A control method and device for high and low voltage distribution cabinet cluster

Abstract

The present invention discloses a control method and device for a high and low voltage distribution cabinet cluster, and relates to the field of distribution cabinet control technology. The method comprises the following specific steps: intelligent monitoring and evaluation: using sensors to collect current and voltage parameters of each distribution cabinet in the distribution cabinet cluster in real time, and analyzing and calculating the load conditions. The present invention adopts intelligent algorithms and sensor technology to monitor key parameters such as current and voltage of each distribution cabinet in the high and low voltage distribution cabinet cluster in real time, and accurately evaluate the load conditions based on these data. During peak load periods, it can give priority to ensuring the power supply stability of important loads, and at the same time, appropriately limit the power of non-critical loads to avoid overload operation of equipment. During low load periods, it can identify idle distribution cabinets and switch them to energy-saving mode, thereby significantly improving the energy utilization efficiency of the entire distribution cabinet cluster.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution cabinet control, and in particular to a control method and device for a high and low voltage power distribution cabinet cluster. Background Art

[0002] With the continuous development of the power system and the growing demand for electricity, high and low voltage distribution cabinets, as an important part of the power system, are expanding in number and scale. High and low voltage distribution cabinets play a key role in the distribution and transmission of electric energy in the power system, ensuring that all electrical equipment can obtain a stable and reliable power supply. The progress in this technical field has not only promoted the intelligent development of the power system, but also put forward higher requirements for the control and management of distribution cabinets.

[0003] However, the traditional control method of high and low voltage distribution cabinets has obvious shortcomings. First, the traditional control method lacks intelligence. The control of the distribution cabinet mainly relies on manual operation or simple automation devices. These methods cannot be dynamically adjusted according to the actual load conditions and energy consumption. Therefore, when the load changes greatly, the traditional control method often cannot respond in time, resulting in energy waste and unstable equipment operation. Secondly, the traditional control method lacks efficiency. Since it is impossible to obtain the operating data of the distribution cabinet in real time, the traditional control method often cannot accurately control and optimize the distribution cabinet, which not only reduces the energy utilization efficiency, but also increases the risk of equipment failure. In addition, with the continuous expansion of the scale of the power system, the number of high and low voltage distribution cabinets is also increasing, which makes the complexity and workload of the traditional control method increase sharply, and it is difficult to achieve effective management of the distribution cabinet cluster.

[0004] To sum up, the traditional high and low voltage distribution cabinet control method can no longer meet the needs of the current power system. Therefore, a new type of high and low voltage distribution cabinet cluster control method and device is needed to improve energy utilization efficiency, enhance power supply stability, reduce energy waste, and improve the level of intelligence. Summary of the invention

[0005] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a control method and device for a high and low voltage distribution cabinet cluster. It can realize real-time monitoring and evaluation of the distribution cabinet system cluster by utilizing intelligent algorithms and sensor technology, and perform dynamic regulation and energy-saving optimization according to load conditions and energy consumption. At the same time, through intelligent prediction and start-stop optimization technology, it can further reduce energy waste and improve the stability and reliability of equipment operation. It not only solves the deficiencies of traditional control methods in intelligence, automation, cluster management, energy saving and consumption reduction, but also provides new ideas and solutions for the intelligent development of power systems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a control method for a high and low voltage distribution cabinet cluster comprises the following specific steps:

[0007] Intelligent monitoring and evaluation: The current and voltage parameters of each distribution cabinet in the distribution cabinet cluster are collected in real time through sensors, the load conditions are analyzed and calculated, and the loads of different distribution cabinets are classified according to the importance level according to the preset importance evaluation standards;

