An intelligent control method and system for medium-voltage power distribution

Abstract

The present invention relates to the field of power distribution technology, and particularly relates to a medium-voltage power distribution method, system, electronic device and readable storage medium with intelligent control. The medium-voltage power distribution system includes a data acquisition and preprocessing module, a power consumption pattern analysis module, an optimization strategy generation module, and a system operation evaluation and feedback module. The intelligent control medium-voltage power distribution method, system, electronic device and readable storage medium of the present invention effectively solve the operation problems of traditional medium-voltage power distribution systems through data acquisition and preprocessing, power consumption pattern analysis and risk identification, optimization strategy generation, system operation stability evaluation and strategy feedback improvement, significantly improve the intelligent level of the power distribution system, and provide technical support for the development of smart grids.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution, and particularly to a medium-voltage power distribution method and system with intelligent control. Background Art

[0002] With the rapid development of social economy and the continuous growth of power demand, as an important link in power transmission and distribution, the operation stability and efficiency of the medium-voltage power distribution system directly affect the overall performance of the power grid. However, the traditional medium-voltage power distribution system faces various challenges during operation, including:

[0003] Load fluctuations and changes in electricity consumption patterns: During daily electricity consumption and special time nodes (such as holidays or peak hours), the electricity load may suddenly increase or decrease, resulting in the long-term non-optimal operation state of power distribution equipment and increasing the risk of equipment failure;

[0004] Low utilization rate of operation data: Although modern power distribution systems have the ability to collect a large amount of real-time data, the traditional solutions have a low utilization rate of data and cannot effectively identify and predict system risks and trends through historical data;

[0005] Insufficient optimization ability: Most of the optimization strategies of the existing power distribution systems rely on preset rules, lack dynamic adjustment and adaptive ability, and are difficult to adapt to the complex and changeable electricity consumption environment. Summary of the Invention

[0006] In order to overcome the disadvantages and deficiencies existing in the prior art, the purpose of the present invention is to provide a medium-voltage power distribution method, system, electronic device and readable storage medium with intelligent control. Through data acquisition and preprocessing, electricity consumption pattern analysis and risk identification, optimization strategy generation, system operation stability evaluation and strategy feedback improvement, the operation problems of the traditional medium-voltage power distribution system are effectively solved, the intelligent level of the power distribution system is significantly improved, and technical support is provided for the development of the smart grid.

[0007] The present invention is achieved by the following technical solutions:

[0008] In the first aspect, the present invention discloses a medium-voltage power distribution method with intelligent control, which includes the following steps:

[0009] S100. Real-time collect the electricity consumption data and environmental data of the power distribution system, and perform cleaning, denoising and outlier processing on the electricity consumption data and environmental data;

[0010] S200. Analyze the electricity consumption pattern of users, identify the electricity consumption characteristics of users in different time periods, and obtain the analysis data set of users;

[0011] S300. Based on the analysis of the data set, by interacting with the simulation environment, adjust the power distribution logic, maximize the power consumption efficiency and system stability, and optimize the power distribution strategy;

[0012] S400. Simulate multiple possible operation scenarios and evaluate the stability of the system under the optimized strategy;

[0013] S500. According to the evaluation results of the operation stability, update the reward function of the reinforcement learning model or adjust the model parameters, and iteratively improve the power distribution optimization strategy.

[0014] Combined with the first aspect, further, in step S100, use wavelet transform to clean, denoise and handle outliers of the power consumption data and environmental data. The calculation formulas for cleaning, denoising and outlier handling are as follows:

[0015] W(j,k) = ∫X(t)ψ j,k (t)dt

[0016]

[0017] where X(t) is the input signal data set of the power consumption data and environmental data, W(j,k) is the wavelet coefficient, ψ j,k (t)dt is the wavelet basis function, λ is the threshold, is the wavelet coefficient after threshold processing, is the denoised signal.

[0018] Combined with the first aspect, further, in step S200, use the clustering algorithm to analyze the user's power consumption pattern and identify the power consumption characteristics of users in different time periods. The calculation formula of the clustering algorithm is as follows:

[0019]

[0020] where C is the set of clustering results, n is the number of data points, k is the number of clusters, X i is the i-th signal segment, μ j is the i-th cluster center.

