A power distribution network distributed compensation control system and method

Abstract

The application relates to a power distribution network distributed compensation control system and method, the system comprising: a distributed multi-source module for collecting node data, equipment operation state data and system global operation data of each key node in real time; an edge decision module for obtaining local node data of a local key node, generating preliminary compensation instructions of a plurality of distributed compensation devices according to the local node data; a collaborative optimization module for receiving and fusing all the preliminary compensation instructions, the equipment operation state data and the system global operation data, and calculating a system comprehensive operation index through a unified nonlinear comprehensive evaluation function to generate a global collaborative optimization instruction; and a control execution module for distributing the global collaborative optimization instruction to the corresponding edge decision module, so as to drive the plurality of distributed compensation devices to perform a collaborative compensation operation. Preventive maintenance and fault early warning are realized, the equipment life is effectively prolonged, and the overall reliability of the system is improved.

Description

Technical Field

[0001] This invention belongs to the field of distributed compensation control technology, and particularly relates to a distributed compensation control system and method for power distribution networks. Background Technology

[0002] As a key component of the power system, the distribution network directly serves end users, and its power quality, supply reliability, and operating efficiency directly affect the overall performance of the power system and user satisfaction. With the large-scale integration of distributed energy sources (such as photovoltaic and wind power), the rapid growth of electric vehicle charging loads, and the widespread application of nonlinear loads, the distribution network faces multiple power quality problems such as voltage fluctuations, harmonic pollution, and reactive power imbalance. Traditional centralized compensation methods are no longer sufficient to meet the dynamic control requirements of modern distribution networks.

[0003] In existing technologies, common compensation devices such as Static Var Compensators (SVCs) and Static Synchronous Compensators (STATCOMs) are mostly centrally deployed, resulting in limited response speed, limited coverage, and a lack of collaborative perception and intelligent decision-making capabilities for the entire network. Especially in scenarios with frequent load changes and large fluctuations in the output of distributed power sources, traditional systems often experience compensation lag, overcompensation, or undercompensation, leading to voltage exceeding limits, increased line losses, shortened equipment lifespan, and even system stability risks. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a distributed compensation control system and method for power distribution networks.

[0005] The technical solution adopted in this invention is:

[0006] Firstly, a distributed compensation control system for a power distribution network is provided, comprising:

[0007] Multiple distributed multi-source modules, multiple edge decision modules, collaborative optimization modules, and control execution modules;

[0008] Multiple distributed multi-source modules are deployed at key nodes of the power distribution network.

[0009] Each edge decision module is coupled to the local controller of its corresponding multiple distributed compensation devices;

[0010] The distributed multi-source module is used to collect node data, device operating status data and global system operating data in real time from each key node;

[0011] The edge decision module is used to obtain local node data of local key nodes through the distributed multi-source module, and generate preliminary compensation instructions for multiple distributed compensation devices based on the local node data.

[0012] The collaborative optimization module is used to receive and integrate the preliminary compensation instructions uploaded by all edge decision modules, as well as the device operation status data and system global operation data uploaded by the distributed multi-source modules. It calculates the system comprehensive operation index through a unified nonlinear comprehensive evaluation function and generates global collaborative optimization instructions based on the system comprehensive operation index.

[0013] The control execution module is used to distribute global collaborative optimization instructions to the corresponding edge decision modules, thereby driving multiple distributed compensation devices corresponding to the edge decision modules to perform collaborative compensation operations.

[0014] Furthermore, local node data includes node voltage, node current, node phase, and node harmonic content;

[0015] The preliminary compensation instructions include the preliminary reactive power compensation instructions and the preliminary harmonic compensation instructions.

[0016] Furthermore, the formula for calculating the system's overall operating index U is as follows:

[0017] ;

[0018] in, This is the overall network reactive power demand urgency index calculated based on all preliminary reactive power compensation instructions. The value range of is [0, 1];

[0019] This is the overall network harmonic pollution severity index calculated based on all preliminary harmonic compensation instructions. The value range of is [0, 1];

[0020] This is the average health index of all devices in the network, calculated based on device operating status data. The value range of is [0, 1];

[0021] p, m, and n are all preset adjustment indices, all of which are real numbers greater than 0; μ and ν are both preset weighting coefficients, satisfying μ+ν=1; η is a preset coordination gain coefficient, which is a real number between 0 and 1; θ is a preset difference adjustment coefficient, which is a positive real number; exp(.) is the natural exponential function; || is the absolute value function.