[0008] Dynamic load control and energy saving: According to the monitoring and evaluation results, when it is detected that the peak load period is in progress, the central control system sends a control instruction to the intelligent circuit breaker based on the distribution result calculated by the intelligent algorithm. The power distribution formula during the peak load period is: Where P allocated represents the power allocated to a distribution cabinet, S is the importance score of the distribution cabinet, P total is the total available power of the system, S total It is the sum of the importance scores of all distribution cabinets. The intelligent circuit breaker adjusts the parameters according to the instructions, and gives priority to the power supply lines of the high-importance distribution cabinets to provide stable power supply for important loads. At the same time, the power of the distribution cabinets with non-critical loads is limited. The input power is reduced by adjusting the parameters of the intelligent circuit breaker. During the off-peak load period, the central control system sends instructions to the intelligent power regulator to adjust the power of some idle distribution cabinets. The intelligent power regulator reduces the input power of the idle distribution cabinets according to the instructions to achieve energy-saving mode control. At the same time, for the distribution cabinets in operation, according to the power adjustment results calculated by the intelligent algorithm, the intelligent power regulator automatically adjusts the operating power of the equipment. The power adjustment formula during the off-peak load period is: Where P adjusted represents the adjusted power,

[0009] P current is the current power, λ is the power adjustment factor, T low is the length of time during the low load period, T total is the length of a complete load cycle;

[0010] Start-stop optimization: Use intelligent algorithms to analyze historical load data and equipment operating time, predict future load change trends, and optimize equipment start-stop times based on the prediction results to avoid unnecessary equipment operation during low-load periods, further reducing unnecessary energy waste;

[0011] Continuous monitoring and adjustment: Continuously monitor the operating status of the distribution cabinet cluster, and dynamically adjust power distribution, energy-saving mode, and equipment start and stop time based on real-time data and changes.

[0012] Furthermore, in the intelligent monitoring and evaluation step, the current and voltage parameters of each distribution cabinet in the distribution cabinet cluster are collected in real time by sensors, and the load conditions are analyzed and calculated. The load power calculation formula is P=U×I×η, where P represents the load power of the distribution cabinet, U is the real-time voltage of the distribution cabinet, I is the real-time current of the distribution cabinet, and η is the efficiency coefficient dynamically adjusted according to the equipment type and operating status in the distribution cabinet.

[0013] Furthermore, in the intelligent monitoring and evaluation step, sensors are deployed in each power distribution cabinet and connected to the central control system via wired or wireless means.

[0014] Furthermore, in the start-stop optimization step, an intelligent algorithm is used to analyze historical load data and equipment operating time to predict future load change trends. The algorithm formula is: Among them, L future Indicates the predicted load at a certain time in the future, L current is the current load, k is the prediction coefficient, n is the number of historical data points considered, and α i is the weight coefficient of the i-th historical data point, L current-i It is the load at the i-th time point before the current moment.

[0015] Furthermore, in the start-stop optimization step, the start-stop time of the equipment is optimized according to the prediction results to avoid unnecessary equipment operation during low-load periods. The equipment start-stop time optimization formula is: Among them, T start Indicates the equipment startup advance time, T stop represents the equipment stop advance time, R is the load response rate of the equipment, and μ and ν are the start and stop time adjustment coefficients.

[0016] Furthermore, in the continuous monitoring and adjustment step, the operating status of the distribution cabinet cluster is continuously monitored, including current, voltage, load power and equipment temperature parameters, and the working status of the intelligent circuit breaker and the intelligent power regulator is monitored at the same time. The real-time monitoring data is compared with the preset standard parameters to analyze whether there are any abnormal conditions. If the load power of a distribution cabinet exceeds the preset safety threshold or the temperature is too high, the system will issue an alarm, analyze the difference between real-time data and historical data, and dynamically adjust the control strategy according to the analysis results.

[0017] Furthermore, in the continuous monitoring and adjustment step, the power allocation during peak load periods, the energy-saving mode during valley load periods, and the start and stop time of the equipment are dynamically adjusted according to real-time data and changes. The algorithm formula for dynamic adjustment is: Among them, C newrepresents the adjusted control strategy parameter value, C current is the current control strategy parameter value, ΔC is the adjustment step of the control strategy parameter, θ is the adjustment weight coefficient, which is used to control the adjustment amplitude, and D real is the actual monitored system performance index value, D expected It is the expected system performance index value, that is, the set target performance index value.

