A fuzzy state machine-based wind storage system black start method and device

Abstract

The application relates to the technical field of new energy black start control, and discloses a wind storage system black start method and device based on a fuzzy state machine, which comprises the following steps: obtaining state variables of wind storage system black start; wherein the state variables of wind storage system black start include a direct-current bus voltage, an alternating-current bus frequency and a storage battery capacity; performing fuzzy processing on the state variables of wind storage system black start by using a membership function to obtain membership values; performing fuzzy reasoning based on the membership values to determine a wind storage system black start fuzzy state; inputting the wind storage system black start fuzzy state into a pre-set fuzzy state machine to obtain a wind storage system black start control strategy; and controlling wind storage system black start based on the wind storage system black start control strategy. The application realizes soft switching of different operating states in the wind storage system black start process and black start control of the wind storage system, and has stronger robustness.

Description

Technical Field

[0001] This invention relates to the field of new energy black-start control technology, specifically to a black-start method and device for a wind-storage system based on a fuzzy state machine. Background Technology

[0002] The large-scale grid connection of new energy sources has reduced the inertia capacity of the power grid, exacerbating the risk of power outages. Black start is the first step in restoring power after a major blackout, and more reliable black start power sources are needed to restore power as quickly as possible. To improve the system's startup capability, the possibility of using wind-storage power generation systems as black start power sources is considered. Black start power sources need to provide stable power to the units being started and establish stable voltage and frequency during major grid outages.

[0003] However, the black-start control mechanism of the relevant wind-storage power generation system does not consider the transition between different black-start states, and the robustness of the black-start control is not strong enough. Summary of the Invention

[0004] In view of this, the present invention provides a black start method and apparatus for wind and energy storage systems based on fuzzy state machines to solve the problem that the black start control mechanism of wind and energy storage power generation systems does not consider the transition between different black start states and the robustness of black start control is not strong enough.

[0005] In a first aspect, the present invention provides a black-start method for a wind-storage system based on a fuzzy state machine, the method comprising:

[0006] Obtain the state variables for black start of the wind-storage system; among which, the state variables for black start of the wind-storage system include DC bus voltage, AC bus frequency and battery capacity;

[0007] The membership function is used to fuzzify the state variables of the wind-storage system during black start-up to obtain the membership values;

[0008] Fuzzy inference based on membership values ​​is used to determine the black start fuzzy state of the wind-storage system.

[0009] The black start fuzzy state of the wind and storage system is input into a pre-set fuzzy state machine to obtain the black start control strategy of the wind and storage system.

[0010] Controlling the black start of the wind and storage system based on the black start control strategy of the wind and storage system.

[0011] This embodiment provides a black-start method for a wind-storage system based on a fuzzy state machine. A fuzzy state machine is pre-set for the operating states during the black-start of the wind-storage system. Then, by fuzzifying the state variables of the wind-storage system during black-start and performing fuzzy inference based on membership values, a transition method for the fuzzy states of the wind-storage system during black-start is established. The fuzzy state machine is used to obtain the black-start control strategies corresponding to different fuzzy states of the wind-storage system during black-start, realizing soft switching between different operating states during the black-start process and black-start control of the wind-storage system, thus exhibiting stronger robustness.

[0012] In one optional implementation, fuzzy reasoning based on membership values ​​is used to determine the black-start fuzzy state of the wind-storage system, including:

[0013] The operational status of the wind-storage system during black start is obtained. Based on the operational status and state variables of the wind-storage system during black start, an expert survey questionnaire is constructed using the analytic hierarchy process.

[0014] Fuzzy reasoning rules are constructed based on the expert knowledge corresponding to the expert survey questionnaire.

[0015] Based on fuzzy inference rules, the black-start fuzzy state of the wind-storage system corresponding to the membership degree value is determined by using a lookup table method.

[0016] This embodiment provides a black-start method for wind and energy storage systems based on fuzzy state machines. It constructs an expert questionnaire using the analytic hierarchy process (AHP), builds fuzzy inference rules based on expert knowledge, and then accurately constructs fuzzy state transition decisions for black-start wind and energy storage systems based on the fuzzy inference rules. This lays the foundation for the accurate acquisition of the fuzzy states of wind and energy storage systems corresponding to membership values ​​during black-start, and improves the speed and accuracy of acquiring the fuzzy states of wind and energy storage systems during black-start.

