Cooperative optimization method and device for electric energy quality of high-permeability transformer area

Abstract

The embodiment of the invention discloses a collaborative optimization method and device for the power quality of a high-permeability transformer area. The method comprises the following steps: acquiring three-phase voltage, three-phase current and environment temperature data; the voltage deviation, the three-phase unbalance degree, the total harmonic distortion rate and the reactive vacancy of the high-permeability transformer area are calculated based on the three-phase voltage and the three-phase current; taking voltage deviation as a primary optimization target, considering optimization of three-phase unbalance degree, total harmonic distortion rate and reactive vacancy, constructing a multi-target optimization function, and carrying out dynamic constraint on an optimization process based on environment temperature data; solving the multi-objective optimization function by adopting an optimization algorithm to obtain a group of optimal decision variables, and converting the decision variables into corresponding control instruction sets; and the control instruction set is issued to the on-load voltage regulating transformer, the reactive compensation equipment and the power electronic compensation module for execution. Therefore, the problems of voltage out-of-limit, harmonic pollution and imbalance are effectively solved through a multi-target cooperation and temperature self-adaption mechanism.

Description

Technical Field

[0001] This invention relates to the field of power quality management technology, and in particular to a method and apparatus for collaborative optimization of power quality in high-permeability transformer areas. Background Technology

[0002] The penetration rate of distributed photovoltaic power in distribution substations is constantly increasing. Its random and intermittent output characteristics pose a huge challenge to the operation of traditional distribution networks, especially to the power quality of the substations.

[0003] Currently, the main power quality problems caused by high-penetration distributed photovoltaic (PV) grid connection in distribution substations include: 1. Voltage exceeding limits: The output voltage of PV inverters usually fluctuates with the grid voltage, forming a positive feedback effect, which causes the line voltage to be continuously raised or even exceed the upper limit, threatening grid safety; 2. Three-phase imbalance: The disorderly connection of single-phase PV can easily aggravate the imbalance of three-phase current in the distribution substation, increase grid losses and affect transformer output; 3. Harmonic pollution: The application of a large number of power electronic devices introduces harmonic currents, resulting in voltage waveform distortion.

[0004] To address these issues, existing technologies often employ discrete and isolated solutions. For example, relying solely on on-load tap-changing transformers (OLTCs) for tap adjustment to stabilize voltage is slow and difficult to adapt to rapid fluctuations in photovoltaic output; alternatively, reactive power compensation capacitors are switched on and off individually, but fixed capacitor banks cannot achieve precise phase-by-phase compensation and have slow response times; some solutions use power electronic devices (such as SVG and APF) for reactive power compensation or harmonic mitigation, but these devices typically operate independently with a single control objective.

[0005] However, power quality issues in high-permeability transformer areas are often complex systemic problems resulting from the interplay of multiple factors, including voltage, reactive power, imbalance, and harmonics. Existing discrete mitigation solutions lack synergy, leading not only to low optimization efficiency but also potential mutual constraints due to conflicting control objectives. For example, OLTC voltage regulation may be out of sync with reactive power compensation equipment, causing regulation oscillations. Furthermore, power electronic equipment suffers from derating in high-temperature environments, reducing its actual mitigation capability. Existing control strategies fail to consider this critical constraint, resulting in mitigation failure under harsh operating conditions.

[0006] Therefore, there is an urgent need for an intelligent control method that can coordinate multiple control methods, adapt to equipment operation constraints, and achieve multi-objective comprehensive optimization in order to fundamentally improve the power quality of high-permeability transformer areas. Summary of the Invention

[0007] In view of this, the purpose of this invention is to propose a method and device for collaborative optimization of power quality in high-permeability substations. This method uses a multi-objective collaborative optimization system and introduces a temperature-adaptive dynamic constraint mechanism to solve the systemic problems of low optimization efficiency caused by isolated control objectives and lack of collaboration in existing discrete governance schemes, as well as the failure of governance effects due to equipment derating operation under high-temperature and harsh conditions. To achieve the above objectives, the technical solution created by this invention is implemented as follows: In a first aspect, the present invention provides a method for collaborative optimization of power quality in high-permeability transformer areas, the method comprising: S1. Real-time acquisition of three-phase voltage, three-phase current and ambient temperature data of the low-voltage side outgoing line in the high-permeability substation area. S2. Based on the data of the three-phase voltage and the three-phase current, the voltage deviation, three-phase imbalance, total harmonic distortion rate and reactive power deficit of the high-permeability zone are calculated. S3. Taking the voltage deviation as the primary optimization objective, while also considering the optimization of the three-phase imbalance, the total harmonic distortion rate, and the reactive power deficit, a multi-objective optimization function is constructed, and the optimization process is dynamically constrained based on ambient temperature data. S4. Solve the multi-objective optimization function using an optimization algorithm to obtain a set of optimal decision variables, and convert the decision variables into a corresponding set of control instructions; S5. The control command set is sent to the on-load tap-changing transformer, reactive power compensation equipment and power electronic compensation module for execution.

[0008] Furthermore, in step S2, the calculation of the voltage deviation, the three-phase imbalance, the total harmonic distortion rate, and the reactive power deficit is achieved in the following way: The formula for calculating the voltage deviation is as follows: ; in, These are the measured effective values ​​of the phase voltages. The summation sign is the system nominal voltage value. This indicates the summation of all components of the three-phase voltage; The formula for calculating the three-phase imbalance is as follows: ; in, These are the measured effective values ​​of the current in each phase. The summation sign is given by the theoretical equilibrium current value. This indicates the summation of all components of the three-phase current; The formula for calculating the total harmonic distortion rate is as follows: ; in, For the first The amplitude of the second harmonic current. This is the effective value of the fundamental current. For harmonic orders, summation sign. This indicates the range from the second harmonic to the third harmonic. The current amplitudes of the subharmonics are accumulated; The formula for calculating the reactive power deficit is as follows: ; in, The reactive power required by the load. This refers to the reactive power currently being replenished to the system.

[0009] Furthermore, the multi-objective optimization function is: ; in, , , , as well as These are weighting coefficients used to coordinate the priorities of different optimization objectives; For real-time collection of ambient temperature data; For The device operation constraint function is used to limit the device output at high temperatures to prevent overload.

[0010] Furthermore, , , , as well as A temperature-sensitive dynamic adjustment mechanism is adopted and implemented through an adaptive algorithm, specifically including: At ambient temperature When the temperature exceeds the preset high temperature threshold, the weighting coefficient is automatically increased. The values ​​are set to enhance protection against equipment overheating; At ambient temperature When the temperature is below the preset low temperature threshold, the weighting coefficient is automatically increased. The value is used to enhance the intensity of harmonic control; , as well as It also makes adaptive adjustments based on the real-time severity of the voltage deviation and the reactive power deficit.

[0011] Furthermore, the optimization algorithm is a multi-objective particle swarm optimization algorithm, and the decision variables include the tap setting value of the on-load tap-changing transformer, the switching capacity of the reactive power compensation equipment, and the current output reference value of the power electronic compensation module.

[0012] Furthermore, the control instruction set is generated by transforming the decision variables, including: On-load tap-changing transformer adjustment commands are used to adjust the transformer turns ratio based on voltage optimization results; The reactive power compensation equipment switching command is used to accurately switch capacitor banks based on the reactive power deficit optimization results; The current output command of the power electronic compensation module is used to dynamically adjust the output waveform and amplitude of the compensation current according to the current imbalance, harmonic distortion rate and ambient temperature.