[0018] On the other hand, a control device for a high and low voltage distribution cabinet cluster includes the following components:

[0019] The monitoring and evaluation module uses sensor technology to collect the current and voltage parameters of each distribution cabinet in the distribution cabinet cluster in real time, and analyzes and processes the parameters through intelligent algorithms to determine the load conditions and importance classification;

[0020] The energy-saving control module prioritizes the power supply stability of important loads during peak load periods based on the results of the monitoring and evaluation module, while appropriately limiting the power of non-critical loads. During off-peak load periods, it identifies idle distribution cabinets and switches to energy-saving mode, automatically adjusting the operating power of the equipment.

[0021] The start-stop optimization module uses intelligent algorithms to analyze historical load data and equipment operating time, predict future load change trends, and optimize the start-stop time of the equipment accordingly;

[0022] The dynamic adjustment module continuously monitors the operating status of the power distribution cabinet cluster and dynamically adjusts power distribution, energy-saving mode, and equipment start and stop time according to real-time data and changes.

[0023] Compared with the prior art, the control method and device of the high and low voltage distribution cabinet cluster have the following beneficial effects:

[0024] 1. The present invention adopts intelligent algorithms and sensor technology to monitor the key parameters such as current and voltage of each distribution cabinet in the high and low voltage distribution cabinet cluster in real time, and accurately evaluate the load conditions based on these data. During peak load periods, it can give priority to ensuring the power supply stability of important loads, and at the same time, appropriately limit the power of non-critical loads to avoid equipment overload. During low load periods, it can identify idle distribution cabinets and switch them to energy-saving mode, thereby significantly improving the energy utilization efficiency of the entire distribution cabinet cluster.

[0025] 2. The present invention continuously monitors the operating status of the distribution cabinet cluster and makes dynamic adjustments based on real-time data to promptly discover and handle potential failure risks. In the case of large load fluctuations, it can quickly adjust power distribution to ensure the power supply stability of key equipment. In addition, by intelligently predicting future load change trends, the system can also optimize the start and stop time of the equipment to avoid unnecessary equipment operation during low-load periods, thereby further reducing the possibility of failures.

[0026] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a schematic diagram of a control device for a high and low voltage distribution cabinet cluster;

[0029] Figure 2 The present invention is a flow chart of a control method for a high and low voltage distribution cabinet cluster. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Embodiment 1

[0032] This embodiment describes in detail a control method and device for a high and low voltage distribution cabinet cluster in the specific application of the method in the field of industrial production, aiming to provide reliable power guarantee for industrial production and reduce energy consumption and operating costs through the present invention.

[0033] In the intelligent monitoring and evaluation stage, high-precision current sensors and voltage sensors are carefully installed in each high and low voltage distribution cabinet in the industrial production plant. These sensors have advanced measurement technology and can accurately capture the subtle changes in current and voltage in the distribution cabinet. The sensors are connected to the central control system through a reliable industrial Ethernet to ensure that data can be transmitted in real time in a high-speed and stable manner. The advantages of industrial Ethernet are its high bandwidth, low latency and strong anti-interference ability, which can meet the strict requirements of data transmission in industrial environments. The sensors continuously collect data and transmit the electrical parameters of the distribution cabinet to the central control system in real time. The central control system uses The load power calculation formula P=U×I×η in the intelligent algorithm analyzes and calculates the load situation, where P represents the load power of the distribution cabinet, U is the real-time voltage of the distribution cabinet, I is the real-time current of the distribution cabinet, and η is an efficiency coefficient that is dynamically adjusted according to the equipment type and operating status in the distribution cabinet. Different types of equipment have different power conversion efficiencies under different operating conditions. For example, the efficiency of some large motors may be low during the startup phase, while the efficiency will increase during the stable operation phase. By analyzing the equipment type and operating status, the central control system can accurately calculate the load power of each distribution cabinet. At the same time, according to the importance evaluation formula S=a×P critical +b×N critical +c×T operatiom , comprehensively consider multiple factors to evaluate the importance of the distribution cabinet, among which the power proportion of key load power supply (P critical ) reflects the support level of the distribution cabinet for key production equipment, and the number of key equipment connected (N critical ) reflects the importance of the distribution cabinet in the entire production system, and the continuous operation time (T operation ) is also an important indicator. The long-term operation of the distribution cabinet usually plays a key role in the continuity of production. The weight coefficients a, b, and c can be adjusted according to the actual application scenarios to adapt to the production characteristics and needs of different factories.

