A control method for regulating energy storage based on transformer operating state

Abstract

The application provides a control method for adjusting energy storage based on transformer operating state, and relates to the field of power system operation optimization and energy storage control. The method obtains transformer operating parameters, establishes a heat loss characteristic matrix, and performs singular value decomposition on the heat loss characteristic matrix using a sliding time window to extract the dominant loss mode. Then, the energy storage compensation amount is dynamically calculated according to the space-time distribution characteristics. The energy storage compensation amount is dynamically distributed by combining the particle swarm-fuzzy collaborative optimization method, and the power output of the energy storage converter is controlled based on the energy storage power instruction sequence, which can effectively improve the collaborative adjustment capability between the energy storage system and the transformer. The application can significantly improve the energy efficiency and reliability of the transformer operation, optimize the overall operation state of the power grid, help reduce heat loss, realize precise control of the energy storage equipment, and thus improve the economy and stability of the power system.

Description

Technical Field

[0001] This invention relates to the field of power system operation optimization and energy storage control, and more specifically, to a control method for adjusting energy storage based on transformer operating status. Background Technology

[0002] With the rapid development of modern power systems and the continuous growth of electricity demand, transformers, as core equipment in power transmission and distribution systems, directly affect the overall stability of the power grid through their operating efficiency and reliability. However, transformers are inevitably affected by multiple factors such as load fluctuations and changes in the external environment during long-term operation, leading to increasingly prominent problems of heat loss and energy consumption. Traditional transformer operation control methods are mostly based on static parameter optimization or empirical models, making it difficult to adjust in real time for complex operating conditions. Furthermore, in recent years, with the increasing proportion of renewable energy integration and the gradual opening of the electricity market, the demand for flexible regulation of transformer operation in the power system has become increasingly urgent, placing higher demands on existing technologies.

[0003] Existing methods for regulating transformer operation status still have significant shortcomings in addressing heat loss management and optimizing energy utilization efficiency. On the one hand, traditional methods often employ fixed operating modes, lacking a deep understanding of load characteristics and the operating environment, making it difficult to dynamically balance the relationship between transformer heat loss and energy demand. On the other hand, although some studies have attempted to improve transformer operating efficiency by introducing artificial intelligence algorithms or multi-objective optimization methods, in practical applications, these methods generally suffer from high computational complexity, insufficient real-time performance, and limited ability to coordinate control with energy storage systems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention is proposed. This invention provides a control method for regulating energy storage based on transformer operating conditions, which can, to some extent, solve the problem of drastic fluctuations in winding heat loss caused by the coupling effect of load fluctuations and voltage regulation actions during voltage regulation of large-capacity transformers in the power grid.

[0005] According to one aspect of the present invention, a control method for adjusting energy storage based on transformer operating status is provided, comprising:

[0006] Obtain the transformer's operating parameters, and establish a heat loss feature matrix H based on the operating parameters;

[0007] The heat loss feature matrix H is decomposed using a sliding time window to extract the dominant loss mode, and the energy storage compensation is calculated based on the spatiotemporal distribution characteristics of the dominant loss mode.

[0008] The energy storage compensation amount is dynamically allocated based on the particle swarm optimization-fuzzy collaborative optimization method, and an energy storage power command sequence P is established.

[0009] According to the energy storage power command sequence P, the energy storage converter is driven by a dead-zone compensation controller to complete the power output control of the energy storage converter.

[0010] Furthermore, the operating parameters include the three-phase winding currents on the high-voltage side and the low-voltage side, the top and bottom oil temperatures, the active and reactive power of each phase, and the tap position signal of the voltage regulating switch.

[0011] Calculate the copper loss of each winding, and construct the heat loss feature matrix based on the copper loss, top and bottom oil temperatures, and tap position signal.

[0012] Furthermore, the heat loss feature matrix is ​​truncated using a sliding time window, and the truncated submatrix within each time window is subjected to singular value decomposition to obtain an orthogonal matrix containing time features, a singular value matrix containing energy features, and an orthogonal matrix containing spatial features.

[0013] The contribution rate of the i-th singular value is calculated based on the values ​​of the diagonal elements of the singular value matrix.

