Control optimization method for improving consumption capability of distributed power supply

Abstract

The invention discloses a control optimization method for improving the consumption capability of a distributed power supply, and the method comprises the steps: S1, obtaining the real-time load data of each node in a power distribution network and the output power of the distributed power supply, S2, calculating the consumption capability of the power distribution network based on the real-time load data and the output power of the distributed power supply, and S3, calculating the consumption capability of the power distribution network based on the calculation result. The method comprises the steps of S1, adjusting the operation mode of a distributed power supply to reduce electromagnetic interference, S4, determining the working state of a relay in the optimal operation mode, and monitoring the abrasion condition of a relay contact, and S5, optimizing the control strategy of the relay according to the monitoring result to prolong the service life of the relay. According to the control optimization method for improving the consumption capability of the distributed power supply, the problem that the service life of a relay contact is affected by electromagnetic interference generated by an inverter in a high-frequency switching state in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical fields of power electronics and electromagnetic compatibility, and particularly relates to a control optimization method for improving the consumption capacity of distributed power sources. Background Art

[0002] In the field of power electronics, inverters have been widely used in various power equipment and systems due to their flexible frequency switching and precise power regulation characteristics. However, inverters in the prior art will cause relatively serious electromagnetic interference problems in high-frequency switching modes. Such electromagnetic interference will affect the electromagnetic compatibility of the system, and thus have a negative impact on the stable operation of the system.

[0003] A typical problem of the prior art is that due to the strong electromagnetic radiation of the inverter in the frequent switching state, electrical components such as relays in the system are easily interfered. When the relay works in an electromagnetic interference environment, abnormal arc discharges will occur at its contacts, resulting in increased contact wear and thus shortening the service life of the relay. Especially in high-frequency application scenarios, the fast switching frequency of the inverter further aggravates the wear speed of the contacts. To solve this problem, measures such as shielding or filtering are usually adopted to weaken the interference, but these methods cannot fundamentally solve the problem of contact wear. In summary, inverters in the prior art, due to the electromagnetic interference they generate affecting the life of relay contacts in the high-frequency switching state, have become an inevitable problem in the power system. Summary of the Invention

[0004] The purpose of the present invention is to provide a control optimization method for improving the consumption capacity of distributed power sources, so as to solve the problem that the electromagnetic interference generated by inverters in the prior art affects the life of relay contacts in the high-frequency switching state.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A control optimization method for improving the consumption capacity of distributed power sources, the method includes:

[0006] S1. Obtain the real-time load data of each node in the distribution network and the output power of the distributed power source;

[0007] S2. Calculate the consumption capacity of the distribution network based on the real-time load data and the output power of the distributed power source;

[0008] S3. Based on the calculation result, adjust the operation mode of the distributed power source to reduce electromagnetic interference, including constructing a matrix to represent the change probability of the electromagnetic interference level in different modes, simulating the mode substitution process in different time periods, and finding the mode with the least impact on electromagnetic interference. The specific formula is: P(t) = e Qt ;

[0009] Among them, P(t) represents the switching probability of the distributed power source between different operating modes, t represents time, Q represents the change rate matrix of the electromagnetic interference level under different operating modes, and e represents the base of the natural logarithm;

[0010] S4. Determine the working state of the relay in the optimal operating mode and monitor the wear condition of the relay contacts;

[0011] S5. Optimize the control strategy of the relay according to the monitoring results to extend its service life.

[0012] Preferably, the said S1 includes:

[0013] Construct the load distribution coefficient matrix between the nodes of the distribution network, use the real-time load data as input, and calculate the total load demand of each node. The specific formula is: x = (I - A) -1 ×d;

[0014] Among them, x represents the total load demand of each node in the distribution network, I represents the unit matrix, A represents the mutual influence coefficient between the loads of each node in the distribution network, and d represents the direct load demand of each node in the distribution network.

[0015] Preferably, the said S2 includes:

[0016] Calculate the energy change in the power flow through the real-time load difference between the nodes of the distribution network, and combine with the output power of the distributed power source to determine the power transmission efficiency and the total accommodation capacity of the distribution network. The specific formula is:

[0017]

[0018] Among them, R represents the load demand intensity of a certain node in the distribution network, ρ represents the distribution concentration of power in the distribution network per unit time, g represents the power level coefficient of the distributed power source, v represents the transmission rate of power from the distributed power source to each node, h represents the load priority of a certain node in the distribution network, and C represents the stable value of power distribution and consumption in the distribution network.

