Power grid dynamic support optimization method adaptive to hybrid converter

Abstract

The invention discloses a power grid dynamic support optimization method adaptive to a hybrid converter, and relates to the technical field of power grid optimization, and the method comprises the steps: collecting voltage deviation data at a power grid node, combining a loss index of the hybrid converter and a power grid response index, constructing a dynamic optimization model, and carrying out the comprehensive optimization of voltage deviation, loss and power grid response; dynamically adjusting the hybrid converter according to a calculation result of the dynamic optimization model so as to support and optimize the operation of the power grid; according to the method, accurate quantification of multi-working-condition loss of the power semiconductor device and the magnetic element is realized by constructing the loss index model, an overheat fault caused by accumulated loss is effectively prevented in combination with a threshold value alarm mechanism, and the long-term operation efficiency of equipment is improved. In addition, the life index model dynamically triggers a protection mechanism through temperature data acquisition and analysis, such as reduction of switching frequency or load balance, so that overheating aging of components is avoided, the service life of equipment is prolonged, and the maintenance cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid optimization, and in particular to a power grid dynamic support optimization method suitable for a hybrid converter. BACKGROUND

[0002] With the large-scale access of new energy power generation technologies (such as wind energy, solar energy, etc.) to the power grid, the operation environment of the power system is becoming increasingly complex. The volatility and intermittency characteristics of new energy have significantly increased the random disturbance of the voltage and frequency of the power grid. The traditional rigid power grid structure dominated by fossil energy has gradually become difficult to adapt to the dynamic regulation demand brought by high proportion of renewable energy penetration. Against this background, the hybrid converter, as the core device of new energy grid connection and power quality control, undertakes multiple functions such as power conversion, voltage regulation and reactive power compensation, and its performance directly affects the transient stability and dynamic response capability of the power grid.

[0003] However, the existing hybrid converter has many technical bottlenecks in the dynamic operation process: first, the loss mechanism of power semiconductor devices and magnetic elements is complex, and it is difficult to accurately quantify the cumulative loss under multiple working conditions, resulting in a decrease in the long-term operation efficiency of the equipment and even overheating failure; second, when the power grid fails or the load suddenly changes, the switching frequency and topology structure cannot be adaptively adjusted according to the real-time power grid state, causing the frequency recovery time to be prolonged and the voltage support capability to be insufficient; in addition, the high-temperature operating environment accelerates the aging of electronic components, significantly shortens the service life of the converter, increases the maintenance cost, and further affects the stable operation of the power grid.

[0004] In view of the above technical defects, the present application provides a solution. SUMMARY

[0005] The present application aims to solve the problems of the existing hybrid converter, such as the complex loss mechanism of power semiconductor devices and magnetic elements, the difficulty in accurately quantifying the cumulative loss under multiple working conditions, the decrease in the long-term operation efficiency of the equipment and even overheating failure, the inability to adaptively adjust the switching frequency and topology structure according to the real-time power grid state when the power grid fails or the load suddenly changes, the prolongation of the frequency recovery time and the insufficient voltage support capability, and the acceleration of the aging of electronic components in a high-temperature operating environment, which significantly shortens the service life of the converter, increases the maintenance cost, and further affects the stable operation of the power grid.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a power grid dynamic support optimization method suitable for a hybrid converter, comprising the following steps:

[0007] Step one, in the working process of the hybrid converter, the loss data and quantity of the internal electronic components of the hybrid converter are collected, and the collected data is calculated and analyzed to obtain the loss index of the hybrid converter, which is used to quantify the loss of the hybrid converter in the running process;

[0008] Step two, collect voltage change rate data and grid frequency offset data and integrate them to form a mutation set, calculate and analyze the mutation set data to obtain the grid response index, which is used to evaluate the stability of the grid;

[0009] Step three, collect voltage deviation data at the grid node, and combine the loss index of the hybrid converter and the grid response index to build a dynamic optimization model, and comprehensively optimize the voltage deviation, loss and grid response;

[0010] Step four, according to the calculation result of the dynamic optimization model, the hybrid converter is dynamically adjusted to support the optimization of the grid operation;

[0011] Step five, in the working process of the hybrid converter, the temperature data of the internal electronic components of the hybrid converter are collected, and the temperature data are calculated and analyzed to obtain the life index of the hybrid converter, which is used to reflect the service life of the hybrid converter and avoid overheating of the internal electronic components of the hybrid converter.

