Power transformer life loss optimization method considering bidirectional power flow switching

Abstract

The invention relates to a power transformer life loss optimization method considering bidirectional power flow switching, and belongs to the field of distribution network optimization control, and the method comprises the steps: S1, building a transformer three-dimensional high-order safe operation domain omega: taking a relative aging rate V < = 1.5 as an evaluation index, selecting a load rate and a current harmonic distortion rate as key input parameters, and carrying out the optimization of the three-dimensional high-order safe operation domain omega; establishing a transformer three-dimensional safe operation domain of the current harmonic distortion rate, the transformer load rate and the relative aging rate; s2, configuring a distribution network optimization control device; s3, establishing an optimization model for minimizing the life loss of the transformer; s4, setting a multi-dimensional operation constraint condition; s5, solving the objective function in the step S3 by adopting a particle swarm optimization algorithm, and realizing theoretical calculation, simulation verification and iterative optimization in combination with an OpenDSS simulation platform; transformer aging is accurately inhibited, and the service life of equipment is prolonged; an optimization model with the aim of minimizing the relative aging rate is established, and the problem of aging acceleration caused by bidirectional power flow switching is effectively reduced in combination with multi-device cooperative control.

Description

Technical Field

[0001] This invention belongs to the field of distribution network optimization and control technology, and relates to an optimization method for power transformer life loss considering bidirectional power flow switching. Background Technology

[0002] Driven by the goals of "carbon peaking and carbon neutrality," my country is actively promoting the green and low-carbon transformation of its energy system. A new power system centered on new energy sources is gradually becoming the main direction of power development. Compared to centralized grid connection, distributed renewable energy integration has seen particularly significant growth in recent years. With the large-scale grid connection of distributed renewable energy, the operating status and load characteristics of power transformers in the distribution network are significantly affected, and operational challenges are becoming increasingly prominent.

[0003] As the penetration rate of renewable energy increases, its power generation cannot always be fully absorbed locally, easily leading to backflow of electricity and causing reverse power flow in the distribution network. Due to the significant randomness and volatility of renewable energy output, the power flow direction may frequently switch within a day, resulting in bidirectional power flow characteristics in the grid. Bidirectional power flow switching refers to the phenomenon where the power flow direction in the distribution network alternates at different times due to the randomness and volatility of its output power after a high proportion of distributed renewable energy sources are connected to the distribution network. When the output of distributed power sources is high, some nodes may backflow electricity to the upper-level grid, forming a reverse power flow; while when the output of distributed power sources is low or the load is high, the power flow direction reverts to supplying power from the upper-level grid to the load side, i.e., a forward power flow. With the dynamic changes in renewable energy output and load levels, the power flow direction will frequently switch between forward and reverse, forming the "bidirectional power flow switching" operating characteristic.

[0004] In addition, the large-scale integration of distributed renewable energy sources will exacerbate problems such as harmonic pollution, voltage fluctuations, and increased network losses, adversely affecting the safe, stable operation and economic efficiency of the distribution network.

[0005] However, many studies have not fully considered the impact of reverse power flow on distribution network transformers. At the transformer level, frequent switching between bidirectional power flows can lead to frequent voltage fluctuations and significant harmonic pollution on the low-voltage side of the transformer, resulting in frequent operation of on-load tap changers and increased transformer losses, accelerating transformer aging and ultimately shortening transformer lifespan. This issue has not been thoroughly explored in existing research. Summary of the Invention

[0006] In view of this, in order to solve the problem that frequent switching of bidirectional power flow may lead to frequent voltage fluctuations and a large amount of harmonic pollution on the low-voltage side of the transformer, resulting in frequent operation of the on-load tap changer and increased transformer losses, which accelerates transformer aging and shortens the transformer life, the present invention provides an optimization method for power transformer life loss considering bidirectional power flow switching.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An optimization method for power transformer life loss considering bidirectional power flow switching includes the following steps:

[0009] S1. Constructing a three-dimensional high-order safe operation domain Ω for transformers: Using the relative aging rate V≤1.5 as the evaluation index, and taking into account both short-term minor overload and long-term life balance, load rate and current harmonic distortion rate are selected as key input parameters to establish a three-dimensional safe operation domain for transformers based on current harmonic distortion rate, transformer load rate, and relative aging rate; the critical isosurface of the relative aging rate is characterized by the level set method, and the boundary is fitted by convex approximation technology to obtain the mathematical expression of the safe operation domain, thus clarifying the parameter combination range for safe operation of transformers;

[0010] S2. Configure distribution network optimization control equipment: In distribution networks containing distributed new energy sources, configure four types of key control equipment, including power transformers with on-load tap changers, reactive power compensation devices, energy storage systems, and passive filters, to provide hardware support for subsequent collaborative optimization.

[0011] S3. Establish an optimization model to minimize transformer life loss: with the objective function being the relative aging rate function F. V (x), where The decision variables include the operating status parameters of the four types of control equipment in step S2. The parameters are: the tap position of the on-load tap changer, the charging and discharging power value of the energy storage system, the reactive power compensation output value, and the number of filters switched on and off.

[0012] S4. Set multi-dimensional operating constraints: Based on the relative aging rate in step S1, integrate the safety and equipment operation constraints of the distribution network to form an optimization boundary, including configuring the voltage limit of all network nodes, the voltage harmonic distortion rate limit, the daily operation limit of on-load tap changers and filters, and the three-dimensional safe operation domain limit of transformers in step S1.

[0013] S5. The objective function in step S3 is solved using the particle swarm optimization algorithm, and theoretical calculations, simulation verification, and iterative optimization are performed using the OpenDSS simulation platform.

[0014] Furthermore, step S1, which involves constructing the three-dimensional safe operation domain of the transformer, specifically involves:

[0015] According to IEEE Std C57.110, transformer load loss P LL Due to resistive loss P R Eddy current loss P EC and stray loss P OSL The transformer load losses, considering harmonic effects, are as follows:

[0016] (1)

[0017] In the formula, I is the actual current value of the transformer, I N F is the rated current of the transformer. HL-R P is the harmonic loss factor of the winding resistance. R-R For rated resistance loss, F HL-EC P is the winding eddy current harmonic loss factor. EC-R For rated eddy current losses, F HL-OSL P is the stray harmonic loss factor. OSL-R The rated stray loss; where the winding resistance harmonic loss factor F HL-R Winding eddy current harmonic loss factor F HL-EL and stray harmonic loss factor F HL-OSL As shown in the following formula:

[0018] (2)

[0019] In the formula, h is the harmonic order, h max For the maximum harmonic order, I (h) ) represents the h-th harmonic current, I (1) It is the fundamental current;

[0020] Referring to IEC 60076-7, the formula for calculating transformer winding hot spots is based on the ambient temperature θ. a Temperature rise θ of the top fluid relative to the environment TO Temperature rise θ of hot spot relative to top liquid g It consists of three parts; the load loss P in Formula 1 is... LL Substituting the no-load loss PNL into the temperature rise θ TO and θ g The calculation formula is as follows:

[0021] (3)

[0022] In the formula, θ TO-R θ represents the temperature rise of the transformer top liquid under rated conditions. g-R P represents the temperature rise of the hot spot relative to the top liquid under rated conditions. LL-R This represents the load and no-load loss of the transformer under rated conditions.

[0023] Furthermore, the relative aging rate V uses the hot spot temperature as an intermediate variable, and Formula 3 provides the temperature parameter; the derivation of the relative aging rate V and thermal life loss D of the transformer considering bidirectional power flow switching is as follows:

[0024] (4)

[0025] (5)

[0026] In the formula, θ h V represents the hot spot temperature of the transformer winding, t1 represents the initial aging time of the calculated lifespan, t2 represents the end time of the calculated lifespan, and V... n Let t be the relative aging rate of the nth interval. n Let n be the time interval.

