Intelligent control medium-voltage power distribution method and system

Abstract

The invention relates to the technical field of power distribution, in particular to an intelligent control medium-voltage power distribution method and system, electronic equipment and a readable storage medium, and the medium-voltage power distribution system comprises a data acquisition and preprocessing module, a power utilization mode analysis module, an optimization strategy generation module and a system operation evaluation and feedback module. According to the intelligent control medium-voltage power distribution method and system, the electronic equipment and the readable storage medium, through data acquisition and preprocessing, power utilization mode analysis and risk identification, optimization strategy generation, system operation stability evaluation and strategy feedback improvement, the operation problem of a traditional medium-voltage power distribution system is effectively solved; the intelligent level of the power distribution system is remarkably improved, and technical support is provided for development of an intelligent power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution, and in particular to an intelligently controlled medium-voltage power distribution method and system. Background Art

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

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

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

[0005] Insufficient optimization capabilities: Most of the optimization strategies of existing power distribution systems rely on preset rules and lack dynamic adjustment and adaptive capabilities, making it difficult to adapt to the complex and changing power consumption environment. Summary of the invention

[0006] In order to overcome the shortcomings and deficiencies in the prior art, the purpose of the present invention is to provide an intelligently controlled medium-voltage power distribution method, system, electronic device and readable storage medium, which effectively solves the operating 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 improves the intelligence level of the distribution system, and provides technical support for the development of smart grids.

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

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

[0009] S100. Collect power consumption data and environmental data of the power distribution system in real time, and clean, denoise and process outliers of the power consumption data and environmental data;

[0010] S200. Analyze the user's electricity usage pattern, identify the user's electricity usage characteristics in different time periods, and obtain the user's analysis data set;

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

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

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

[0014] In combination with the first aspect, further, in step S100, wavelet transform is used to clean, denoise and process outliers for power consumption data and environmental data. The calculation formulas for cleaning, denoising and outlier processing are:

[0015] W(j,k)=∫X(t)ψ j,k (t)dt

[0016]

[0017] Among them, X(t) is the input signal set of 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.

[0018] In combination with the first aspect, 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:

[0019]

[0020] Among them, C is the clustering result set, 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] In combination with the first aspect, further, 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:

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

[0023]

[0024] Among them, F is the comprehensive objective function, C costis the operating cost, R r For power supply reliability, L i is the load imbalance, α1, α2, α3 are weight functions, P loss,i is the power loss of the ith line, t i is the running time, C maintenance,j is the maintenance cost of the jth equipment, F k is the frequency of the kth power outage, D k is the duration, T is the total time, P l is the load power of the lth line, P total is the total system load, L is the number of lines;

[0025] The mathematical model must meet 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 of the i-th power generation unit, P load,j is the power requirement of the jth load unit, V i is the voltage threshold of the ith node in the distribution network, P i is the active power of the ith node.

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

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

[0032]

[0033] Among them, S is the comprehensive evaluation index of system operation stability, β1, β2, β3, β4 are weight coefficients, and 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 ith node, V refis the reference voltage, f is the frequency of the current system operation, and f ref is the system nominal frequency, P load (t) is the load power at time t, is the average load power per unit time, P miss is the unsatisfied load power, P total is the total load power.

[0034] In combination with the first aspect, further, in step S500, the calculation formula of the optimized feedback formula is:

[0035]

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

[0037] In a second aspect, the present invention further discloses an intelligently controlled medium voltage power distribution system, comprising:

[0038] The data acquisition and preprocessing module is used to collect the real-time operation data and environmental data of the power distribution system, and clean, denoise and process the operation data and environmental data;

[0039] The power consumption pattern analysis module is used to analyze historical power consumption data and identify the power consumption characteristics of different time periods and different users;

[0040] The optimization strategy generation module is used to simulate various power usage scenarios and automatically generate optimized power distribution strategies;

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

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

[0043] In a third aspect, the present invention further discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the medium voltage power distribution method as described above.

[0044] In a fourth aspect, the present invention further discloses a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the medium voltage power distribution method as described above.

