifically, risk weight parameters are constructed based on the thermal risk index of each distribution node to describe the thermal risk distribution status of each corresponding distribution circuit; target load parameters for each distribution circuit are calculated based on the risk weight parameters of each distribution circuit and the current load parameters of each distribution circuit; load adjustment parameters for each distribution circuit are generated based on the difference between the current load parameters and the target load parameters; and a distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit is generated based on the load adjustment parameters of each distribution circuit.
[0040] Specifically, the target load parameters for each distribution circuit are calculated based on the risk weight parameters of each distribution circuit and the current load parameters of each distribution circuit. This includes: constructing a load allocation coefficient based on the risk weight parameters of each distribution circuit to describe the load allocation ratio of each distribution circuit; calculating the total load parameters of the high-voltage distribution system based on the current load parameters of each distribution circuit; calculating the initial target load parameters for each distribution circuit based on the load allocation coefficient and the total load parameters; and performing load correction processing based on the difference between the initial target load parameters and the current load parameters of each distribution circuit to obtain the target load parameters for each distribution circuit.
[0041] Specifically, the distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit is generated based on the load adjustment parameters of each distribution circuit, including: calculating the load change rate of each distribution circuit within a preset scheduling period based on the load adjustment parameters of each distribution circuit, and constructing a load adjustment sequence for each distribution circuit based on the load change rate; calculating the load difference parameter between adjacent distribution circuits based on the load adjustment sequence of each distribution circuit, and constructing a load gradient parameter to characterize the load balance degree of each distribution circuit based on the load difference parameter; performing load balance correction processing based on the load adjustment sequence and the load gradient parameter of each distribution circuit to obtain the corrected load adjustment parameters for each distribution circuit; and generating a distribution load reconfiguration strategy for each distribution circuit based on the corrected load adjustment parameters of each distribution circuit.
[0042] In this embodiment of the invention, during the actual operation of a high-voltage power distribution system, distribution circuits typically bear different types of loads, such as production equipment loads, power equipment loads, or public facility loads, and the load levels of each circuit are not entirely consistent. When the thermal risk index of certain distribution nodes increases significantly, it often means that the thermal load of the corresponding circuit or adjacent circuits is at a high level. If the original load distribution method is still followed, local hotspots may further develop. Therefore, by analyzing the thermal risk index and performing power distribution load reconfiguration, the power distribution system can dynamically optimize the load distribution based on the thermal state of the equipment.
[0043] In this embodiment, risk weight parameters are first constructed based on the thermal risk index of each distribution node to describe the thermal risk distribution state of each corresponding distribution circuit. Since a distribution circuit typically corresponds to multiple distribution nodes, such as circuit breaker contact nodes, busbar connection nodes, and cable joint nodes, the overall risk level of the circuit is comprehensively calculated based on the thermal risk indices of these nodes. The risk weight parameters are expressed as follows: ; in, This represents the risk weight parameter for the k-th distribution circuit; This represents the thermal risk index of the i-th distribution node belonging to this circuit; This represents the set of nodes corresponding to the k-th power distribution circuit; This represents the number of nodes in the set. Through the above calculations, the thermal risk information at the node level is converted into risk weight parameters at the loop level, thereby reflecting the comprehensive thermal load level of each distribution loop under the current operating condition.
[0044] After obtaining the risk weight parameters for each distribution circuit, the target load parameters are further calculated by combining the current load parameters of each circuit. The current load parameters are calculated using circuit current and system voltage; for example, in a three-phase system, the circuit power value is obtained by measuring the circuit current and combining it with the rated voltage. To ensure the system maintains stable overall power supply capacity during load adjustments, the total load parameters of the high-voltage distribution system must first be calculated. The total load parameters are expressed as: ; in, This represents the total load parameters of the system; This represents the current load parameters of the k-th distribution circuit; N represents the number of distribution circuits in the system.
[0045] Based on this, a load allocation coefficient is constructed using the risk weight parameters of each distribution circuit to describe the load distribution ratio. The load allocation coefficient reflects the load proportion of each circuit in the new load allocation strategy. To avoid high-risk circuits bearing excessive loads, the load allocation coefficient is inversely proportional to the risk weight parameters, expressed as follows: ; in, This represents the load distribution coefficient for the k-th distribution circuit; This represents the risk weight parameter of the loop. Through the above relationship, loops with higher thermal risk will receive a smaller load distribution coefficient, while loops with lower risk will bear more load, thereby achieving a rebalancing of thermal load within the system.