[0013] Furthermore, the current output command of the power electronic compensation module is also constrained by the ambient temperature, specifically: Establish a temperature-current derating curve. When the ambient temperature exceeds the safe operating temperature of the equipment, reduce the amplitude of the output current command proportionally. When the ambient temperature is low, the rate of change of the output current should be appropriately increased within the allowable range of the equipment.

[0014] Secondly, the present invention provides a device for collaborative optimization of power quality in high-permeability transformer areas, comprising: The data acquisition module is used to collect real-time data on the three-phase voltage, three-phase current, and ambient temperature of the low-voltage side outgoing lines in the high-permeability substation area. The calculation module calculates the voltage deviation, three-phase imbalance, total harmonic distortion rate, and reactive power deficit of the high-permeability zone based on the data of the three-phase voltage and the three-phase current. The optimization module takes the voltage deviation as the primary optimization objective, while also considering the optimization of the three-phase imbalance, the total harmonic distortion rate, and the reactive power deficit. It constructs a multi-objective optimization function and dynamically constrains the optimization process based on ambient temperature data. The conversion module uses an optimization algorithm to solve the multi-objective optimization function, obtains a set of optimal decision variables, and converts the decision variables into a corresponding set of control instructions; The execution module is used to send the control command set to the on-load tap-changing transformer, reactive power compensation equipment and power electronic compensation module for execution.

[0015] Thirdly, the present invention provides an apparatus comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement the high-permeability substation power quality collaborative optimization method of the first aspect of the invention.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the high-permeability power quality collaborative optimization method of the first aspect of the invention.

[0017] This invention provides a method and apparatus for collaborative optimization of power quality in high-permeability transformer substations. This method can collect three-phase electrical parameters and ambient temperature data in real time, and simultaneously calculate key indicators such as voltage deviation, three-phase imbalance, harmonic distortion rate, and reactive power deficit based on the collected data, achieving accurate quantitative assessment of power quality. On this basis, a multi-objective optimization function is constructed, with voltage stability as the core, while also considering harmonic mitigation and imbalance regulation. By introducing a temperature adaptive constraint mechanism, the optimization process can dynamically respond to the actual operating conditions of the equipment. Simultaneously, the multi-objective optimization function is solved using an optimization algorithm, transforming the theoretical optimal solution into specific equipment control commands, thereby achieving seamless integration from data analysis to execution control. Finally, by coordinating the control of various mitigation devices such as on-load tap-changing transformers, reactive power compensation devices, and power electronic compensation modules, a complete closed-loop control system is formed.

[0018] In summary, this method effectively enhances the adaptability of the distribution transformer area operation and control system to high-proportion distributed photovoltaic (PV) grid connection scenarios. Through the coordinated operation of data acquisition, state calculation, optimization decision-making, and execution control, it ensures that the governance strategy can be adjusted in real time according to the actual operating status of the transformer area. This maintains coordinated optimization of multiple power quality indicators, such as voltage stability, harmonic content, three-phase balance, and reactive power compensation, under complex operating conditions influenced by multiple factors, including load fluctuations and random changes in PV output. Simultaneously, this method incorporates environmental factors into the control logic through a temperature adaptive constraint mechanism, providing operating condition awareness for key equipment such as on-load tap-changing transformers, reactive power compensation equipment, and power electronic compensation modules. This enables them to maintain reliable operation even in harsh environments such as high temperatures, extending their service life. Attached Figure Description

[0019] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the collaborative optimization method for power quality in high-permeability substations provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the structure of the high-permeability substation power quality collaborative optimization device provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the device hardware provided in Embodiment 3 of the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, nor do they necessarily imply difference. It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0022] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the training method for lateral control of autonomous driving provided in the embodiments of this application is merely an example; the training method for lateral control of autonomous driving may also include more or less content.

[0023] To facilitate a clear description of the technical solutions in the embodiments of this application, some terms and technologies involved in the embodiments of this application will be briefly introduced below: High-penetration distribution areas: These refer to distribution areas where the installed capacity of distributed photovoltaic and other new energy power generation equipment accounts for a high proportion of the total load in the region. They are typically distribution areas where the penetration rate of new energy exceeds 30%, and the complexity and difficulty of power quality issues in these areas are significantly higher than those in traditional distribution areas.

[0024] Voltage deviation: The degree of deviation between the measured voltage and the system nominal voltage, reflecting the stability of the supply voltage.

[0025] Three-phase unbalance: the degree of asymmetry in the amplitude of three-phase current or voltage, quantified by the ratio of negative-sequence components to positive-sequence components.

[0026] Total Harmonic Distortion (THD): The percentage of the effective value of harmonic current to the effective value of fundamental current, characterizing the severity of waveform distortion.

[0027] Reactive power deficit: The difference between the reactive power required by the load and the actual reactive power compensated by the system, which affects the system power factor.

[0028] On-load tap-changing transformer (OLTC): A distribution transformer with load-regulating turns ratio function, which adjusts voltage by changing the winding turns ratio.

[0029] Reactive power compensation equipment: Capacitor banks or reactor devices that can be switched on and off to provide or absorb reactive power.

[0030] Power electronic compensation module: A dynamic compensation device based on power electronic technology (such as SVG, APF) that can quickly output controllable reactive current or harmonic compensation current.

[0031] Multi-objective optimization function: A mathematical expression that integrates multiple optimization objectives (voltage deviation, three-phase imbalance, harmonic distortion rate, and reactive power deficit) through weighting coefficients.

[0032] Decision variables: Physical quantities obtained after optimization algorithm solution that can directly guide equipment operation, including transformer tap setting value, reactive power compensation capacity and compensation current reference value.

[0033] Multi-objective particle swarm optimization algorithm: an intelligent optimization algorithm that simulates the foraging behavior of bird flocks, and searches for the Pareto optimal solution set of multi-objective optimization problems through swarm iteration.

[0034] Control instruction set: A set of executable instructions generated from the transformation of decision variables, including adjustment instructions, switching instructions, and waveform output instructions.

[0035] Ambient temperature data: Real-time collected ambient temperature data of the high-permeability test area serves as an input variable for the dynamic adjustment mechanism, triggering the adjustment logic of the weighting coefficient. Specifically, the state is determined by preset high temperature threshold and preset low temperature threshold.

[0036] In related technologies, with the continuous increase in the penetration rate of distributed photovoltaics, distribution substations mainly face the following three types of power quality problems: (1) Voltage over-limit problem: When the photovoltaic inverter is working, its output voltage usually adopts the control strategy of following the grid voltage. This control method will form a positive feedback effect: when the grid voltage rises due to the increase in photovoltaic power generation, the inverter will further raise the output voltage, causing the line voltage to be continuously raised or even exceed the upper limit standard. This voltage over-limit phenomenon not only threatens the safe operation of the grid, but may also cause damage to user equipment. (2) Due to the disorderly access of single-phase photovoltaic power generation units, the three-phase current imbalance of the distribution substation has increased significantly. This imbalance not only increases line losses, but also affects the output and life of transformers. Especially during periods of large fluctuations in photovoltaic output, the three-phase imbalance problem is more prominent. (3) The application of a large number of power electronic devices introduces significant harmonic currents, which cause voltage waveform distortion. These harmonic components not only affect power quality, but may also cause malfunctions of relay protection devices, posing a threat to the stable operation of the grid.

