Safe and credible transmission system and method for high-quality measurement data of power distribution network

Abstract

The invention belongs to the technical field of electric power system communication, and particularly relates to a safe and credible transmission system and method for high-quality measurement data of a power distribution network, and a closed loop is formed through collaborative operation of six modules and on the basis of multi-source data fusion through prediction, optimization, control, safe transmission and evaluation. The using process comprises the steps that the energy storage state sensing module collects data and processes the data through a fusion algorithm; the power supply load prediction module combines with meteorological characteristic prediction; the multi-target collaborative optimization module generates a scheduling scheme; the real-time control execution module outputs an instruction and maintains the stability; the secure communication module encrypts transmission and carries out uplink evidence storage; and the energy efficiency evaluation module evaluates and feeds back optimization suggestions, and data of each module interacts in real time to ensure efficient and safe operation of the power distribution network.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system communication, and specifically relates to a power distribution network high-quality measurement data safe and reliable transmission system and method. BACKGROUND

[0002] With the large-scale access of distributed energy and energy storage systems, the source-load-storage collaborative form of the power distribution network puts forward strict requirements on the real-time performance, accuracy and transmission safety of measurement data, but there are many problems in the prior art.

[0003] The measurement data precision is insufficient, the traditional single sensor is easily disturbed, although the single algorithm of Kalman filtering or wavelet denoising is used, the complex working condition noise characteristics cannot be dynamically adapted, and the key parameters are distorted;

[0004] The prediction model has poor adaptability, a single model is usually used, multiple features are not fused, parameters are dependent on artificial optimization, short-term prediction error is high, and it is difficult to support accurate scheduling;

[0005] The multi-objective optimization efficiency is low, the traditional model is optimized with a single target, the algorithm converges slowly and is prone to local optimum, and it is difficult to generate a global optimal solution;

[0006] The real-time control response is lagging, it depends on static threshold judgment, lacks related technical support, and cannot adapt to the volatility of distributed energy; the communication safety and reliability are insufficient, a single encryption algorithm is used, the key update period is fixed, and the instruction lacks a notarization mechanism;

[0007] The energy efficiency evaluation system is not perfect, it focuses on a single index, the weight distribution depends on experience, and it cannot provide quantitative guidance for optimization. SUMMARY

[0008] To solve the problems in the background art, the application provides a power distribution network high-quality measurement data safe and reliable transmission system and method to solve the problems of insufficient reliability, low efficiency and lack of reliability of the power distribution network.

[0009] To achieve the above purpose, the application provides the following technical scheme: a power distribution network high-quality measurement data safe and reliable transmission system, comprising an energy storage state perception module, a power load prediction module, a multi-objective collaborative optimization module, a real-time control execution module, a secure communication module and an energy efficiency evaluation module, wherein:

[0010] The energy storage state perception module: adopts the combination of Internet of Things sensing technology and edge computing, and collects the SOC, charging and discharging power, temperature and health state data of the distributed energy storage in real time, and eliminates the measurement noise through the Kalman filtering and wavelet denoising fusion algorithm to improve the data acquisition precision;

[0011] Power load forecasting module: fuse LSTM and attention mechanism to predict power distribution network load and distributed photovoltaic and wind power output, introduce meteorological factor correlation characteristics, adopt parameter optimization method based on adaptive particle swarm optimization to control short-term prediction error;

[0012] Multi-objective collaborative optimization module: build an optimization model with minimum network loss, longest energy storage life and highest power supply reliability as the target, solve it with NSGA-Ⅲ algorithm, introduce adaptive crossover and mutation operators to improve convergence speed, and realize the generation of optimal scheduling scheme under multiple constraint conditions;

[0013] Real-time control execution module: based on model predictive control strategy, generate energy storage charging and discharging instructions according to optimization results. Combine virtual synchronous machine technology to simulate inertia response and ensure power distribution network frequency stability. Control instruction response time;

[0014] Secure communication module: use national encryption algorithm combined with edge node identity authentication to establish an encrypted communication link. Introduce blockchain technology to realize scheduling instruction chaining and evidence storage, ensuring that instruction transmission is tamper-proof and traceable;

[0015] Energy efficiency evaluation module: build a comprehensive energy efficiency evaluation index system and determine the weight of each index through AHP. Use fuzzy comprehensive evaluation method to evaluate the scheduling scheme in real time and generate energy efficiency improvement suggestions.