[0046] Using the load allocation factor and the total system load parameters, the initial target load parameters for each distribution circuit are further calculated, and their expressions are as follows: ; in, This represents the initial target load parameter for the k-th distribution circuit. Since load switching in actual power distribution systems is typically limited by factors such as equipment capacity, power supply structure, and operational stability, after obtaining the initial target load parameter, load correction processing is required, combining it with the current load parameter. For example, if the difference between the current load and the target load of a circuit is too large, the maximum adjustment range is limited to avoid large power fluctuations in the system. After correction processing, the final target load parameter is obtained.
[0047] After determining the target load parameters for each distribution circuit, the difference between the current load parameters and the target load parameters is further calculated to obtain the load adjustment parameters for each circuit. The load adjustment parameters represent the amount of load that needs to be increased or decreased in the next scheduling cycle and are an important basis for generating distribution load reconfiguration strategies.
[0048] To make the load adjustment process smoother, this embodiment further calculates the load change rate of each distribution circuit within a preset scheduling period based on the load adjustment parameters. Assume the scheduling period is... The rate of change of load is then expressed as: ; in, This represents the rate of load change of the k-th distribution circuit; This represents the load adjustment parameters for that circuit. By analyzing the load change rate, a load adjustment sequence for each distribution circuit is constructed, thereby describing the load adjustment trend of each circuit in a continuous dispatch cycle.
[0049] After obtaining the load adjustment sequence, the load difference parameter between adjacent distribution circuits is further calculated. For example, for two adjacent circuits within a distribution cabinet, the load difference is obtained by comparing their target load or adjusted load changes. Based on this load difference parameter, a load gradient parameter is constructed to describe the degree of spatial variation in load distribution within the system. When the load gradient between some circuits is too large, it indicates that there is a significant imbalance in the load distribution within the system.
[0050] To prevent certain circuits from bearing excessive load during load reconfiguration, load balancing correction is performed based on load adjustment sequences and load gradient parameters. This process is achieved by redistributing load adjustment parameters, ensuring that load changes not only meet risk weight distribution requirements but also maintain load gradients between circuits within a reasonable range. For example, in some industrial power distribution scenarios, if the load gradient between two adjacent circuits exceeds a preset threshold, the target load of the high-load circuit is appropriately reduced, and some load is allocated to circuits with lower loads.
[0051] After the above correction process, the corrected load adjustment parameters for each distribution circuit are obtained. Finally, a distribution load reconfiguration strategy is generated based on the corrected load adjustment parameters. This strategy includes information such as the load adjustment direction, adjustment magnitude, and adjustment sequence for each circuit in the next scheduling cycle. In actual engineering operation, this strategy is implemented by controlling distribution switching devices or load switching devices, enabling the distribution system to dynamically adjust the load distribution relationship of each distribution circuit according to the thermal state of the equipment. This effectively avoids certain circuits from being under high heat load for extended periods, while simultaneously improving the overall safety and stability of the high-voltage distribution system.
[0052] In other embodiments, in actual operating environments, the temperature changes of high-voltage power distribution equipment typically exhibit a certain degree of thermal inertia; that is, the equipment temperature does not change significantly immediately after a load change, but rather accumulates gradually over a period of time. When the power distribution system performs load reconfiguration solely based on the current thermal risk index, it may be difficult to identify emerging potential hotspots in a timely manner.
[0053] Therefore, in this implementation, after calculating the thermal risk index of each distribution node, a node temperature rise trend parameter is constructed by combining historical temperature data. Specifically, by analyzing the temperature change sequence of a node over multiple consecutive monitoring periods, the temperature rise trend coefficient of the node is calculated, and the current thermal risk index is predicted and corrected based on this temperature rise trend coefficient. If a node's current temperature is within the normal range, but its temperature rise trend is significantly higher than that of other nodes, the system will include the distribution circuit corresponding to that node in the load reconfiguration scope in advance, thereby reducing the risk of potential local hotspots in the future.