[0034] In the stage of dynamic load regulation and energy saving, the central control system plays a key role in peak load period regulation, such as during full-load production in factories. According to the power allocation formula during peak load period, The central control system distributes power according to the importance score of each distribution cabinet, where P allocated represents the power allocated to a distribution cabinet, S is the importance score of the distribution cabinet, P total is the total available power of the system, S totalIt is the sum of the importance scores of all distribution cabinets. The intelligent circuit breaker adjusts the parameters according to the instructions, and gives priority to the power supply lines of the high-importance distribution cabinets to provide stable power supply for important loads. At the same time, the power of the distribution cabinets with non-critical loads is limited. The input power is reduced by adjusting the parameters of the intelligent circuit breaker. For energy saving during low-load periods, during low-load periods such as the shutdown of some production lines at night, the system identifies idle distribution cabinets through monitoring. This can be achieved by analyzing the load power and current changes of the distribution cabinets. If the load power of a distribution cabinet is lower than the preset idle threshold for a period of time, the system will mark it as an idle distribution cabinet. For these idle distribution cabinets, the system will switch them to energy-saving mode. For example, the operating power of the ventilation system is reduced. The ventilation system is usually driven by a motor. By adjusting the speed or running time of the motor, the energy consumption of the ventilation system can be reduced. At the same time, turning off some unnecessary indicator lights can also reduce standby power consumption. For the distribution cabinets that are in operation, the power adjustment formula is used according to the low-load period. The system will automatically adjust the equipment operating power according to the actual energy consumption. During low-load periods, the equipment operating power can be reduced by lowering the output voltage of the transformer, thereby achieving energy-saving control.

[0035] During the start-stop optimization phase, the central control system actively collects and stores the historical load data and equipment operation time of the distribution cabinet cluster. These data provide an important basis for subsequent analysis and prediction. Advanced data analysis tools are used to conduct in-depth analysis of these data. For example, by statistically analyzing the average load, load peak and valley values ​​in different time periods, a load change model can be established. This model can reflect the periodicity of factory production and the law of load changes, and provide a basis for predicting future load change trends. Based on historical data and load change models, the central control system uses the load change trend prediction formula in the intelligent algorithm to Predict future load change trends, including:

[0036] L future Indicates the predicted load at a certain time in the future, L current is the current load, k is the prediction coefficient, n is the number of historical data points considered, and α i is the weight coefficient of the i-th historical data point, L current-i is the load at the i-th time point before the current moment. The prediction coefficient and weight coefficient can be adjusted according to the characteristics of historical data and actual needs to improve the accuracy of the prediction. For example, if the load changes in recent time periods are more drastic, the prediction coefficient can be appropriately increased to pay more attention to the recent load change trend. For the optimization of equipment start and stop time, according to the load change trend prediction results, the central control system uses the equipment start and stop time optimization formula and Optimize the start and stop time of equipment. When it is predicted that the load will increase in the future, the central control system will start some key equipment in advance. This can be achieved by sending a start command to the corresponding equipment controller. The advance start time is calculated by the parameters in the formula, where the load response rate R of the equipment reflects the time required for the equipment to reach a stable operating state from startup. The start time adjustment coefficient μ can be adjusted according to actual conditions to ensure that the equipment can be started in time before the load increases to ensure the stability of power supply. When it is predicted that the load will decrease, the central control system will shut down some unnecessary equipment in advance to reduce unnecessary energy waste. The stop time adjustment coefficient ν can also be adjusted according to actual conditions to ensure that the equipment can stop running in time after the load decreases.