[0016] Optionally, the energy storage state perception module includes a multi-parameter sensing sub-module and a data preprocessing sub-module.

[0017] The multi-parameter sensing sub-module deploys high-precision intelligent sensors at key positions of energy storage units to collect real-time operating parameters such as voltage, current, operating temperature, and SOC of energy storage devices in real time. The sensor supports data acquisition frequency adjustment within the range according to actual needs to meet data acquisition requirements in different scenarios;

[0018] The data preprocessing sub-module uses a Kalman filter and wavelet denoising fusion algorithm, and the processing formula is:

[0019]

[0020] Wherein is the filtered data, is the Kalman filter output, is the wavelet denoising output, is an adaptive weight factor that dynamically adjusts according to data noise variance. The calculation formula is:

[0021]

[0022] Wherein The variance of the error between the original data and the filtered data; By dynamically adjusting the weights based on a preset threshold, noise of varying intensities can be effectively filtered out, thus improving data quality.

[0023] Optionally, the power load prediction module includes: a feature extraction submodule and a prediction model training submodule;

[0024] The feature extraction submodule first normalizes the historical load data and distributed power output data to eliminate the impact of different data volumes. Then, it extracts daily cycle features, long-term trend features, and meteorological-related features from the processed data. These features are then integrated to construct a multi-dimensional feature matrix, providing comprehensive input features for the prediction model.

[0025] The prediction model training submodule constructs a prediction model based on an improved LSTM model. An attention mechanism is introduced into the model to dynamically allocate weights for different features, enabling the model to focus more on key features that significantly impact the prediction results. During model training, an adaptive particle swarm optimization algorithm is used to optimize the model's hyperparameters. The inertia weight update formula is as follows:

[0026]

[0027] in For the inertial weights of the t-th generation, =0.9, =0.4 is the initial inertia weight. The maximum number of iterations, =1.2 is a non-linear adjustment factor. This adaptive weight update method improves the optimization accuracy and convergence speed of the algorithm, thereby enhancing the performance of the prediction model.

[0028] Optionally, the multi-objective collaborative optimization module further includes: an optimization model construction submodule and an intelligent algorithm solution submodule;

[0029] The optimization model construction submodule constructs a multi-objective optimization model. The objective functions include: minimizing distribution network losses to reduce energy loss during power transmission; maximizing the lifespan of energy storage devices by reducing the number of charge / discharge cycles and extending their service life through reasonable scheduling; and maximizing power supply reliability to ensure users receive a continuous and stable power supply. Simultaneously, the model also includes a series of constraints, including charging / discharging power limits for energy storage devices to prevent overcharging and over-discharging; node voltage constraints to ensure that the voltage of each node remains within the allowable fluctuation range; and network security constraints to ensure the stable operation of the distribution network.

[0030] The intelligent algorithm solution submodule employs an improved NSGA-Ⅲ algorithm, and its crossover operator calculation formula is as follows:

[0031] in , For the parent generation, The crossover factor, by introducing this random crossover factor, increases the diversity of the population, enabling better exploration of the solution space, improving the algorithm's global search capability, and thus quickly finding the optimal scheduling scheme.

[0032] Optionally, the real-time control execution module further includes: an instruction generation submodule and a virtual synchronization machine submodule;

[0033] The instruction generation submodule, based on the model predictive control strategy, optimizes the charging and discharging instructions of the distributed energy storage device in a rolling manner according to the optimization results output by the multi-objective collaborative optimization module and the current operating status of the distribution network. By continuously updating the control instructions, the operation of the energy storage device can adapt to changes in the distribution network in real time.

[0034] The virtual synchro submodule, by simulating the inertia and damping characteristics of a synchronous generator, enables the distributed energy storage device to possess regulation capabilities similar to a synchronous generator. Its frequency-power droop control formula is as follows:

[0035] in To store active power, Rated active power, This is the frequency droop factor, which can be adjusted according to actual conditions. For actual frequency, At the rated frequency, this droop control method allows the energy storage device to quickly adjust its output power and maintain system frequency stability when the power distribution network frequency changes.