[0017] In one optional implementation, fuzzy reasoning rules are constructed based on expert knowledge corresponding to the expert questionnaire, including:

[0018] The expert questionnaire was evaluated using expert knowledge to obtain expert evaluation parameters, and an expert reasoning result matrix was constructed based on the expert evaluation parameters.

[0019] Obtain the expert authority evaluation matrix, and then weight the expert reasoning result matrix based on the expert authority evaluation matrix to obtain the weighted expert reasoning result matrix;

[0020] Fuzzy inference rules are constructed based on the weighted matrix of expert inference results.

[0021] This embodiment provides a black-start method for a wind-storage system based on a fuzzy state machine. It weights the expert inference result matrix based on the expert authority evaluation matrix and accurately divides the membership values ​​of state variables corresponding to different fuzzy states, laying the foundation for the construction of subsequent fuzzy inference rules.

[0022] In one optional implementation, the black-start fuzzy state of the wind-storage system is input into a pre-set fuzzy state machine to obtain a black-start control strategy for the wind-storage system, including:

[0023] Obtain the current operating status of the wind-storage system during black start, and compare the fuzzy state of the wind-storage system during black start with the current operating status of the wind-storage system during black start;

[0024] If the fuzzy state of the black start of the wind and energy storage system is consistent with the current operating state of the black start of the wind and energy storage system, then the current control strategy will be used as the black start control strategy of the wind and energy storage system.

[0025] If the fuzzy state of the black start of the wind and energy storage system is inconsistent with the current operating state of the black start of the wind and energy storage system, then the control strategy corresponding to the fuzzy state of the black start of the wind and energy storage system shall be used as the black start control strategy of the wind and energy storage system.

[0026] This embodiment provides a black-start method for a wind and energy storage system based on a fuzzy state machine. By comparing the fuzzy state of the wind and energy storage system during black-start with the current operating state of the wind and energy storage system during black-start, the black-start control strategy of the wind and energy storage system is determined based on the comparison result. This realizes soft switching between different operating states during the black-start process of the wind and energy storage system, as well as black-start control of the wind and energy storage system, and has stronger robustness.

[0027] In one optional implementation, the black start fuzzy states of the wind-storage system include the black start wind turbine DC capacitor charging state, the black start wind turbine low power operation state, the black start wind turbine stable operation state, the black start battery charging state, the black start battery discharging state, the other wind turbines being put into operation state, and the load recovery state.

[0028] In one optional implementation, the black start control strategy of the wind-storage system includes a black start wind turbine DC capacitor charging control strategy, a black start wind turbine low-power operation control strategy, a black start wind turbine stable operation control strategy, a black start battery charging control strategy, a black start battery discharging control strategy, other wind turbine input control strategies, and a load input control strategy.

[0029] Secondly, the present invention provides a black-start device for a wind-storage system based on a fuzzy state machine, the device comprising:

[0030] The acquisition module is used to acquire the state variables of the wind-storage system during black start-up; among which, the state variables of the wind-storage system during black start-up include DC bus voltage, AC bus frequency and battery capacity;

[0031] The fuzzification module is used to fuzzify the state variables of the black start of the wind-storage system using a membership function to obtain membership values.

[0032] The fuzzy inference module is used to perform fuzzy inference based on membership values ​​to determine the black start fuzzy state of the wind-storage system.

[0033] The determination module is used to input the black-start fuzzy state of the wind-storage system into a pre-set fuzzy state machine to obtain the black-start control strategy of the wind-storage system.

[0034] The control module is used to control the black start of the wind and storage system based on the black start control strategy of the wind and storage system.

[0035] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the black start method for a wind-storage system based on a fuzzy state machine as described in the first aspect or any corresponding embodiment.

[0036] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the fuzzy state machine-based black-start method for a wind-storage system described in the first aspect or any of its corresponding embodiments.

[0037] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the fuzzy state machine-based black-start method for a wind and energy storage system described in the first aspect or any of its corresponding embodiments. Attached Figure Description

[0038] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific 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 from these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating a black start method for a wind-storage system based on a fuzzy state machine according to an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the structure of a wind storage system according to an embodiment of the present invention;

[0041] Figure 3 This is a flowchart illustrating another black-start method for a wind-storage system based on a fuzzy state machine according to an embodiment of the present invention.