[0014] Furthermore, the feature vectors corresponding to the k singular values ​​whose cumulative contribution rate exceeds a preset percentage are selected as the dominant loss modes, and wavelet transform is performed on the time feature vectors of the dominant loss modes to calculate the energy distribution characteristics at different time scales. ;

[0015] The spatial distribution feature vector of the dominant loss mode is projected onto a preset loss classification space to obtain the spatial distribution features of basic loss and additional loss. ;

[0016] Based on the energy distribution characteristics at different time scales and spatial distribution characteristics Establish the energy balance equation set and optimize the objective function;

[0017] The optimal time-frequency compensation coefficient matrix is ​​obtained by solving the objective function using the augmented Lagrange method, and then the optimal energy storage compensation amount is obtained.

[0018] The obtained energy storage compensation amount is reconstructed in time and frequency to obtain a continuous energy storage compensation power sequence.

[0019] Furthermore, the optimization objective function is shown in the following equation:

[0020] ;

[0021] ;

[0022] in, The elements of the time-frequency compensation coefficient matrix to be optimized are: The rated power of the energy storage system. The rated capacity of the energy storage system, The characteristics of energy distribution over a time scale. Spatial distribution characteristics, The energy storage efficiency coefficient. To compensate for energy storage power, The number of scale decomposition layers, The time translation step size.

[0023] Furthermore, establishing the energy storage power command sequence includes:

[0024] Establish a fuzzy evaluation system to conduct online assessments of energy storage compensation characteristics;

[0025] When the fuzzy evaluation system receives the input variables, it uses an adaptive membership function to fuzzify the input variables.

[0026] After completing the fuzzy evaluation, a multi-objective optimization model for energy storage allocation is established.

[0027] The multi-objective optimization model is solved using the particle swarm optimization algorithm, and the energy storage power command sequence is constructed based on the solution results.

[0028] Furthermore, the fuzzy evaluation system includes determining response speed indicators, capacity utilization indicators, and compensation accuracy indicators;

[0029] The response speed index is calculated by the deviation between the power change rate and the command value change rate;

[0030] The capacity utilization rate is calculated by the deviation between the current state of charge and the reference state of charge.

[0031] The compensation accuracy index is calculated by the tracking error between the output power and the command power.

[0032] Furthermore, the energy storage power command sequence includes a basic allocated power and a dynamic adjustment amount, as shown in the following formula:

[0033] ;

[0034] in, Power allocation based on base:

[0035] ;

[0036] For dynamic adjustment:

[0037] ;

[0038] ;

[0039] in, To represent the actual output power command value of the i-th energy storage unit at time t, To represent the base power allocation of the i-th energy storage unit at time t, To represent the dynamic power adjustment of the i-th energy storage unit at time t, To represent the rated maximum energy capacity of the i-th energy storage unit, To represent the current remaining energy of the i-th energy storage unit at time t, To represent the total number of energy storage units in an energy storage system, To represent the target reference power value of the i-th energy storage unit at time t, the unit is kW. This is the proportional control coefficient. The integral control coefficient, This is the differential control coefficient.

[0040] Furthermore, the energy storage power command sequence also needs to undergo constraint processing, including:

[0041] When the power command value is between the maximum and minimum power limits, the original command value is output directly;

[0042] When the power command value exceeds the maximum charging and discharging power limit of the energy storage unit, the command value will be limited to the allowable range;

[0043] When the power command value is lower than the minimum power limit, the command value is set to the minimum power limit value.

[0044] Furthermore, the dead-zone compensation controller eliminates the effects of dead-zone through forward and reverse compensation channels.