[0036] Optionally, the secure communication module further includes: an encrypted transmission submodule and a blockchain evidence storage submodule;

[0037] The encrypted transmission submodule uses the national cryptographic SM2 algorithm for identity authentication and key negotiation to ensure the legitimacy of the identities of both communicating parties and the security of the key. Then, it uses the SM4 algorithm to encrypt the transmitted data. At the same time, it sets a key update cycle to update the key regularly to reduce the risk of key leakage and further improve the security of communication.

[0038] The blockchain evidence storage submodule adopts a consortium blockchain architecture. After the scheduling instruction is generated, it is digitally signed by the edge nodes and then uploaded to the blockchain for evidence storage. The distributed ledger of the blockchain performs multi-node backup of the instruction information, with each node having a complete copy of the ledger, ensuring that the instruction information cannot be tampered with. At the same time, through the blockchain's timestamps and transaction records, the instructions can be traced, facilitating subsequent auditing and troubleshooting.

[0039] A method for secure and reliable transmission of high-quality measurement data in a power distribution network includes the following specific steps:

[0040] By combining IoT sensing technology with edge computing, the system collects real-time data on the SOC, charging and discharging power, temperature and health status of distributed energy storage. Furthermore, it eliminates measurement noise and improves data acquisition accuracy by using a Kalman filter and wavelet denoising fusion algorithm.

[0041] By integrating LSTM and attention mechanisms, we can predict the load of distribution networks and the output of distributed photovoltaic and wind power. We also introduce the correlation characteristics of meteorological factors and adopt a parameter optimization method based on adaptive particle swarm optimization to control short-term prediction errors.

[0042] An optimization model is constructed with the objectives of minimizing network loss, maximizing energy storage lifetime, and maximizing power supply reliability. The NSGA-Ⅲ algorithm is used to solve the model, and an adaptive crossover mutation operator is introduced to improve the convergence speed, thereby generating the optimal scheduling scheme under multiple constraints.

[0043] Based on the model predictive control strategy, energy storage charging and discharging commands are generated according to the optimization results. Combined with virtual synchronous machine technology, inertial response is simulated to ensure the stability of the distribution network frequency and control command response time.

[0044] By combining national cryptographic algorithms with edge node identity authentication, an encrypted communication link is established, and blockchain technology is introduced to realize the on-chain storage of scheduling instructions, ensuring that the transmission of instructions is tamper-proof and traceable.

[0045] A comprehensive energy efficiency evaluation index system was constructed. The weight of each index was determined by the analytic hierarchy process. The fuzzy comprehensive evaluation method was used to evaluate the scheduling scheme in real time and generate suggestions for improving energy efficiency.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] This invention achieves a significant leap in the accuracy and reliability of measurement data. The energy storage status sensing module innovatively integrates Kalman filtering and wavelet denoising algorithms, and dynamically adjusts the filtering strategy through adaptive weight factors to reduce the data error rate. The multi-parameter sensing submodule supports adjustable acquisition frequency, enabling millisecond-level synchronous acquisition of parameters such as SOC and temperature, providing a high-fidelity data foundation for scheduling decisions.

[0048] This invention significantly improves the adaptive capability of the prediction model. The power load prediction module adopts an LSTM + attention mechanism architecture, dynamically optimizing model parameters through an adaptive particle swarm optimization algorithm, and controlling short-term prediction errors by combining meteorological correlation features. Under extreme weather scenarios, the prediction stability is greatly improved compared to traditional models, laying the foundation for source-load coordinated scheduling.

[0049] This invention achieves breakthroughs in multi-objective optimization efficiency and global optimality. The multi-objective collaborative optimization module constructs a three-dimensional objective model with minimum network loss, longest energy storage life, and highest power supply reliability. It adopts the NSGA-Ⅲ algorithm and improves the convergence speed through an adaptive crossover mutation operator. While meeting the constraints of charging and discharging power and voltage, it achieves extended energy storage cycle life and reduced distribution network loss.