[0054] In practical applications, such as in the power distribution systems of large industrial plants, when a busbar connection point in a distribution cabinet experiences a slow temperature rise due to changes in contact resistance, the load distribution of the relevant power distribution circuits can be adjusted in advance before the temperature reaches the alarm threshold through predictive analysis of temperature rise trend parameters. In this way, the power distribution system can shift from a passive temperature monitoring to an active thermal risk prevention operating mode, thereby further improving the operational safety and stability of the high-voltage power distribution system.
[0055] Step S4: Execute power distribution control of the high-voltage power distribution system based on the power distribution load reconfiguration strategy to achieve dynamic adjustment of the load distribution status of each power distribution circuit.
[0056] Specifically, the target load parameters for each distribution circuit are extracted based on the distribution load reconfiguration strategy, and load scheduling instructions for each distribution circuit are generated based on the target load parameters. Based on the load scheduling instructions for each distribution circuit, the distribution switchgear of that circuit is controlled to perform switch adjustment operations to adjust the load distribution status of each distribution circuit. After performing the switch adjustment operations, temperature and current data for each distribution node are re-acquired, and an updated thermal risk index for each distribution node is calculated based on the re-acquired temperature and current data. Based on the updated thermal risk index, feedback correction processing is performed on the distribution load reconfiguration strategy to form the distribution control result for the next scheduling cycle.
[0057] In this embodiment of the invention, the high-voltage power distribution system typically consists of multiple distribution circuits, each connected to the busbar via devices such as circuit breakers, disconnectors, or load switches. To enable dynamic optimization of load distribution based on equipment thermal status, the power distribution load reconfiguration strategy needs to be translated into specific load dispatching instructions, and control operations executed through corresponding power distribution switching devices.
[0058] Specifically, when executing distribution control, the target load parameters for each distribution circuit are first extracted based on the distribution load reconfiguration strategy. The target load parameter represents the load level that each distribution circuit should bear within the current dispatch cycle; this parameter is expressed in the form of circuit power or circuit current. In practical applications, the target load parameter is usually associated with the circuit's rated capacity and system operating constraints. For example, some circuits may be responsible for supplying power to critical equipment, therefore their load adjustment range needs to be limited.
[0059] After obtaining the target load parameters, corresponding load dispatch instructions are generated based on the target load parameters of each distribution circuit. Load dispatch instructions include load increase instructions, load decrease instructions, or load transfer instructions. For example, when the current load of a circuit is significantly higher than the target load, a dispatch instruction to reduce the load of that circuit is generated, and some load is transferred to other circuits with lower risk. Conversely, when some circuits are under low load and have a low thermal risk index, the load they bear is increased through dispatch instructions, thereby optimizing the overall load distribution of the system.
[0060] After generating load dispatch instructions, switching operations are performed by controlling the corresponding distribution circuit's switching devices. For example, in an industrial park's power distribution system, by controlling the opening and closing status of bus tie switches or circuit breakers, some loads are switched from the original circuit to other circuits. In some highly automated power distribution systems, the above operations are also remotely controlled through distribution automation terminals (DTUs) or distribution management systems, thereby achieving automated load dispatch.
[0061] After completing the switching adjustment operation, it is necessary to re-collect temperature and current data for each distribution node to obtain the operating parameters of the power distribution system under the new load distribution state. By analyzing the updated temperature and current data, the thermal risk index of each distribution node is recalculated to determine whether the load reconfiguration strategy has achieved the expected results. For example, in some operating scenarios, if the thermal risk index of a certain node remains at a high level after load adjustment, it indicates that the node may have problems such as abnormal contact resistance or poor heat dissipation.
[0062] After obtaining the updated thermal risk index, feedback correction processing is performed on the distribution load reconfiguration strategy based on this index. The purpose of feedback correction processing is to adjust the original strategy according to the new operating conditions, such as recalculating risk weight parameters or reallocating some circuit loads. After the correction processing is completed, the distribution control results for the next scheduling cycle are generated, and a new scheduling loop is entered. In this way, the distribution system can form a closed-loop scheduling mechanism with the thermal risk index as its core, enabling load allocation to be continuously optimized as equipment operating conditions change, thereby improving the safety and stability of the high-voltage distribution system during operation.