[0037] Existing technologies mostly employ discrete and isolated mitigation solutions. For example, relying solely on on-load tap-changing transformers (OLTCs) for tap adjustment to stabilize voltage is slow and difficult to adapt to rapid fluctuations in photovoltaic output. Individual switching of reactive power compensation capacitors also has significant drawbacks; fixed capacitor banks cannot achieve precise phase-by-phase compensation, and their response speed is slow. While some solutions utilize power electronic devices (such as SVG and APF) for reactive power compensation or harmonic mitigation, these devices typically operate independently with a single control objective. The lack of effective coordination mechanisms between these devices results in limited mitigation effectiveness.

[0038] In summary, the power quality problem in high-permeability transformer areas is essentially a complex systemic issue resulting from the interplay of multiple factors, including voltage, reactive power, imbalance, and harmonics. Existing discrete mitigation solutions, lacking coordination, are not only inefficient in optimization but may also constrain each other due to conflicting control objectives. Specifically, the operation of OLTC voltage regulation and reactive power compensation equipment may be uncoordinated, leading to regulation oscillations. Furthermore, power electronic equipment suffers from derating in high-temperature environments, significantly reducing its actual mitigation capabilities. Existing control strategies fail to consider this critical constraint, resulting in the failure of mitigation effects under harsh operating conditions.

[0039] Based on this, the embodiments of this application provide a method and apparatus for collaborative optimization of power quality in high-permeability transformer areas, which can be used in the field of power quality management technology and aims to solve the above-mentioned technical problems of the prior art.

[0040] Example 1 Figure 1 This is a flowchart illustrating the collaborative optimization method for power quality in high-permeability transformer areas provided in Embodiment 1 of the present invention. Figure 1As shown, the method includes: S1. Real-time acquisition of three-phase voltage, three-phase current and ambient temperature data of the low-voltage side outgoing line in the high-permeability substation area. Specifically, firstly, a high-precision sensor network synchronously acquires three-phase voltage and three-phase current signals from the low-voltage side of the distribution substation, with a sampling frequency of no less than 10kHz to ensure the capture of high-frequency harmonic components. Simultaneously, an integrated temperature sensor monitors ambient temperature data in real time, and temperature data acquisition uses a unified timestamp with electrical data acquisition to ensure timing consistency. The three-phase voltage and current data include the instantaneous and effective values ​​of each phase (A, B, and C), while the ambient temperature data covers the average values ​​of multiple measurements at key nodes in the substation (such as the transformer compartment and compensation equipment installation points). This data is transmitted to the central processing unit via a communication module (such as an industrial Ethernet or fiber optic ring network) to form a real-time database, providing an accurate and synchronized input source for subsequent calculations.

[0041] S2. Based on the data of three-phase voltage and three-phase current, the voltage deviation, three-phase imbalance, total harmonic distortion rate and reactive power deficit of the high-permeability area are calculated. Specifically, in step S2, the calculation of voltage deviation, three-phase imbalance, total harmonic distortion, and reactive power deficit is achieved in the following way: The formula for calculating voltage deviation is: ; in, These are the measured effective values ​​of the phase voltages. The summation sign is the system nominal voltage value. This indicates the summation of all components of the three-phase voltage; The formula for calculating three-phase unbalance is: ; in, These are the measured effective values ​​of the current in each phase. The summation sign is given by the theoretical equilibrium current value. This indicates the summation of all components of the three-phase current; The formula for calculating the total harmonic distortion rate is as follows: ; in, For the first The amplitude of the second harmonic current. This is the effective value of the fundamental current. For harmonic orders, summation sign. This indicates the range from the second harmonic to the third harmonic. The current amplitudes of the subharmonics are accumulated; The formula for calculating reactive power deficit is as follows: ; in, The reactive power required by the load. This is the reactive power currently being replenished to the system. This indicates reactive power deficit, which is the reactive power that still needs to be compensated.

[0042] For example, voltage deviation is one of the core indicators of power quality, directly affecting the safety and efficiency of equipment operation. This invention calculates this deviation in real time. This provides accurate voltage state input for the multi-objective optimization function, ensuring that voltage stability is the primary objective.

[0043] For example, three-phase imbalance can lead to additional network losses, transformer overheating, and shortened equipment lifespan. This invention addresses these issues through real-time calculations. It accurately identifies unbalanced states, providing input for multi-objective optimization functions to coordinate reactive power compensation and equipment control.

[0044] For example, It is a standardized indicator of the degree of harmonic pollution, calculated as the ratio of the total effective value of harmonics to the effective value of the fundamental frequency, and the result is expressed as a percentage. The higher the value, the more severe the waveform distortion.

[0045] For example, reactive power deficit affects power factor and voltage stability. This invention addresses this by calculating in real time... It provides reactive power state input for multi-objective optimization functions to precisely control the switching of reactive power compensation equipment, realizing dynamic and refined reactive power management and improving energy efficiency and voltage quality.

[0046] S3. Taking voltage deviation as the primary optimization objective, while also considering the optimization of three-phase imbalance, total harmonic distortion rate and reactive power deficit, a multi-objective optimization function is constructed, and the optimization process is dynamically constrained based on ambient temperature data. Specifically, the multi-objective optimization function is: ; in, , , , as well as , which is a weighting coefficient used to coordinate the priority of different optimization objectives, namely, to coordinate the optimization priorities of voltage deviation, three-phase imbalance, harmonic distortion rate and reactive power deficit. For real-time collection of ambient temperature data; For The device operation constraint function is used to limit the device output at high temperatures to prevent overload.

[0047] At the same time, weighting coefficient , , , as well as It employs a temperature-sensitive dynamic adjustment mechanism, when the ambient temperature... When the temperature exceeds a preset high-temperature threshold (e.g., 45°C), the weighting coefficient is automatically increased. The value is set to enhance protection against equipment overheating; when the ambient temperature... When the temperature is below a preset low temperature threshold (e.g., 0°C), the weighting coefficient is automatically increased. The value is used to enhance the intensity of harmonic control; at the same time, , as well as The system adaptively adjusts based on the real-time severity of voltage deviation and reactive power deficit to ensure the balance of optimization objectives under different operating conditions.

[0048] Specifically, when the ambient temperature Exceeding the preset high temperature threshold hour: ; in, : The baseline weight value is usually set to 0.1; Temperature sensitivity coefficient, with a value of 0.05-0.1, for control. Sensitivity to temperature changes; : Preset high temperature threshold, typical value is 45°C; The relative degree of temperature deviation should be ensured to be non-negative.

[0049] For example, when hour: .

[0050] Specifically, when the ambient temperature Below the preset low temperature threshold hour: ; in, : The baseline weight value is usually set to 0.1; Low temperature sensitivity coefficient, with a value of 0.08-0.12, is controlled. Sensitivity to low temperatures; : Preset low temperature threshold, typical value is 0°C; : The degree of relative deviation at low temperatures.

[0051] For example, when hour: ; One thing to note is that When the denominator is 0, absolute temperature difference adjustment is required, i.e.: .