[0037] For the continuous monitoring and adjustment stage, the system continuously monitors the operating status of the distribution cabinet cluster in real time through sensors. In addition to basic parameters such as current, voltage and load power, it also monitors important indicators such as equipment temperature. The monitoring of equipment temperature can be achieved through temperature sensors installed in the distribution cabinet. Temperature sensors can detect abnormal conditions such as equipment overheating in time to avoid equipment damage and failure. At the same time, it is also very important to monitor the working status of control devices such as intelligent circuit breakers and intelligent power regulators. The normal operation of these control devices is the key to achieving dynamic load regulation and energy saving. By monitoring their operating parameters and status indicators, etc., it can be ensured that they can correctly execute control instructions when needed, compare the real-time monitoring data with the preset standard parameters, and analyze whether there are abnormal conditions. If the load power of a distribution cabinet exceeds the preset safety threshold, or the temperature is too high, the system will issue an alarm. The alarm can be sent to relevant personnel through sound and light signals, SMS notifications or emails, so that timely measures can be taken to deal with it. For example, if the temperature of a distribution cabinet is found to be too high, the system can automatically start the ventilation system or reduce the load power of the distribution cabinet to reduce the temperature and avoid equipment damage. The dynamic adjustment formula According to the difference between the actual monitored system performance indicators and the expected performance indicators, the control strategy parameters are dynamically adjusted. For example, if the actual total energy consumption is higher than the expected total energy consumption, the system can appropriately adjust the power allocation coefficient, the power adjustment coefficient in the energy-saving mode and other parameters. The adjustment weight coefficient (θ) can be adjusted according to the actual situation to control the adjustment range. The adjustment step (ΔC) can also be adjusted according to the response speed and stability requirements of the system. By continuously and dynamically adjusting the control strategy parameters, the system performance can gradually approach the expected performance, ensuring that the entire distribution cabinet cluster always maintains an efficient, stable and energy-saving operating state.

[0038] In summary, this embodiment achieves efficient and intelligent control of high and low voltage distribution cabinet clusters through intelligent monitoring and evaluation, dynamic load regulation and energy saving, start-stop optimization, and continuous monitoring and adjustment, providing reliable power guarantee for industrial production and reducing energy consumption and operating costs.

[0039] Embodiment 2

[0040] This embodiment describes in detail a control method and device for a high and low voltage distribution cabinet cluster in the field of commercial buildings, and aims to achieve intelligent management of the high and low voltage distribution cabinet cluster in commercial buildings through the present invention.

[0041] In the intelligent monitoring and evaluation stage, high-precision current and voltage sensors are installed in each high and low voltage distribution cabinet in the commercial complex. These sensors are connected to the central control system through stable wired or wireless communication methods to ensure that data can be transmitted in a timely and accurate manner. For example, ZigBee wireless communication technology can be used, which has the advantages of low power consumption and self-organizing network, and is suitable for deployment in complex environments of commercial buildings. The sensors collect the electrical parameters of the distribution cabinet in real time, and the central control system uses the load power calculation formula P=U×I×η in the intelligent algorithm to calculate the load power of each distribution cabinet, where the voltage U and current I are measured by the sensor, and the dynamic efficiency coefficient η is dynamically adjusted according to the type and operating status of different equipment in the commercial building. For example, for the lighting system, the efficiency coefficient will be different under different brightness settings. For the air-conditioning system, the efficiency coefficient is adjusted according to its operating mode and load conditions. At the same time, according to the importance evaluation formula S=a×P critical +b×N critical +c×T operation Assess the importance score of each distribution cabinet. In commercial buildings, the distribution cabinet S that supplies power to the main lighting and elevators of the shopping mall usually has a higher importance. critical The power proportion for powering critical loads can be determined by analyzing the power distribution of the equipment connected to the power distribution cabinet. critical The number of key devices connected, such as the number of elevators, the number of lighting fixtures in important areas, etc. operation It is the continuous operating time. Considering the business hours of commercial buildings and the continuous operation requirements of equipment, by reasonably setting the weight coefficients a, b, and c, the influence of different factors on the importance of the distribution cabinet can be accurately reflected.