[0063] To further illustrate the technical effects of this invention, in a practical engineering application scenario, a 10kV high-voltage power distribution system is installed in an industrial park substation. This system is connected to the public power grid by a 16000kVA main transformer and provides power to multiple production workshops through a single busbar distribution structure. The power distribution system has a total of 6 high-voltage outgoing circuits, supplying power to the machining workshop, power workshop, compressed air system, cooling system, and two auxiliary production lines, respectively. Each outgoing circuit is connected to the busbar through a high-voltage circuit breaker and equipped with a current transformer to collect circuit current data. Temperature sensors are installed at key heat-generating locations inside the distribution cabinet to continuously monitor the equipment's operating status. Specific locations include busbar connection bars, circuit breaker contact connection points, and cable terminal joints. Each distribution circuit corresponds to 3 monitoring nodes, and the system has a total of 18 distribution nodes. The sampling period of the temperature sensors is set to 10 seconds, and the current sampling period is 1 second. All monitoring data is uploaded to the power distribution control system through a power distribution automation terminal.
[0064] During system operation, the control system first acquires temperature data of each power distribution node and current data of each power distribution circuit, and constructs thermal state parameters of the power distribution nodes based on the temperature and current data. Table 1 shows some node operating data collected at a certain operating moment.
[0065] Table 1. Partial Node Operation Data ; Based on the above temperature data, the average temperature of the monitoring area can be calculated, and the temperature gradient parameters of each node can be further calculated. When the average temperature of the monitoring area is 65.3℃, the temperature gradient parameter of node N2 is approximately 6.7℃. Simultaneously, by analyzing the current change sequence within a preset monitoring period, the current change parameters of the corresponding loops at each node can be obtained. When the current in a certain loop rises from 280A to 312A within 30 seconds, its current change rate will be recorded as approximately 1.07A / s.
[0066] Based on this, the system further calculates the thermal coupling coefficient according to the physical adjacency relationship between nodes, thereby constructing the thermal state parameters of the distribution nodes. Subsequently, the system calculates the thermal risk index based on the thermal state parameters of each node. In this embodiment, the thermal risk index calculation model adopts the following form: ; in, Represents a node The heat risk index; Indicates the real-time temperature of the node; Represents the temperature gradient parameter; Indicates the rate of change of current; This represents the thermal coupling coefficient between nodes; Represents the set of adjacencies of nodes; , , , These are weighting coefficients. In this embodiment, they are taken as 0.5, 0.2, 0.2, and 0.1, respectively. The thermal risk index of each node can be calculated. The thermal risk index of node N2 is 83.6, while the thermal risk index of node N7 is 72.4.
[0067] The system then ranks all nodes according to their thermal risk index, forming a node risk sequence. Within a certain scheduling cycle, the risk values of nodes N2 and N3 rank first and second in the system, indicating that the heat load level of loop 1 is relatively high. By analyzing the differences in risk values between adjacent nodes, risk gradient parameters can be further calculated. When the risk gradient of a certain area increases significantly, it indicates that there is a significant concentration of heat load in that area.
[0068] After obtaining the node risk assessment results, the system further performs distribution load reconfiguration analysis. First, the circuit risk weight parameters are calculated based on the thermal risk index of the nodes to which each circuit belongs. Circuit 1 corresponds to nodes N1, N2, and N3, and its calculated risk weight is 81.5, while the risk weight of the node corresponding to circuit 3 is 74.2. Based on this, the total system load is calculated, currently at 4.2 MW. Load allocation coefficients are constructed based on the risk weights, and the target load parameters for each circuit are calculated. The results are shown in Table 2.
[0069] Table 2 Target load parameters for each circuit ; By comparing the difference between the current load and the target load, the load adjustment parameters for each loop can be obtained. Loop 1 needs to reduce its load by approximately 100kW, while loops 2 and 3 need to increase their loads by approximately 60kW and 40kW, respectively. The system further calculates the load change rate based on the load adjustment parameters and constructs a load adjustment sequence. Simultaneously, it obtains the load gradient parameters by calculating the load difference between adjacent loops. When the load gradient between some loops is too large, the system corrects the load adjustment parameters to avoid excessive load shifting.
[0070] After generating the distribution load reconfiguration strategy, the system generates load dispatching instructions based on the target load parameters of each circuit, and controls the corresponding circuit breakers or load transfer devices to execute the dispatching operation through the distribution automation terminal. In this embodiment, by adjusting the operating status of two tie switches, part of the machining equipment load is transferred from circuit 1 to circuit 2. After the dispatching is completed, the system re-collects the temperature and current data of each distribution node and recalculates the thermal risk index. Within 5 minutes after the load transfer is completed, the temperature of node N2 drops from 72°C to 68°C, and the corresponding thermal risk index drops from 83.6 to 77.2. The system performs feedback correction on the distribution load reconfiguration strategy based on the new thermal risk index and enters the next dispatching cycle, thus forming a continuously operating dynamic optimization control process for the distribution load.