[0021] Combined with the first aspect, further, in step S300, construct a mathematical model based on multi-objective optimization to optimize the power distribution strategy. The mathematical formula of the mathematical model is as follows:

[0022] minF = α1C cost +α2R reliability +α3L imbalance

[0023]

[0024] where F is the comprehensive objective function, C costFor the operating cost, R r For the power supply reliability, L i For the load unbalance degree, α1, α2, α3 are weight functions, P loss,i For the power consumption loss of the i-th line, t i For the operating time, C maintenance,j Is the maintenance cost of the j-th device, F k For the frequency of the k-th power outage, D k For the duration, T is the total time, P l For the load power of the l-th line, P total For the total system load, L is the number of lines;

[0025] The mathematical model needs to satisfy the following constraints:

[0026]

[0027] V min ≤V i ≤V max , i = 1, 2,..., n

[0028] P i ≤P max,i , i = 1, 2,..., n

[0029] Among them, P gen,i Is the power generation power of the i-th power generation unit, P load,j Is the power demand of the j-th load unit, V i Is the voltage threshold of the i-th node in the distribution network, P i Is the active power of the i-th node.

[0030] Combined with the first aspect, further, in step S400, the calculation formula for optimizing the distribution strategy is:

[0031] S = β1F voltage +β2F frequency +β3F load +β4F reliability

[0032]

[0033] Among them, S is the comprehensive evaluation index of the system operation stability, β1, β2, β3, β4 are weight coefficients, F voltage Is the voltage deviation function, F frequency Is the frequency deviation function, F load Is the load fluctuation function, F reliability Is the power supply reliability function, V i Is the voltage of the i-th node, V refis the reference voltage, f is the frequency at which the current system is operating, and f ref is the nominal frequency of the system, and P load (t) is the load power at time t, is the average load power per unit time, and P miss is the unmet load power, and P total is the total load power.

[0034] Combined with the first aspect, further, in step S500, the calculation formula for optimizing the feedback formula is:

[0035]

[0036] where θ is the parameter set of the optimization strategy, and θ (k) is the optimization strategy parameter for the k-th iteration, and S (k+1) is the optimization strategy parameter after the (k + 1)-th iteration, γ is the learning rate, is the gradient of the loss function.

[0037] In the second aspect, the present invention also discloses a medium-voltage distribution system with intelligent control, which includes:

[0038] A data acquisition and preprocessing module, which is used to collect the real-time operation data and environmental data of the distribution system, and perform cleaning, denoising, and outlier processing on the operation data and environmental data;

[0039] An electricity consumption pattern analysis module, which is used to analyze historical electricity consumption data to identify the electricity consumption characteristics of different time periods and different users;

[0040] An optimization strategy generation module, which is used to simulate various electricity consumption scenarios and automatically generate optimized distribution strategies;

[0041] A system operation evaluation and feedback module, which is used to quantitatively evaluate the long-term stability of the distribution system under the optimization strategy;

[0042] The medium-voltage distribution system is used to implement the medium-voltage distribution method as described above.

[0043] In the third aspect, the present invention also discloses an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor runs the computer program to implement the medium-voltage distribution method as described above.

[0044] In the fourth aspect, the present invention also discloses a computer-readable storage medium, on which a computer program is stored. The program is executed by the processor to implement the medium-voltage distribution method as described above.

[0045] The beneficial effects of the present invention:

[0046] An intelligent control medium-voltage power distribution method, system, electronic device, and readable storage medium of the present invention collect relevant data during the operation of the power distribution system, analyze it in combination with relevant patterns and characteristics of users, and cooperate with relevant mathematical models, simulations, and evaluations to effectively improve the system performance, stability, and operation efficiency, while achieving the effects of quantifying risks and optimization and realizing closed-loop optimization. Description of the Drawings

[0047] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on the following drawings without creative efforts.