[0019] Preferably, the said S4 includes:

[0020] Model the force-bearing process of the relay contacts, use sensors to collect the contact area, pressure and deformation amount during the wear process of the contacts, calculate the contact stress, predict the strain and fatigue life of the contacts, and monitor the wear condition of the contacts in real time. The specific formula is: σ = E × δ;

[0021] Among them, σ represents the mechanical stress borne by the relay contacts during operation, E represents the elastic modulus of the contact material, and δ represents the relative deformation degree of the contacts under the action of mechanical stress.

[0022] Preferably, S3 further includes determining the high-frequency switching mode of the inverter, calculating the electromagnetic interference intensity based on the high-frequency switching mode, reducing the electromagnetic interference intensity by changing the operating frequency of the inverter, monitoring the current change of the relay contact, and adjusting the operating state of the relay.

[0023] Preferably, the calculating the electromagnetic interference intensity based on the high-frequency switching mode includes obtaining the operating frequency f and operating mode m of the inverter, and calculating the electromagnetic interference intensity G based on the operating frequency f and operating mode m. The formula is: G = A×f + B×m, where A is the frequency coefficient, B is the mode coefficient, and a preset threshold G is set. max to determine whether the electromagnetic interference intensity G exceeds the preset threshold G max If the electromagnetic interference intensity G exceeds the preset threshold G max then trigger the adjustment of the inverter operating frequency.

[0024] Preferably, the method for obtaining the real-time load data of each node in the distribution network in S1 includes using intelligent sensors to collect the load changes of each node in the distribution network in real time and transmitting the data to the control center through wireless communication technology.

[0025] Preferably, the collection of the real-time load data in S1 is realized through a hierarchical network architecture, including the aggregation of node loads in the regional layer, the data analysis in the substation layer, and the optimal scheduling in the central control layer.

[0026] Preferably, S5 includes:

[0027] Define the utility function, including the trade-off between maximizing the contact life and minimizing the electromagnetic interference, and obtain the control strategy that satisfies the global optimum. The specific formula is:

[0028] where, u i represents the utility function of the control strategy of the i-th relay, y * represents the optimal control strategy combination, y i represents the specific control strategy currently adopted by the i-th relay, y -i represents the control strategy combination of other relays in the distribution network except the i-th relay, and i represents the relay number.

[0029] Preferably, the method for monitoring the wear process of the relay contact in S4 includes measuring the contact pressure of the contact during each switching process through a high-precision pressure sensor and calculating the cumulative wear amount based on the switching frequency.

[0030] From the above technical solutions, the present invention has the following beneficial effects:

[0031] The control optimization method for improving the accommodation capacity of distributed power sources obtains the real-time load data of each node in the distribution network and the output power of distributed power sources, calculates the accommodation capacity of the distribution network based on the real-time load data and the output power of distributed power sources, adjusts the operation mode of distributed power sources based on the calculation results to reduce electromagnetic interference, determines the working state of relays in the optimal operation mode, monitors the wear condition of relay contacts, optimizes the control strategy of relays according to the monitoring results to extend their service life, can effectively reduce the intensity of electromagnetic radiation in the high-frequency switching mode, improve the electromagnetic compatibility of the system, thereby reducing the negative impact of electromagnetic interference on other electrical components; the reduced electromagnetic interference significantly reduces the abnormal arc discharge phenomenon generated by the contacts of relays in the high-frequency switching environment, reduces the wear and loss of contacts, thereby effectively extending the service life of relays, improving the overall stability and reliability of the system, avoiding misoperation or faults caused by contact wear or arc discharge, improving the reliability and safety of system operation, reducing the maintenance cost and downtime caused by frequent relay replacement, thereby improving the economy and operation efficiency of the system, being applicable to a wider range of high-frequency and high-precision control application scenarios, and expanding the application scope of inverters in power electronic systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] As Figure 1 shown, the present invention provides a technical solution: a control optimization method for improving the accommodation capacity of distributed power sources, including:

[0035] S1. Obtain the real-time load data of each node in the distribution network and the output power of distributed power sources;

[0036] S2. Calculate the accommodation capacity of the distribution network based on the real-time load data and the output power of distributed power sources;

[0037] S3. Based on the calculation results, adjust the operation mode of distributed power sources to reduce electromagnetic interference, including constructing a matrix to represent the change probability of electromagnetic interference levels in different modes, simulating the mode substitution process in different time periods, and finding the mode with the least impact on electromagnetic interference. The specific formula is: P(t) = e Qt ;

[0038] Among them, P(t) represents the switching probability of the distributed power source between different operating modes, t represents time, Q represents the rate-of-change matrix of the electromagnetic interference level under different operating modes, and e represents the base of the natural logarithm;

[0039] S4. Determine the operating state of the relay in the optimal operating mode and monitor the wear condition of the relay contacts;

[0040] S5. Optimize the control strategy of the relay according to the monitoring results to extend its service life.

[0041] In the above embodiment, by obtaining the real-time load data of each node of the distribution network and the output power of the distributed power source through S1, the real-time operation state of the distribution network is mastered, providing basic data support for subsequent calculations and optimizations. In step S2, by combining the real-time load and the output power of the distributed power source, the absorption capacity of the distribution network is analyzed and calculated, enabling the distribution network to reasonably absorb and distribute the power of the distributed power source under the current operating conditions. In step S3, by establishing a mathematical model, the change of the electromagnetic interference level in different operating modes is represented in matrix form, and the switching probability is derived through the formula P(t) = e Qt to simulate the multi-mode switching process to reduce electromagnetic interference. This process realizes the dynamic optimization of the operating mode of the distributed power source, effectively improving the stability of system operation. In step S4, further combined with the monitoring of the operating state of the relay, the wear condition of the contacts is evaluated to realize the real-time evaluation of the equipment operation health. In step S5, based on the monitoring results, the control strategy of the relay is optimized, and the operation of the relay is dynamically adjusted to extend its service life. By calculating the absorption capacity of the distribution network in real time, reasonably distributing the power of the distributed power source, improving the energy utilization efficiency, through the optimization and adjustment of the operating mode, using the mathematical model and probability analysis, the influence of electromagnetic interference on the system stability is effectively reduced, the wear condition of the relay contacts is monitored in real time, and the control strategy is optimized based on the data, thereby reducing the wear rate, extending the service life of the relay, reducing the operation and maintenance costs, dynamically adjusting the operating mode, ensuring the efficient cooperation of the distribution network and the distributed power source under various operating conditions, and improving the reliability of the overall system.

[0042] S1 includes constructing a load distribution coefficient matrix between the nodes of the distribution network, using the real-time load data as input, and calculating the total load demand of each node. The specific formula is: x = (I - A) -1 ×d;

[0043] Among them, x represents the total load demand of each node in the distribution network, I represents the identity matrix, A represents the mutual influence coefficient between the loads of each node in the distribution network, and d represents the direct load demand of each node in the distribution network.

[0044] In the above embodiments, by constructing the load distribution coefficient matrix A between the nodes of the distribution network, the mutual influence relationship of the load between each node is clarified, laying a foundation for calculating the total load demand of each node. In the application of the formula x = (I - A) -1 ×d, the identity matrix I represents the load reference of the node when it is not affected by other nodes, while the load distribution coefficient matrix A describes the load coupling degree between the nodes. By calculating the matrix inversion (I - A) -1 , the comprehensive analysis of the influence of load transfer between nodes can be carried out. After inputting the direct load demand d, the total load demand x of each node can be obtained. This calculation method fully considers the load linkage effect between nodes, thus more truly reflecting the load distribution of the distribution network. Through matrix calculation, the mutual influence between nodes is comprehensively considered, improving the accuracy of load demand calculation. The load distribution coefficient matrix A is dynamically adjusted based on real-time load data, which can adapt to the changes in the operation state of the distribution network, improve the applicability of the model, and quickly calculate the total load demand by using the matrix inversion method, improving the efficiency of load distribution analysis, contributing to real-time applications, providing accurate load distribution information, and providing reliable data support for subsequent consumption capacity analysis and operation mode optimization.