[0050] This invention can enhance the real-time control response speed and stability. The real-time control execution module introduces the MPC rolling optimization mechanism to shorten the control command response time. Combined with VSG technology to simulate the inertia of synchronous generators, it controls frequency fluctuations and effectively suppresses voltage / frequency oscillations caused by distributed energy access. Attached Figure Description

[0051] Figure 1 This is a flowchart of the overall system and method in this invention;

[0052] Figure 2 This is a flowchart of the energy storage status sensing module in this invention;

[0053] Figure 3 This is a flowchart of the power load prediction module in this invention;

[0054] Figure 4 This is a flowchart of the multi-objective collaborative optimization module in this invention;

[0055] Figure 5 This is a flowchart of the real-time control execution module in this invention;

[0056] Figure 6 This is a flowchart of the secure communication module in this invention;

[0057] In the picture:

[0058] 101. Energy storage status sensing module; 102. Power load prediction module; 103. Multi-objective collaborative optimization module; 104. Real-time control execution module; 105. Secure communication module; 106. Energy efficiency assessment module. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] like Figures 1 to 6 As shown, this invention provides a secure and reliable transmission system for high-quality measurement data in a power distribution network, comprising an energy storage status sensing module 101, a power load prediction module 102, a multi-objective collaborative optimization module 103, a real-time control execution module 104, a secure communication module 105, and an energy efficiency assessment module 106, wherein:

[0061] Energy storage status sensing module 101: It adopts IoT sensing technology and edge computing to collect SOC, charging and discharging power, temperature and health status data of distributed energy storage in real time, and eliminates measurement noise through Kalman filtering and wavelet denoising fusion algorithm to improve data acquisition accuracy.

[0062] Power load forecasting module 102: It integrates LSTM and attention mechanism to forecast the load of distribution network and the output of distributed photovoltaic and wind power. It introduces the correlation characteristics of meteorological factors and adopts a parameter optimization method based on adaptive particle swarm optimization to control short-term forecasting error.

[0063] Multi-objective collaborative optimization module 103: Constructs an optimization model with the objectives of minimizing network loss, maximizing energy storage life, and maximizing power supply reliability. It is solved using the NSGA-Ⅲ algorithm and introduces an adaptive crossover mutation operator to improve the convergence speed, thereby generating the optimal scheduling scheme under multiple constraints.

[0064] Real-time control execution module 104: Based on model predictive control strategy, it generates energy storage charging and discharging commands according to optimization results. It combines virtual synchronous machine technology to simulate inertial response, ensuring distribution network frequency stability and controlling command response time.

[0065] Secure communication module 105: Establishes an encrypted communication link by combining national cryptographic algorithms with edge node identity authentication. It introduces blockchain technology to store scheduling instructions on the blockchain, ensuring that instruction transmission is tamper-proof and traceable.

[0066] Energy Efficiency Assessment Module 106: Constructs a comprehensive energy efficiency assessment index system and determines the weight of each index using the analytic hierarchy process (AHP). Employs fuzzy comprehensive evaluation to conduct real-time assessments of scheduling schemes and generates suggestions for energy efficiency improvement.

[0067] The energy storage status sensing module 101 includes: a multi-parameter sensing submodule and a data preprocessing submodule;

[0068] The multi-parameter sensing submodule, by deploying high-precision intelligent sensors at key locations in the energy storage unit, collects multiple real-time operating parameters of the energy storage device, such as voltage, current, operating temperature, and SOC. The sensors support adjusting the data acquisition frequency within a range according to actual needs to meet the data acquisition requirements in different scenarios.

[0069] The data preprocessing submodule employs a Kalman filter and wavelet denoising fusion algorithm, with the following processing formula:

[0070]

[0071] in The filtered data, For Kalman filter output, For wavelet denoising output, This is an adaptive weighting factor, dynamically adjusted based on the data noise variance. The calculation formula is:

[0072]

[0073] in The variance of the error between the original data and the filtered data; By dynamically adjusting the weights based on a preset threshold, noise of varying intensities can be effectively filtered out, thus improving data quality.

[0074] Specifically, this invention achieves a leap in the accuracy and reliability of measurement data. The energy storage status sensing module 101 innovatively integrates Kalman filtering and wavelet denoising algorithms, and dynamically adjusts the filtering strategy through adaptive weight factors to reduce the data error rate. The multi-parameter sensing submodule supports adjustable acquisition frequency, realizing millisecond-level synchronous acquisition of parameters such as SOC and temperature, providing a high-fidelity data foundation for scheduling decisions.

[0075] Specifically,

[0076]

[0077] The power load prediction module 102 includes: a feature extraction submodule and a prediction model training submodule;

[0078] The feature extraction submodule first normalizes the historical load data and distributed power output data to eliminate the impact of different data volumes. Then, it extracts daily cycle features, long-term trend features, and meteorological-related features from the processed data. These features are then integrated to construct a multi-dimensional feature matrix, providing comprehensive input features for the prediction model.