[0022] Furthermore, the collaborative optimization module includes:

[0023] The global collaborative optimization unit is used to perform the following steps:

[0024] Obtain the first preset mode switching threshold U1, the second preset mode switching threshold U2, and the third preset mode switching threshold U3, where U1>U2>U3;

[0025] When U≥U1, the economic optimization mode is adopted. Based on the economic optimization mode, with minimizing the overall network line loss as the main goal, global collaborative optimization instructions are generated.

[0026] When U2≤U<U1, the power quality priority mode is adopted. Based on the power quality priority mode, with suppressing voltage deviation and harmonics as the main goal, global collaborative optimization instructions are generated.

[0027] When U3≤U<U2, the equipment protection mode is adopted. Based on the equipment protection mode, with degraded operation and ensuring equipment safety as the main goal, global collaborative optimization instructions are generated.

[0028] When U<U3, the safe shutdown mode is adopted. Based on the safe shutdown mode, shutdown instructions for gradually exiting operation are generated and used as global collaborative optimization instructions.

[0029] Furthermore, the preliminary reactive power compensation instruction Q

[0036] ,

[0035] ,

[0037] , , is calculated by the following formula:

[0030] ;

[0031] where is the reference value of the node voltage, is obtained by the collaborative optimization module according to the global collaborative optimization instructions; is the measured value of the node voltage, is the preset power factor reference value, is the measured power factor value calculated from the node current and the node phase, and are preset proportional integral coefficients, β is a preset weight coefficient, represents the sampling period.

[0032] Furthermore, the edge decision module includes:

[0033] The preliminary harmonic compensation instruction calculation unit is used to perform the following steps:

[0034] Perform windowed fast Fourier transform analysis on the node current, identify the harmonic orders and the corresponding amplitudes, and generate a preliminary harmonic spectrum;

[0035] According to the preliminary harmonic spectrum, dynamically adjust the cut-off frequency of the low-pass filter in the harmonic detection algorithm based on instantaneous reactive power theory and / or allocate different harmonic order compensation priority weights;

[0036] According to the dynamically adjusted harmonic detection algorithm based on instantaneous reactive power theory, extract the instantaneous harmonic current instruction;

[0037] The instantaneous harmonic current command is used as the preliminary harmonic compensation command, which is used to drive the active power filter to generate the opposite compensation current.

[0038] Furthermore, the preliminary harmonic compensation command calculation unit includes:

[0039] The dynamic adjustment subunit is used to increase the compensation priority weight of the target subharmonic for targets whose amplitude exceeds the set amplitude threshold, and correspondingly reduce the cutoff frequency of the low-pass filter for the target subharmonic's preset frequency band.

[0040] Furthermore, the distributed multi-source module includes:

[0041] The synchronization phasor measurement unit is used to provide network-wide synchronization phase data and upload it to the collaborative optimization module, so that the collaborative optimization module uses the network-wide synchronization phase data as the benchmark for synchronization and accuracy when performing global optimization calculations.

[0042] Furthermore, the equipment operating status data includes the number of times the capacitor bank is switched on and off and the junction temperature of the power devices;

[0043] Average health index of all network devices The calculation formula is:

[0044] ;

[0045] ;

[0046] Where M represents the number of distributed compensation devices; For the health sub-indicator of the j-th distributed compensation device; The cumulative number of switching operations on the capacitor bank in the j-th distributed compensation device; Let J be the maximum allowable number of switching operations for the capacitor bank in the j-th distributed compensation device. The real-time junction temperature of the power devices in the j-th distributed compensation device; The ambient temperature; Let be the maximum allowable junction temperature of the power device in the j-th distributed compensation device; and All are preset weighting coefficients, and + =1.