[0045] Compared with existing technologies, the control method for regulating energy storage based on transformer operating status provided by this invention obtains transformer operating parameters, establishes a heat loss characteristic matrix, and uses a sliding time window to perform singular value decomposition on the heat loss characteristic matrix to extract the dominant loss mode. Then, it dynamically calculates the energy storage compensation amount based on the spatiotemporal distribution characteristics. By combining a particle swarm optimization-fuzzy collaborative optimization method to dynamically allocate the energy storage compensation amount and controlling the power output of the energy storage converter based on the energy storage power command sequence, it can effectively improve the coordinated regulation capability between the energy storage system and the transformer. This can significantly improve the energy efficiency and reliability of transformer operation, optimize the overall operating status of the power grid, help reduce heat loss, achieve precise control of energy storage devices, and thus improve the economy and stability of the power system. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0047] Figure 1 This is a flowchart of a control method for adjusting energy storage based on transformer operating status according to an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram comparing energy storage before and after the adjustment of energy storage based on transformer operating status according to an embodiment of the present invention. Detailed Implementation

[0049] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0050] Figure 1 This is a flowchart of a control method for adjusting energy storage based on transformer operating status according to an embodiment of the present invention. Figure 1 As shown, the control method for adjusting energy storage based on transformer operating status includes:

[0051] S1: Obtain the operating parameters of the transformer, including winding current, oil temperature, load power and tap position signal. Based on the operating parameters, establish a heat loss feature matrix H, which includes load power component and additional loss component caused by voltage regulation.

[0052] The transformer's operating parameters are acquired, including the three-phase winding currents Ih_abc and Il_abc on the high-voltage and low-voltage sides, the top and bottom oil temperatures Toil_top and Toil_bottom, the active power Ph_abc and reactive power Qh_abc of each phase, and the tap position signal Ntap of the voltage regulating switch. The winding currents Ih_abc and Il_abc are acquired using current transformers installed at the transformer bushings, with a sampling period of 10ms. The top and bottom oil temperatures Toil_top and Toil_bottom are acquired using a fiber optic temperature measurement system located in the transformer tank, with a sampling period of 1s. The active power Ph_abc and reactive power Qh_abc are acquired using a power measurement device, with a sampling period of 100ms. The tap position signal Ntap... The tap position signal (Ntap) is acquired via a mechanical position sensor to detect position changes during tap switching. The operating parameters are synchronized according to the sampling time stamp. After data preprocessing to eliminate sampling noise, the copper loss Pcu_abc of each phase winding is calculated. Pcu_abc is the product of the square of the winding current and the winding resistance. Based on the copper loss Pcu_abc, the top and bottom oil temperatures Toil_top and Toil_bottom, and the tap position signal Ntap, an m×n dimensional heat loss feature matrix H is constructed, where m is the number of sampling points and n is the feature dimension. The feature dimension includes the basic loss component and the additional loss component caused by tap switching. Each row in the heat loss feature matrix H represents a loss feature vector at a sampling time, and each column represents the change of different types of loss components over time.

[0053] S2: The heat loss feature matrix H is decomposed using a sliding time window to extract the dominant loss mode. The energy storage compensation is calculated based on the spatiotemporal distribution characteristics of the dominant loss mode. The energy storage compensation is obtained by solving the energy balance equations using the least squares method.

[0054] A sliding time window of length T is used to truncate the heat loss feature matrix H. The length T of the time window is determined to be 300s based on the thermal time constant of the transformer winding and the variation characteristics of the oil temperature. As the time window slides along the time axis, the step size is set to 10. Singular value decomposition is performed on the sub-matrix Ht truncated within each time window to obtain an orthogonal matrix U containing time characteristics, a singular value matrix Σ containing energy characteristics, and an orthogonal matrix VT containing spatial characteristics. Based on the diagonal elements of the singular value matrix Σ, the contribution rate ri of the i-th singular value is calculated.

[0055] ;

[0056] in, For the i-th singular value, The time-related weighting coefficient is the time-related weighting coefficient. Calculated using an adaptive weighting function:

[0057] ;

[0058] in, For the current moment, As the reference time, This is the time decay coefficient.

[0059] Furthermore, the eigenvectors corresponding to the top k singular values ​​with a cumulative contribution rate exceeding 85% are selected as the dominant loss modes. A continuous wavelet transform is performed on the time eigenvectors of these dominant loss modes, using the Morlet wavelet as the mother wavelet function. The scale parameter is set to a range of 1 to 64 to cover the main frequency components of the loss fluctuations. The step size of the translation parameter is set to 1 / 4 of the sampling period to improve the accuracy of the time-frequency analysis. After the wavelet transform is completed, the energy distribution characteristics at different time scales are calculated. As shown in the following formula:

[0060] ;

[0061] in, Let i be the temporal feature vector of the i-th dominant mode. For wavelet basis functions, For scale parameters, For translation parameters, The number of dominant patterns selected.