[0027] Furthermore, the load factor in step S1 characterizes the load current and distinguishes the power flow direction; the load factor on the low-voltage side of the transformer... as follows:

[0028] (6)

[0029] In the formula, P is the active power value on the low-voltage side of the transformer, and S... N Here, φ represents the rated capacity of the transformer, and cosφ represents the power factor on the low-voltage side of the transformer.

[0030] Furthermore, the safe operating domain Ω is defined to satisfy the relative aging rate threshold. For all parameter combinations ≤1.5, the convexity description of the safe operating domain Ω is as follows:

[0031] (7)

[0032] In the formula, This indicates the harmonic content of the current.

[0033] Furthermore, the voltage value V at node i in the distribution network is defined. i With voltage harmonic content (THD) v-i It should be kept within a safe range;

[0034] Configure voltage limits for all nodes in the network:

[0035] (8)

[0036] Harmonic distortion rate limit:

[0037] (9)

[0038] Maximum number of daily operations of on-load tap changers:

[0039] (10)

[0040] Daily maximum switching limit for filters:

[0041] (11)

[0042] In the formula, V min and V maxThese are the upper and lower limits of the voltage value. n is the upper limit of harmonic content. OLTC,tmax and n PF,tmax These are the maximum number of on-load tap changers and filters operated within a day, respectively.

[0043] Furthermore, the boundary of the transformer's safe operating domain is used as a constraint for distribution network optimization. Referring to Formula 7, the constraint conditions for the transformer's safe operating domain under bidirectional power flow switching are as follows:

[0044] (12).

[0045] Furthermore, the combination of the particle swarm optimization algorithm and the OpenDSS simulation platform in step S5 is specifically as follows:

[0046] In each iteration, OpenDSS is called to perform power flow analysis, obtain system operating parameters and performance indicators, and evaluate the constraints and fitness function values, i.e., the relative aging rate.

[0047] Furthermore, step S5 specifically includes:

[0048] S51. Initialize the basic data of the distribution network and the parameters of the particle swarm algorithm, including the number of particles, the maximum number of iterations, the learning factor, and the inertia weight, etc.

[0049] S52. Initialize the initial position and velocity of each particle, and set the iteration counter. =0;

[0050] S53. Apply the current particle's position parameters to the power distribution network model, call the OpenDSS simulation tool to perform power flow calculations, and obtain the system operating parameters;

[0051] S54. Calculate the fitness value of each particle based on the power distribution network operating parameters and constraints to evaluate its optimization effect;

[0052] S55. Update the individual best position (pbest) and global best position (gbest) of each particle, and record the current best solution;

[0053] S56. Based on the velocity and position update formula of the particle swarm optimization algorithm, adjust the velocity and position of each particle, and then let... ;

[0054] S57. Determine whether the preset maximum number of iterations has been reached; if not, return to step S53 to continue iterating; if so, execute the following step S58.

[0055] S58. Output the final optimization result and apply the optimal operating scheme to the distribution network control.

[0056] The beneficial effects of this invention are as follows:

[0057] 1. The present invention discloses an optimization method for power transformer life loss under bidirectional power flow switching, which accurately suppresses transformer aging and extends equipment life. By quantifying the impact of harmonics and temperature rise on transformer aging, an optimization model is established with the goal of minimizing the relative aging rate. Combined with multi-device collaborative control, the aging acceleration problem caused by bidirectional power flow switching is effectively reduced. A three-dimensional safe operating domain for the transformer is established, which includes current harmonic distortion rate, transformer load rate, and relative aging rate. Key operating parameters are constrained within safe thresholds, suppressing low-voltage side voltage fluctuations, suppressing low-voltage side voltage fluctuations and harmonic pollution, reducing frequent operation of on-load tap changers, and lowering the risk of equipment failure.