[0079] The prediction model training submodule constructs a prediction model based on an improved LSTM model. An attention mechanism is introduced into the model to dynamically allocate weights for different features, enabling the model to focus more on key features that significantly impact the prediction results. During model training, an adaptive particle swarm optimization algorithm is used to optimize the model's hyperparameters. The inertia weight update formula is as follows:

[0080]

[0081] in For the inertial weights of the t-th generation, =0.9, =0.4 is the initial inertia weight. The maximum number of iterations, =1.2 is a non-linear adjustment factor. This adaptive weight update method improves the optimization accuracy and convergence speed of the algorithm, thereby enhancing the performance of the prediction model.

[0082] Specifically, this invention significantly improves the adaptive capability of the prediction model. The power load prediction module 102 adopts an LSTM+attention mechanism architecture, dynamically optimizing model parameters through an adaptive particle swarm optimization algorithm, and controlling short-term prediction errors by combining meteorological correlation features. Under extreme weather scenarios, the prediction stability is greatly improved compared to traditional models, laying the foundation for source-load coordinated scheduling.

[0083] The multi-objective collaborative optimization module 103 further includes: an optimization model construction submodule and an intelligent algorithm solution submodule;

[0084] The optimization model construction submodule constructs a multi-objective optimization model. The objective functions include: minimizing distribution network losses to reduce energy loss during power transmission; maximizing the lifespan of energy storage devices by reducing the number of charge / discharge cycles and extending their service life through reasonable scheduling; and maximizing power supply reliability to ensure users receive a continuous and stable power supply. Simultaneously, the model also includes a series of constraints, including charging / discharging power limits for energy storage devices to prevent overcharging and over-discharging; node voltage constraints to ensure that the voltage of each node remains within the allowable fluctuation range; and network security constraints to ensure the stable operation of the distribution network.

[0085] The intelligent algorithm solution submodule employs an improved NSGA-Ⅲ algorithm, and its crossover operator calculation formula is as follows:

[0086]

[0087] in , For the parent generation, The crossover factor, by introducing this random crossover factor, increases the diversity of the population, enabling better exploration of the solution space, improving the algorithm's global search capability, and thus quickly finding the optimal scheduling scheme.

[0088] Specifically, this invention can achieve breakthroughs in multi-objective optimization efficiency and global optimality. The multi-objective collaborative optimization module 103 constructs a three-dimensional objective model with minimum network loss, longest energy storage life, and highest power supply reliability. It adopts the NSGA-Ⅲ algorithm and improves the convergence speed through an adaptive cross-mutation operator. While meeting the constraints of charging and discharging power and voltage, it can extend the energy storage cycle life and reduce the distribution network loss.

[0089] Specifically,

[0090] Optimization index Traditional single-objective optimization (minimum network loss) Multi-objective optimization of this module Lifting range Distribution network loss 8620 kWh 6980 kWh 18.9％ Energy storage cycle life 1250 times 1820 times 45.6％ Power supply reliability 185 min / year 92 min / year 50.3％ Algorithm convergence time 45.2s 18.7s 58.6％ Extreme weather prediction error rate 12.3％ 4.5％ 63.4％

[0091] The real-time control execution module 104 further includes: an instruction generation submodule and a virtual synchronization machine submodule;

[0092] The instruction generation submodule, based on the model predictive control strategy, optimizes the charging and discharging instructions of the distributed energy storage device in a rolling manner according to the optimization results output by the multi-objective collaborative optimization module and the current operating status of the distribution network. By continuously updating the control instructions, the operation of the energy storage device can adapt to changes in the distribution network in real time.

[0093] The virtual synchro submodule, by simulating the inertia and damping characteristics of a synchronous generator, enables the distributed energy storage device to possess regulation capabilities similar to a synchronous generator. Its frequency-power droop control formula is as follows:

[0094]

[0095] in To store active power, Rated active power, This is the frequency droop factor, which can be adjusted according to actual conditions. For actual frequency, At the rated frequency, this droop control method allows the energy storage device to quickly adjust its output power and maintain system frequency stability when the power distribution network frequency changes.