[0042] Figure 4 This is a flowchart illustrating another black-start method for a wind-storage system based on a fuzzy state machine according to an embodiment of the present invention.

[0043] Figure 5 This is a schematic diagram of a fuzzy state machine according to an embodiment of the present invention;

[0044] Figure 6 This is a structural block diagram of a black start device for a wind-storage system based on a fuzzy state machine according to an embodiment of the present invention;

[0045] Figure 7 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] The converter decoupling control algorithm, based on PI control (a linear control method that achieves system stability through proportional and integral control), is the mainstream control method used in grid-side converters of wind power generation systems. This method employs a dual-loop control structure: an outer voltage loop and an inner current loop. The outer voltage loop achieves stable control of the DC-side capacitor voltage, and its output serves as a reference value for the inner current loop. Utilizing the speed of the inner current loop, the AC-side current is adjusted promptly to suppress the effects of load disturbances, enabling the actual current to quickly track the current reference value, thereby achieving unity power factor control.

[0048] In addition, by combining some control methods (such as predictive control), predictive models and rolling optimization models of the system are designed, and the system output power can be optimized and black-started by solving the models.

[0049] However, the black-start control mechanism of the aforementioned wind-storage power generation system determines different states (DC capacitor charging stage, black-start wind turbine low-power operation stage, black-start wind turbine stable operation stage, battery charging stage, black-start battery discharging stage, other wind turbine commissioning stage, load recovery stage) and control mechanisms, but does not consider the transition between different states, and the control robustness is not strong enough.

[0050] This invention provides a black-start method for a wind and energy storage system based on a fuzzy state machine. By comprehensively analyzing the operating states of the wind and energy storage system during black-start, a finite state machine for black-start is constructed. Different control strategies are determined for different operating states of the wind and energy storage system during black-start. Considering the switching between different operating states, fuzzy reasoning is introduced to establish a fuzzy state machine for black-start of the wind and energy storage system. Soft switching between different states is then implemented to achieve black-start of the wind and energy storage system.

[0051] According to an embodiment of the present invention, a black start method for a wind storage system based on a fuzzy state machine is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0052] This embodiment provides a black-start method for a wind-storage system based on a fuzzy state machine, which can be used in wind-storage systems. Figure 1 This is a flowchart of a black-start method for a wind-storage system based on a fuzzy state machine according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0053] Step S101: Obtain the state variables for black start of the wind-storage system; wherein, the state variables for black start of the wind-storage system include DC bus voltage, AC bus frequency and battery capacity.

[0054] Specifically, such as Figure 2 As shown, the wind-storage system includes a wind turbine 1, a battery 1, an AC / DC converter, a DC / DC converter, a DC / AC converter, a DC bus (DC bus1), a DC capacitor T1, and an AC bus 2. Wind turbine 1 is connected in series with the AC / DC converter; battery 1 is connected in series with the DC / DC converter; the DC bus is connected in parallel with both the AC / DC converter and the DC / DC converter; the DC bus is connected to AC bus 2 via the DC / AC converter and the DC capacitor; AC bus 2 is connected to AC loads (L1, L2, and L3); other wind turbines (e.g., wind turbine 2) are connected to the DC bus (DC bus1) via the AC / DC converter. The DC bus (DCbus2) is connected to the AC bus 1 via the DC / AC converter, DC capacitor T2, and AC bus 1. AC bus 1 is connected to AC bus 2 via an AC line. Among them, the fan 1, battery 1, AC / DC converter, DC / DC converter, DC / AC converter, and DC bus (DCbus1) constitute VSC1, where VSC (Variable Speed ​​Controller) is a frequency converter. Meanwhile, other fans, AC / DC converters, DC bus (DC bus2), and DC / AC converter constitute VSC2.

[0055] Furthermore, the DC bus voltage, AC bus 2 frequency, and battery 1 capacity are selected as the state variables for the black start of the wind-storage system.

[0056] Step S102: Use the membership function to fuzzify the state variables of the wind-storage system during black start-up to obtain the membership values.

[0057] Specifically, the membership function uses the trigonometric membership function, and its expression is shown below:

[0058]

[0059] Where μ(x) represents the membership value, x represents the state variable of the wind-storage system during black start, and a, b, and c represent the starting point, peak value, and ending point of the triangular membership function, respectively.