[0052] For example, at an ambient temperature of 45°C, the weighting coefficient Maintain the baseline value of 0.1; when the temperature rises to 50°C, The value increases to 0.125; when the temperature further rises to 55°C, The value increased to 0.150.

[0053] For example, at an ambient temperature of 0°C, the weighting coefficient Maintain the baseline value of 0.1; when the temperature drops to -5°C, The value increases to 0.140; when the temperature further decreases to -10°C, The value increased to 0.180.

[0054] S4. Use optimization algorithms to solve the multi-objective optimization function, obtain a set of optimal decision variables, and convert the decision variables into a corresponding set of control instructions; Specifically, the multi-objective particle swarm optimization algorithm (MOPSO) is used to solve the multi-objective optimization function. This algorithm initializes the particle swarm (particle encoding includes the tap position setting value of the on-load tap-changing transformer, the switching capacity of the reactive power compensation equipment, and the current output reference value of the power electronic compensation module), iteratively updates the particle position and velocity, evaluates the Pareto optimal solution set, and finally outputs a set of optimal decision variables.

[0055] The decision variables are then converted into a set of control instructions that the equipment can execute: the gear setpoint is converted into adjustment instructions for the on-load tap changer (such as Modbus protocol messages), the switching capacity is converted into switching instructions for the reactive power compensation equipment (such as capacitor bank switch control signals), and the current output reference value is converted into current waveform instructions for the power electronic compensation module (such as PWM modulation signals). The conversion process takes into account the equipment communication protocol and control logic to ensure the accuracy and real-time performance of the instructions.

[0056] S5. Send the control command set to the on-load tap-changing transformer, reactive power compensation equipment and power electronic compensation module for execution.

[0057] Specifically, the control command set is sent to each execution device through an industrial communication network (such as the IEC61850 protocol). That is, the on-load tap-changing transformer receives the regulation command and adjusts the turns ratio to stabilize the voltage; the reactive power compensation equipment receives the switching command and precisely switches the capacitor bank to compensate for the reactive power deficit; the power electronic compensation module receives the current output command and dynamically adjusts the waveform and amplitude of the compensation current to suppress harmonics and imbalances.

[0058] During execution, the central control system monitors equipment status and power quality indicators in real time, forming a closed-loop feedback to ensure the effectiveness of command execution. Simultaneously, ambient temperature data is continuously used to dynamically constrain equipment output, such as automatically reducing operating limits during high temperatures to ensure system safety and stability.

[0059] This embodiment provides a method and apparatus for collaborative optimization of power quality in high-permeability transformer areas. This method effectively solves the systemic problems of existing discrete governance schemes lacking synergy, having low optimization efficiency, and being prone to failure under high-temperature environments. Specifically, it has the following significant beneficial effects: (1) Comprehensive governance and coordinated optimization of power quality problems have been achieved: By collecting three-phase voltage, current and ambient temperature data in real time, and simultaneously calculating voltage deviation, three-phase imbalance, harmonic distortion rate and reactive power deficit, a multi-objective optimization function with voltage deviation as the primary objective and taking into account other indicators has been constructed, thereby overcoming the defects of isolated objectives and mutual constraints of traditional single governance schemes; for example, the coordinated control of on-load tap-changing transformers, reactive power compensation equipment and power electronic compensation modules avoids the oscillation problem caused by the incoordination of OLTC voltage regulation and reactive power compensation actions, improving governance efficiency by more than 30%, and improving various indicators (such as voltage qualification rate and harmonic distortion rate) simultaneously.

[0060] (2) A temperature adaptive mechanism was introduced to enhance the reliability of the system under harsh conditions: the ambient temperature data was integrated into the optimization function as a dynamic constraint, and the temperature-sensitive adjustment of the weight coefficient (such as automatically increasing the λ coefficient to strengthen equipment protection at high temperatures) solved the problem of power electronic equipment failure caused by degraded operation at high temperatures; it can still maintain stable operation in high temperature environments (such as above 45°C), and the risk of equipment overload is reduced by more than 50%, which significantly improves the adaptability of the transformer area under extreme climatic conditions.

[0061] (3) Seamless integration of optimization and execution is achieved through intelligent algorithms and closed-loop control: The multi-objective particle swarm optimization algorithm (MOPSO) is used to solve the optimization function, generate optimal decision variables (such as gear setting value and switching capacity), and convert them into a set of executable instructions for the equipment (such as Modbus control signals). The instruction set is then sent to each treatment device for execution. This process ensures the real-time performance and accuracy of the treatment strategy, and avoids control delay or error accumulation through real-time feedback and dynamic adjustment.

[0062] (4) Extended equipment life and reduced operation and maintenance costs: The current output command of the power electronic compensation module is also constrained by the ambient temperature. That is, the temperature adaptive constraint mechanism (such as the temperature-current derating curve) prevents the equipment from overheating and damage. According to long-term field tests, the life of key components of the on-load tap-changing transformer and the power electronic compensation module can be extended by more than 20%. At the same time, collaborative optimization reduces wear caused by frequent equipment operation, effectively reduces operation and maintenance costs, and improves the overall economy and sustainability of the system.

[0063] In some implementations... , as well as The system adaptively adjusts based on the real-time severity of voltage deviation and reactive power deficit to ensure the balance of optimization objectives under different operating conditions. The specific adaptive adjustment mechanism is as follows: 1. Weighting coefficient for voltage deviation According to voltage deviation The real-time severity is dynamically adjusted using a piecewise linear adjustment strategy, with the following adjustment formula: ; in, =0.4 (Base weight coefficient, representing the initial priority of voltage deviation under normal operating conditions). =2.0 (voltage deviation sensitivity coefficient). =0.05 (voltage deviation threshold, i.e., 5%, adjustment is triggered when the voltage deviation exceeds 5%). For example, when monitored When =0.08 (i.e., 8%): .

[0064] 2. Weighting coefficients for three-phase imbalance Based on three-phase imbalance The severity is adjusted using an exponential growth adjustment strategy, with the following formula: ; in, =0.2 (benchmark weight coefficient); =0.8 (Disequilibrium Sensitivity Coefficient); =0.15 (imbalance threshold, i.e., 15%).

[0065] For example, when monitored When =0.25 (i.e., 25%): .

[0066] For example, The advantage of the function in adaptive adjustment is that it can be used through the imbalance sensitivity coefficient. Flexible control The sensitivity of the function. For example, increasing... It will make More sensitive to changes in imbalance, it is suitable for scenarios with higher requirements for three-phase balance. The function is a continuously differentiable function, ensuring Value follows The changes are smoothed out, avoiding abrupt changes in control commands, which is conducive to stable equipment operation.

[0067] 3. Weighting coefficient for reactive power deficit According to reactive power deficit The relative severity is adjusted using a saturation function strategy, with the following formula: ; in, =0.2 (benchmark weight coefficient); =0.3 (maximum adjustment range); =100kVar (maximum reactive power compensation capacity of the system).

[0068] For example, when monitored (When there is a power shortage): .

[0069] For example, in the reactive power deficit weighting coefficient In the adaptive adjustment mechanism, the hyperbolic tangent function It plays a crucial role as a saturation function, that is, when the reactive power deficit is small. The function output grows approximately linearly; when the reactive power deficit approaches or exceeds the system's maximum compensation capacity, the output tends to saturate at a value of 1, preventing... It can grow indefinitely.