[0047] Secondly, a distributed compensation control method for a distribution network is provided, applied to the distributed compensation control system for the distribution network described in the first aspect. The distributed compensation control method for the distribution network includes:

[0048] The distributed multi-source module collects node data, equipment operating status data, and global system operating data from each key node in real time.

[0049] The edge decision module obtains local node data of local key nodes through the distributed multi-source module, and generates preliminary compensation instructions for multiple distributed compensation devices based on the local node data.

[0050] The collaborative optimization module receives and integrates the preliminary compensation instructions uploaded by all edge decision modules, as well as the device operation status data and system global operation data uploaded by the distributed multi-source module. It then calculates the system comprehensive operation index through a unified nonlinear comprehensive evaluation function and generates global collaborative optimization instructions based on the system comprehensive operation index.

[0051] The control execution module distributes global collaborative optimization instructions to the corresponding edge decision modules, thereby driving multiple distributed compensation devices corresponding to the edge decision modules to perform collaborative compensation operations.

[0052] The beneficial effects achieved by this invention are as follows:

[0053] The distributed multi-source module is used to collect node data, equipment operating status data, and global system operating data of each key node in real time. The edge decision module is used to obtain local node data of local key nodes through the distributed multi-source module and generate preliminary compensation instructions for multiple distributed compensation devices based on the local node data. The collaborative optimization module is used to receive and integrate the preliminary compensation instructions uploaded by all edge decision modules, as well as the equipment operating status data and global system operating data uploaded by the distributed multi-source module, and calculate the system comprehensive operating index through a unified nonlinear comprehensive evaluation function. Based on the system comprehensive operating index, a global collaborative optimization instruction is generated. The control execution module is used to distribute the global collaborative optimization instruction to the corresponding edge decision module, thereby driving multiple distributed compensation devices corresponding to the edge decision module to perform collaborative compensation operations.

[0054] The system comprehensive operation index is calculated through a unified nonlinear comprehensive evaluation function, which deeply integrates three heterogeneous indicators: reactive power demand, harmonic status, and equipment health. Furthermore, it can dynamically switch operating modes based on the value of the system comprehensive operation index, achieving an optimal balance between economy, power quality, and equipment safety.

[0055] By combining "edge decision-making" with "centralized optimization" in a two-tier architecture, it not only ensures rapid response of local control but also achieves system-level global optimal coordination. This overcomes the drawbacks of traditional centralized or fully distributed control. By using equipment health status as a core evaluation indicator and integrating it into the control logic, it enables preventive maintenance and fault early warning, effectively extending equipment life and improving the overall reliability of the system. Attached Figure Description

[0056] Figure 1This is a structural diagram of the distributed compensation control system for power distribution networks of the present invention;

[0057] Figure 2 This is a flowchart of the calculation unit for the preliminary harmonic compensation command of the present invention.

[0058] Figure 3 This is a flowchart of the distributed compensation control method for power distribution networks according to the present invention. Detailed Implementation

[0059] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0060] like Figure 1 As shown, an embodiment of the present invention provides a distributed compensation control system for a power distribution network, comprising:

[0061] Multiple distributed multi-source modules 101, multiple edge decision modules 102, collaborative optimization module 103, and control execution module 104;

[0062] Multiple distributed multi-source modules 101 are deployed at key nodes of the power distribution network.

[0063] Each edge decision module 102 is coupled to the local controller of the corresponding multiple distributed compensation devices;

[0064] The distributed multi-source module 101 is used to collect node data, equipment operating status data and global system operating data of each key node in real time.

[0065] The edge decision module 102 is used to obtain local node data of local key nodes through the distributed multi-source module 101, and generate preliminary compensation instructions for multiple distributed compensation devices based on the local node data.

[0066] The collaborative optimization module 103 is used to receive and integrate the preliminary compensation instructions uploaded by all edge decision modules 102, as well as the device operation status data and system global operation data uploaded by the distributed multi-source module 101, and calculate the system comprehensive operation index through a unified nonlinear comprehensive evaluation function, and generate global collaborative optimization instructions based on the system comprehensive operation index.