[0058] 2. The present invention discloses an optimization method for power transformer life loss under bidirectional power flow switching, which improves system safety and stability; by using energy storage system to smooth out the power output fluctuations of new energy sources and reactive power compensation device to adjust the power factor, the frequency and duration of power flow switching are reduced, the impact of bidirectional power flow on the distribution network is mitigated, and the safe and stable operation of the distribution network is ensured.

[0059] 3. The present invention discloses an optimization method for the life loss of power transformers under bidirectional power flow switching, which reduces the total life cycle cost. On the one hand, it reduces the direct cost of transformer replacement and maintenance, and on the other hand, it reduces operation and maintenance costs by optimizing equipment operating status, while reducing distribution network losses and improving energy utilization efficiency.

[0060] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0062] Figure 1 This is a schematic diagram illustrating the changes in current harmonic distortion rate, load factor, and relative aging rate of the transformer in this invention.

[0063] Figure 2 This is a schematic diagram of the safe operation domain of the transformer in this invention;

[0064] Figure 3 This is a flowchart illustrating the solution process of combining the particle swarm optimization algorithm with the OpenDSS simulation platform in this invention.

[0065] Figure 4This is a schematic diagram showing the configuration of the energy storage system at each node in this invention. Detailed Implementation

[0066] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0067] An optimization method for power transformer life loss considering bidirectional power flow switching includes the following steps:

[0068] S1. Constructing a three-dimensional high-order safe operation domain Ω for transformers: Using the relative aging rate V≤1.5 as the evaluation index, and taking into account both short-term minor overload and long-term lifespan balance, load rate and current harmonic distortion rate are selected as key input parameters to establish a three-dimensional safe operation domain for transformers based on current harmonic distortion rate, transformer load rate, and relative aging rate; the critical isosurface of the relative aging rate is characterized by the level set method, and the boundary is fitted by convex approximation technology to obtain the mathematical expression of the safe operation domain, thus clarifying the parameter combination range for safe operation of transformers.

[0069] Step S1, constructing the three-dimensional safe operating domain of the transformer, specifically involves:

[0070] According to IEEE Std C57.110, transformer load loss P LL Due to resistive loss P R Eddy current loss P EC and stray loss P OSL The transformer load losses, considering harmonic effects, are as follows:

[0071] (1)

[0072] In the formula, I is the actual current value of the transformer, I N F is the rated current of the transformer. HL-R P is the harmonic loss factor of the winding resistance. R-R For rated resistance loss, F HL-EC P is the winding eddy current harmonic loss factor. EC-R For rated eddy current losses, F HL-OSL P is the stray harmonic loss factor. OSL-R The rated stray loss is given; the winding resistance harmonic loss factor, winding eddy current harmonic loss factor, and stray harmonic loss factor are given by the following formulas:

[0073] (2)

[0074] In the formula, h is the harmonic order, h max For the maximum harmonic order, I (h) ) represents the h-th harmonic current, I (1) It is the fundamental current;

[0075] Referring to IEC 60076-7, the formula for calculating transformer winding hot spots is based on the ambient temperature θ. a Temperature rise θ of the top fluid relative to the environment TO Temperature rise θ of hot spot relative to top liquid g It consists of three parts; the load loss P in Formula 1 is... LL Substituting the no-load loss PNL into the temperature rise θ TO and θ g The calculation formula is as follows:

[0076] (3)

[0077] In the formula, θ TO-R θ represents the temperature rise of the transformer top liquid under rated conditions. g-R P represents the temperature rise of the hot spot relative to the top liquid under rated conditions. LL-R This represents the load and no-load loss of the transformer under rated conditions.

[0078] The relative aging rate V uses the hot spot temperature as an intermediate variable, and Formula 3 provides the temperature parameter. Taking 98℃ as the reference hot spot temperature, the relative aging rate V and thermal life loss D of the transformer considering bidirectional power flow switching are derived as follows:

[0079] (4)

[0080] (5)

[0081] In the formula, θ h V represents the hot spot temperature of the transformer winding, t1 represents the initial aging time of the calculated lifespan, t2 represents the end time of the calculated lifespan, and V... n Let t be the relative aging rate of the nth interval. n Let n be the time interval.