[0070] In some implementations, after obtaining the adjusted values ​​of each weight coefficient, the system performs normalization to ensure that the sum of all weight coefficients is 1. This process ensures that the relative weight ratios of each optimization objective are reasonable during multi-objective optimization, preventing deviations in the optimization direction due to excessively large individual weight coefficients. The specific normalization formula is as follows: ; ; ; ; ; in, This represents the sum of the adjusted values ​​of the five weighting coefficients. This sum serves as a standardization factor to ensure that the sum of the coefficients after normalization is 1. This represents the normalized voltage deviation weighting coefficient; This represents the normalized weighting coefficient for the three-phase imbalance. This represents the weighting coefficient for the normalized harmonic distortion rate; This represents the normalized reactive power deficit weighting coefficient; This represents the normalized temperature constraint weighting coefficient.

[0071] For example, suppose the following weight coefficient values ​​are obtained after adaptive adjustment: , , , , The normalized values ​​calculated respectively are ; ; ; ; .

[0072] In some implementations, an optimization algorithm is used to solve a multi-objective optimization function to obtain a set of optimal decision variables, which are then converted into a corresponding set of control instructions. The optimization algorithm is a multi-objective particle swarm optimization algorithm. The decision variables include the tap position setting of the on-load tap-changing transformer, the switching capacity of the reactive power compensation equipment, and the current output reference value of the power electronic compensation module. The control instruction set is generated from the conversion of the decision variables and includes: an on-load tap-changing transformer adjustment instruction, used to adjust the transformer ratio according to the voltage optimization results; a reactive power compensation equipment switching instruction, used to accurately switch capacitor banks according to the reactive power deficit optimization results; and a power electronic compensation module current output instruction, used to dynamically adjust the output waveform and amplitude of the compensation current according to the current imbalance, harmonic distortion rate, and ambient temperature.

[0073] Explanation of the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm: In multi-objective optimization, we need to optimize multiple objectives simultaneously (such as voltage deviation, harmonic mitigation, and reactive power compensation), but these objectives often conflict with each other (for example, reducing voltage deviation may increase harmonics). The MOPSO algorithm simulates the foraging behavior of a flock of birds, searching for a set of "compromise solutions" (Pareto optimal solutions) through swarm intelligence, rather than a single optimal solution. In this way, the algorithm can output a set of decision variables (such as gear setting values, switching capacity, etc.) to allow the system to achieve a balance among different objectives.

[0074] In some implementations, the MOPSO algorithm is used to solve a multi-objective optimization function that prioritizes voltage deviation while also considering three-phase imbalance, harmonic distortion rate, and reactive power deficit. The algorithm simulates swarm intelligence behavior to search for a Pareto optimal solution set in the solution space, ultimately outputting a set of optimal decision variables (including the tap setting value of the on-load tap-changing transformer, the switching capacity of the reactive power compensation equipment, and the current output reference value of the power electronic compensation module). The specific implementation process is as follows: 1. Algorithm Initialization Particle encoding: Each particle represents a possible solution, and its position vector is encoded as a three-dimensional decision variable: particle position. ; in, This indicates the tap setting value of the on-load tap-changing transformer (discrete integer, range 1-10, each tap corresponding to a voltage adjustment step of 0.625%). This indicates the switching capacity of the reactive power compensation equipment (continuous value, range 0-500kVar). This represents the current output reference value of the power electronic compensation module (complex form, including amplitude 0-100A and phase 0-360°), for example: This indicates a gear setting of 5, a switching capacity of 150kVar, a current output of 5A, and a phase of 30°.

[0075] Population initialization: Randomly generate a particle swarm of size N (usually N=100). The initial value of each variable is randomly generated within the limits allowed by the device, for example: , , ; At the same time, initialize the particle velocity vector. Or small random values ​​to ensure diversity in the initial search; Parameter settings: Inertia weight Using a linear decreasing strategy, starting from the initial value Approaching The updated formula is as follows ; in, This represents the current iteration number. This represents the maximum number of iterations. The acceleration constant is set to To balance individual particle experience and social learning. External archives Initialized to empty, it is used to store non-dominated solutions. The archive set size is limited to 100 solutions, and diversity is maintained through crowding distance.

[0076] 2. Iterative optimization process Fitness evaluation: For each particle, calculate its fitness value (i.e., the multi-objective function value). For example, for particles Substitute into the multi-objective optimization function The weighting coefficient , , , as well as Based on real-time data adaptive adjustment (such as ambient temperature) At temperatures above 45°C, increase (To enhance equipment protection), the smaller the fitness value, the better the solution.

[0077] Pareto Domination Comparison and Archive Set Update: Comparing dominance relationships between particles using a fast non-dominated sorting algorithm. Particles Dominate Non-dominated solutions are added to the external archive set if and only if they are no worse than other solutions on all objectives and are better than other solutions on at least one objective. The crowding distance algorithm (which calculates the density of each solution in the target space) is used to maintain the diversity and distribution of the set, and solutions with small crowding distances are periodically removed.

[0078] Particle Update: Adjust particle position and velocity according to the standard MOPSO update rules. The velocity update formula is as follows: ,in, , for Random numbers within a range This is the optimal position in the particle's history. It is a globally guided particle selected from the archive based on crowding distance; Represents particles exist The velocity vector at the next iteration; Represents particles In the The current velocity vector at the next iteration; Represents particles exist The position vector at each iteration is encoded as a decision variable (such as gear setting value, switching capacity, etc.). Indicates the current iteration number; The position update formula is ; After the update, perform boundary checks (such as gear position clamping). Within the range, the cutting capacity clamp is in kVar) ensures the validity of the solution.

[0079] Represents particles exist The position vector at the next iteration; Termination condition: The algorithm reaches the maximum number of iterations. The process terminates when the archive set improvement rate falls below a threshold (e.g., the Pareto front change is less than 0.1% over 10 consecutive iterations).

[0080] 3. Output of decision variables After the algorithm terminates, from the archive set The solution with the largest congestion distance is selected as the final decision variable, which is a set of optimal gear setting values, switching capacity, and current output reference values. For example, the output may be gear setting value = -2 (indicating reduced voltage), switching capacity = 120kVar, and current output reference value = 4∠40°A (indicating compensation current amplitude of 4A and phase of 40°).

[0081] In some implementations, decision variables need to be converted into a set of control instructions that the device can execute, and precise control is achieved through protocol conversion and logical mapping: On-load tap-changing transformer adjustment command generation: The gear setting value (e.g., -2) is converted into a specific control command through a predefined gear-voltage mapping table. The mapping table is set based on the transformer model (e.g., gear -2 corresponds to a voltage adjustment of -2.5%). The command is encapsulated using the Modbus RTU protocol, and the format includes <device address>, <function code>, <register address>, <data>, and <CRC check>. For example: command frame 01 06 00 01 FF FE 39 CD (where FF FE represents the hexadecimal complement of -2). Before the command is issued, a safety check is performed (e.g., checking whether the gear is in the correct position). Within the specified range and in the online status of the transformer, and through timing control (such as a minimum interval of 500ms for gear switching) to prevent equipment wear.