[0042] In the stage of dynamic load regulation and energy saving, for peak load period regulation, during the peak business hours of the mall, such as holidays or promotional activities, the central control system uses the peak load period power allocation formula Power distribution, where P allocatedrepresents the power allocated to a distribution cabinet, S is the importance score of the distribution cabinet, P total is the total available power of the system, S total It is the sum of the importance scores of all distribution cabinets. The intelligent circuit breaker adjusts the parameters according to the instructions, and gives priority to the power supply lines of the high-importance distribution cabinets to provide a stable power supply for important loads. At the same time, the power of the distribution cabinets with non-critical loads is limited. The input power is reduced by adjusting the parameters of the intelligent circuit breaker. For example, the brightness of some decorative lighting can be reduced or the lighting of some non-emergency passages can be turned off to reduce the power consumption of non-critical loads. During the non-business hours or low passenger flow periods of the mall, the system identifies idle distribution cabinets through monitoring and switches them to energy-saving mode. For example, for some distribution cabinets that supply power to temporary display areas, their power can be turned off during non-business hours. For the distribution cabinets that are in operation, the power adjustment formula is used according to the low load period. Automatically adjust the equipment operating power, where P adjusted Indicates the adjusted power, P current is the current power, λ is the power adjustment factor, T low is the length of time during the low load period, T total It is the length of a complete load cycle. During low-load periods, the operating power of the air-conditioning system can be reduced, such as adjusting the fan speed or reducing the cooling / heating output. At the same time, for the lighting system, intelligent dimming technology can be used to reduce the light brightness according to actual needs to achieve energy-saving control.

[0043] For the start-stop optimization phase, based on historical data and load change models, the load change trend prediction formula in the intelligent algorithm is used Predict future load change trends, where L future Indicates the predicted load at a certain time in the future, L current is the current load, k is the prediction coefficient, n is the number of historical data points considered, and α i is the weight coefficient of the i-th historical data point, L current-i It is the load at the i-th time point before the current moment. In commercial buildings, the load demand in different time periods in the future can be predicted based on the current mall operation, weather forecast, promotional activities and other factors. For example, if the weather forecast shows that the temperature will be high in the next few days, it may cause the load of the air-conditioning system to increase. If there are large-scale promotional activities, the lighting and elevator loads of the mall may increase significantly. By accurately predicting the load change trend, the start and stop plan of the equipment can be made in advance. According to the load change trend prediction results, the equipment start and stop time optimization formula can be used and Optimize the start and stop time of equipment. Before the mall opens, start key equipment such as elevators and air conditioners in advance based on the predicted load increase potential to ensure that the equipment can operate stably when the mall opens. After the mall closes, when it is predicted that the load will drop significantly, shut down some non-critical equipment in time, such as some lighting systems and advertising display screens, to reduce unnecessary energy waste. At the same time, according to parameters such as the equipment's load response rate (R), start time adjustment coefficient (μ) and stop time adjustment coefficient (ν), reasonably adjust the equipment's start and stop time to achieve the best energy-saving effect.