[0045] S2 includes calculating the energy change in power flow through the real-time load difference between the nodes of the distribution network, and combining the output power of distributed power sources to determine the power transmission efficiency and the total consumption capacity of the distribution network. The specific formula is as follows:

[0046] Among them, R represents the load demand intensity of a certain node in the distribution network, ρ represents the distribution concentration of power in the distribution network per unit time, g represents the power level coefficient of the distributed power source, v represents the transmission rate of power from the distributed power source to each node, h represents the load priority of a certain node in the distribution network, and C represents the stable value of power distribution and consumption in the distribution network.

[0047] In the above embodiments, step S2 dynamically calculates the energy change in power flow by analyzing the real-time load difference between the nodes of the distribution network. First, the direction and amplitude of power flow are determined by using the real-time load difference, so as to capture the dynamic characteristics of power distribution. Through the formula Combined with the load demand intensity (R), power transmission rate (v), power distribution concentration (ρ), and node priority (h), the total efficiency of power transmission in the distribution network is calculated, and the total consumption capacity is further determined. Among them, the energy change is through 1 / 2(ρv 2)The dynamic energy part representing power transmission, ρgh represents the influence of power flow distribution caused by height or node priority, and finally the parameter C ensures the stability of power distribution. The whole process dynamically combines the output characteristics of distributed power sources and the load demands of nodes, ensuring the efficient energy transmission and consumption of the distribution network under real-time operating conditions. By calculating the energy change through real-time load differences, the dynamic characteristics in power flow can be captured, providing a more comprehensive reference for the evaluation of consumption capacity. Considering factors such as power transmission rate, concentration, and node priority, the power transmission efficiency of the distribution network is accurately calculated, improving the scientificity and accuracy of the evaluation. Combining the output power of distributed power sources and node characteristics, the total consumption capacity of the distribution network is dynamically determined to ensure the high efficiency and stability of system operation. Through the calculation of power dynamic characteristics and distribution characteristics, this method is applicable to various complex operating scenarios, such as high-load fluctuation environments or scenarios with frequent switching of distributed power sources.

[0048] S4 includes modeling the force-bearing process of relay contacts, using sensors to collect the contact area, pressure, and deformation during contact wear, calculating the contact stress, predicting the strain and fatigue life of the contacts, and real-time monitoring the contact wear situation. The specific formula is: σ = E × δ;

[0049] Among them, σ represents the mechanical stress borne by the relay contacts during operation, E represents the elastic modulus of the contact material, and δ represents the relative deformation degree of the contacts under the action of mechanical stress.

[0050] In the above embodiments, by modeling the force application process of the relay contacts, the system can accurately analyze the mechanical characteristics of the contacts during operation. Sensors are used to collect key data such as the contact area, pressure, and deformation of the contacts, providing a data basis for calculating the mechanical stress of the contacts. According to the formula σ = E×δ, combined with the elastic modulus E and the relative deformation degree δ of the contact material, the stress distribution of the contacts under mechanical loads is calculated in real time. By establishing the mapping relationship between stress and strain, the fatigue life and damage degree of the contacts can be further predicted. In addition, the contact area and deformation data collected during the real-time monitoring can dynamically reflect the wear state of the contacts. By evaluating the fatigue condition of the contacts through the stress analysis results, it provides a basis for optimizing the control strategy, thereby extending the service life of the relay and improving the operation reliability. Through the formula σ = E×δ, combined with the real-time collected data, the mechanical stress of the relay contacts during operation is accurately calculated, providing a scientific basis for wear prediction. Based on the relationship between stress and strain, the fatigue life of the contacts can be effectively predicted, potential faults can be detected in advance, and the risk of downtime caused by contact failure can be reduced. By using sensors to collect the contact area, pressure, and deformation, the dynamic monitoring of the contact wear is realized, ensuring the safety and stability of the system operation. By optimizing the control strategy through the real-time monitoring of contact wear and the feedback of the life prediction results, the force loss of the contacts is reduced, and the service life of the relay is significantly extended. By monitoring the entire life cycle of the contact state, the operation reliability of the distribution network equipment is improved, and the maintenance and replacement costs are reduced.

[0051] S3 also includes determining the high-frequency switching mode of the inverter, calculating the electromagnetic interference intensity based on the high-frequency switching mode, reducing the electromagnetic interference intensity by changing the operating frequency of the inverter, monitoring the current change of the relay contacts, and adjusting the operating state of the relay.