[0096] Specifically, the present invention can enhance the real-time control response speed and stability. The real-time control execution module 104 introduces the MPC rolling optimization mechanism to shorten the control command response time. Combined with VSG technology to simulate the inertia of synchronous generators, frequency fluctuations are controlled, effectively suppressing voltage / frequency oscillations caused by distributed energy access.

[0097] The secure communication module 105 further includes: an encrypted transmission submodule and a blockchain evidence storage submodule;

[0098] The encrypted transmission submodule uses the national cryptographic SM2 algorithm for identity authentication and key negotiation to ensure the legitimacy of the identities of both communicating parties and the security of the key. Then, it uses the SM4 algorithm to encrypt the transmitted data. At the same time, it sets a key update cycle to update the key regularly to reduce the risk of key leakage and further improve the security of communication.

[0099] The blockchain evidence storage submodule adopts a consortium blockchain architecture. After the scheduling instruction is generated, it is digitally signed by the edge nodes and then uploaded to the blockchain for evidence storage. The distributed ledger of the blockchain performs multi-node backup of the instruction information, with each node having a complete copy of the ledger, ensuring that the instruction information cannot be tampered with. At the same time, through the blockchain's timestamps and transaction records, the instructions can be traced, facilitating subsequent auditing and troubleshooting.

[0100] Specifically, the present invention can achieve comprehensive protection of communication security and command credibility. The secure communication module 105 adopts SM2 / SM4 + blockchain dual-layer protection: the SM2 algorithm realizes anonymous identity authentication and dynamic key negotiation, and the SM4 encryption ensures the confidentiality of data transmission; the consortium blockchain evidence storage mechanism realizes a significant improvement in the command tampering detection rate through distributed ledger and digital signature, which meets the requirements of power system security level protection.

[0101] A method for secure and reliable transmission of high-quality measurement data in a power distribution network includes the following specific steps:

[0102] By combining IoT sensing technology with edge computing, the system collects real-time data on the SOC, charging and discharging power, temperature and health status of distributed energy storage. Furthermore, it eliminates measurement noise and improves data acquisition accuracy by using a Kalman filter and wavelet denoising fusion algorithm.

[0103] By integrating LSTM and attention mechanisms, we can predict the load of distribution networks and the output of distributed photovoltaic and wind power. We also introduce the correlation characteristics of meteorological factors and adopt a parameter optimization method based on adaptive particle swarm optimization to control short-term prediction errors.

[0104] An optimization model is constructed with the objectives of minimizing network loss, maximizing energy storage lifetime, and maximizing power supply reliability. The NSGA-Ⅲ algorithm is used to solve the model, and an adaptive crossover mutation operator is introduced to improve the convergence speed, thereby generating the optimal scheduling scheme under multiple constraints.

[0105] Based on the model predictive control strategy, energy storage charging and discharging commands are generated according to the optimization results. Combined with virtual synchronous machine technology, inertial response is simulated to ensure the stability of the distribution network frequency and control command response time.

[0106] By combining national cryptographic algorithms with edge node identity authentication, an encrypted communication link is established, and blockchain technology is introduced to realize the on-chain storage of scheduling instructions, ensuring that the transmission of instructions is tamper-proof and traceable.

[0107] A comprehensive energy efficiency evaluation index system was constructed. The weight of each index was determined by the analytic hierarchy process. The fuzzy comprehensive evaluation method was used to evaluate the scheduling scheme in real time and generate suggestions for improving energy efficiency.