[0060] Furthermore, in the state variables of the black start of the wind-storage system, the DC bus voltage is set to three states: high, medium, and low; the AC bus frequency is set to five states: high, relatively high, medium, relatively low, and low; and the battery capacity is set to five states: high, relatively high, medium, relatively low, and low.

[0061] Furthermore, the state variables for the black start of the wind-storage system correspond to 13 membership values: high membership value, medium membership value, and low membership value for DC bus voltage, with the largest membership value representing the fuzzy state of DC bus voltage; high membership value, relatively high membership value, medium membership value, relatively low membership value, and low membership value for AC bus frequency, with the largest membership value representing the fuzzy state of AC bus frequency; and high membership value, relatively high membership value, medium membership value, relatively low membership value, and low membership value for battery capacity, with the largest membership value representing the fuzzy state of battery capacity.

[0062] Step S103: Perform fuzzy inference based on membership values ​​to determine the black start fuzzy state of the wind-storage system.

[0063] Step S104: Input the black start fuzzy state of the wind and storage system into a pre-set fuzzy state machine to obtain the black start control strategy of the wind and storage system.

[0064] Step S105: Control the black start of the wind and storage system based on the black start control strategy of the wind and storage system.

[0065] Specifically, the black start process of the wind-storage system is as follows: Battery 1 charges the DC capacitor of the black start wind turbine 1; after the DC capacitor is fully charged, the black start wind turbine 1 is set to operate in a low-power mode; after the wind turbine speed stabilizes, the black start wind turbine 1 starts to generate electricity stably in MPPT mode; if the power generation of wind turbine 1 is greater than the load power, battery 1 is started to charge; if the power generation of wind turbine 1 is less than the load power, battery 1 is started to discharge; other wind turbines are put into operation and the load is gradually put into operation to ensure that the power generation of the entire system is consistent with the load power.

[0066] This embodiment provides a black-start method for a wind-storage system based on a fuzzy state machine. A fuzzy state machine is pre-set for the operating states during the black-start of the wind-storage system. Then, by fuzzifying the state variables of the wind-storage system during black-start and performing fuzzy inference based on membership values, a transition method for the fuzzy states of the wind-storage system during black-start is established. The fuzzy state machine is used to obtain the black-start control strategies corresponding to different fuzzy states of the wind-storage system during black-start, realizing soft switching between different operating states during the black-start process and black-start control of the wind-storage system, thus exhibiting stronger robustness.

[0067] This embodiment provides a black-start method for a wind-storage system based on a fuzzy state machine, which can be used in the aforementioned wind-storage system. Figure 3 This is a flowchart of a black-start method for a wind-storage system based on a fuzzy state machine according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0068] Step S301: Obtain the state variables for black start of the wind-storage system; these state variables include DC bus voltage, AC bus frequency, and battery capacity. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0069] Step S302: The state variables of the wind-storage system during black start are fuzzified using a membership function to obtain membership values. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0070] Step S303: Perform fuzzy inference based on membership values ​​to determine the black start fuzzy state of the wind-storage system.

[0071] Specifically, step S303 includes:

[0072] Step S3031: Obtain the operating status of the wind-storage system during black start. Based on the operating status and state variables of the wind-storage system during black start, construct an expert survey questionnaire using the analytic hierarchy process (AHP).

[0073] Specifically, experts scored the state variables of the 13 wind-storage system during black start-up, with a scoring range of 1-7. These correspond to the following states: black start wind turbine DC capacitor charging state, black start wind turbine low-power operation state, black start wind turbine stable operation state, black start battery charging state, black start battery discharging state, other wind turbine activation state, and load recovery state. The expert questionnaire is shown in Table 1 below.

[0074] Table 1:

[0075]

[0076] Step S3032: Construct fuzzy reasoning rules based on the expert knowledge corresponding to the expert survey questionnaire.

[0077] In some optional implementations, step S3032 above includes:

[0078] Step a1: Use expert knowledge to evaluate the expert questionnaire, obtain expert evaluation parameters, and construct an expert reasoning result matrix based on the expert evaluation parameters.

[0079] Specifically, let the matrix of expert reasoning results be S, where S∈R e Where e represents the number of experts, and s is the element in the nth row of the expert reasoning result matrix. n This represents the reasoning result of the nth expert.

[0080] Step a2: Obtain the expert authority assessment matrix, and weight the expert reasoning result matrix based on the expert authority assessment matrix to obtain the weighted expert reasoning result matrix.