[0052] In the above embodiments, by determining the high-frequency switching mode of the inverter, analyzing its influence on the electromagnetic interference intensity under different operating conditions, and combining the actual operating conditions to calculate the electromagnetic interference intensity. The high-frequency switching mode determines the operating frequency and switching behavior of the inverter, which has a significant impact on the surrounding electromagnetic environment. By dynamically adjusting the operating frequency of the inverter, the electromagnetic interference intensity caused by it can be effectively reduced, and the stability of the system operation can be improved. At the same time, by real-time monitoring the current change of the relay contact, capturing the abnormal state of the relay contact that may be caused by electromagnetic interference, and adjusting the operating state of the relay according to the monitoring data, such as optimizing the opening and closing frequency or changing the operating mode to ensure the stable operation of the system. The overall process ensures that while reducing electromagnetic interference, the safety and stable operation of the relay contact are ensured, thus realizing the coordinated promotion of the optimization goal of the distribution network. By adjusting the operating frequency of the inverter, the electromagnetic interference intensity is significantly reduced, and the electromagnetic compatibility of the system operating environment is improved. By monitoring the current change of the relay contact, abnormal situations can be detected in time, and the operating state of the relay can be dynamically adjusted to avoid faults, reduce the influence of electromagnetic interference on the relay contact, and ensure the stable operation of the distribution network system under various working conditions. Combining the relay state monitoring and electromagnetic interference suppression strategy, the abnormal wear rate of the relay contact is reduced, and the service life of the equipment is extended. By jointly optimizing the operating modes of the inverter and the relay, the efficiency and stability of the distribution network optimization evaluation method coexist, and the accommodation capacity of distributed power sources is further improved.

[0053] Based on the high-frequency switching mode, calculating the electromagnetic interference intensity includes obtaining the operating frequency f and the operating mode m of the inverter. Based on the operating frequency f and the operating mode m, calculating the electromagnetic interference intensity G, and the formula is: G = A×f + B×m, where A is the frequency coefficient and B is the mode coefficient, and a threshold G is set. max , determining whether the electromagnetic interference intensity G exceeds the preset threshold G. max , if the electromagnetic interference intensity G exceeds the preset threshold G. max , then trigger the adjustment of the inverter operating frequency.

[0054] In the above embodiments, by obtaining the operating frequency f and the operating mode m of the inverter, the influence of its operating parameters on the electromagnetic interference intensity is clarified. According to the formula G = A×f + B×m, the frequency coefficient A is used to describe the linear influence of the operating frequency f on the electromagnetic interference intensity, and the mode coefficient B reflects the different contributions of different operating modes m to the interference intensity. After substituting the actual values of f and m into the formula, the electromagnetic interference intensity G of the current inverter is calculated. To ensure the electromagnetic compatibility of the system, a threshold G of the electromagnetic interference intensity is set. max . If the calculated G exceeds the preset threshold G. max, the system automatically triggers the adjustment of the inverter's operating frequency, reducing the electromagnetic interference intensity by decreasing the operating frequency f or switching to a working mode m with lower interference, thereby maintaining the stability and reliability of the system operation. The electromagnetic interference intensity is accurately calculated through the formula G = A×f + B×m to quantitatively evaluate the electromagnetic interference level of the inverter, and a preset threshold G is introduced. max By triggering the frequency adjustment, it can dynamically reduce electromagnetic interference, quickly respond to the situation of excessive interference, and improve the system stability. Combining with the switching strategy of the working mode m, it can optimize the interference intensity under different operating scenarios, enhance the adaptability and compatibility of the system, effectively reduce the electromagnetic interference generated by the inverter, reduce the impact on sensitive equipment in the distribution network, and improve the overall reliability and safety of the system. By optimizing the operating frequency and mode, while suppressing electromagnetic interference, it can maintain the efficient operating state of the inverter as much as possible, taking into account both performance and stability.

[0055] The method for obtaining the real-time load data of each node in the distribution network in S1 includes using intelligent sensors to collect the load changes of each node in the distribution network in real time and transmitting the data to the control center through wireless communication technology.