[0045] Beneficial effects of the present invention:

[0046] The present invention provides an intelligently controlled medium-voltage power distribution method, system, electronic device and readable storage medium, which collects relevant data during the operation of the power distribution system, analyzes the relevant patterns and characteristics of the users, and cooperates with relevant mathematical models, simulations and evaluations to effectively improve the system performance, stability and operating efficiency, while achieving the effect of quantifying risks and optimization and realizing closed-loop optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0048] Figure 1 A flow chart of the steps of a medium voltage power distribution method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so 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 electricity demand, the medium voltage distribution system is an important link in power transmission and distribution. Its operation stability and efficiency directly affect the overall performance of the power grid. However, the traditional medium voltage distribution system faces many challenges during operation, including:

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

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

[0053] Insufficient optimization capabilities: Most of the optimization strategies of existing power distribution systems rely on preset rules and lack dynamic adjustment and adaptive capabilities, making it difficult to adapt to the complex and changing power consumption environment.

[0054] In order to solve the above problems, this embodiment discloses an intelligently controlled 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 power distribution system, and clean, denoise and process the operation data and environmental data;

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

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

[0058] System operation evaluation and feedback module, 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 power distribution method executed in the above system, the method comprising the following steps:

[0060] S100. Collect power consumption data and environmental data of the power distribution system in real time, and clean, denoise and process outliers of the power consumption data and environmental data;

[0061] S200. Analyze the user's electricity usage pattern, identify the user's electricity usage characteristics in different time periods, and obtain the user's analysis data set;

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

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

[0064] S500. Update the reward function of the reinforcement learning model or adjust the model parameters according to the operation stability evaluation result, and iteratively improve the power distribution optimization strategy.

[0065] Furthermore, in step S100, wavelet transform is used to clean, denoise and process outliers for the power consumption data and the environmental data. The calculation formulas for cleaning, denoising and outlier processing are:

[0066] W(j,k)=∫X(t)ψ j,k (t)dt

[0067]

[0068] Among them, X(t) is the input signal set of 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] Furthermore, 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] Among them, C is the clustering result set, 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.

[0072] Furthermore, 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] Among them, F is the comprehensive objective function, which represents the total objective value of the optimization strategy and comprehensively considers the weights of different objectives; C c R is the operating cost, which refers to the operating cost of the power distribution system in a given period of time, including power loss and maintenance costs; r For power supply reliability, the power supply reliability index of the system under different load conditions usually uses the system average outage frequency index (SAIFI) or the system average outage duration index (SAIDI); L i is the load imbalance, reflecting the imbalance of load distribution among the lines; α1, α2, and α3 are weight functions used to balance the importance of each target and are set according to system requirements and operation goals.

[0075] Furthermore,

[0076]

[0077] Among them, P loss,i is the power loss of the ith line, t i For the running time.

[0078] Furthermore,

[0079]

[0080] Among them, C maintenance,j is the maintenance cost of the jth equipment, F k is the frequency of the kth power outage, D k is the duration and T is the total time.

[0081] Furthermore,

[0082]

[0083] Among them, P l is the load power of the lth line, P total is the total system load, L is the number of lines;

[0084] The mathematical model must meet 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] Among them, P gen,i is the power generation of the i-th power generation unit, P load,j is the power requirement of the jth load unit, V i is the voltage threshold of the ith node in the distribution network, P i is the active power of the ith node.

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

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

[0091]

[0092]

[0093] Among them, S is the comprehensive evaluation index of system operation stability, β1, β2, β3, β4 are weight coefficients, and 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 ith node, V ref is the reference voltage, f is the frequency of the current system operation, and f ref is the system nominal frequency, P load (t) is the load power at time t, is the average load power per unit time, P miss is the unsatisfied load power, P total is the total load power.

[0094] Furthermore, in step S500, the calculation formula of the optimized feedback formula is:

[0095]

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

[0097] The present invention provides an intelligently controlled medium-voltage power distribution method, system, electronic device and readable storage medium, which collects relevant data during the operation of the power distribution system, analyzes the relevant patterns and characteristics of the users, and cooperates with relevant mathematical models, simulations and evaluations to effectively improve the system performance, stability and operating efficiency, while achieving the effect of quantifying risks and optimization and realizing closed-loop optimization.

[0098] Some embodiments of the present application also provide an electronic device, comprising: a processor, a memory, a bus and a communication interface, wherein the processor, the communication interface and the memory are connected via the bus; the memory stores a computer program that can be run on the processor, and when the processor runs the computer program, the method provided in any of the aforementioned embodiments of the present application is executed.

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

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

[0101] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. 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 the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module may be located in a mature storage medium in the field 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 a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0102] The electronic device provided in the embodiment of the present application and the medium voltage power distribution method provided in the embodiment of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0103] An embodiment of the present application also provides a computer-readable storage medium corresponding to the medium-voltage power distribution method provided in the aforementioned embodiment, on which a computer program is stored. The computer-readable storage medium is a CD, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the medium-voltage power distribution method provided in any of the aforementioned embodiments.