[0082] This invention establishes a safe operating domain for transformers under a bidirectional power flow switching mode. It constrains the real-time and long-term cumulative values ​​of key operating indicators within allowable safety thresholds and constructs a control strategy for distribution network operation with the optimization objective of minimizing the relative aging rate of the transformer. By analyzing the main factors affecting the transformer aging rate, the load current and current harmonic distortion rate are identified as key variables. The load current is characterized by the low-voltage side load rate, as shown in Formula 6 below. A positive value represents a positive power flow direction, and an amplitude value represents a negative power flow direction. Figure 1 This figure illustrates the change in the relative aging rate of a transformer as the current harmonic distortion rate and load factor change. As shown in the figure, the relative aging rate increases significantly with increasing load factor. (Transformer low-voltage side load factor) as follows:

[0083] (6)

[0084] In the formula, P is the active power value on the low-voltage side of the transformer, and S... N Here, φ represents the rated capacity of the transformer, and cosφ represents the power factor on the low-voltage side of the transformer.

[0085] Under rated load, the relative aging rate of a transformer is defined as 1. If the average aging rate remains below 1.0 during long-term operation, its lifespan can be considered within the normal range. Although short-term overloads may cause an increase in the aging rate, if compensated for during low-load periods, the cumulative aging level remains acceptable. Based on this, this invention sets the upper limit of the relative aging rate to 1.5 when constructing the safe operating domain, to balance short-term minor overloads with long-term lifespan, ensuring that the transformer maintains safe and reliable operation under frequent bidirectional power flow switching conditions. To characterize this process, load factor and current harmonic distortion rate are selected as key input parameters, with the relative aging rate... As an evaluation metric, a three-dimensional high-order safe operating domain function relationship for the transformer is established. The safe operating domain Ω is defined as satisfying the relative aging rate threshold. All parameter combinations ≤1.5. The critical isosurface of the relative aging rate is characterized using the level set method, and its boundary is further fitted using a convex approximation method to achieve a convex description of the safe operating domain, as shown in Equation 7 below. The transformer safe operating domain is composed of... Figure 2 It is evident that as the current harmonic content increases, the range of safe load rates that a transformer can tolerate gradually narrows.

[0086] (7)

[0087] In the formula, This indicates the harmonic content of the current.

[0088] S2. Configure distribution network optimization control equipment: In distribution networks containing distributed renewable energy sources, configure four types of key control equipment, including power transformers with on-load tap changers (OLTC), reactive power compensation devices, energy storage systems (BESS), and passive filters (PF), to provide hardware support for subsequent collaborative optimization.

[0089] S3. Establish an optimization model to minimize transformer life loss: with the objective function being the relative aging rate function F. V (x), represented by Formula 4. The decision variables include the operating status parameters of the four types of control equipment in step S2. Specifically, the parameters are: the tap position of the on-load tap changer, the charging and discharging power value of the energy storage system, the reactive power compensation output value, and the number of filters switched on and off.

[0090] S4. Set multi-dimensional operating constraints: Based on the relative aging rate in step S1, integrate the power distribution network safety and equipment operation constraints to form an optimization boundary, including configuring the voltage limit of all network nodes, the voltage harmonic distortion rate limit, the daily operation limit of on-load tap changers and filters, and the three-dimensional safe operation domain limit of transformers in step S1.

[0091] Define the voltage value V at node i in the distribution network. i With voltage harmonic content (THD) v-i It should be kept within a safe range;

[0092] Configure voltage limits for all nodes in the network:

[0093] (8)

[0094] Harmonic distortion rate limit:

[0095] (9)

[0096] Maximum number of daily operations of on-load tap changers:

[0097] (10)

[0098] Daily maximum switching limit for filters:

[0099] (11)

[0100] In the formula, V min and V max These are the upper and lower limits of the voltage value. n is the upper limit of harmonic content. OLTC,tmax and n PF,tmax These are the maximum number of on-load tap changers and filters operated within a day, respectively.