[0048] Figure 1 It is a flowchart of the steps of the medium-voltage power distribution method provided by the embodiment of the present invention. Detailed Embodiments

[0049] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made with reference to the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein. Those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0050] With the rapid development of social economy and the continuous growth of power demand, as an important link in power transmission and distribution, the operation stability and efficiency of the medium-voltage power distribution system directly affect the overall performance of the power grid. However, the traditional medium-voltage power distribution system faces various challenges during operation, including:

[0051] Load fluctuations and changes in electricity consumption patterns: During daily electricity consumption and special time nodes (such as holidays or peak hours), the electricity load may suddenly increase or decrease, resulting in the long-term non-optimal operation state of power distribution equipment and increasing the risk of equipment failure;

[0052] Low utilization rate of operation data: Although modern power distribution systems have the ability to collect a large amount of real-time data, the traditional solutions have a low utilization rate of data and cannot effectively identify and predict system risks and trends through historical data;

[0053] Insufficient optimization ability: Most of the optimization strategies of existing power distribution systems rely on preset rules, lack dynamic adjustment and adaptive ability, and are difficult to adapt to the complex and changeable electricity consumption environment.

[0054] In order to solve the above problems, this embodiment discloses an intelligent control medium-voltage power distribution system, including:

[0055] The data acquisition and preprocessing module is used to collect the real-time operation data and environmental data of the distribution system, and clean, denoise and handle outliers of the operation data and environmental data;

[0056] The electricity consumption pattern analysis module is used to analyze the historical electricity consumption data to identify the electricity consumption characteristics of different time periods and different users;

[0057] The optimization strategy generation module is used to simulate various electricity consumption scenarios and automatically generate optimized distribution strategies;

[0058] The system operation evaluation and feedback module is used to quantitatively evaluate the long-term stability of the distribution system under the optimization strategy;

[0059] In addition, this embodiment also discloses a medium-voltage distribution method executed in the above system. The method includes the following steps:

[0060] S100. Real-time collect the electricity consumption data and environmental data of the distribution system, and clean, denoise and handle outliers of the electricity consumption data and environmental data;

[0061] S200. Analyze the electricity consumption pattern of users, identify the electricity consumption characteristics of users in different time periods, and obtain the analysis dataset of users;

[0062] S300. Based on the analysis dataset, by interacting with the simulation environment, adjust the distribution logic, maximize the electricity consumption efficiency and system stability, and optimize the distribution strategy;

[0063] S400. Simulate various possible operation scenarios and evaluate the stability of the system under the optimization strategy;

[0064] S500. According to the operation stability evaluation results, update the reward function of the reinforcement learning model or adjust the model parameters, and iteratively improve the distribution optimization strategy.

[0065] Further, in step S100, wavelet transform is used to clean, denoise and handle outliers of the electricity consumption data and environmental data. The calculation formulas for cleaning, denoising and handling outliers are as follows:

[0066] W(j,k) = ∫X(t)ψ j,k (t)dt

[0067]

[0068] where X(t) is the input signal dataset of the electricity consumption data and environmental data, W(j,k) is the wavelet coefficient, ψ j,k (t)dt is the wavelet basis function, λ is the threshold, is the wavelet coefficient after threshold processing, is the signal after denoising.

[0069] Further, in step S200, a clustering algorithm is used to analyze the user's power consumption pattern and identify the power consumption characteristics of the user in different time periods. The calculation formula of the clustering algorithm is:

[0070]

[0071] where C is the set of clustering results, n is the number of data points, k is the number of clusters, and X i is the i-th signal segment, and μ j is the i-th cluster center.

[0072] Further, in step S300, a mathematical model based on multi-objective optimization is constructed to optimize the power distribution strategy. The mathematical formula for constructing the mathematical model is:

[0073] minF = α1C c +α2R r +α3L i

[0074] where F is the comprehensive objective function, representing the total objective value of the optimization strategy, and comprehensively considering the weights of different objectives; C c is the operating cost, which refers to the operating cost of the power distribution system within a given time period, including power loss and maintenance cost; R r is the power supply reliability, which is the power supply reliability index of the system under different load conditions, usually using the system average interruption frequency index (SAIFI) or the system average interruption duration index (SAIDI); L i is the load imbalance degree, reflecting the uneven degree of load distribution between each line; α1, α2, α3 are weight functions, used to balance the importance of each objective, and are set according to system requirements and operating objectives.