[0062] The spatial distribution feature vector of the dominant loss mode is projected onto a preset loss classification space to obtain the spatial distribution features Es of the basic loss and additional loss. The loss classification space is spanned by the basic loss feature basis vector and the additional loss feature basis vector. When performing feature projection, the basic loss feature basis vector is first set to reflect the loss component caused by load changes, and its vector elements are related to the square value of the winding current. The additional loss feature basis vector is set to reflect the loss component caused by the tap switching process, and its vector elements are related to the tap position change rate. The spatial distribution feature Es is shown in the following formula:

[0063] ;

[0064] in, and These are the eigenvectors of the basic loss and the additional loss, respectively. Let be the spatial feature vector of the i-th dominant mode.

[0065] Furthermore, based on the time-scale energy distribution characteristics Et and spatial distribution characteristics Es, an energy balance equation set considering the dynamic characteristics of losses is established to obtain the energy storage compensation power. When establishing the energy balance equation set, the energy storage compensation power is decomposed at different time scales, corresponding to loss fluctuations of different frequency components. If the amplitude of loss fluctuation at a certain time scale exceeds a preset threshold, the compensation coefficient corresponding to that scale is increased. When calculating the energy storage compensation amount, both the power constraint and capacity constraint of the energy storage system are considered. The power constraint is determined based on the rated capacity of the energy storage converter, and the capacity constraint is determined based on the available capacity of the energy storage device. If constraint conflicts occur during the optimization process, priority is given to ensuring the satisfaction of the power constraint. The energy balance equation set is shown below:

[0066] ;

[0067] in, To compensate for energy storage power, The energy storage efficiency coefficient. This is the time-frequency compensation coefficient matrix. The number of scale decomposition layers, The time translation step size.

[0068] The time-frequency compensation coefficient matrix is ​​obtained by optimizing the objective function:

[0069] ;

[0070] ;

[0071] in, The elements of the time-frequency compensation coefficient matrix to be optimized are: The rated power of the energy storage system. The rated capacity of the energy storage system, The characteristics of energy distribution over a time scale. Spatial distribution characteristics, Let be the energy storage efficiency coefficient. An improved augmented Lagrangian method is used to solve the above optimization problem, directly yielding the optimal value. Thus, the optimal energy storage compensation amount is obtained, and the improved augmented Lagrangian function is:

[0072] ;

[0073] in, For Lagrange multipliers, As a penalty factor, These are constraints.

[0074] The obtained energy storage compensation amount is reconstructed in time and frequency. The reconstruction process uses a reconstruction basis function corresponding to wavelet transform. During reconstruction, a smoothing factor is used to process the compensation power sequence. If there is a jump in the reconstruction results of two adjacent time windows, an exponential weighted average method is used for smoothing, so that the final energy storage compensation power sequence has good continuity.

[0075] ;

[0076] in, For reconstruction coefficients, To reconstruct the basis functions, This is the reconfigured energy storage compensation power.

[0077] It is worth noting that although the above description outlines the general process for calculating energy storage compensation, specific implementation details may vary depending on the transformer. For example, when a transformer undergoes a tap switching operation, singular value decomposition reveals three dominant loss modes within a time window: the first mode reflects base load loss, accounting for the majority of total loss; the second mode reflects short-term loss fluctuations caused by tap switching, a minor component of total loss; and the third mode reflects loss changes caused by load fluctuations, accounting for the smallest proportion. Wavelet transform analysis shows that loss fluctuations caused by tap switching are mainly concentrated on a short time scale, with amplitudes reaching a significant proportion of base losses. Based on these time-frequency characteristics, the optimization algorithm proposes that the energy storage system needs to provide a large short-term compensation during tap switching, with the compensation curve exhibiting a "peak" characteristic to correspond to the instantaneous loss fluctuations during tap switching; simultaneously, it provides continuous compensation with smaller power during load fluctuation periods, resulting in a relatively flat compensation curve. This hierarchical compensation strategy effectively suppresses large short-term loss fluctuations caused by tap switching and smooths out loss fluctuations caused by regular load changes, thereby achieving refined adjustment of transformer loss characteristics.