[0101] Using the boundary of the transformer's safe operating domain as a constraint for distribution network optimization, and referring to Formula 7, the constraint conditions for the transformer's safe operating domain under bidirectional power flow switching are as follows:

[0102] (12).

[0103] S5. The objective function in step S3 is solved using the particle swarm optimization algorithm, and theoretical calculations, simulation verification, and iterative optimization are performed using the OpenDSS simulation platform.

[0104] The specific steps in step S5 involving the integration of the particle swarm optimization algorithm with the OpenDSS simulation platform are as follows:

[0105] In each iteration, OpenDSS is called to perform power flow analysis, obtain system operating parameters and performance indicators, and evaluate the constraints and fitness function values, i.e., the relative aging rate.

[0106] Step S5 process is as follows Figure 3 Specifically, it includes:

[0107] S51. Initialize the basic data of the distribution network and the parameters of the particle swarm algorithm, including the number of particles, the maximum number of iterations, the learning factor, and the inertia weight, etc.

[0108] S52. Initialize the initial position and velocity of each particle, and set the iteration counter. =0;

[0109] S53. Apply the current particle's position parameters to the power distribution network model, call the OpenDSS simulation tool to perform power flow calculations, and obtain the system operating parameters;

[0110] S54. Calculate the fitness value of each particle based on the power distribution network operating parameters and constraints to evaluate its optimization effect;

[0111] S55. Update the individual best position (pbest) and global best position (gbest) of each particle, and record the current best solution;

[0112] S56. Based on the velocity and position update formula of the particle swarm optimization algorithm, adjust the velocity and position of each particle, and then let... ;

[0113] S57. Determine whether the preset maximum number of iterations has been reached; if not, return to step S53 to continue iterating; if so, execute the following step S58.

[0114] S58. Output the final optimization result and apply the optimal operating scheme to the distribution network control.

[0115] Example

[0116] This method is used for simulation verification to demonstrate the optimization effect.

[0117] Modeling and parameter configuration were performed based on the IEEE-33 node distribution network system. The system reference voltage was set to 10.5kV, and a three-phase oil-immersed self-cooled double-winding on-load tap-changing power transformer of model S(F)Z11-6300 / 110 was configured at the head end of the distribution network. Its rated voltage is 110 / 10.5kV, the tap changer is located on the high-voltage side, and the operating range is 1±8×1.25%. The nameplate parameters are shown in Table 1.

[0118] Table 1: Nameplate Parameters of S(F)Z11-6300 / 110 Transformer

[0119]

[0120] Wind power stations with a capacity of 2.5 MVA and 1.5 MVA are connected at nodes 14 and 20 respectively, and photovoltaic power stations with a capacity of 2.3 MVA are connected at nodes 25 and 30 respectively. The rated irradiance is 1000 W / m2, and the power factor of both photovoltaic and wind turbines is set to 0.95.

[0121] A 3.5MWh BESS (Battery Shielded Energy Storage) is connected at nodes 25 and 30, a 1.5MWh BESS at node 14, and a 1MWh BESS at node 20. All have a charge / discharge efficiency of 0.9, and the minimum energy storage capacity is 20% of the rated capacity, forming a distributed power source-energy storage system. A power supply (PF) is connected at node 33, filtering harmonics of the 3rd, 5th, 7th, and 11th orders, with a maximum of 5 harmonic groups that can be switched on. A reactive power regulation (SVC) is connected at node 18, with a reactive power regulation range of -1Mvar to 1Mvar. The specific configuration is as follows... Figure 4 As shown.

[0122] Typical summer wind speed, temperature, irradiance, and load data are input into wind power and photovoltaic power generation systems and distribution network loads to simulate the bidirectional power flow switching conditions caused by changes in new energy output and load within a day.