[0075] Further,

[0076]

[0077] where P loss,i is the power loss of the i-th line, and t i is the operating time.

[0078] Further,

[0079]

[0080] where C maintenance,j is the maintenance cost of the j-th device, F k is the frequency of the k-th power outage, D k is the duration, and T is the total time.

[0081] Further,

[0082]

[0083] where P l is the load power of the l-th line, P total is the total system load, and L is the number of lines;

[0084] The mathematical model needs to satisfy the following constraints:

[0085]

[0086] V min ≤V i ≤V max , i = 1, 2,..., n

[0087] P i ≤P max,i , i = 1, 2,..., n

[0088] where P gen,i is the power generation of the i-th power generation unit, P load,j is the power demand of the j-th load unit, V i is the voltage threshold of the i-th node in the distribution network, and P i is the active power of the i-th node.

[0089] Combined with the first aspect, further, in step S400, the calculation formula for optimizing the distribution strategy is:

[0090] S = β1F voltage + β2F frequency + β3F load + β4F reliability

[0091]

[0092]

[0093] where S is the comprehensive evaluation index of system operation stability, β1, β2, β3, β4 are weight coefficients, F voltage is the voltage deviation function, F frequency is the frequency deviation function, F load is the load fluctuation function, F reliability is the power supply reliability function, V i is the voltage of the i-th node, V ref is the reference voltage, f is the current operating frequency of the system, f ref is the nominal frequency of the system, P load (t) is the load power at time t, is the average load power per unit time, P miss is the unmet load power, P total is the total load power.

[0094] Furthermore, in step S500, the calculation formula for optimizing the feedback formula is:

[0095]

[0096] where θ is the parameter set of the optimization strategy, θ (k) is the optimization strategy parameter for the k-th iteration, S (k+1) is the optimization strategy parameter after the (k + 1)-th iteration, γ is the learning rate, is the gradient of the loss function.

[0097] An intelligent control medium-voltage power distribution method, system, electronic device and readable storage medium of the present invention collect relevant data during the operation of the power distribution system, analyze it in combination with the relevant patterns and characteristics of users, and cooperate with relevant mathematical models, simulations and evaluations to effectively improve the system performance, stability and operation efficiency, and at the same time achieve the effects of quantifying risks and optimization and realizing closed-loop optimization.

[0098] Some embodiments of the present application also provide an electronic device, which includes: a processor, a memory, a bus and a communication interface, and the processor, communication interface and memory are connected through the bus; a computer program that can run on the processor is stored in the memory, and when the processor runs the computer program, it executes the method provided in any one of the foregoing embodiments of the present application.

[0099] Among them, the memory may include a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface (which can be wired or wireless), a communication connection is realized between the system network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0100] The bus can be an ISA bus, a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Among them, the memory is used to store the program, and after receiving the execution instruction, the processor executes the program. The soft-pack battery cell recycling management method disclosed in any one of the foregoing embodiments of the present application can be applied to the processor or implemented by the processor.

[0101] A processor may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute each method, step and logic block diagram disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed by the hardware decoding processor, or executed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0102] The electronic device provided by the embodiment of the present application and the medium-voltage power distribution method provided by the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by it.

[0103] The embodiment of the present application also provides a computer-readable storage medium corresponding to the medium-voltage power distribution method provided by the foregoing embodiment. A computer program is stored thereon. The computer-readable storage medium is an optical disc, and a computer program (i.e., a program product) is stored thereon. When the computer program is run by a processor, it will execute the medium-voltage power distribution method provided by any foregoing embodiment.

[0104] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here one by one.

[0105] The computer-readable storage medium provided by the above embodiment of the present application and the soft-pack battery cell recycling management method provided by the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored thereon.