[0012] Further, the loss index analysis and calculation process of the hybrid converter is as follows:

[0013] S11, collect the loss data and quantity of the internal electronic components of the hybrid converter, and calculate and analyze the loss data;

[0014] S12, calculate the loss index P according to the following formula loss :

[0015]

[0016] Wherein, n is the number of power semiconductor devices, m is the number of magnetic elements, P cdt,i is the conduction loss of the i-th device, P s,i is the switching loss of the i-th device, P core,j is the core loss of the j-th magnetic element, the larger the loss index, the higher the loss degree of the hybrid converter in the running process;

[0017] S13, obtain the preset loss threshold, when the calculated loss index exceeds the preset loss threshold, it means that the loss of the internal electronic components of the hybrid converter is abnormal, and an alarm prompt will be sent to the remote monitoring platform.

[0018] Further, the analysis and calculation process of the grid response index is as follows:

[0019] S21, collect voltage rate of change data and grid frequency offset data and integrate to form a mutation set, and calculate and analyze the mutation set data;

[0020] S22, calculate the response index T according to the following formula:

[0021]

[0022] Wherein, p is the number of grid monitoring nodes, is divided according to the access point of the electrical equipment or user, w k is the voltage weight coefficient of node k, μ is the frequency sensitive factor, Δf is the grid frequency offset, |dV k / dt| is the voltage rate of change of node k, the larger the grid response index, the more stable the grid, on the contrary, it shows that the grid operation is unstable.

[0023] Further, the construction process of the dynamic optimization model is as follows:

[0024] S31, collect voltage deviation data at the grid node, and combine the loss index of the hybrid converter and the grid response index to construct the dynamic optimization model;

[0025] The expression of the dynamic optimization model function F opt is:

[0026]

[0027] Wherein, α, β, γ are respectively the preset weight coefficients of voltage deviation, loss and response index, p is the number of grid monitoring nodes, ΔV k is the voltage deviation of node k;

[0028] S33, taking the minimum dynamic optimization model function F opt as the optimization goal, the dynamic optimization model is constructed to comprehensively optimize the voltage deviation, loss and grid response.

[0029] Further, the dynamic optimization grid operation process of the hybrid converter is as follows:

[0030] S41, adjust the switching frequency and duty cycle of the hybrid converter, and optimize the output voltage and power;

[0031] S42, in the low voltage scenario of the grid, the reactive power support function of the hybrid converter is activated, and the inductive reactive power is injected to suppress the voltage drop, and when the grid is in the high voltage scenario, the hybrid converter will absorb the excess inductive reactive power to reduce the voltage level;

[0032] S43, dynamically switching the converter topology according to the loss model.

[0033] Further, the analysis and calculation process of the life index of the hybrid converter is as follows:

[0034] S51, collecting temperature data of internal electronic components of the hybrid converter, and performing calculation and analysis on the temperature data;

[0035] S52, calculating the life index L of the hybrid converter according to the following formula life :

[0036]

[0037] Wherein, T e is the preset optimal operating temperature of the internal electronic components of the hybrid converter, L0 is the preset standard operating life of the internal electronic components of the hybrid converter, is the preset temperature acceleration coefficient, T avg is the average operating temperature of the internal electronic components of the hybrid converter;

[0038] S53, a preset life threshold is obtained, when the life index is less than or equal to the preset life threshold, the hybrid converter protection mechanism is triggered to protect the hybrid converter, and when the life index is greater than the preset life threshold, the hybrid converter protection mechanism is not triggered.