[0078] S3: The energy storage compensation amount is dynamically allocated based on the particle swarm optimization-fuzzy collaborative optimization method, and an energy storage power command sequence P is established. The energy storage power command sequence P is optimized according to the loss mitigation effect and energy storage capacity constraints.

[0079] Based on the energy storage compensation amount P_comp, a multi-objective optimization problem for dynamic energy storage allocation is constructed. First, a fuzzy evaluation system is established to evaluate the energy storage compensation characteristics online. The input variables of the fuzzy evaluation system include a response speed index Rs, a capacity utilization index Cu, and a compensation accuracy index Cp. The response speed index Rs is determined by the deviation between the power change rate of the energy storage unit and the command value change rate. The capacity utilization index Cu is calculated based on the deviation between the current state of charge of the energy storage unit and the reference state of charge. The compensation accuracy index Cp is obtained based on the tracking error between the energy storage output power and the commanded power.

[0080] The response speed index is calculated by obtaining the actual output power of the energy storage unit at two adjacent sampling times, calculating the difference, and dividing it by the sampling time interval to obtain the actual power change rate ΔP_real; simultaneously, the value of the energy storage power command at two adjacent sampling times is obtained, the difference is calculated, and divided by the sampling time interval to obtain the command power change rate ΔP_ref; after calculating the actual power change rate and the command power change rate, a response speed evaluation function is constructed based on their ratio. If the actual power change rate is greater than the command power change rate, a sigmoid function is used to penalize the excess, with a larger penalty for greater excess; if the actual power change rate is less than the command power change rate, an exponential function is used to penalize the deficiency, with a larger penalty for greater deficiency; the output of the evaluation function is then averaged over a time window T to obtain the response speed index Rs.

[0081] The capacity utilization index Cu is calculated based on the ampere-hour integral method to obtain the current state of charge (SOC_now). Simultaneously, a reference state of charge (SOC_ref) is determined according to the energy storage system's operating strategy. The deviation between the current state of charge and the reference state of charge is calculated, and an evaluation function is constructed by combining the historical capacity loss rate of the energy storage unit. When the deviation is positive and exceeds the capacity equilibrium dead zone, a quadratic function is used to penalize capacity redundancy; when the deviation is negative and exceeds the capacity equilibrium dead zone, an exponential function is used to penalize capacity insufficiency. The calculation results of the evaluation function are smoothed using a low-pass filter to obtain the value of the capacity utilization index Cu.

[0082] The compensation accuracy index Cp is calculated based on the difference between the actual output power P_real of the energy storage unit and the power command value P_ref at the current moment to obtain the instantaneous power tracking error e(t). At the same time, considering the rate of change of the power tracking error de(t) / dt, a combined evaluation function is constructed based on the instantaneous error evaluation value and the dynamic evaluation value, and its root mean square value is calculated within the sliding time window. The output result of the evaluation function is transformed to the [0,1] interval through nonlinear mapping to obtain the value of the compensation accuracy index Cp.

[0083] Furthermore, after receiving the input variables, the fuzzy evaluation system uses an adaptive membership function to fuzzify the input variables. That is, if the response speed index Rs is in the high-speed response range, its membership value is calculated using an exponential membership function; if the capacity utilization index Cu is in the balanced operating range, its membership value is calculated using a bell-shaped membership function; and if the compensation accuracy index Cp is in the high-precision range, its membership value is calculated using an S-shaped membership function.

[0084] After completing the fuzzy evaluation, a multi-objective optimization model for energy storage allocation is established. The first optimization objective is the compensation accuracy objective, obtained by calculating the weighted squared error between the output power of each energy storage unit and the desired compensation power. The second optimization objective is the power smoothing objective, obtained by calculating the weighted sum of squares of the time derivative terms of the energy storage output power. The third optimization objective is the energy balance objective, obtained by calculating the weighted squared error between the remaining capacity of each energy storage unit and the reference capacity level. The multi-objective optimization model is shown in the following equation:

[0085] ;

[0086] ;

[0087] ;

[0088] in, Indicates the target accuracy for compensation. Indicates the power smoothing target. Indicates the goal of energy balance. , , These are the weighting coefficients for each objective. Let be the power allocation for the i-th energy storage unit. Let be the remaining capacity of the i-th energy storage unit. This represents the expected capacity level.