[0106] It should be noted that in the above text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus including a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or apparatus including such element. In addition, it should be pointed out that the scope of the methods and apparatuses in the embodiments of the present application is not limited to performing functions in the order shown or discussed, but may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0107] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0108] The above describes the embodiments of the present application in conjunction with the accompanying drawings, which are only specific embodiments of the present application. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all belong to the protection scope of the present application.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the protection scope of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. An intelligent control method for medium-voltage power distribution, characterized in that, It includes the following steps: S100. Collect the power consumption data and environmental data of the power distribution system in real time, and clean, denoise, and handle outliers for the power consumption data and environmental data; S200. Analyze the user's power consumption pattern, identify the power consumption characteristics of users in different time periods, and obtain the user's analysis dataset; S300. Based on the analysis dataset, by interacting with the simulation environment, adjust the power distribution logic, maximize the power consumption efficiency and system stability, and optimize the power distribution strategy; S400. Simulate multiple possible operating scenarios and evaluate the stability of the system under the optimized strategy; S500. According to the operating stability evaluation results, update the reward function of the reinforcement learning model or adjust the model parameters, and iteratively improve the power distribution optimization strategy; In step S300, a mathematical model based on multi-objective optimization is constructed to optimize the power distribution strategy, and the mathematical formula of the mathematical model is: in, is the comprehensive objective function, For operating costs, For power supply reliability, is the load imbalance, , is the weight function, For the The power loss of the line, is the running time, It is The maintenance cost of each device, For the The frequency of power outages, is the duration, is the total time, For the The load power of the line, is the total system load, is the number of lines; The mathematical model needs to meet the following constraints: wherein, is the power generation power of the th power generation unit, is the power demand of the th load unit, is the voltage threshold of the th node in the power distribution network, is the active power of the th node.

2. The intelligent control medium voltage power distribution method according to claim 1, wherein In step S100, wavelet transform is used to clean, denoise, and handle outliers for the power consumption data and environmental data, and the calculation formulas for cleaning, denoising, and handling outliers are: Among them, is the input signal dataset of power consumption data and environmental data, is the wavelet coefficient, is the wavelet basis function, is the threshold, is the wavelet coefficient after threshold processing, is the denoised signal.

3. The medium-voltage power distribution method with intelligent control according to claim 2, characterized in that In step S200, a clustering algorithm is used to analyze the user's power consumption pattern and identify the power consumption characteristics of users in different time periods, and the calculation formula of the clustering algorithm is: Among them, is the set of clustering results, is the number of data points, is the number of clusters, is the th signal segment, is the th cluster center.

4. A medium-voltage power distribution method with intelligent control according to claim 1, characterized in that, In step S400, the calculation formula for optimizing the power distribution strategy is: Among them, is the comprehensive evaluation index for the system operation stability, is the weight coefficient, is the voltage deviation function, is the frequency deviation function, is the load fluctuation function, is the power supply reliability function, is the voltage of the th node, is the reference voltage, is the frequency of the current system operation, is the nominal frequency of the system, is the load power at time is the average load power per unit time, is the unmet load power, is the total load power.

5. The medium-voltage power distribution method with intelligent control according to claim 4, characterized in that In step S500, the calculation formula for the optimization feedback formula is: Among them, is the parameter set of the optimization strategy, is the optimization strategy parameter for the -th iteration, is the optimization strategy parameter after the -th iteration, is the learning rate, is the gradient of the loss function.

6. An intelligent control medium voltage distribution system, characterized in that, It includes: A data collection and preprocessing module, which is used to collect the real-time operation data and environmental data of the power distribution system, and clean, denoise, and handle outliers for the operation data and environmental data; A power consumption pattern analysis module, which is used to analyze the historical power consumption data and identify the power consumption characteristics of different users in different time periods; An optimization strategy generation module, which is used to simulate multiple power consumption scenarios and automatically generate optimized power distribution strategies; A system operation evaluation and feedback module, which is used to quantitatively evaluate the long-term stability of the power distribution system under the optimized strategy; The medium-voltage power distribution system is used to implement the medium-voltage power distribution method according to any one of claims 1-5.

7. An electronic device, characterized in that it includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor runs the computer program to implement the medium-voltage power distribution method according to any one of claims 1-5.

8. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor to implement the medium-voltage power distribution method according to any one of claims 1-5.

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