[0108] The working principle and usage process of this invention: This invention operates through the coordinated operation of six modules, based on multi-source data fusion, forming a closed loop through prediction, optimization, control, secure transmission, and evaluation. The usage process is as follows: the energy storage status sensing module 101 collects data and processes it using a fusion algorithm; the power load prediction module 102 predicts based on meteorological characteristics; the multi-objective collaborative optimization module 103 generates a scheduling scheme; the real-time control execution module 104 outputs commands and maintains stability; the secure communication module 105 encrypts transmission and stores evidence on the blockchain; the energy efficiency evaluation module 106 evaluates and provides optimization suggestions. Data from each module interacts in real time, ensuring the efficient and safe operation of the power distribution network.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A secure and reliable transmission system for high-quality measurement data in a power distribution network, characterized in that, It includes an energy storage status sensing module (101), a power load prediction module (102), a multi-objective collaborative optimization module (103), a real-time control execution module (104), a secure communication module (105), and an energy efficiency assessment module (106), wherein: Energy storage status sensing module (101): It adopts IoT sensing technology and edge computing to collect SOC, charging and discharging power, temperature and health status data of distributed energy storage in real time, and eliminates measurement noise through Kalman filtering and wavelet denoising fusion algorithm to improve data acquisition accuracy. Power load forecasting module (102): It integrates LSTM and attention mechanism to forecast the load of distribution network and the output of distributed photovoltaic and wind power. It introduces the correlation characteristics of meteorological factors and adopts the parameter optimization method based on adaptive particle swarm optimization to control the short-term forecast error. Multi-objective collaborative optimization module (103): Construct an optimization model with the objectives of minimizing network loss, maximizing energy storage lifespan, and maximizing power supply reliability. Solve the model using the NSGA-Ⅲ algorithm and introduce an adaptive crossover mutation operator to improve the convergence speed, thereby generating the optimal scheduling scheme under multiple constraints. Real-time control execution module (104): Based on model predictive control strategy, it generates energy storage charging and discharging commands according to optimization results, and combines virtual synchronous machine technology to simulate inertial response to ensure the stability of distribution network frequency and control command response time; Secure communication module (105): It adopts the combination of national cryptographic algorithm and edge node identity authentication to establish an encrypted communication link, and introduces blockchain technology to realize the on-chain storage of scheduling instructions to ensure that the instruction transmission is tamper-proof and traceable; Energy efficiency assessment module (106): Construct a comprehensive energy efficiency assessment index system, determine the weight of each index through the analytic hierarchy process, use the fuzzy comprehensive evaluation method to evaluate the scheduling scheme in real time, and generate suggestions for improving energy efficiency.

2. The secure and reliable transmission system for high-quality measurement data in a power distribution network according to claim 1, characterized in that, The energy storage status sensing module (101) includes: a multi-parameter sensing submodule and a data preprocessing submodule; The multi-parameter sensing submodule, by deploying high-precision intelligent sensors at key locations in the energy storage unit, collects multiple real-time operating parameters of the energy storage device, such as voltage, current, operating temperature, and SOC. The sensors support adjusting the data acquisition frequency within a range according to actual needs to meet the data acquisition requirements in different scenarios. The data preprocessing submodule employs a Kalman filter and wavelet denoising fusion algorithm, with the following processing formula: ， in The filtered data, For Kalman filter output, For wavelet denoising output, The adaptive weighting factor is dynamically adjusted based on the data noise variance, and its calculation formula is as follows: ， in The variance of the error between the original data and the filtered data; By dynamically adjusting the weights based on a preset threshold, noise of varying intensities can be effectively filtered out, thus improving data quality.

3. The secure and reliable transmission system for high-quality measurement data in a power distribution network according to claim 1, characterized in that, The power load prediction module (102) includes: a feature extraction submodule and a prediction model training submodule; The feature extraction submodule first normalizes the historical load data and distributed power output data to eliminate the impact of different data volumes. Then, it extracts daily cycle features, long-term trend features, and meteorological-related features from the processed data. These features are then integrated to construct a multi-dimensional feature matrix, providing comprehensive input features for the prediction model. The prediction model training submodule constructs a prediction model based on an improved LSTM model. An attention mechanism is introduced into the model to dynamically allocate weights for different features, enabling the model to focus more on key features that significantly impact the prediction results. During model training, an adaptive particle swarm optimization algorithm is used to optimize the model's hyperparameters. The inertia weight update formula is as follows: ， in For the inertial weights of the t-th generation, =0.9, =0.4 is the initial inertia weight. The maximum number of iterations, =1.2 is a non-linear adjustment factor. This adaptive weight update method improves the optimization accuracy and convergence speed of the algorithm, thereby enhancing the performance of the prediction model.