[0081] For example, the expert authority assessment matrix can be constructed based on the data shown in Table 2 below.

[0082] Table 2:

[0083]

[0084] Specifically, the expert authority assessment matrix is ​​A, where A∈R e×k k is the number of evaluation items, and a is an element in the expert authority evaluation matrix. nm This represents the m-th competency assessment value of the nth expert; the expert authority weight coefficient matrix C∈R 1×e The authority weight coefficient c of the nth expert n for:

[0085]

[0086] Furthermore, the formula for calculating the weighted expert reasoning result W is as follows:

[0087] W = CS (3)

[0088] Step a3: Construct fuzzy inference rules based on the weighted expert inference result matrix.

[0089] Specifically, after calculating the weighted expert inference result W, the result is rounded down from 1 to 7 to obtain the fuzzy inference rules corresponding to the black start fuzzy states of different wind storage systems.

[0090] For example, the fuzzy inference rule can be as follows: when the fuzzy states of the DC bus voltage, AC bus frequency, and battery capacity during the black start of the wind-storage system are medium, high, and medium respectively, the power generation is greater than the load power, and the fuzzy state machine is the load recovery turntable, controlling the gradual connection of the load; when the fuzzy states of the DC bus voltage, AC bus frequency, and battery capacity during the black start of the wind-storage system are medium, high, and low respectively, the power generation is greater than the load power, and the fuzzy state machine is the black start battery charging state, controlling the battery charging; when the fuzzy states of the DC bus voltage, AC bus frequency, and battery capacity during the black start of the wind-storage system are low, low, and high respectively, the fuzzy state machine is the black start wind turbine DC capacitor charging state, controlling the black start wind turbine DC capacitor charging.

[0091] Step S3033: Based on fuzzy inference rules, the black start fuzzy state of the wind-storage system corresponding to the membership degree value is determined by using a lookup table method.

[0092] Specifically, the membership value is matched with the value of the state variable of the wind-storage system during black start in the fuzzy inference rule, and the fuzzy state of the wind-storage system during black start is determined based on the matching result.

[0093] Step S304: Input the black-start fuzzy state of the wind-storage system into a pre-set fuzzy state machine to obtain the black-start control strategy for the wind-storage system. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0094] Step S305: Control the black start of the wind and storage system based on the black start control strategy. For details, please refer to [link to relevant documentation]. Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0095] This embodiment provides a black-start method for wind and energy storage systems based on fuzzy state machines. It constructs an expert questionnaire using the analytic hierarchy process (AHP), builds fuzzy inference rules based on expert knowledge, and then accurately constructs fuzzy state transition decisions for black-start wind and energy storage systems based on the fuzzy inference rules. This lays the foundation for the accurate acquisition of the fuzzy states of wind and energy storage systems corresponding to membership values ​​during black-start, and improves the speed and accuracy of acquiring the fuzzy states of wind and energy storage systems during black-start.

[0096] This embodiment provides a black-start method for a wind-storage system based on a fuzzy state machine, which can be used in the aforementioned wind-storage system. Figure 4 This is a flowchart of a black-start method for a wind-storage system based on a fuzzy state machine according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0097] Step S401: Obtain the state variables for black start of the wind-storage system; these state variables include DC bus voltage, AC bus frequency, and battery capacity. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0098] Step S402: The state variables of the wind-storage system during black start are fuzzified using a membership function to obtain membership values. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0099] Step S403: Perform fuzzy inference based on membership values ​​to determine the black-start fuzzy state of the wind-storage system. For details, please refer to [link to relevant documentation]. Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0100] Step S404: Input the black start fuzzy state of the wind and storage system into a pre-set fuzzy state machine to obtain the black start control strategy of the wind and storage system.

[0101] Specifically, step S404 includes:

[0102] Step S4041: Obtain the current operating status of the black start of the wind and storage system, and compare the fuzzy state of the black start of the wind and storage system with the current operating status of the black start of the wind and storage system.

[0103] Specifically, such as Figure 5 As shown, the fuzzy states of the black start of the wind-storage system include the black start wind turbine DC capacitor charging state, the black start wind turbine low power operation state, the black start wind turbine stable operation state, the black start battery charging state, the black start battery discharging state, the other wind turbines being put into operation state, and the load recovery state.