[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 or magnetic storage media, which are not listed here one by one.

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

[0106] It should be noted that: in the above text, the term "comprises", "includes" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

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

[0108] The embodiments of the present application are described above in conjunction with the accompanying drawings, which are only specific implementation modes of the present application. However, the present application is not limited to the above-mentioned specific implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A medium voltage power distribution method with intelligent control, characterized in that: The following steps are involved: S100. Collect power consumption data and environmental data of the power distribution system in real time, and clean, denoise and process outliers of the power consumption data and environmental data; S200. Analyze the user's electricity usage pattern, identify the user's electricity usage characteristics in different time periods, and obtain the user's analysis data set; S300. Based on the analysis data set, adjust the power distribution logic, maximize power efficiency and system stability, and optimize the power distribution strategy by interacting with the simulation environment; S400. Simulate multiple possible operation scenarios and evaluate the stability of the system under the optimization strategy; S500. Update the reward function of the reinforcement learning model or adjust the model parameters according to the operation stability evaluation result, and iteratively improve the power distribution optimization strategy.

2. The medium voltage power distribution method of intelligent control according to claim 1, characterized in that: In step S100, wavelet transform is used to clean, denoise and process outliers for power consumption data and environmental data. The calculation formulas for cleaning, denoising and outlier processing are: W(j,k)=∫X(t)ψ j,k (t)dt Among them, X(t) is the input signal set of 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.

3. The medium voltage power distribution method of intelligent control according to claim 2, characterized in that: In step S200, a clustering algorithm is used to analyze the user's power usage pattern and identify the power usage characteristics of the user in different time periods. The calculation formula of the clustering algorithm is: Among them, C is the clustering result set, 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.

4. The medium voltage power distribution method of intelligent control according to claim 1, characterized in that: In step S300, a mathematical model based on multi-objective optimization is constructed to optimize the power distribution strategy. The mathematical formula of the mathematical model is: minF=α1C cost +α2R reliability +α3L imbalance Among them, F is the comprehensive objective function, C cost is the operating cost, R r For power supply reliability, L i is the load imbalance, α1, α2, α3 are weight functions, P loss,i is the power loss of the ith line, t i is the running time, C maintenance,j is the maintenance cost of the jth equipment, F k is the frequency of the kth power outage, D k is the duration, T is the total time, P l is the load power of the lth line, P total is the total system load, L is the number of lines; The mathematical model must meet the following constraints: V min ≤V i ≤V max ,i=1,2,...,n P i ≤P max,i ,i=1,2,...,n Among them, P gen,i is the power generation of the i-th power generation unit, P load,j is the power requirement of the jth load unit, V i is the voltage threshold of the ith node in the distribution network, P i is the active power of the ith node.

5. The medium voltage power distribution method of intelligent control according to claim 1, characterized in that: In step S400, the calculation formula for optimizing the power distribution strategy is: S=β1F voltage +β2F frequency +β3F load +β4F reliability Among them, S is the comprehensive evaluation index of system operation stability, β1, β2, β3, β4 are weight coefficients, and 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 ith node, V ref is the reference voltage, f is the frequency of the current system operation, and f ref is the system nominal frequency, P load (t) is the load power at time t, is the average load power per unit time, P miss is the unsatisfied load power, P total is the total load power.

6. The medium voltage power distribution method of intelligent control according to claim 5, characterized in that: In step S500, the calculation formula of the optimized feedback formula is: Among them, θ is the parameter set of the optimization strategy, C (k) is the optimization strategy parameter for the kth iteration, S (k+1) is the optimization strategy parameter after the k+1th iteration, γ is the learning rate, is the gradient of the loss function.

7. An intelligently controlled medium voltage power distribution system, characterized in that: include: The data acquisition and preprocessing module is used to collect the real-time operation data and environmental data of the power distribution system, and clean, denoise and process the operation data and environmental data; The power consumption pattern analysis module is used to analyze historical power consumption data and identify the power consumption characteristics of different time periods and different users; The optimization strategy generation module is used to simulate various power usage scenarios and automatically generate optimized power distribution strategies; System operation evaluation and feedback module, used to quantitatively evaluate the long-term stability of the distribution system under the optimization strategy; The medium voltage power distribution system is used to implement the medium voltage power distribution method as described in any one of claims 1-6.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the medium voltage power distribution method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, 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 to 6.

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