[0067] The control execution module 104 is used to distribute global collaborative optimization instructions to the corresponding edge decision module 102, thereby driving multiple distributed compensation devices corresponding to the edge decision module 102 to perform collaborative compensation operations.

[0068] Preferably, combining the above Figure 1In the embodiment shown, the edge decision module 102 collects local node data of the corresponding key nodes through the distributed multi-source module 101. The local node data includes node voltage, node current, node phase and node harmonic content. The preliminary compensation instructions include preliminary reactive power compensation instructions and preliminary harmonic compensation instructions.

[0069] Preferably, the edge decision module 102 calculates the preliminary reactive power compensation instruction Q. local The calculation formula is:

[0070] ;

[0071] in, The reference value for the node voltage is calculated globally by the collaborative optimization module 103 and then distributed to each edge decision module 102.

[0072] This represents the measured value of the node voltage. The preset power factor reference value, The power factor is a measured value calculated from node current and node phase. and β is the preset proportional-integral coefficient, and β is the preset weight coefficient. , And β can be adaptively adjusted based on the device temperature and load rate in the equipment operating status data; Indicates the sampling period.

[0073] Preferably, combining the above Figure 1 The embodiment shown includes an edge decision module 102, comprising:

[0074] The preliminary harmonic compensation command calculation unit is used to execute, for example... Figure 2 The steps shown include:

[0075] 201. Windowed Fast Fourier Transform analysis is performed on the node current to identify the harmonic order and corresponding amplitude, and a preliminary harmonic spectrum is generated.

[0076] In the process of windowed Fast Fourier Transform analysis: First, the purpose of windowing is to smoothly transition the two ends of the node current to zero by weighting in the time domain (e.g., Hanning window, Hamming window, etc.), reducing the jump caused by truncation, thereby suppressing spectral leakage; then, the node current is subjected to Fast Fourier Transform (FFT) to transform the node current from the time domain to the frequency domain, identify the main harmonic orders and the amplitude corresponding to each harmonic, and thus obtain the preliminary harmonic spectrum.

[0077] 202. Based on the preliminary harmonic spectrum, dynamically adjust the cutoff frequency of the low-pass filter and / or assign compensation priority weights for different harmonic orders in the harmonic detection algorithm based on instantaneous reactive power theory.

[0078] This step is performed by the dynamic adjustment subunit in the preliminary harmonic compensation command calculation unit, and the process is as follows:

[0079] Harmonics whose amplitudes exceed a set amplitude threshold in the preliminary harmonic spectrum are designated as target harmonics.

[0080] Increase the compensation priority weight of the target subharmonic in the harmonic detection algorithm based on instantaneous reactive power theory; and correspondingly reduce the cutoff frequency of the low-pass filter for the target subharmonic in the preset frequency band, which is generally the frequency band near the target subharmonic.

[0081] 203. Based on the dynamically adjusted harmonic detection algorithm based on instantaneous reactive power theory, the instantaneous harmonic current command is extracted.

[0082] 204, the instantaneous harmonic current command is used as the initial harmonic compensation command.

[0083] After inputting the preliminary harmonic compensation command into the active power filter, the active power filter can be driven to generate the opposite compensation current.

[0084] Preferably, combining the above Figure 1 In the embodiments shown, in some embodiments of the present invention, the formula for calculating the comprehensive operating index U of the system by the collaborative optimization module 103 is as follows:

[0085] ;

[0086] in, The reactive power demand urgency index for the entire network is calculated based on all preliminary reactive power compensation instructions. The normalized value range of the reactive power demand urgency index for the entire network is [0, 1].

[0087] The harmonic pollution severity index for the entire network is calculated based on all preliminary harmonic compensation instructions. The normalized value range of the harmonic pollution severity index for the entire network is [0, 1].

[0088] The average health index of all network devices is calculated based on the device operation status data. The normalized value range of the average health index of all network devices is [0, 1].

[0089] p, m, and n are preset adjustment indices, all of which are real numbers greater than 0. These three preset adjustment indices are used to adjust the shape of various sensitivities and nonlinearities.