[0104] Furthermore, the DC capacitor charging state of the black-start fan is that the fan and the battery charge the DC capacitor through DC buses connected in parallel with the AC / DC converter and DC / DC converter, respectively; the low-power operation state of the black-start fan is that the fan operates in low-power mode (at the start of black start, the fan is not operating in MPPT mode); the stable operation state of the black-start fan is that the fan is in a stable wind speed state, and the fan operates in MPPT (Maximum power point) mode. The system operates in maximum power point tracking (MPPT) mode. Black start battery charging occurs when the wind turbine's power output exceeds the preset load power and the DC bus voltage is greater than the rated voltage (or the battery capacity is low), at which point the battery is charging. Black start battery discharging occurs when the wind turbine's power output is less than the preset load power (or when the wind turbine is not running, the DC capacitor is charging), at which point the battery is discharging, maintaining power balance. Other wind turbines are in operation when they are running (i.e., after the black start wind turbines have stabilized power generation and the AC bus voltage and frequency have stabilized, other wind turbines gradually start). Load recovery occurs when other loads connected to the AC bus are in recovery mode.

[0105] Step S4042: If the fuzzy state of the black start of the wind and storage system is consistent with the current operating state of the black start of the wind and storage system, then the current control strategy shall be used as the black start control strategy of the wind and storage system.

[0106] Step S4043: If the fuzzy state of the black start of the wind and storage system is inconsistent with the current operating state of the black start of the wind and storage system, then the control strategy corresponding to the fuzzy state of the black start of the wind and storage system shall be used as the black start control strategy of the wind and storage system.

[0107] Specifically, the black start control strategies for the wind-storage system include: black start wind turbine DC capacitor charging control strategy, black start wind turbine low power operation control strategy, black start wind turbine stable operation control strategy, black start battery charging control strategy, black start battery discharging control strategy, other wind turbine input control strategy, and load input control strategy.

[0108] Furthermore, the black-start wind turbine DC capacitor charging control strategy is to control the black-start battery discharge current to slowly charge the DC capacitor; the black-start wind turbine low-power operation control strategy is to control the wind turbine to operate in low-power mode; the black-start wind turbine stable operation control strategy is to control the wind turbine to operate in MPPT mode; the black-start battery charging control strategy is to control the DC / DC converter to discharge the battery when the power generation is less than the load power; the black-start battery discharge control strategy is to control the DC / DC converter to charge the storage battery when the power generation is greater than the load power; the other wind turbine commissioning control strategy is to control other wind turbines to be commissioned when the power generation is less than the load power after the black-start wind turbine is running stably; the load commissioning control strategy is to control the load commissioning when the power generation is greater than the load power and the battery capacity is high after the black-start wind turbine is running stably.

[0109] Step S405: Control the black start of the wind and storage system based on the black start control strategy. For details, please refer to [link to relevant documentation]. Figure 2 Step S305 of the illustrated embodiment will not be described again here.

[0110] This embodiment provides a black-start method for a wind and energy storage system based on a fuzzy state machine. By comparing the fuzzy state of the wind and energy storage system during black-start with the current operating state of the wind and energy storage system during black-start, the black-start control strategy of the wind and energy storage system is determined based on the comparison result. This realizes soft switching between different operating states during the black-start process of the wind and energy storage system, as well as black-start control of the wind and energy storage system, and has stronger robustness.

[0111] This embodiment also provides a black-start device for a wind-storage system based on a fuzzy state machine. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0112] This embodiment provides a black-start device for a wind-storage system based on a fuzzy state machine, such as... Figure 6 As shown, it includes:

[0113] The acquisition module 601 is used to acquire the state variables of the wind-storage system during black start; wherein, the state variables of the wind-storage system during black start include DC bus voltage, AC bus frequency and battery capacity;

[0114] The fuzzification processing module 602 is used to perform fuzzification processing on the state variables of the black start of the wind storage system using the membership function to obtain the membership value.

[0115] Fuzzy reasoning module 603 is used to perform fuzzy reasoning based on membership values ​​to determine the black start fuzzy state of the wind-storage system;

[0116] The determination module 604 is used to input the black start fuzzy state of the wind and storage system into a pre-set fuzzy state machine to obtain the black start control strategy of the wind and storage system.

[0117] The control module 605 is used to control the black start of the wind and storage system based on the black start control strategy of the wind and storage system.