[0056] In the above embodiment, through the intelligent sensors deployed at each node of the distribution network, the real-time load changes of the nodes are accurately collected. The intelligent sensors can capture the dynamic information of the node load changes through means such as current and voltage measurement and convert it into digital signals. The collected data is transmitted to the control center through wireless communication technology (such as LoRa, Wi-Fi, NB-IoT, etc.). After the control center receives and stores the data, it further processes and analyzes it to provide basic data support for the subsequent optimization of the distribution network and the evaluation of the absorption capacity of distributed power sources. The application of wireless communication technology overcomes the wiring complexity problem of traditional wired communication, realizes the efficient transmission of data, and enhances the guarantee of real-time performance and reliability. The real-time collection of node load changes by intelligent sensors has high precision and high sensitivity, can quickly respond to the dynamic changes of the load, and through wireless communication technology, reduces the data transmission delay, ensuring that the control center can grasp the state of the distribution network in real time and providing support for optimized scheduling. Wireless communication technology eliminates the complex wiring process, reduces the installation and maintenance costs, and improves the flexibility of the system at the same time. The combination of intelligent sensors and wireless communication makes this method applicable to distribution networks of different scales, especially in the case of wide node distribution or complex environments, and can still stably transmit data. The collection and transmission of real-time load data provide a data basis for the intelligent operation and optimized decision-making of the distribution network, improving the automation level of the system.

[0057] The acquisition of real-time load data in S1 is achieved through a hierarchical network architecture, including load aggregation at the regional layer nodes, data analysis at the substation layer, and optimal scheduling at the central control layer. In the above implementation, the acquisition of real-time load data adopts a hierarchical network architecture to ensure the efficiency of data transmission and the accuracy of scheduling decisions. Regional layer node load aggregation: In each region of the distribution network, intelligent sensors are deployed to collect the load data of each node. After preliminary processing, the node data is aggregated to the control device at the regional layer (such as a regional data gateway). This layer is responsible for the real-time collection and basic statistics of load data, reducing the bandwidth requirements for data transmission. Substation layer data analysis: The data collected by the regional layer is transmitted to the substation layer, where further data analysis and load status evaluation are performed based on the regional load aggregation information. For example, by analyzing the load change trend, high-load areas are identified, and potential load anomalies are predicted. Central control layer optimal scheduling: The data analyzed by the substation layer is uploaded to the central control layer, which performs global optimal scheduling. This layer uses the load data of the entire network and the operating status of distributed power sources, combines optimization algorithms to calculate the optimal distributed power allocation plan, guides the operation and scheduling of the distribution network, and realizes the improvement of the overall operation efficiency and the maximization of the consumption capacity. Through the hierarchical network architecture, the tasks of each layer are clearly defined, and an efficient hierarchical processing flow is formed from node acquisition to global optimization, which not only reduces the pressure of data transmission but also enhances the scalability and response speed of the system. This implementation method has the following advantages compared with the prior art by adopting a hierarchical network architecture for the acquisition and processing of real-time load data: Through the division of labor and cooperation among the regional layer, substation layer, and central control layer, hierarchical processing of data is achieved, reducing the computational pressure and communication burden of a single layer and improving the processing efficiency. The real-time aggregation and analysis of load data by the regional layer and substation layer can quickly respond to load fluctuations within the region and provide more timely data support for the central control layer. The central control layer performs optimal scheduling based on the global load data, reasonably allocates the power output of distributed power sources, and realizes the efficiency and reliability of the operation of the distribution network. The hierarchical architecture is convenient for system expansion, and regional layer or substation layer nodes can be dynamically added according to the scale of the distribution network without major changes to the existing architecture. The hierarchical architecture reduces the need for the central control layer to directly process the original load data. Through the sharing of the regional layer and substation layer, the communication bandwidth and the burden on the central processing device are reduced. The independent processing capabilities between layers ensure the operational stability of the system when local nodes fail and avoid the impact of single-point failures on the operation of the entire network.

[0058] S5 includes defining a utility function, including the trade-off between maximizing contact life and minimizing electromagnetic interference, to obtain a control strategy that satisfies global optimality. The specific formula is:

[0059] where u i represents the utility function of the control strategy of the i-th relay, and y* represents the optimal control strategy combination, y i represents the specific control strategy currently adopted by the i-th relay, y -i represents the control strategy combination of other relays in the distribution network except the i-th relay, where i represents the relay number.