[0089] Furthermore, an improved multi-objective particle swarm optimization (PSO) algorithm is employed to solve the problem. The PSO algorithm's velocity update considers three learning terms: individual optimal position, global optimal position, and neighborhood optimal position. The individual optimal position reflects the historical search experience of a single particle, the global optimal position reflects the global search capability of the entire population, and the neighborhood optimal position enhances the granularity of the local search. The velocity and position update equations for the particles are as follows:

[0090] ;

[0091] ;

[0092] in, The inertial weights are dynamically adjusted using a fuzzy system.

[0093] ;

[0094] in, , , As a learning factor, , , It is a random number. For the individual's optimal position, The optimal position globally. It is the optimal position in the neighborhood.

[0095] Based on the optimization results, an energy storage power command sequence is constructed. This sequence comprises two parts: a basic allocated power and a dynamic adjustment. The basic allocated power is determined by the initial allocation ratio based on the difference in remaining capacity of the energy storage units. The dynamic adjustment is calculated using a fuzzy PID controller. When the power tracking error is large, the PID parameters are adjusted online using fuzzy rules. The proportional coefficient is mainly used to adjust the dynamic response speed, the integral coefficient is used to eliminate steady-state error, and the derivative coefficient is used to suppress overshoot. The energy storage power command sequence takes the following form:

[0096] ;

[0097] in, Power allocation based on base:

[0098] ;

[0099] For dynamic adjustment:

[0100]

[0101] in, To represent the actual output power command value of the i-th energy storage unit at time t, To represent the base power allocation of the i-th energy storage unit at time t, To represent the dynamic power adjustment of the i-th energy storage unit at time t, To represent the rated maximum energy capacity of the i-th energy storage unit, To represent the current remaining energy of the i-th energy storage unit at time t, To represent the total number of energy storage units in an energy storage system, To represent the target reference power value of the i-th energy storage unit at time t, the unit is kW. This is the proportional control coefficient. The integral control coefficient, This is the differential control coefficient.

[0102] Finally, the energy storage power command sequence is constrained. When the power command value exceeds the maximum charge / discharge power limit of the energy storage unit, the command value is limited to the allowable range; when the power command value is between the maximum and minimum power limits, the original command value is directly output; when the power command value is lower than the minimum power limit, the command value is set to the minimum power limit value.

[0103] S4: According to the energy storage power command sequence P, the energy storage converter is driven by the dead-zone compensation controller to complete the power output control of the energy storage converter.

[0104] First, the current command reference value is determined through an adaptive calculation module. When the battery is in a high-temperature or high-rate charging / discharging state, temperature compensation coefficient and rate coefficient are used for correction, respectively. Then, a dual-closed-loop nested control structure is adopted, where the outer loop is the power control loop, which responds quickly through feedforward compensation when the power command changes abruptly. The inner loop is the current control loop, which tracks the current command through an improved proportional resonant controller after it is determined. When a discontinuous change in the switching transistor voltage is detected, the dead zone parameter is identified by edge triggering, and the dead zone effect is eliminated through forward and reverse compensation channels. At the same time, current zero-crossing optimization control is used. When the current approaches the zero-crossing region, the zero-crossing point is captured by a high-precision detection circuit. If a reverse zero crossing occurs, the zero-crossing optimization controller is activated. In addition, the control parameters are optimized through a fuzzy adaptive controller. When the current tracking error is large, the proportional coefficient is increased to improve dynamic performance, and when the harmonic distortion is large, the resonant coefficient is increased to enhance the suppression capability.