[0090] μ and ν are preset weighting coefficients that satisfy μ+ν=1, used to balance the impact of reactive power demand and harmonic pollution on the system state.

[0091] η is the preset coordination gain coefficient, which is a real number between 0 and 1; θ is the preset difference adjustment coefficient, which is a positive real number; exp(.) is the natural exponential function; || is the absolute value function.

[0092] Preferably, in some embodiments of the present invention, the urgency index of reactive power demand across the entire network is... The calculation formula is:

[0093] ;

[0094] Where N is the total number of edge decision modules;

[0095] The initial reactive power compensation instruction uploaded for the xth edge decision module;

[0096] Let x be the importance weight coefficient of the key node of the xth edge decision module in the distribution network;

[0097] This represents the upper limit of the total capacity of all distributed reactive power compensation devices in the distribution network.

[0098] Preferably, in some embodiments of the present invention, the severity index of harmonic pollution across the entire network is... The calculation formula is:

[0099] ;

[0100] in, To determine the initial harmonic compensation command based on the critical node of the nth edge decision module. The calculated total harmonic distortion rate;

[0101] This is the maximum total harmonic distortion rate allowed by power grid specifications.

[0102] Preferably, in some embodiments of the present invention, the device operating status data includes the number of switching operations of the capacitor bank and the junction temperature of the power devices;

[0103] Average health index of all network devices The calculation formula is:

[0104] ;

[0105] ;

[0106] Where, M is the number of distributed compensation devices; H_device_j is the sub - index of the health of the j - th distributed compensation device; is the sub - index of the health of the j - th distributed compensation device; is the cumulative switching times of the capacitor bank in the j - th distributed compensation device; is the maximum allowable switching times designed for the capacitor bank in the j - th distributed compensation device; is the real - time junction temperature of the power device in the j - th distributed compensation device; is the ambient temperature; is the maximum allowable junction temperature of the power device of the j - th distributed compensation device; and are both preset weight coefficients, and + = 1.

[0107] Preferably, in combination with the above Figure 1 shown embodiments, in some embodiments of the present invention, the collaborative optimization module 103 includes a global collaborative optimization unit, which is used to perform the following steps:

[0108] The first preset mode switching threshold U1, the second preset mode switching threshold U2, and the third preset mode switching threshold U3, and U1>U2> U3;

[0109] When U≥U1, an economic optimization mode is adopted, and based on the economic optimization mode, with minimizing the total network line loss as the main goal, a global collaborative optimization instruction is generated;

[0110] When U2≤U<U1, a power quality priority mode is adopted, and based on the power quality priority mode, with suppressing voltage deviation and harmonics as the main goal, a global collaborative optimization instruction is generated;

[0111] When U3≤U<U2, a device protection mode is adopted, and based on the device protection mode, with de - grading operation and ensuring device safety as the main goal, a global collaborative optimization instruction is generated;

[0112] When U<U3, a safe shutdown mode is adopted, and based on the safe shutdown mode, a shutdown instruction for gradually exiting operation is generated as the global collaborative optimization instruction.

[0113] It should be noted that the collaborative optimization module 103 uses a distributed optimization algorithm based on the alternating direction multiplier method (ADMM) to solve the functions in different operation modes, and the preliminary reactive power compensation instruction is input as the initial solution of the objective function of each sub - problem in the ADMM algorithm.

[0114] Preferably, in combination with the above Figure 1 shown embodiments, in some embodiments of the present invention, the distributed multi - source module 101 includes:

[0115] The synchronization phasor measurement unit is used to provide network-wide synchronization phase data and upload it to the collaborative optimization module, so that the collaborative optimization module uses the network-wide synchronization phase data as the benchmark for synchronization and accuracy when performing global optimization calculations.

[0116] It should be noted that, in combination with the above Figure 1 In some embodiments of the present invention, as shown in the examples, the distributed compensation control system for power distribution networks further includes:

[0117] The cloud platform 105 is used to periodically receive and store the system comprehensive performance index U, all node voltages, node currents, node phases, node harmonic content, and equipment operating status data of different distribution network systems. It trains an optimization model using machine learning algorithms and iteratively optimizes p, m, n, μ, ν, η, θ, and the edge decision-making module. , and β.