[0123] The particle swarm optimization algorithm was set to a maximum of 300 iterations and a population size of 200. The optimization time step was set to 1 hour. Constraints were set for the optimization model: the voltage of all network nodes was limited to 0.95 pu-1.05 pu, and the maximum voltage harmonic distortion rate was limited to no more than 4%. The daily action limits for OLTC and PF were 3 and 8, respectively. OpenDSS and Python 3.9 were used for joint computation. Before optimization, there were 8 power flow operation mode switching events between the high- and low-voltage grids within 4 time periods per day. The maximum increase in the per-unit voltage value on the low-voltage side reached 2.44%, and the maximum increase in the voltage harmonic distortion rate on the low-voltage side reached 12.5%.

[0124] The particle swarm optimization algorithm was used for optimization calculations to obtain simulation results for one day. After optimization, the number of power flow switching events decreased from 8 to 4, and the duration decreased from 7 hours to 5 hours. The total 24-hour lifespan loss of the transformer decreased by 48.62% compared to before optimization.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the lifespan loss of power transformers under bidirectional power flow switching, characterized in that, Includes the following steps: S1. Constructing a three-dimensional high-order safe operation domain Ω for transformers: Using the relative aging rate V≤1.5 as the evaluation index, and taking into account both short-term minor overload and long-term life balance, load rate and current harmonic distortion rate are selected as key input parameters to establish a three-dimensional safe operation domain for transformers based on current harmonic distortion rate, transformer load rate, and relative aging rate; the critical isosurface of the relative aging rate is characterized by the level set method, and the boundary is fitted by convex approximation technology to obtain the mathematical expression of the safe operation domain, thus clarifying the parameter combination range for safe operation of transformers; S2. Configure distribution network optimization control equipment: In distribution networks containing distributed new energy sources, configure four types of key control equipment, including power transformers with on-load tap changers, reactive power compensation devices, energy storage systems, and passive filters, to provide hardware support for subsequent collaborative optimization. S3. Establish an optimization model to minimize transformer life loss: with the objective function being the relative aging rate function F. V (x), where The decision variables include the operating status parameters of the four types of control equipment in step S2. The parameters are specifically: the tap position of the on-load tap changer, the charging and discharging power value of the energy storage system, the reactive power compensation output value, and the number of filters switched on and off. S4. Set multi-dimensional operating constraints: Based on the relative aging rate in step S1, integrate the power distribution network safety and equipment operation constraints to form an optimization boundary, including configuring the voltage limit of all network nodes, voltage harmonic distortion rate limit, daily operation limit of on-load tap changers and filters, and the three-dimensional safe operation domain limit of transformers in step S1. S5. The objective function in step S3 is solved using the particle swarm optimization algorithm, and theoretical calculations, simulation verification, and iterative optimization are performed using the OpenDSS simulation platform.

2. The method for optimizing the lifespan loss of power transformers as described in claim 1, characterized in that, The specific steps of constructing the three-dimensional safe operating domain of the transformer in step S1 are as follows: According to IEEE Std C57.110, transformer load loss P LL Due to resistive loss P R Eddy current loss P EC and stray loss P OSL The transformer load losses, considering the effects of harmonics, are as follows: （1） In the formula, I is the actual current value of the transformer, I N F is the rated current of the transformer. HL-R P is the harmonic loss factor of the winding resistance. R-R For rated resistance loss, F HL-EC P is the winding eddy current harmonic loss factor. EC-R For rated eddy current losses, F HL-OSL P is the stray harmonic loss factor. OSL-R The rated stray loss is given; the winding resistance harmonic loss factor, winding eddy current harmonic loss factor, and stray harmonic loss factor are given by the following formulas: （2） In the formula, h is the harmonic order, h max For the maximum harmonic order, I (h) ) represents the h-th harmonic current, I (1) It is the fundamental current; Referring to IEC 60076-7, the formula for calculating transformer winding hot spots is based on the ambient temperature θ. a Temperature rise θ of the top fluid relative to the environment TO Temperature rise θ of hot spot relative to top liquid g It consists of three parts; the load loss P in Formula 1 is... LL Substituting the no-load loss PNL into the temperature rise θ TO and θ g The calculation formula is as follows: （3） In the formula, θ TO-R θ represents the temperature rise of the transformer top liquid under rated conditions. g-R P represents the temperature rise of the hot spot relative to the top liquid under rated conditions. LL-R This represents the load and no-load loss of the transformer under rated conditions.