[0039] As described above, due to the adoption of the above technical scheme, the present application has the following beneficial effects:

[0040] The power grid dynamic support optimization method suitable for the hybrid converter realizes accurate quantification of the multi-working condition loss of the power semiconductor device and the magnetic element by constructing a loss index model, effectively prevents overheating failure caused by cumulative loss in combination with a threshold alarm mechanism, and improves the long-term operation efficiency of the equipment. Secondly, based on the power grid response index calculation of the voltage change rate and the frequency offset data, in combination with the dynamic optimization model, the hybrid converter can realize real-time sensing of the power grid state, and by adjusting the switching frequency, the duty ratio and the topology structure, the power grid transient stability and dynamic response capability are enhanced, especially in the low voltage scene, the inductive reactive power is injected to suppress voltage drop, and in the high voltage scene, the excess reactive power is absorbed, which significantly optimizes the voltage support effect. In addition, the life index model collects and analyzes temperature data, dynamically triggers the protection mechanism, such as reducing the switching frequency or load balancing, avoids overheating and aging of the components, prolongs the service life of the equipment, and reduces the maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The method flowchart of the present application is shown. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0043] Embodiment:

[0044] As shown in the figure, the power grid dynamic support optimization method suitable for the hybrid converter includes the following steps: Figure 1

[0045] Step one, collect the loss data and quantity of the internal electronic components of the hybrid converter during the working process of the hybrid converter, number the electronic components, and calculate and analyze the collected data to obtain the loss index of the hybrid converter, which is used to quantify the loss of the hybrid converter during the running process;

[0046] The loss index analysis and calculation process of the hybrid converter is as follows:

[0047] S11, collect the loss data and quantity of the internal electronic components of the hybrid converter, and calculate and analyze the loss data;

[0048] S12, calculate the loss index P according to the following formula loss :

[0049]

[0050] Wherein, n is the number of power semiconductor devices (such as MOSFET, IGBT), m is the number of magnetic components (such as inductance, transformer), P cdt,i is the conduction loss of the i-th device, P s,i is the switching loss of the i-th device, P core,j is the core loss of the j-th magnetic component, the larger the loss index, the higher the loss degree of the hybrid converter during the running process;

[0051] S13, obtain the preset loss threshold, when the calculated loss index exceeds the preset loss threshold, it means that the loss of the internal electronic components of the hybrid converter is abnormal, an alarm prompt will be sent to the remote monitoring platform, and the staff will be dispatched to carry out on-site maintenance of the hybrid converter.

[0052] Step two, collect the voltage change rate data and power grid frequency offset data and integrate them to form a mutation set, calculate and analyze the mutation set data to obtain the power grid response index, which is used to evaluate the stability of the power grid;

[0053] ​The analysis and calculation process of the grid response index is as follows:

[0054] S21, collect voltage rate of change data and grid frequency offset data and integrate to form a mutation set, and calculate and analyze the mutation set data;

[0055] S22, calculate the response index T according to the following formula:

[0056]

[0057] Wherein, p is the number of grid monitoring nodes, is divided according to the access point of the electrical equipment or user, w k is the voltage weight coefficient of node k, μ is the frequency sensitive factor, Δf is the grid frequency offset, |dV k / dt| is the voltage rate of change of node k, the larger the grid response index, the more stable the grid, on the contrary, the grid operation is unstable, and optimization adjustment needs to be made to the grid operation.

[0058] Step three, collect the voltage deviation data at the grid nodes, and combine the loss index of the hybrid converter and the grid response index to build a dynamic optimization model, and comprehensively optimize the voltage deviation, loss and grid response;

[0059] The construction process of the dynamic optimization model is as follows:

[0060] S31, collect the voltage deviation data at the grid nodes, and combine the loss index of the hybrid converter and the grid response index to build a dynamic optimization model;

[0061] S32, the expression of the dynamic optimization model function F opt is:

[0062]

[0063] Wherein, α, β, γ are respectively the preset weight coefficients of voltage deviation, loss and response index, p is the number of grid monitoring nodes, ΔV k is the voltage deviation (the difference between the actual value and the rated value) of node k;

[0064] S33, taking minimizing the dynamic optimization model function F opt as the optimization goal, the dynamic optimization model is built to comprehensively optimize the voltage deviation, loss and grid response.