[0118] It should be noted that, in combination with the above Figure 1 In the embodiments shown, and in some embodiments of the present invention, the control execution module 104 includes:

[0119] The instruction parsing unit is used to receive the global collaborative optimization instruction sent by the collaborative optimization module 103, and parse out the target device identifier, instruction type and control parameters contained in the global collaborative optimization instruction; the target device identifier is used to uniquely identify the corresponding target distributed compensation device;

[0120] The communication protocol adaptation unit is used to convert the parsed control parameters into a communication protocol and data format that matches the local controller of the target distributed compensation device;

[0121] The instruction distribution and verification unit is used to send the formatted instruction to the edge decision module 102 coupled with the target distributed compensation device through the industrial bus network according to the target device identifier, and to receive the instruction response signal returned by the edge decision module 102 to verify whether the instruction has been successfully delivered.

[0122] The execution status monitoring unit is used to monitor whether the target distributed compensation device performs operations according to the instructions and to feed back the execution status to the collaborative optimization module 103.

[0123] The beneficial effects achieved by the embodiments of the present invention are as follows:

[0124] The distributed multi-source module 101 continuously collects real-time electrical quantities and equipment status quantities of key nodes in the distribution network to form the system perception layer. The edge decision module 102 generates preliminary compensation instructions based on local data parallel calculation to achieve rapid local response. The collaborative optimization module 103 gathers global information and local decision results, and performs deep integration and intelligent evaluation of the system operation status through a unified nonlinear comprehensive evaluation function to calculate a comprehensive operation index that characterizes the overall health level of the system, and decides the global operation mode accordingly. Then, a suitable distributed optimization algorithm is adopted to generate system-level global collaborative optimization instructions. The control execution module 104 ensures the reliable parsing, protocol conversion and accurate distribution of optimization instructions. After receiving the instructions, each distributed compensation device performs collaborative compensation operations, and its execution status is monitored in real time and fed back to the entire system. At the same time, the cloud platform 105 iteratively optimizes the key parameters of the system through historical and real-time data, enabling the system to have continuous self-learning and self-adaptive capabilities. The whole process is repeated, realizing multi-objective, adaptive and collaborative intelligent control of reactive power, harmonics, voltage and equipment status of the distribution network.

[0125] The system comprehensive operation index is calculated through a unified nonlinear comprehensive evaluation function, which deeply integrates three heterogeneous indicators: reactive power demand, harmonic status, and equipment health. Furthermore, it can dynamically switch operating modes based on the value of the system comprehensive operation index, achieving an optimal balance between economy, power quality, and equipment safety.

[0126] By combining "edge decision-making" with "centralized optimization" in a two-tier architecture, it not only ensures rapid response of local control but also achieves system-level global optimal coordination. This overcomes the drawbacks of traditional centralized or fully distributed control. By using equipment health status as a core evaluation indicator and integrating it into the control logic, it enables preventive maintenance and fault early warning, effectively extending equipment life and improving the overall reliability of the system.

[0127] The above embodiments describe in detail the distributed compensation control system for distribution networks. The following embodiments illustrate the distributed compensation control method for distribution networks applied to the distributed compensation control system for distribution networks.

[0128] like Figure 3 As shown in the figure, an embodiment of the present invention provides a distributed compensation control method for a distribution network, comprising:

[0129] 301, The distributed multi-source module collects node data, equipment operating status data and global system operating data of each key node in real time;

[0130] 302. The edge decision module obtains local node data of local key nodes through the distributed multi-source module, and generates preliminary compensation instructions for multiple distributed compensation devices based on the local node data.

[0131] 303. The collaborative optimization module receives and integrates the preliminary compensation instructions uploaded by all edge decision modules, as well as the device operation status data and system global operation data uploaded by the distributed multi-source module. It then calculates the system comprehensive operation index through a unified nonlinear comprehensive evaluation function and generates global collaborative optimization instructions based on the system comprehensive operation index.