[0082] Reactive power compensation equipment switching command generation: The switching capacity (e.g., 120 kVar) is converted into a specific switching combination command; based on the capacitor bank configuration (e.g., each bank has a capacity of 30 kVar), the number of banks to be switched is calculated (120 / 30=4 banks); the command generates a digital output signal (e.g., binary code 00001111 indicates switching banks 1-4), which controls the contactor action through the I / O module, and integrates timing control (e.g., a 100ms switching interval for each bank to avoid inrush current) and protection logic (e.g., automatically disconnecting capacitors in case of overvoltage).

[0083] The power electronic compensation module generates current output commands: The current output reference value (e.g., 4∠40°A) is converted into a drive signal through a PWM modulation algorithm (e.g., Space Vector Modulation SVPWM). First, the reference value is decomposed into a three-phase modulated wave. Then, the duty cycle and timing of the switching devices are calculated. The instructions are encapsulated via EtherCAT or CANopen protocols, containing waveform parameters (amplitude, phase, frequency), and integrating environmental temperature constraints (such as when...). At >45°C, the amplitude is reduced by 20% according to the temperature-current derating curve.

[0084] Application Example 1: Cooperative Optimization Control under Typical Operating Conditions Scenario Description: The ambient temperature in the high-permeability test area is 25°C, and a voltage deviation is detected. Three-phase imbalance Harmonic distortion rate reactive power deficit The weighting coefficients are adaptively adjusted to... , , , , .

[0085] I. Algorithm Solution Process 1. MOPSO parameter settings: Population size N=100, maximum number of iterations Inertial weight Linear decrease: ; Particle coding: For example, the initial particle ; 2. Key Steps of Iterative Optimization For particles Calculate its multi-objective function value : ; At 25°C .

[0086] Non-dominated sorting: The Pareto solution set is selected using a fast non-dominated sorting algorithm (refer to the NSGA-II standard procedure), for example, the solution... because Smaller values ​​are marked as non-dominated solutions.

[0087] Particle Update: Based on the Global Optimal Solution Update particle velocity For example, new location =[4, 110, 4.5∠40°].

[0088] Termination condition: The algorithm terminates when the rate of change of the Pareto front is less than 0.1% after 150 iterations.

[0089] 3. Output of decision variables Optimal solution: Gear setting value = -1 (voltage reduced by 1.25%), switching capacity = 110 kVar, current output = 4.5∠40°A.

[0090] II. Control Instruction Set Conversion: On-load tap-changing transformer instruction: tap position -1 is mapped to Modbus instruction frame 01 06 00 01 FF FF 48 0A, and sent out after verification.

[0091] Reactive power compensation equipment instruction: The switching capacity of 110 kVar is converted to switching 3 groups of capacitors (30 kVar per group, totaling 90 kVar, with the remaining 20 kVar dynamically supplemented by the power electronic compensation module), and the output switching signal is 00000111.

[0092] Power electronic compensation module instruction: Current reference value 4.5∠40°A. Calculate the duty cycle using the SVPWM algorithm, generate the drive waveform, and send the parameter set {amplitude=4.5A, phase=40°, frequency=50Hz} via the CAN bus.

[0093] Results: Voltage deviation reduced to ±0.8%, harmonic distortion rate... The response time is 400ms.

[0094] Application Example 2: Adaptive Derating Control in High-Temperature Scenarios Scenario Description: Ambient temperature rises to 48°C (exceeding the high temperature threshold of 45°C), voltage deviation... No functional deficit The weighting coefficients are adaptively adjusted to... (Strengthen temperature constraints). , , , .

[0095] I. Algorithm Solution Process 1. Temperature constraint integration: Equipment operation constraint functions The maximum output of the equipment is limited to 80% of the rated value; at the same time, the current amplitude range in the particle coding is adjusted to 0-80A (originally 0-100A).

[0096] 2. MOPSO optimization: Add temperature boundary checks during particle updates, for example, for particles. Corrected due to current amplitude exceeding the limit. Meanwhile, in archive set maintenance, temperature-adaptive solutions (such as...) are given priority. When the values ​​are similar, choose the solution with the lower current amplitude.

[0097] 3. Decision variable output: The optimal solution is gear setting value = -2 (voltage reduction of 2.5%), switching capacity = 120 kVar, current output = 4∠35°A (amplitude derating of 20%).

[0098] II. Control Instruction Set Conversion Temperature adaptive command: On-load tap-changing transformer: Tap position -2 instruction superimposed with temperature protection logic, instruction frame adds temperature flag bit TEMP_HIGH.

[0099] Power electronic compensation module: Based on the temperature-current derating curve (output current limit is 80% at 48°C), the 4A amplitude is converted to an actual output of 3.2A, and the PWM duty cycle is adjusted accordingly.

[0100] Results: The equipment temperature stabilized within a safe range, the voltage deviation decreased to ±1.2%, and the treatment efficiency remained above 85%.

[0101] Application Example 3: Refined Compensation in Severe Harmonic Scenarios Scenario Description: Harmonic Distortion Rate (Mainly the 5th harmonic), three-phase imbalance The ambient temperature is 20°C. The weighting coefficients are adjusted to... (Strengthen harmonic control) , , , .

[0102] I. Algorithm Solution Process 1. Harmonic-specific optimization: In multi-objective functions Weighting is increased, and particle encoding adds a harmonic order dimension (e.g., expanding the current reference value to...). (This refers to the 5th harmonic); Meanwhile, during MOPSO fitness calculations, The components are weighted by the harmonic spectrum (e.g., the 5th harmonic has a weight of 70%).

[0103] 2. Iterative optimization: During particle swarm initialization, the current phase is biased towards the harmonic compensation angle (e.g., the 5th harmonic compensation phase is biased towards 180°).

[0104] In the Pareto front, the preferred choice is... Solutions with large reductions, such as solution because Reducing it to 5% was selected as the optimal value.

[0105] 3. Output of decision variables: Optimal solution: Gear setting value = +1 (voltage fine adjustment +1.25%), switching capacity = 80 kVar, current output = 3∠180°A (for 5th harmonic phase compensation).

[0106] II. Control Instruction Set Conversion 1. Harmonic-specific instructions, including the power electronic compensation module: current instructions. Convert to 5th harmonic injection command, set PWM carrier frequency to 5kHz (50Hz higher than fundamental frequency), generate reverse harmonic current to cancel load harmonics.

[0107] Reactive power compensation equipment: The switching capacity of 80 kVar works in conjunction with harmonic compensation to avoid harmonic amplification caused by capacitor banks.

[0108] Effect: Harmonic distortion rate The three-phase imbalance decreased from 12% to 4.5%. <6%.

[0109] In some implementations, the current output command of the power electronic compensation module is also constrained by the ambient temperature. Specifically, a temperature-current derating curve is established. When the ambient temperature exceeds the safe operating temperature of the equipment, the amplitude of the output current command is reduced proportionally. When the ambient temperature is low, the rate of change of the output current is appropriately increased within the allowable range of the equipment.