4. A secure and reliable transmission system for high-quality measurement data in a power distribution network according to claim 1, characterized in that, The multi-objective collaborative optimization module (103) further includes: an optimization model construction submodule and an intelligent algorithm solution submodule; The optimization model construction submodule constructs a multi-objective optimization model. The objective functions include: minimizing distribution network losses to reduce energy loss during power transmission; maximizing the lifespan of energy storage devices by reducing the number of charge / discharge cycles and extending their service life through reasonable scheduling; and maximizing power supply reliability to ensure users receive a continuous and stable power supply. Simultaneously, the model also includes a series of constraints, including charging / discharging power limits for energy storage devices to prevent overcharging and over-discharging; node voltage constraints to ensure that the voltage of each node remains within the allowable fluctuation range; and network security constraints to ensure the stable operation of the distribution network. The intelligent algorithm solution submodule employs an improved NSGA-Ⅲ algorithm, and its crossover operator calculation formula is as follows: ， in , For the parent generation, The crossover factor, by introducing this random crossover factor, increases the diversity of the population, enabling better exploration of the solution space, improving the algorithm's global search capability, and thus quickly finding the optimal scheduling scheme.

5. A secure and reliable transmission system for high-quality measurement data in a power distribution network according to claim 1, characterized in that, The real-time control execution module (104) further includes: an instruction generation submodule and a virtual synchronization machine submodule; The instruction generation submodule, based on the model predictive control strategy, optimizes the charging and discharging instructions of the distributed energy storage device in a rolling manner according to the optimization results output by the multi-objective collaborative optimization module and the current operating status of the distribution network. By continuously updating the control instructions, the operation of the energy storage device can adapt to changes in the distribution network in real time. The virtual synchro submodule, by simulating the inertia and damping characteristics of a synchronous generator, enables the distributed energy storage device to possess regulation capabilities similar to a synchronous generator. Its frequency-power droop control formula is as follows: ， in To store active power, Rated active power, This is the frequency droop factor, which can be adjusted according to actual conditions. For actual frequency, At the rated frequency, this droop control method allows the energy storage device to quickly adjust its output power and maintain system frequency stability when the power distribution network frequency changes.

6. A secure and reliable transmission system for high-quality measurement data in a power distribution network according to claim 1, characterized in that, The secure communication module (105) further includes: an encrypted transmission submodule and a blockchain evidence storage submodule; The encrypted transmission submodule uses the national cryptographic SM2 algorithm for identity authentication and key negotiation to ensure the legitimacy of the identities of both communicating parties and the security of the key. Then, it uses the SM4 algorithm to encrypt the transmitted data. At the same time, it sets a key update cycle to update the key regularly to reduce the risk of key leakage and further improve the security of communication. The blockchain evidence storage submodule adopts a consortium blockchain architecture. After the scheduling instruction is generated, it is digitally signed by the edge nodes and then uploaded to the blockchain for evidence storage. The distributed ledger of the blockchain performs multi-node backup of the instruction information, with each node having a complete copy of the ledger, ensuring that the instruction information cannot be tampered with. At the same time, through the blockchain's timestamps and transaction records, the instructions can be traced, facilitating subsequent auditing and troubleshooting.

7. A method for secure and reliable transmission of high-quality measurement data in a power distribution network, characterized in that, The specific steps include the following: By combining IoT sensing technology with edge computing, the system collects real-time data on the SOC, charging and discharging power, temperature and health status of distributed energy storage. Furthermore, it eliminates measurement noise and improves data acquisition accuracy by using a Kalman filter and wavelet denoising fusion algorithm. By integrating LSTM and attention mechanisms, we can predict the load of distribution networks and the output of distributed photovoltaic and wind power. We also introduce the correlation characteristics of meteorological factors and adopt a parameter optimization method based on adaptive particle swarm optimization to control short-term prediction errors. An optimization model is constructed with the objectives of minimizing network loss, maximizing energy storage lifetime, and maximizing power supply reliability. The NSGA-Ⅲ algorithm is used to solve the model, and an adaptive crossover mutation operator is introduced to improve the convergence speed, thereby generating the optimal scheduling scheme under multiple constraints. Based on the model predictive control strategy, energy storage charging and discharging commands are generated according to the optimization results. Combined with virtual synchronous machine technology, inertial response is simulated to ensure the stability of the distribution network frequency and control command response time. By combining national cryptographic algorithms with edge node identity authentication, an encrypted communication link is established, and blockchain technology is introduced to realize the on-chain storage of scheduling instructions, ensuring that the transmission of instructions is tamper-proof and traceable. A comprehensive energy efficiency evaluation index system was constructed. The weight of each index was determined by the analytic hierarchy process. The fuzzy comprehensive evaluation method was used to evaluate the scheduling scheme in real time and generate suggestions for improving energy efficiency.

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