[0132] 304. The control execution module distributes the global collaborative optimization instructions to the corresponding edge decision modules, thereby driving multiple distributed compensation devices corresponding to the edge decision modules to perform collaborative compensation operations.

[0133] In this embodiment, the specific details of each step are as described in the above embodiments regarding the distributed compensation control system for power distribution networks.

[0134] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A distributed compensation control system for a power distribution network, characterized in that, Including: Multiple distributed multi-source modules, multiple edge decision modules, a collaborative optimization module, and a control execution module; Multiple of the distributed multi-source modules are respectively arranged at each key node of the distribution network; Each of the edge decision modules is coupled with the local controllers of the corresponding multiple distributed compensation devices; The distributed multi-source module is used to collect in real time the node data, device operation status data, and system global operation data of each key node; The edge decision module is used to obtain the local node data of the local key node through the distributed multi-source module, and generate preliminary compensation instructions for the corresponding multiple distributed compensation devices according to the local node data; the local node data includes node voltage, node current, node phase, and node harmonic content; the preliminary compensation instructions include preliminary reactive power compensation instructions and preliminary harmonic compensation instructions; The collaborative optimization module is used to receive and fuse the preliminary compensation instructions uploaded by all the edge decision modules, and the device operation status data and the system global operation data uploaded by the distributed multi-source module, and calculate the system comprehensive operation index through the system comprehensive operation index calculation formula, and generate a global collaborative optimization instruction according to the system comprehensive operation index; The control execution module is used to distribute the global collaborative optimization instruction to the corresponding edge decision module, so as to drive the corresponding multiple distributed compensation devices of the edge decision module to perform collaborative compensation operations; The calculation formula of the system comprehensive operation index U is: ； Among them, the The reactive power demand urgency index for the entire network, calculated based on all the aforementioned preliminary reactive power compensation instructions, is... The value range is [0, 1]; The The harmonic pollution severity index for the entire network, calculated based on all the aforementioned preliminary harmonic compensation instructions, is... The value range is [0, 1]; The The average health index of all network devices is calculated based on the device operating status data. The value range is [0, 1]; The p, m, and n are all preset adjustment indexes, and their values are all real numbers greater than 0; the μ and ν are all preset weight coefficients, satisfying μ + ν = 1; the η is a preset coordination gain coefficient, and its value range is a real number between 0 and 1; the θ is a preset difference adjustment coefficient, which is a positive real number; the exp(.) is the natural exponential function; the || is the absolute value function.

2. The distributed compensation control system for power distribution networks according to claim 1, characterized in that, The collaborative optimization module includes: A global collaborative optimization unit, which is used to perform the following steps: Obtain the first preset mode switching threshold U1, the second preset mode switching threshold U2, and the third preset mode switching threshold U3, and U1 > U2 > U3; When U ≥ U1, adopt the economic optimization mode, and generate a global collaborative optimization instruction with the main goal of minimizing the network line loss according to the economic optimization mode; When U2 ≤ U < U1, adopt the power quality priority mode, and generate a global collaborative optimization instruction with the main goal of suppressing voltage deviation and harmonics according to the power quality priority mode; When U3 ≤ U < U2, adopt the equipment protection mode, and generate a global collaborative optimization instruction with the main goal of degraded operation and ensuring equipment safety according to the equipment protection mode; When U < U3, adopt the safe shutdown mode, and generate a shutdown instruction for gradually exiting the operation according to the safe shutdown mode, which is used as the global collaborative optimization instruction.

3. The distributed compensation control system for power distribution networks according to claim 1, characterized in that, The preliminary reactive power compensation command Q local The calculation formula is: ； Among them, the The reference value for the node voltage is... The value is calculated by the collaborative optimization module according to the global collaborative optimization instruction; The measured value of the node voltage, the As a preset power factor reference value, the The power factor measurement value is calculated from the node current and the node phase. and stated β is a preset proportional-integral coefficient, and β is a preset weighting coefficient. Indicates the sampling period.