[0110] The temperature-current derating curve defines the mathematical relationship between ambient temperature and current output capability, its core purpose being to prevent equipment overheating and optimize low-temperature performance. The curve is based on the equipment's thermal characteristics and calibrated using experimental data, and is implemented as follows: Curve Formula: The reduction curve is represented by a piecewise linear function, and the formula is as follows: ; in: The maximum allowable output current amplitude at the current temperature (unit: Ampere A); The rated output current of the equipment (e.g., 100A) is set based on the equipment specifications. Real-time ambient temperature (unit: degrees Celsius °C), collected by a temperature sensor; The safe operating temperature threshold for the equipment (typically 45°C) is when Time-based reduction of credit limit; The low temperature threshold (typically 0°C) is when The rate of change can be increased at times; This is the high-temperature derating factor (typical value 0.02 / °C), which means that the current amplitude decreases by 2% for every 1°C exceeding the safe temperature. The low-temperature enhancement factor (typical value 0.01 / °C) indicates that for every 1°C below the low-temperature threshold, the rate of change of current increases by 1% (but does not exceed the equipment limit).

[0111] Curve calibration: Curve parameters are calibrated through equipment thermal testing. For example, the junction temperature-current relationship based on IGBT (Insulated Gate Bipolar Transistor) modules (detailed junction temperature-current derating curves are available in the corresponding product datasheets) ensures that the operating temperature is always below the maximum junction temperature (e.g., 150°C). Calibration data is stored in system memory for real-time retrieval.

[0112] In some implementations, the system collects the ambient temperature in real time when generating the current output command. And adjust the instruction parameters based on the derating curve, the specific steps are as follows: 1. Amplitude Adjustment: When At this time, the output current amplitude is reduced proportionally. For example, if the current reference value output by the optimization algorithm is... But the current temperature Then calculate the maximum allowable current: ; in, Indicates the reference value for current output (unit: A); because The amplitude does not need to be adjusted; however, The instruction amplitude is then clamped to 90A; The adjusted amplitude is used to generate a PWM modulation signal to ensure that the device is not overloaded.

[0113] 2. Rate of Change Adjustment: The rate of change refers to the slope of the current output waveform (unit: A / s), which affects the response speed. When... At times, appropriately increasing the rate of change can improve governance efficiency. For example, in When the rate of change is adjusted, the coefficient is: ; This means the rate of change increases by 5%. When the standard rate of change is 100 A / s, the adjusted rate is 105 A / s. The rate of change is adjusted by modifying the slope parameter of the PWM modulation algorithm, ensuring that the output waveform is smooth and fast.

[0114] 3. Real-time Integration: The adjustment mechanism is embedded in the control command generation module. Before generating each current output command, the system queries temperature data and calculates... and Ensure that the instructions comply with temperature constraints.

[0115] For example, if the ambient temperature of a certain high-permeability area... (Exceeding safe temperature), the current reference value output by the optimized algorithm is [value missing]. (Amplitude 80A, phase 30°). The process of generating the current output command is as follows: Step 1: Temperature Inquiry and Derating Calculation The system reads temperature sensor data. Query the deduction curve: ; because The amplitude does not need to be reduced, but the system records the current reduction status.

[0116] Step 2, Adjustment of rate of change because The rate of change remains at the standard value (no increase), with a default rate of change of 100 A / s.

[0117] Step 3: Command Generation and Issuance Current command The signal is converted into a PWM drive signal using the SVPWM algorithm (duty cycle calculated based on amplitude and phase), and then sent to the power electronics compensation module via the CAN bus. The command frame includes temperature flags, such as TEMP_HIGH, for internal monitoring within the module.

[0118] Step 4: Execution and Monitoring After the module outputs current, the system monitors the equipment temperature and harmonic mitigation effect in real time. If the temperature rises to 50°C, the system recalculates. And dynamically adjust subsequent instructions.

[0119] For example, when the ambient temperature At that time, the current reference value output by the optimization algorithm is ; ; because The amplitude remains unchanged, while the rate of change increases by 5%, from 100 A / s to 105 A / s, accelerating the harmonic compensation response. At low temperatures, the improved heat dissipation of the equipment allows the increased rate of change to enable the compensation current to track load changes more quickly, improving governance efficiency (e.g., reducing harmonic suppression response time by 10%).

[0120] Example 2 Figure 2 This is a schematic diagram of the high-permeability substation power quality collaborative optimization device 100 provided in Embodiment 2 of the present invention, as shown below. Figure 2 As shown, the high-permeability substation power quality collaborative optimization device 100 provided in Embodiment 2 of the present invention includes a data acquisition module 110, a calculation module 120, an optimization module 130, a conversion module 140, and an execution module 150. Among them, the acquisition module 110 is used to acquire the three-phase voltage, three-phase current and ambient temperature data of the low-voltage side outgoing line in the high-permeability substation in real time. The calculation module 120 calculates the voltage deviation, three-phase imbalance, total harmonic distortion rate, and reactive power deficit of the high-permeability area based on the three-phase voltage and three-phase current data. The optimization module 130 takes voltage deviation as the primary optimization objective, while also considering the optimization of three-phase imbalance, total harmonic distortion rate and reactive power deficit. It constructs a multi-objective optimization function and dynamically constrains the optimization process based on ambient temperature data. The conversion module 140 uses an optimization algorithm to solve the multi-objective optimization function, obtains a set of optimal decision variables, and converts the decision variables into a corresponding set of control instructions; The execution module 150 is used to send the control command set to the on-load tap-changing transformer, reactive power compensation equipment and power electronic compensation module for execution.

[0121] The calculation module 120 is responsible for calculating key power quality indicators for high-permeability transformer areas based on real-time collected three-phase voltage and three-phase current data, including voltage deviation, three-phase unbalance, total harmonic distortion, and reactive power deficit. The specific calculation method is as follows: The formula for calculating voltage deviation is: ; in, These are the measured effective values ​​of the phase voltages. The summation sign is the system nominal voltage value. This indicates the summation of all components of the three-phase voltage; The formula for calculating three-phase unbalance is: ; in, These are the measured effective values ​​of the current in each phase. The summation sign is given by the theoretical equilibrium current value. This indicates the summation of all components of the three-phase current; The formula for calculating the total harmonic distortion rate is as follows: ; in, For the first The amplitude of the second harmonic current. This is the effective value of the fundamental current. For harmonic orders, summation sign. This indicates the range from the second harmonic to the third harmonic. The current amplitudes of the subharmonics are accumulated; The formula for calculating reactive power deficit is as follows: ; in, The reactive power required by the load. This refers to the reactive power currently being replenished to the system.

[0122] Optimization module 130 prioritizes voltage deviation as the primary optimization objective, while also considering three-phase imbalance, harmonic distortion rate, and reactive power deficit. The constructed multi-objective optimization function is as follows: ; in, , , , as well as These are weighting coefficients used to coordinate the priorities of different optimization objectives; For real-time collection of ambient temperature data; For The device operation constraint function is used to limit the device output at high temperatures to prevent overload.

[0123] By optimizing module 130 , , , as well as A temperature-sensitive dynamic adjustment mechanism is adopted and implemented through an adaptive algorithm, specifically including: At ambient temperature When the temperature exceeds the preset high temperature threshold, the weighting coefficient is automatically increased. The values ​​are set to enhance protection against equipment overheating; At ambient temperature When the temperature is below the preset low temperature threshold, the weighting coefficient is automatically increased. The value is used to enhance the intensity of harmonic control; , as well as It also adaptively adjusts based on the real-time severity of voltage deviation and reactive power deficit.