[0065] Step four, according to the calculation result of the dynamic optimization model, the hybrid converter is dynamically adjusted to support the optimization of grid operation;

[0066] The process of the hybrid converter dynamically optimizing the grid operation is as follows:

[0067] S41, adjust the switching frequency and duty cycle of the hybrid converter, optimize the output voltage and power;

[0068] S42, in the low voltage grid scenario, activate the reactive power support function of the hybrid converter, inject inductive reactive power to suppress voltage drop, when the grid is in high voltage scenario, the hybrid converter will absorb excess inductive reactive power (or equivalent as injecting capacitive reactive power), to reduce the voltage level;

[0069] S43, according to the loss model, dynamically switch the converter topology, for example, from LLC mode to DAB mode at light load to reduce loss.

[0070] Step five, collect the temperature data of the internal electronic components of the hybrid converter during the working process of the hybrid converter, and analyze the temperature data to obtain the life index of the hybrid converter, which is used to reflect the service life of the hybrid converter, and avoid overheating of the internal electronic components of the hybrid converter;

[0071] The analysis and calculation process of the life index of the hybrid converter is as follows:

[0072] S51, collect the temperature data of the internal electronic components of the hybrid converter, and analyze the temperature data;

[0073] S52, calculate the life index L of the hybrid converter according to the following formula life :

[0074]

[0075] Where, T e is the preset optimal operating temperature of the internal electronic components of the hybrid converter, L0 is the preset standard operating life of the internal electronic components of the hybrid converter, is the preset temperature acceleration coefficient, T avg is the average operating temperature of the internal electronic components of the hybrid converter;

[0076] S53, get the preset life threshold, when the life index is less than or equal to the preset life threshold, the hybrid converter protection mechanism will be triggered to protect the hybrid converter, when the life index is greater than the preset life threshold, the hybrid converter protection mechanism will not be triggered, the specific protection mechanism is: dynamically reduce the switching frequency or load current, or distribute power to the components with lower temperature through load balancing algorithm, reduce the heat, avoid overheating of the key components, prolong the service life of the hybrid converter.

[0077] By constructing the loss index model, the multi-working condition loss of power semiconductor devices and magnetic elements is accurately quantified, and combined with the threshold alarm mechanism, the overheat failure caused by cumulative loss is effectively prevented, and the long-term operation efficiency of the equipment is improved. Secondly, based on the grid response index calculation of voltage change rate and frequency offset data, combined with the dynamic optimization model, the hybrid converter can realize real-time sensing of the grid state, adjust the switching frequency, duty ratio and topology structure, and enhance the transient stability and dynamic response ability of the grid. Especially in the low voltage scene, the inductive reactive power is injected to suppress voltage drop, and in the high voltage scene, the excess reactive power is absorbed, which significantly optimizes the voltage support effect. In addition, the life index model collects and analyzes temperature data, dynamically triggers the protection mechanism, such as reducing the switching frequency or load balancing, to avoid overheating and aging of components, prolong the service life of the equipment, and reduce the maintenance cost.

[0078] The setting of the size of the interval and the threshold value is for easy comparison. The size of the threshold value depends on the amount of sample data and the base number set by the person skilled in the art for each group of sample data. As long as it does not affect the proportional relationship of the parameters and the quantized values.

[0079] The above formulas are dimensionless values calculated, the formulas are obtained by collecting a large amount of data to simulate the most recent real situation, and the preset parameters in the formulas are set by the person skilled in the art according to the actual situation;

[0080] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for grid dynamic support optimization for adaptive hybrid converters, characterized in that, Comprising the following steps: Step one, collect the loss data and quantity of the internal electronic components of the hybrid converter during operation, number the electronic components, and calculate and analyze the collected data to obtain the loss index of the hybrid converter, which is used to quantify the loss of the hybrid converter during operation; Step two, collect voltage change rate data and grid frequency offset data and integrate them to form a mutation set, calculate and analyze the mutation set data to obtain the grid response index, which is used to evaluate the stability of the grid; Step three, collect voltage deviation data at the grid node, and combine the loss index of the hybrid converter and the grid response index to build a dynamic optimization model, and comprehensively optimize the voltage deviation, loss and grid response; Step four, based on the calculation results of the dynamic optimization model, dynamically adjust the hybrid converter to support the optimization of grid operation; Step five, collect the temperature data of the internal electronic components of the hybrid converter during operation, and calculate and analyze the temperature data to obtain the life index of the hybrid converter, which is used to reflect the service life of the hybrid converter.