[0105] The elimination of dead-zone effect through forward and reverse compensation channels involves dividing the current operating range into three states: forward conduction, reverse conduction, and zero-crossing region. When the current is in the forward conduction region, the turn-on delay time and turn-off advance time of the switching transistor are first calculated to obtain the theoretical value of the forward dead-zone voltage drop. Then, an adaptive gain coefficient is introduced into the forward compensation channel, and the fluctuation of the dead-zone parameter is tracked in real time and the gain value is dynamically adjusted through an online parameter identification method. When the current is in the reverse conduction region, the turn-off delay time and turn-on advance time of the switching transistor are first calculated to obtain the theoretical value of the reverse dead-zone voltage rise. Then, a dynamic compensation coefficient is set in the reverse compensation channel, and the dead-zone parameter fluctuation is tracked in real time and the gain value is dynamically adjusted. The compensation coefficient is updated by monitoring the dead zone parameter drift caused by temperature changes. When the current crosses from positive to negative or vice versa, it is in the zero-crossing region. At this time, a smooth switching strategy is constructed using the sigmoid function. The smoothness of the compensation channel switching process is controlled by adjusting the shape parameter of the sigmoid function. At the same time, an oscillation detection mechanism is introduced. If oscillation is detected during the switching process, the smoothing coefficient is adaptively increased. In order to further improve the compensation accuracy, a feedforward decoupling link and a feedback correction mechanism are added to the compensation channel. The coupling effect of dead zone nonlinearity is eliminated by the dead zone effect decoupling matrix, and the compensation amount is corrected in real time based on the output voltage error signal.

[0106] Finally, an anti-saturation protection mechanism is established. When the controller output exceeds the limit, the integrator state is reset. When the grid voltage is abnormal or the battery is overcharged or over-discharged, a power limiting strategy is used for protection, thereby achieving safe and reliable operation of the energy storage converter.

[0107] In summary, the control method for regulating energy storage based on transformer operating status, as described in this invention, is explained. It acquires transformer operating parameters, establishes a heat loss characteristic matrix, and uses a sliding time window to perform singular value decomposition on the heat loss characteristic matrix to extract the dominant loss mode. Then, it dynamically calculates the energy storage compensation amount based on the spatiotemporal distribution characteristics. By combining a particle swarm optimization-fuzzy collaborative optimization method to dynamically allocate the energy storage compensation amount and controlling the power output of the energy storage converter based on the energy storage power command sequence, the coordinated regulation capability between the energy storage system and the transformer can be effectively improved. This significantly improves the energy efficiency and reliability of transformer operation, optimizes the overall operating status of the power grid, helps reduce heat loss, and achieves precise control of energy storage devices, thereby improving the economy and stability of the power system.

[0108] Here, those skilled in the art will understand that the specific operations of each step in the above-described control method for regulating energy storage based on transformer operating status have been referenced above. Figure 1 and Figure 2 The description of the control method for regulating energy storage based on transformer operating status has been detailed, and therefore, its repeated description will be omitted.

[0109] In summary, the control method for regulating energy storage based on transformer operating status, as described in this invention, is explained. It acquires transformer operating parameters, establishes a heat loss characteristic matrix, and uses a sliding time window to perform singular value decomposition on the heat loss characteristic matrix to extract the dominant loss mode. Then, it dynamically calculates the energy storage compensation amount based on the spatiotemporal distribution characteristics. By combining a particle swarm optimization-fuzzy collaborative optimization method to dynamically allocate the energy storage compensation amount and controlling the power output of the energy storage converter based on the energy storage power command sequence, the coordinated regulation capability between the energy storage system and the transformer can be effectively improved. This can significantly improve the energy efficiency and reliability of transformer operation, optimize the overall operating status of the power grid, help reduce heat loss, achieve precise control of energy storage devices, and thus improve the economy and stability of the power system.