[0060] In the above embodiment, by defining the utility function, the maximization of the relay contact life and the minimization of electromagnetic interference are combined as the optimization objective. In the utility function u 1 the extension of the contact life is reflected in reducing the mechanical wear frequency of the relay contacts, while the reduction of electromagnetic interference is reflected in optimizing the operation strategy to reduce the electromagnetic radiation and inductive effects of the system.

[0061] The formula describes the condition for solving the optimal control strategy combination y globally, that is, ensuring that the control strategy adopted by each relay can achieve the maximum utility within the entire network. During the optimization process, y i represents the current control strategy of each relay, and y -i represents the control strategy combination of other relays. By traversing and adjusting the control strategy combination, the system iteratively optimizes the u i value, and finally obtains y that satisfies the global optimal condition. This method ensures that the relay control strategy of the distribution network achieves the best balance between extending the equipment life and reducing electromagnetic interference. By establishing a trade-off relationship between the goal of maximizing the contact life and minimizing the electromagnetic interference, it ensures the overall optimization of the system operation. Through the mathematical description of the utility function u i and the global optimal condition, it is possible to find the optimal combination among multiple relay control strategies, improve the system optimization accuracy. By optimizing the control strategy, it effectively reduces the mechanical wear rate of the relay, thus significantly extending the equipment service life, reducing the operation and maintenance costs, combining the operation optimization and interference suppression strategies, reducing the impact of electromagnetic interference on the distribution network equipment and the external environment, and improving the electromagnetic compatibility of the system. This method is applicable to distribution networks of different scales and complexities and can efficiently achieve global optimization in complex scenarios with multiple relays and multiple control strategies.

[0062] The method for monitoring the contact wear process in S4 includes measuring the contact pressure of the contact during each switching process through a high-precision pressure sensor, and calculating the cumulative wear amount based on the switching frequency. In the above embodiment, by integrating a high-precision pressure sensor in the relay, the contact pressure of the contact during each switching process is measured in real time, and the change in the force state of the contact is captured. The contact pressure of the contact is an important factor affecting its wear, and its magnitude directly determines the stress distribution and wear rate of the contact surface material. Based on the collected contact pressure data and the switching frequency of the relay, using a cumulative wear calculation model (such as the Archard wear formula), the cumulative wear amount of the contact is evaluated. By recording the pressure data of each switching and combining frequency statistics, the wear process of the contact can be dynamically tracked, and a warning can be issued or adjustment measures can be taken when the wear amount of the contact approaches the threshold, so as to realize the real-time monitoring and life management of the relay contact state. The high-precision pressure sensor can accurately capture the contact pressure during each contact switching, providing a reliable data basis for the accurate calculation of the wear amount. By real-time monitoring the contact pressure and switching frequency, the cumulative wear amount of the contact can be dynamically evaluated, providing timely feedback on the wear state. Through the wear monitoring and warning mechanism, sudden failure of the contact due to excessive wear can be avoided, the service life of the relay can be extended, the operation and maintenance cost can be reduced. Combining the wear monitoring results, the operation strategy of the relay can be adjusted, such as reducing the switching frequency or optimizing the switching conditions, so as to reduce the contact wear rate, dynamically monitor the contact wear state, ensure that the relay is maintained or replaced before the wear reaches the critical value, and improve the operation stability of the distribution network.

[0063] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made in these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control optimization method for improving the absorption capacity of distributed power sources, characterized in that: The method comprises: S1. Obtain the real-time load data of each node in the distribution network and the output power of the distributed power source; S2. Calculate the absorption capacity of the distribution network based on real-time load data and the output power of distributed power sources; S3. Based on the calculation results, the operation mode of the distributed power supply is adjusted to reduce electromagnetic interference, including constructing a matrix to represent the probability of change of electromagnetic interference level under different modes, simulating the mode replacement process in different time periods, and finding the mode with the least impact on electromagnetic interference. The specific formula is: P(t) = e Qt ; Where P(t) represents the switching probability of distributed generation between different operating modes, t represents time, Q represents the rate of change matrix of electromagnetic interference level under different operating modes, and e represents the base of natural logarithm; S4, determining the working state of the relay under the optimal operation mode and monitoring the wear of the relay contacts; S5. Based on the monitoring results, optimize the control strategy of the relay to extend its service life.