[0071] like Figure 2 As shown, this invention provides an intelligent high-voltage power distribution system, comprising: a data acquisition unit for acquiring temperature and current data of each distribution node in the high-voltage power distribution system, and constructing thermal state parameters characterizing the thermal state of each distribution node based on the temperature and current data; a risk index determination unit for calculating the thermal risk index of each distribution node based on the thermal state parameters to obtain a risk assessment result characterizing the heat load level of each distribution node; a reconfiguration unit for performing power distribution load reconfiguration analysis based on the thermal risk index of each distribution node to generate a power distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit; and a control unit for performing power distribution control of the high-voltage power distribution system based on the power distribution load reconfiguration strategy to dynamically adjust the load distribution state of each distribution circuit.
[0072] The present invention also provides a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the above-described intelligent high-voltage power distribution method.
[0073] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3As shown, the computer device includes a processor A01, a network interface A02, a display screen A03, an input device A04, a memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program B02 is executed by the processor A01, it implements an intelligent high-voltage power distribution method.
[0074] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0075] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.
[0076] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.
Claims
1. A smart high-voltage power distribution method, characterized in that, The method includes: Acquire temperature and current data of each distribution node in the high-voltage power distribution system, and construct thermal state parameters to characterize the thermal state of each distribution node based on the temperature and current data. The thermal risk index of each distribution node is calculated based on the thermal state parameters to obtain risk assessment results that characterize the heat load level of each distribution node. Based on the thermal risk index of each distribution node, a distribution load reconfiguration analysis is performed to generate a distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit. Based on the aforementioned power distribution load reconfiguration strategy, power distribution control of the high-voltage power distribution system is executed to achieve dynamic adjustment of the load distribution status of each power distribution circuit.
2. The intelligent high-voltage power distribution method according to claim 1, characterized in that, Based on the temperature data and the current data, thermal state parameters are constructed to characterize the thermal state of each distribution node, including: The average temperature of each distribution node is calculated based on the temperature data of each distribution node, and the temperature gradient parameter of each distribution node is determined based on the difference between the temperature data of each distribution node and the average temperature. The current change rate of each distribution node within a preset monitoring period is calculated based on the current data of each distribution node to obtain the current change parameters of each distribution node. Based on the temperature data, temperature gradient parameters, and current change parameters of each distribution node, node thermal coupling analysis is performed to generate a thermal coupling coefficient that characterizes the thermal diffusion influence relationship between each distribution node and its adjacent distribution nodes. The thermal state parameters of each distribution node are constructed based on the temperature data, temperature gradient parameters, current change parameters, and thermal coupling coefficient.
3. The intelligent high-voltage power distribution method according to claim 2, characterized in that, Based on the thermal state parameters, the thermal risk index for each distribution node is calculated to obtain risk assessment results characterizing the heat load level of each distribution node, including: Temperature parameters, temperature gradient parameters, current change parameters, and thermal coupling coefficients are extracted based on the thermal state parameters of each distribution node. Thermal risk calculation factors for each distribution node are then constructed based on the temperature parameters, temperature gradient parameters, current change parameters, and thermal coupling coefficients. The thermal risk calculation factors of each distribution node are weighted and processed to generate the corresponding thermal risk index for each distribution node; Based on the thermal risk index of each power distribution node, node risk ranking is performed to generate risk assessment results.
4. The intelligent high-voltage power distribution method according to claim 3, characterized in that, Based on the thermal risk index corresponding to each power distribution node, node risk ranking processing is performed to generate risk assessment results, including: Based on the thermal risk index of each distribution node, a node risk sequence is constructed to describe the thermal risk distribution relationship of each corresponding distribution node. The risk difference between adjacent distribution nodes is calculated based on the node risk sequence, and a risk gradient parameter is constructed based on the risk difference to characterize the degree of spatial variation of thermal risk. Based on the node risk sequence and the risk gradient parameters, node risk partitioning is performed to obtain the risk partitioning results for each distribution node. Based on the risk zoning results, the risk assessment results corresponding to each distribution node are generated.