3. The method for optimizing the lifespan loss of power transformers as described in claim 2, characterized in that, The relative aging rate V uses the hot spot temperature as an intermediate variable, and Formula 3 provides the temperature parameter; the relative aging rate V and thermal life loss D of the transformer considering bidirectional power flow switching are derived as follows: (4) (5) In the formula, θ h V represents the hot spot temperature of the transformer winding, t1 represents the initial aging time of the calculated lifespan, t2 represents the end time of the calculated lifespan, and V... n Let t be the relative aging rate of the nth interval. n Let n be the time interval.

4. The method for optimizing the lifespan loss of power transformers as described in claim 3, characterized in that, The load factor in step S1 represents the load current and distinguishes the power flow direction; it is the load factor on the low-voltage side of the transformer. as follows: (6) In the formula, P is the active power value on the low-voltage side of the transformer, and S... N Here, φ represents the rated capacity of the transformer, and cosφ represents the power factor on the low-voltage side of the transformer.

5. The method for optimizing the lifespan loss of power transformers as described in claim 4, characterized in that, The safe operating domain Ω is defined to satisfy the relative aging rate threshold. For all parameter combinations ≤1.5, the convexity description of the safe operating domain Ω is as follows: (7) In the formula, This indicates the harmonic content of the current.

6. The method for optimizing the lifespan loss of power transformers as described in claim 5, characterized in that, Define the voltage value V at node i in the distribution network. i With voltage harmonic content (THD) v-i It should be kept within a safe range; Configure voltage limits for all nodes in the network: (8) Harmonic distortion rate limit: (9) Maximum number of daily operations of on-load tap changers: (10) Daily maximum switching limit for filters: (11) In the formula, V min and V max These are the upper and lower limits of the voltage value. n is the upper limit of harmonic content. OLTC,tmax and n PF,tmax These are the maximum number of on-load tap changers and filters operated within a day, respectively.

7. The method for optimizing the lifespan loss of power transformers as described in claim 6, characterized in that, Using the boundary of the transformer's safe operating domain as a constraint for distribution network optimization, and referring to Formula 7, the constraint conditions for the transformer's safe operating domain under bidirectional power flow switching are as follows: (12)。 8. The method for optimizing the lifespan loss of power transformers as described in claim 7, characterized in that, The combination of particle swarm optimization algorithm and OpenDSS simulation platform in step S5 is specifically as follows: In each iteration, OpenDSS is called to perform power flow analysis, obtain system operating parameters and performance indicators, and evaluate the constraints and fitness function values, i.e., the relative aging rate.

9. The method for optimizing the lifespan loss of power transformers as described in claim 8, characterized in that, Step S5 specifically includes: S51. Initialize the basic data of the distribution network and the parameters of the particle swarm algorithm, including the number of particles, the maximum number of iterations, the learning factor, and the inertia weight. S52. Initialize the initial position and velocity of each particle, and set the iteration counter. =0; S53. Apply the current particle's position parameters to the power distribution network model, call the OpenDSS simulation tool to perform power flow calculations, and obtain the system operating parameters; S54. Calculate the fitness value of each particle based on the power distribution network operating parameters and constraints to evaluate its optimization effect; S55. Update the individual best position (pbest) and global best position (gbest) of each particle, and record the current best solution; S56. Based on the velocity and position update formula of the particle swarm optimization algorithm, adjust the velocity and position of each particle, and then let... ; S57. Determine whether the preset maximum number of iterations has been reached; if not, return to step S53 to continue iterating; if so, execute the following step S58. S58. Output the final optimization result and apply the optimal operating scheme to the distribution network control.

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