4. The distributed compensation control system for power distribution networks according to claim 1, characterized in that, The edge decision module includes: A preliminary harmonic compensation instruction calculation unit, which is used to perform the following steps: Perform windowed fast Fourier transform analysis on the node current, identify the harmonic order and the corresponding amplitude, and generate a preliminary harmonic spectrum; Based on the preliminary harmonic spectrum, the cutoff frequency of the low-pass filter and / or the compensation priority weights for different harmonic orders in the harmonic detection algorithm based on instantaneous reactive power theory are dynamically adjusted. Based on the dynamically adjusted harmonic detection algorithm based on instantaneous reactive power theory, the instantaneous harmonic current command is extracted. The instantaneous harmonic current command is used as a preliminary harmonic compensation command, which is used to drive the active power filter to generate an opposite compensation current.

5. The distributed compensation control system for power distribution networks according to claim 4, characterized in that, The preliminary harmonic compensation command calculation unit includes: The dynamic adjustment subunit is used to increase the compensation priority weight of the target subharmonic for the target subharmonic whose amplitude exceeds a set amplitude threshold, and correspondingly reduce the cutoff frequency of the low-pass filter for the preset frequency band of the target subharmonic.

6. The distributed compensation control system for power distribution networks according to claim 1, characterized in that, The distributed multi-source module includes: The synchronization phasor measurement unit is used to provide network-wide synchronization phase data and upload the network-wide synchronization phase data to the collaborative optimization module, so that the collaborative optimization module uses the network-wide synchronization phase data as the benchmark for synchronization and accuracy when performing global optimization calculations.

7. The distributed compensation control system for power distribution networks according to claim 3, characterized in that, The equipment operating status data includes the number of times the capacitor bank is switched on and off and the junction temperature of the power devices; The average health index of all network devices The calculation formula is: ； ； Wherein, M represents the number of the distributed compensation devices; The health sub-index of the j-th distributed compensation device; The cumulative number of switching operations on the capacitor bank in the j-th distributed compensation device; The maximum allowable number of switching operations for the capacitor bank in the j-th distributed compensation device; The real-time junction temperature of the power device in the j-th distributed compensation device; The ambient temperature; The maximum allowable junction temperature of the power device in the j-th distributed compensation device; and stated All are preset weighting coefficients, and + =1.

8. A distributed compensation control method for a power distribution network, characterized in that, The distributed compensation control method for distribution networks, applied to any one of claims 1-7, comprises: The distributed multi-source module collects node data, equipment operating status data, and global system operating data from each key node in real time. The edge decision module obtains local node data of local key nodes through the distributed multi-source module, and generates preliminary compensation instructions for multiple corresponding distributed compensation devices based on the local node data; the local node data includes node voltage, node current, node phase, and node harmonic content; the preliminary compensation instructions include preliminary reactive power compensation instructions and preliminary harmonic compensation instructions. The collaborative optimization module receives and integrates the preliminary compensation instructions uploaded by all the edge decision modules, the device operating status data and the system global operating data uploaded by the distributed multi-source module, and calculates the system comprehensive operating index through the system comprehensive operating index calculation formula, and generates a global collaborative optimization instruction based on the system comprehensive operating index. The control execution module distributes the global collaborative optimization instructions to the corresponding edge decision modules, thereby driving multiple distributed compensation devices corresponding to the edge decision modules to perform collaborative compensation operations. The formula for calculating the system's comprehensive operating index U is as follows: ； Among them, the The reactive power demand urgency index for the entire network, calculated based on all the aforementioned preliminary reactive power compensation instructions, is... The value range is [0, 1]; The The harmonic pollution severity index for the entire network, calculated based on all the aforementioned preliminary harmonic compensation instructions, is... The value range is [0, 1]; The The average health index of all network devices is calculated based on the device operating status data. The value range is [0, 1]; p, m, and n are all preset adjustment indices, all of which are real numbers greater than 0; μ and ν are both preset weighting coefficients, satisfying μ + ν = 1; η is a preset coordination gain coefficient, which is a real number between 0 and 1; θ is a preset difference adjustment coefficient, which is a positive real number; exp(.) is a natural exponential function; and || is an absolute value function.

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