2. The method of grid dynamics support optimization for adaptive hybrid converter according to claim 1, characterized in that, The loss index analysis and calculation process of the hybrid converter is as follows: S11, collect the loss data and quantity of the internal electronic components of the hybrid converter, and calculate and analyze the loss data; S12. Calculate the loss index P according to the following formula loss : wherein n is the number of power semiconductor devices, m is the number of magnetic elements, P cdt,i is the conduction loss of the i-th device, P s,i is the switching loss of the i-th device, P core,j is the core loss of the j-th magnetic element, the greater the loss index, the higher the degree of loss of the hybrid converter during operation; S13, obtain the preset loss threshold, when the calculated loss index exceeds the preset loss threshold, it indicates that the loss of the internal electronic components of the hybrid converter is abnormal, and an alarm prompt will be sent to the remote monitoring platform.

3. The grid dynamic support optimization method for adaptive hybrid converter according to claim 1, characterized in that, The analysis and calculation process of the grid response index is as follows: S21, collect voltage change rate data and grid frequency offset data and integrate them to form a mutation set, and calculate and analyze the mutation set data; S22, calculate the response index T according to the following formula: wherein p is the number of grid monitoring nodes, w k is the voltage weight coefficient of node k, μ is the frequency sensitive factor, Δf is the grid frequency offset, |dV k / dt| is the voltage change rate of node k, the larger the grid response index, the more stable the grid, otherwise, it indicates that the grid is unstable.

4. The grid dynamic support optimization method for adaptive hybrid converter according to claim 1, characterized in that, The construction process of the dynamic optimization model is as follows: S31, collect voltage deviation data at the grid node, and combine the loss index of the hybrid converter and the grid response index to build a dynamic optimization model; S32, dynamically optimize the model function F opt The expression is: Wherein, α, β, γ are respectively preset weight coefficients of voltage deviation, loss, response index, p is the number of grid monitoring nodes, ΔV k is the voltage deviation of node k; S33, minimizing the dynamic optimization model function F opt As the optimization objective, a dynamic optimization model is constructed to comprehensively optimize the voltage deviation, loss and grid response.

5. The grid dynamic support optimization method for adaptive hybrid converter according to claim 1, characterized in that, The process of dynamic optimization of hybrid converter for grid operation is as follows: S41, adjust the switching frequency and duty cycle of the hybrid converter to optimize the output voltage and power; S42, in the low voltage scenario of the grid, activate the reactive power support function of the hybrid converter to inject inductive reactive power to suppress voltage drop, and in the high voltage scenario of the grid, the hybrid converter will absorb excess inductive reactive power to reduce the voltage level; S43, dynamically switch the converter topology according to the loss model.

6. The grid dynamic support optimization method for adaptive hybrid converter according to claim 1, characterized in that, The analysis and calculation process of the life index of the hybrid converter is as follows: S51, collect the temperature data of the internal electronic components of the hybrid converter, and calculate and analyze the temperature data; S52, calculate the life index L of the hybrid converter according to the following formula life : Wherein, T e is the preset optimal operating temperature of the internal electronic components of the hybrid converter, L0 is the preset standard operating life of the internal electronic components of the hybrid converter, is the preset temperature acceleration coefficient, T avg is the average operating temperature of the internal electronic components of the hybrid converter; S53, obtain the preset life threshold, when the life index is less than or equal to the preset life threshold, the hybrid converter protection mechanism will be triggered to protect the hybrid converter, and when the life index is greater than the preset life threshold, the hybrid converter protection mechanism will not be triggered.

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