Claims

1. A control method for adjusting energy storage based on transformer operating status, characterized in that, include: Obtain the transformer's operating parameters, and establish a heat loss feature matrix H based on the operating parameters; The heat loss feature matrix H is decomposed using a sliding time window to extract the dominant loss mode, and the energy storage compensation is calculated based on the spatiotemporal distribution characteristics of the dominant loss mode. The energy storage compensation amount is dynamically allocated based on the particle swarm optimization-fuzzy collaborative optimization method, and an energy storage power command sequence P is established. According to the energy storage power command sequence P, the energy storage converter is driven by a dead-zone compensation controller to complete the power output control of the energy storage converter. The heat loss feature matrix is ​​truncated using a sliding time window. Singular value decomposition is performed on the sub-matrix truncated within each time window to obtain an orthogonal matrix containing time features, a singular value matrix containing energy features, and an orthogonal matrix containing spatial features. Based on the values ​​of the diagonal elements of the singular value matrix, calculate the contribution rate of the i-th singular value; The eigenvectors corresponding to the k singular values ​​whose cumulative contribution rate exceeds a preset percentage are selected as the dominant loss modes. Wavelet transform is then performed on the time eigenvectors of the dominant loss modes to calculate the energy distribution characteristics at different time scales. ; The spatial distribution feature vector of the dominant loss mode is projected onto a preset loss classification space to obtain the spatial distribution features of basic loss and additional loss. ; Based on the energy distribution characteristics at different time scales and spatial distribution characteristics Establish the energy balance equation set and optimize the objective function; The optimal time-frequency compensation coefficient matrix is ​​obtained by solving the objective function using the augmented Lagrange method, and then the optimal energy storage compensation amount is obtained. The obtained energy storage compensation amount is reconstructed in time and frequency to obtain a continuous energy storage compensation power sequence.

2. The control method for adjusting energy storage based on transformer operating status according to claim 1, characterized in that, The operating parameters include the three-phase winding currents on the high-voltage and low-voltage sides, the top and bottom oil temperatures, the active and reactive power of each phase, and the tap position signal of the voltage regulating switch. Calculate the copper loss of each winding, and construct the heat loss feature matrix based on the copper loss, top and bottom oil temperatures, and tap position signal.

3. The control method for adjusting energy storage based on transformer operating status according to claim 1, characterized in that, Establishing the energy storage power command sequence includes: Establish a fuzzy evaluation system to conduct online assessments of energy storage compensation characteristics; When the fuzzy evaluation system receives the input variables, it uses an adaptive membership function to fuzzify the input variables. After completing the fuzzy evaluation, a multi-objective optimization model for energy storage allocation is established. The multi-objective optimization model is solved using the particle swarm optimization algorithm, and the energy storage power command sequence is constructed based on the solution results.

4. The control method for adjusting energy storage based on transformer operating status according to claim 3, characterized in that, The fuzzy evaluation system includes determining response speed indicators, capacity utilization indicators, and compensation accuracy indicators; The response speed index is calculated by the deviation between the power change rate and the command value change rate; The capacity utilization rate is calculated by the deviation between the current state of charge and the reference state of charge. The compensation accuracy index is calculated by the tracking error between the output power and the command power.

5. The control method for adjusting energy storage based on transformer operating status according to claim 3, characterized in that, The energy storage power command sequence includes a base allocated power and a dynamic adjustment amount, as shown in the following formula: ； in, The calculation formula is as follows: ； The calculation formula is as follows: ； ； in, To represent the actual output power command value of the i-th energy storage unit at time t, To represent the base power allocation of the i-th energy storage unit at time t, To represent the dynamic power adjustment of the i-th energy storage unit at time t, To represent the rated maximum energy capacity of the i-th energy storage unit, To represent the current remaining energy of the i-th energy storage unit at time t, To represent the total number of energy storage units in an energy storage system, To represent the target reference power value of the i-th energy storage unit at time t, the unit is kW. This is the proportional control coefficient. The integral control coefficient, This is the differential control coefficient.

6. The control method for adjusting energy storage based on transformer operating status according to claim 5, characterized in that, The energy storage power command sequence also needs to undergo constraint processing, including: When the power command value is between the maximum and minimum power limits, the original command value is output directly; When the power command value exceeds the maximum charging and discharging power limit of the energy storage unit, the command value will be limited to the allowable range; When the power command value is lower than the minimum power limit, the command value is set to the minimum power limit value.

7. The control method for adjusting energy storage based on transformer operating status according to claim 1, characterized in that, The dead zone compensation controller eliminates the effects of dead zone through forward and reverse compensation channels.

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