5. The intelligent high-voltage power distribution method according to claim 1, characterized in that, Based on the thermal risk index of each distribution node, a distribution load reconfiguration analysis is performed to generate a distribution load reconfiguration strategy for adjusting the load distribution relationship of each distribution circuit, including: Risk weight parameters are constructed based on the thermal risk index of each power distribution node to describe the thermal risk distribution status of each corresponding power distribution circuit. The target load parameters for each distribution circuit are calculated based on the risk weight parameters of each distribution circuit and the current load parameters of each distribution circuit. The load adjustment parameters for each distribution circuit are generated based on the difference between the current load parameters and the target load parameters. The power distribution load reconfiguration strategy is generated based on the load adjustment parameters of each power distribution circuit to adjust the load distribution relationship of each power distribution circuit.
6. The intelligent high-voltage power distribution method according to claim 5, characterized in that, Calculate the target load parameters for each distribution circuit based on the risk weight parameters and the current load parameters of each distribution circuit, including: A load allocation coefficient is constructed based on the risk weight parameters of each distribution circuit to describe the load allocation ratio of each corresponding distribution circuit. Calculate the total load parameters of the high-voltage power distribution system based on the current load parameters of each distribution circuit; Calculate the initial target load parameters for each distribution circuit based on the load allocation coefficient and the total load parameters; Load correction processing is performed based on the difference between the initial target load parameters and the current load parameters for each distribution circuit to obtain the target load parameters for each distribution circuit.
7. The intelligent high-voltage power distribution method according to claim 5, characterized in that, The distribution load reconfiguration strategy, which generates a load adjustment parameter for each distribution circuit to adjust the load distribution relationship of each distribution circuit, includes: The load change rate of each distribution circuit within a preset scheduling period is calculated based on the load adjustment parameters of each distribution circuit, and a load adjustment sequence for each distribution circuit is constructed based on the load change rate. The load difference parameter between adjacent distribution circuits is calculated based on the load adjustment sequence of each distribution circuit, and a load gradient parameter is constructed based on the load difference parameter to characterize the load balance of each distribution circuit. Based on the load adjustment sequence and load gradient parameters of each distribution circuit, a load balancing correction process is performed to obtain the corrected load adjustment parameters for each distribution circuit. Based on the corrected load adjustment parameters of each distribution circuit, a distribution load reconfiguration strategy is generated for each distribution circuit.
8. The intelligent high-voltage power distribution method according to claim 1, characterized in that, Based on the aforementioned power distribution load reconfiguration strategy, power distribution control of the high-voltage power distribution system is executed to dynamically adjust the load distribution status of each power distribution circuit, including: Based on the aforementioned power distribution load reconfiguration strategy, target load parameters for each power distribution circuit are extracted, and load scheduling instructions for each power distribution circuit are generated based on the target load parameters for each power distribution circuit. Based on the load dispatching instructions of each power distribution circuit, the power distribution switch device of the corresponding power distribution circuit is controlled to perform switch adjustment operation to adjust the load distribution status of each power distribution circuit. After performing the switch adjustment operation, the temperature and current data of each power distribution node are re-collected, and the updated thermal risk index of each power distribution node is calculated based on the re-collected temperature and current data. Based on the updated thermal risk index, the power distribution load reconfiguration strategy is subjected to feedback correction processing to form the power distribution control result for the next scheduling cycle.
9. An intelligent high-voltage power distribution system, characterized in that, The system includes: The data acquisition unit is used to acquire temperature data and current data of each distribution node in the high-voltage power distribution system, and to construct thermal state parameters to characterize the thermal state of each distribution node based on the temperature data and the current data. The risk index determination unit is used to calculate the thermal risk index of each distribution node based on the thermal state parameters, so as to obtain the risk assessment results used to characterize the heat load level of each distribution node. The reconfiguration unit is used to perform distribution load reconfiguration analysis and processing based on the thermal risk index of each distribution node, and generate distribution load reconfiguration strategies for adjusting the load distribution relationship of each distribution circuit. The control unit is used to perform power distribution control of the high-voltage power distribution system based on the power distribution load reconfiguration strategy, so as to complete the dynamic adjustment of the load distribution status of each power distribution circuit.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the intelligent high-voltage power distribution method as described in any one of claims 1-8.