[0044] During the continuous monitoring and adjustment stage, the operating status of the high and low voltage distribution cabinet clusters in commercial buildings is continuously monitored in real time through sensors, including parameters such as current, voltage, load power, and equipment temperature. At the same time, the working status of control equipment such as intelligent circuit breakers and intelligent power regulators is monitored to ensure their normal operation. For example, for the air-conditioning system, the operating status of its compressor, refrigerant pressure, inlet and outlet temperature and other parameters are monitored. For the lighting system, the brightness and power consumption of the lamps are monitored. Through real-time monitoring, equipment failures or abnormal conditions can be discovered in time so that appropriate measures can be taken to deal with them. The real-time monitoring data is compared with the preset standard parameters to analyze whether there are abnormal conditions. If the load power of a distribution cabinet exceeds the preset safety threshold, or the equipment temperature is too high, the system will issue an alarm. For example, if the current of a distribution cabinet is too large, it may mean that the equipment in the area is faulty or overloaded. The system can automatically cut off the power supply of the distribution cabinet and notify the maintenance personnel to check. In the case of excessively high equipment temperature, the ventilation system can be started or the operating power of the equipment can be reduced to prevent equipment damage. The dynamic adjustment formula is used Among them, C new represents the adjusted control strategy parameter value, C current is the current control strategy parameter value, ΔC is the adjustment step of the control strategy parameter, θ is the adjustment weight coefficient, which is used to control the adjustment amplitude, and D real is the actual monitored system performance index value, D expectedIt is the expected system performance index value, that is, the set target performance index value. According to the difference between the actual monitored system performance index and the expected performance index, the control strategy parameters are dynamically adjusted. In commercial buildings, the system performance indicators may include total energy consumption, average equipment load rate, customer satisfaction, etc. For example, if the actual total energy consumption is higher than the expected total energy consumption, the system can appropriately adjust the power allocation coefficient, the power adjustment coefficient in the energy-saving mode and other parameters to further reduce energy consumption. If the customer gives feedback on the lighting brightness or air-conditioning comfort of the mall, the system can adjust the corresponding control parameters according to the feedback information to improve customer satisfaction. By continuously and dynamically adjusting the control strategy parameters, the high and low voltage distribution cabinet clusters of commercial buildings can always maintain an efficient, stable and energy-saving operating state.

[0045] To summarize, this embodiment realizes the intelligent management of high and low voltage distribution cabinet clusters in commercial buildings by implementing intelligent monitoring and evaluation, dynamic load regulation and energy saving, start-stop optimization, and continuous monitoring and adjustment in commercial complexes, ensuring that the entire distribution cabinet cluster is always in an efficient, stable, and energy-saving operating state, thereby improving the operational efficiency and sustainability of commercial buildings.

[0046] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A control method for a high and low voltage distribution cabinet cluster, characterized in that: The method comprises the following specific steps: Intelligent monitoring and evaluation: The current and voltage parameters of each distribution cabinet in the distribution cabinet cluster are collected in real time through sensors, the load conditions are analyzed and calculated, and the loads of different distribution cabinets are classified according to the importance level according to the preset importance evaluation standards; Dynamic load control and energy saving: According to the monitoring and evaluation results, when it is detected that the peak load period is in progress, the central control system sends a control instruction to the intelligent circuit breaker based on the distribution result calculated by the intelligent algorithm. The power distribution formula during the peak load period is: Where P allocated represents the power allocated to a distribution cabinet, S is the importance score of the distribution cabinet, P total is the total available system power, S total It is the sum of the importance scores of all distribution cabinets. The intelligent circuit breaker adjusts the parameters according to the instructions, and gives priority to the power supply lines of the high-importance distribution cabinets to provide stable power supply for important loads. At the same time, the power of the distribution cabinets with non-critical loads is limited. The input power is reduced by adjusting the parameters of the intelligent circuit breaker. During the off-peak load period, the central control system sends instructions to the intelligent power regulator to adjust the power of some idle distribution cabinets. The intelligent power regulator reduces the input power of the idle distribution cabinets according to the instructions to achieve energy-saving mode control. At the same time, for the distribution cabinets in operation, according to the power adjustment results calculated by the intelligent algorithm, the intelligent power regulator automatically adjusts the operating power of the equipment. The power adjustment formula during the off-peak load period is: Where P adjusted Indicates the adjusted power, P current is the current power, λ is the power adjustment factor, T low is the length of time during the low load period, T total is the length of a complete load cycle; Start-stop optimization: Use intelligent algorithms to analyze historical load data and equipment operating time, predict future load change trends, and optimize equipment start-stop times based on the prediction results to avoid unnecessary equipment operation during low-load periods, further reducing unnecessary energy waste; Continuous monitoring and adjustment: Continuously monitor the operating status of the distribution cabinet cluster, and dynamically adjust power distribution, energy-saving mode, and equipment start and stop time based on real-time data and changes.