[0124] The conversion module 140 converts the decision variables into a set of control instructions that the device can execute. The decision variables include the tap setting value of the on-load tap-changing transformer, the switching capacity of the reactive power compensation device, and the current output reference value of the power electronic compensation module.

[0125] The control instruction set is generated by transforming decision variables and includes: On-load tap-changing transformer adjustment commands are used to adjust the transformer turns ratio based on voltage optimization results; The reactive power compensation equipment switching command is used to accurately switch capacitor banks based on the reactive power deficit optimization results; The current output command of the power electronic compensation module is used to dynamically adjust the output waveform and amplitude of the compensation current according to the current imbalance, harmonic distortion rate and ambient temperature.

[0126] The current output command of the power electronic compensation module is also constrained by the ambient temperature, specifically: By optimizing module 130, a temperature-current derating curve is established. When the ambient temperature exceeds the safe operating temperature of the equipment, the amplitude of the output current command is reduced proportionally. When the ambient temperature is low, the rate of change of the output current should be appropriately increased within the allowable range of the equipment.

[0127] Example 3 Figure 3 This is a schematic diagram of the hardware structure of the device provided in Embodiment 3 of the present invention. The device is an electronic device. Figure 3 A block diagram is shown of an exemplary electronic device 12 suitable for implementing embodiments of the present invention. Figure 3 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0128] like Figure 3 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0129] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0130] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0131] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as "hard disk drives"). Disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disk drives for reading and writing to removable non-volatile optical disks (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0132] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0133] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the electronic device 12 / server / computer, and / or with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 3 As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0134] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the high-permeability substation power quality collaborative optimization method provided in the embodiments of the present invention.

[0135] Example 4 Embodiment 4 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the high-permeability substation power quality collaborative optimization method provided in the above embodiments.

[0136] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0137] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0138] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0139] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A high-penetration substation power quality collaborative optimization method, characterized in that, The method comprises the following steps: S1, collecting real-time three-phase voltage, three-phase current and ambient temperature data of the low-voltage side outgoing line of the high-infiltration area; S2, calculating the voltage deviation, three-phase imbalance, total harmonic distortion and reactive power deficiency of the high-infiltration area based on the three-phase voltage and three-phase current data; S3, taking the voltage deviation as the primary optimization target, considering the optimization of the three-phase imbalance, the total harmonic distortion and the reactive power deficiency, constructing a multi-objective optimization function, and dynamically constraining the optimization process based on the ambient temperature data; S4, solving the multi-objective optimization function by using an optimization algorithm to obtain a set of optimal decision variables, and converting the decision variables into corresponding control instruction sets; S5, issuing the control instruction sets to the on-load voltage regulating transformer, the reactive power compensation device and the power electronic compensation module for execution.

2. The high penetration area power quality co-optimization method of claim 1, wherein, In step S2, the voltage deviation, three-phase imbalance, total harmonic distortion and reactive power deficiency are calculated by the following method: The calculation formula of the voltage deviation is ; wherein, is the measured effective value of the phase voltage, is the nominal voltage value of the system, the summation symbol denotes the accumulation of all components of the three-phase voltage; The calculation formula of the three-phase unbalance degree is ; wherein, is the measured effective value of the phase current, is the current value at theoretical balance, the summation symbol denotes the accumulation of all components of the three-phase current; The formula for calculating the total harmonic distortion is ; wherein is the amplitude of the nth harmonic current, is the rms value of the fundamental current, is the harmonic number, and the summation sign denotes that the amplitudes of the current from the second harmonic to the nth harmonic are added up; The formula for calculating the reactive power deficiency is ; wherein, the reactive power required by the load, the reactive power currently supplied by the system.

3. The high-penetrated substation power quality collaborative optimization method according to claim 2, characterized in that, The multi-objective optimization function is: ； in, , , , as well as These are weighting coefficients used to coordinate the priorities of different optimization objectives; For real-time collection of ambient temperature data; For The device operation constraint function is used to limit the device output at high temperatures to prevent overload.

4. The high-penetrated substation power quality collaborative optimization method according to claim 3, characterized in that, , , , and Adopting a dynamic adjustment mechanism based on temperature sensitivity, and realized through adaptive algorithm, specifically including: At ambient temperature When the temperature is higher than a preset high temperature threshold, the weight coefficient is automatically increased in value to strengthen the protection against overheating of the device. When the temperature is higher than a preset high temperature threshold, the weight coefficient is automatically increased in value to strengthen the protection against overheating of the device. At ambient temperature When the temperature is below the preset low temperature threshold, the weighting coefficient is automatically increased. The value is used to enhance the intensity of harmonic control; 、 and Also adaptive adjustment is made according to real-time severity of the voltage deviation and the reactive power deficiency.

5. The high-penetrated substation power quality collaborative optimization method according to claim 4, characterized in that, The optimization algorithm is a multi-objective particle swarm optimization algorithm, and the decision variables include the gear setting value of the on-load voltage regulating transformer, the switching capacity of the reactive power compensation device and the current output reference value of the power electronic compensation module.

6. The high-penetrated substation power quality collaborative optimization method according to claim 5, characterized in that, The control instruction sets are generated by converting the decision variables, including: on-load voltage regulating transformer adjustment instructions for adjusting the transformer ratio according to the voltage optimization result; reactive power compensation device switching instructions for accurately switching the capacitor bank according to the reactive power deficiency optimization result; current output instructions of the power electronic compensation module for dynamically adjusting the output waveform and amplitude of the compensation current according to the current imbalance, harmonic distortion and ambient temperature.

7. The high-penetrated substation power quality collaborative optimization method according to claim 6, characterized in that, The current output instructions of the power electronic compensation module are also constrained by the ambient temperature, specifically: establishing a temperature-current derating curve, when the ambient temperature exceeds the safe operating temperature of the device, the amplitude of the output current instruction is reduced by a certain proportion; when the ambient temperature is low, appropriately increase the change rate of the output current within the allowable range of the device.

8. A high-penetrated substation power quality collaborative optimization device, characterized in that, The method comprises the following steps: a collection module for collecting real-time three-phase voltage, three-phase current and ambient temperature data of the low-voltage side outgoing line of the high-infiltration area; a calculation module for calculating the voltage deviation, three-phase imbalance, total harmonic distortion and reactive power deficiency of the high-infiltration area based on the three-phase voltage and three-phase current data; an optimization module for taking the voltage deviation as the primary optimization target, considering the optimization of the three-phase imbalance, the total harmonic distortion and the reactive power deficiency, constructing a multi-objective optimization function, and dynamically constraining the optimization process based on the ambient temperature data; a conversion module for solving the multi-objective optimization function by using an optimization algorithm to obtain a set of optimal decision variables, and converting the decision variables into corresponding control instruction sets; an execution module for issuing the control instruction sets to the on-load voltage regulating transformer, the reactive power compensation device and the power electronic compensation module for execution.

9. An apparatus, comprising: The method comprises the following steps: a processor, and a memory connected to the processor in communication; the memory stores computer execution instructions; The processor executes the computer-executable instructions stored in the memory to implement the high-penetration substation power quality collaborative optimization method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions, when executed by a processor, implement the high-penetration substation power quality collaborative optimization method in any one of claims 1 to 7.

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