2. A control optimization method for improving the absorption capacity of distributed power sources according to claim 1, characterized in that: The S1 includes: Construct the load distribution coefficient matrix between the nodes of the distribution network, use the real-time load data as input, and calculate the total load demand of each node. The specific formula is: x = (IA) -1 ×d; Among them, x represents the total load demand of each node in the distribution network, I represents the unit matrix, A represents the mutual influence coefficient between the loads of each node in the distribution network, and d represents the direct load demand of each node in the distribution network.

3. A control optimization method for improving the absorption capacity of distributed power sources according to claim 1, characterized in that: The S2 includes: The energy change in power flow is calculated by the real-time load difference between distribution network nodes. Combined with the output power of distributed power sources, the power transmission efficiency and the total absorption capacity of the distribution network are determined. The specific formula is: Among them, R represents the load demand intensity of a node in the distribution network, ρ represents the distribution concentration of power in the distribution network per unit time, g represents the power level coefficient of distributed power sources, v represents the transmission rate of power from distributed power sources to each node, h represents the load priority of a node in the distribution network, and C represents the stable value of power allocation and consumption in the distribution network.

4. A control optimization method for improving the absorption capacity of distributed power sources according to claim 1, characterized in that: The S4 includes: Model the force process of the relay contact, use sensors to collect the contact area, pressure and deformation during the contact wear process, calculate the contact stress, predict the strain and fatigue life of the contact, and monitor the contact wear in real time. The specific formula is: σ=E×δ; Among them, σ represents the mechanical stress that the relay contact is subjected to during operation, E represents the elastic modulus of the contact material, and δ represents the relative deformation degree of the contact under the action of mechanical stress.

5. The control optimization method for improving the absorption capacity of distributed power sources according to claim 1 is characterized in that: The S3 also includes determining the high-frequency switching mode of the inverter, calculating the electromagnetic interference intensity based on the high-frequency switching mode, reducing the electromagnetic interference intensity by changing the operating frequency of the inverter, monitoring the current change of the relay contacts, and adjusting the working state of the relay.

6. A control optimization method for improving the absorption capacity of distributed power sources according to claim 5, characterized in that: The electromagnetic interference intensity is calculated based on the high-frequency switching mode, including obtaining the working frequency f and the working mode m of the inverter, and calculating the electromagnetic interference intensity G based on the working frequency f and the working mode m. The formula is: G = A × f + B × m, where A is the frequency coefficient, B is the mode coefficient, and the threshold G is set. max , determine whether the electromagnetic interference intensity G exceeds the preset threshold G max If the electromagnetic interference intensity G exceeds the preset threshold G max , then the inverter operating frequency adjustment is triggered.

7. A control optimization method for improving the absorption capacity of distributed power sources according to claim 1, characterized in that: The method for obtaining the real-time load data of each node in the distribution network in S1 includes using intelligent sensors to collect the load changes of each node in the distribution network in real time, and transmitting the data to the control center through wireless communication technology.

8. A control optimization method for improving the absorption capacity of distributed power sources according to claim 2, characterized in that: The collection of real-time load data in S1 is achieved through a layered network architecture, including regional layer node load aggregation, substation layer data analysis and central control layer optimization scheduling.

9. The control optimization method for improving the absorption capacity of distributed power sources according to claim 1, characterized in that: The S5 includes: Define the utility function, including the trade-off between maximizing contact life and minimizing electromagnetic interference, and obtain the control strategy that satisfies the global optimum. The specific formula is: Among them, u i represents the utility function of the ith relay control strategy, y * represents the optimal control strategy combination, y i represents the specific control strategy currently adopted by the i-th relay, y -i It represents the control strategy combination of other relays in the distribution network except the i-th relay, and i represents the number of the relay.

10. A control optimization method for improving the absorption capacity of distributed power sources according to claim 4, characterized in that: The method for monitoring the wear process of the relay contacts in S4 includes measuring the contact pressure of the contacts during each switching process by a high-precision pressure sensor, and calculating the accumulated wear amount based on the switching frequency.

Images