2. A control method for a high and low voltage distribution cabinet cluster according to claim 1, characterized in that: In the intelligent monitoring and evaluation step, the current and voltage parameters of each distribution cabinet in the distribution cabinet cluster are collected in real time by sensors, and the load situation is analyzed and calculated. The load power calculation formula is P=U×I×η, where P represents the load power of the distribution cabinet, U is the real-time voltage of the distribution cabinet, I is the real-time current of the distribution cabinet, and η is the efficiency coefficient dynamically adjusted according to the equipment type and operating status in the distribution cabinet.

3. A control method for a high and low voltage distribution cabinet cluster according to claim 1, characterized in that: In the intelligent monitoring and evaluation step, sensors are deployed in each distribution cabinet and connected to the central control system via wired or wireless means.

4. The control method of a high and low voltage distribution cabinet cluster according to claim 1 is characterized in that: In the start-stop optimization step, an intelligent algorithm is used to analyze historical load data and equipment operating time to predict future load change trends. The algorithm formula is: Among them, L future Indicates the predicted load at a certain time in the future, L current is the current load, k is the prediction coefficient, n is the number of historical data points considered, and α i is the weight coefficient of the i-th historical data point, L current-i It is the load at the i-th time point before the current moment.

5. The control method of a high and low voltage distribution cabinet cluster according to claim 1 is characterized in that: In the start-stop optimization step, the start-stop time of the equipment is optimized according to the prediction results to avoid unnecessary equipment operation during low-load periods. The equipment start-stop time optimization formula is: Where T start Indicates the equipment startup advance time, T stop represents the equipment stop advance time, R is the load response rate of the equipment, and μ and ν are the start and stop time adjustment coefficients.

6. The control method of a high and low voltage distribution cabinet cluster according to claim 1 is characterized in that: In the continuous monitoring and adjustment step, the operating status of the distribution cabinet cluster is continuously monitored, including current, voltage, load power and equipment temperature parameters, and the working status of the intelligent circuit breaker and the intelligent power regulator is monitored at the same time. The real-time monitoring data is compared with the preset standard parameters to analyze whether there are any abnormal conditions. If the load power of a distribution cabinet exceeds the preset safety threshold or the temperature is too high, the system will issue an alarm, analyze the difference between real-time data and historical data, and dynamically adjust the control strategy according to the analysis results.

7. The control method of a high and low voltage distribution cabinet cluster according to claim 1 is characterized in that: In the continuous monitoring and adjustment step, the power allocation during peak load periods, the energy-saving mode during valley load periods, and the start and stop time of the equipment are dynamically adjusted according to real-time data and changes. The algorithm formula for dynamic adjustment is: Among them, C new represents the adjusted control strategy parameter value, C current is the current control strategy parameter value, ΔC is the adjustment step of the control strategy parameter, θ is the adjustment weight coefficient, which is used to control the adjustment amplitude, and D real is the actual monitored system performance index value, D expected It is the expected system performance index value, that is, the set target performance index value.

8. A control device for a high and low voltage distribution cabinet cluster according to any one of claims 1 to 7, characterized in that: The device comprises the following components: The monitoring and evaluation module uses sensor technology to collect the current and voltage parameters of each distribution cabinet in the distribution cabinet cluster in real time, and analyzes and processes the parameters through intelligent algorithms to determine the load conditions and importance classification; The energy-saving control module prioritizes the power supply stability of important loads during peak load periods based on the results of the monitoring and evaluation module, while appropriately limiting the power of non-critical loads. During off-peak load periods, it identifies idle distribution cabinets and switches them to energy-saving mode, automatically adjusting the operating power of equipment. The start-stop optimization module uses intelligent algorithms to analyze historical load data and equipment operating time, predict future load change trends, and optimize the start-stop time of the equipment accordingly; The dynamic adjustment module continuously monitors the operating status of the distribution cabinet cluster, and dynamically adjusts power distribution, energy-saving mode, and equipment start and stop time according to real-time data and changes.

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