[0118] In some alternative implementations, the fuzzy inference module 603 includes:

[0119] The first building unit is used to obtain the operating status of the wind-storage system during black start. Based on the operating status and state variables of the wind-storage system during black start, an expert survey questionnaire is constructed using the analytic hierarchy process.

[0120] The second building unit is used to construct fuzzy inference rules based on the expert knowledge corresponding to the expert survey questionnaire.

[0121] The determination unit is used to determine the black-start fuzzy state of the wind-storage system corresponding to the membership value based on fuzzy inference rules and using a lookup table method.

[0122] In some alternative implementations, the second building unit includes:

[0123] The evaluation subunit is used to evaluate the expert questionnaire using expert knowledge, obtain expert evaluation parameters, and construct an expert reasoning result matrix based on the expert evaluation parameters.

[0124] The weighted sub-unit is used to obtain the expert authority evaluation matrix, and to weight the expert reasoning result matrix based on the expert authority evaluation matrix to obtain the weighted expert reasoning result matrix.

[0125] Construct sub-units to build fuzzy inference rules based on the weighted expert inference result matrix.

[0126] In some alternative implementations, the determining module 604 includes:

[0127] The comparison unit is used to obtain the current operating state of the wind-storage system during black start and compare the fuzzy state of the wind-storage system during black start with the current operating state of the wind-storage system during black start.

[0128] The first judgment unit is used to take the current control strategy as the black start control strategy of the wind and storage system if the black start fuzzy state of the wind and storage system is consistent with the current operating state of the wind and storage system.

[0129] The second judgment unit is used to take the control strategy corresponding to the black start fuzzy state of the wind and energy storage system as the black start control strategy if the black start fuzzy state of the wind and energy storage system is inconsistent with the current operating state of the black start of the wind and energy storage system.

[0130] In some optional implementations, the black-start fuzzy states of the wind-storage system in the comparison unit include the black-start wind turbine DC capacitor charging state, the black-start wind turbine low-power operation state, the black-start wind turbine stable operation state, the black-start battery charging state, the black-start battery discharging state, the other wind turbines being put into operation state, and the load recovery state.

[0131] In some optional implementations, the black start control strategy of the storage system in the first judgment unit includes a black start fan DC capacitor charging control strategy, a black start fan low power operation control strategy, a black start fan stable operation control strategy, a black start battery charging control strategy, a black start battery discharging control strategy, other fan input control strategies, and a load input control strategy.

[0132] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0133] In this embodiment, a black start device for a wind-storage system based on a fuzzy state machine is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0134] This invention also provides a computer device having the above-described features. Figure 6 The diagram shows a black start device for a wind-storage system based on a fuzzy state machine.

[0135] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 7 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0136] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0137] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0138] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0139] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0140] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0141] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0142] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0143] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0144] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A black-start method for a wind-storage system based on a fuzzy state machine, characterized in that, The method includes: Obtain the state variables for black start of the wind-storage system; wherein, the state variables for black start of the wind-storage system include DC bus voltage, AC bus frequency and battery capacity; The state variables of the wind-storage system during black start are fuzzified using membership functions to obtain membership values. Specifically, the state variables for black start of the wind-storage system include: DC bus voltage (high, medium, low) with three states; AC bus frequency (high, relatively high, medium, relatively low, low) with five states; and battery capacity (high, relatively high, medium, relatively low, low) with five states. The state variables for black start of the wind-storage system correspond to 13 membership values: DC bus voltage (high, medium, low), AC bus frequency (high, relatively high, medium, relatively low, low), and battery capacity (high, relatively high, medium, relatively low, low). The highest membership value represents the fuzzy state of the DC bus voltage. Fuzzy inference is performed based on the membership value to determine the black start fuzzy state of the wind-storage system. The black start fuzzy state of the wind and storage system is input into a pre-set fuzzy state machine to obtain the black start control strategy of the wind and storage system. The black start control strategy of the wind and energy storage system is used to control the black start of the system. The black start process of the wind and energy storage system is as follows: the battery charges the DC capacitor of the black start wind turbine; after the DC capacitor is charged, the black start wind turbine is set to operate in a low-power operation mode; after the wind turbine speed stabilizes, the black start wind turbine starts to generate electricity stably in MPPT mode; if the wind turbine's power generation is greater than the load power, the battery is started to charge; if the wind turbine's power generation is less than the load power, the battery is started to discharge; other wind turbines are put into operation and the load is gradually added.

2. The method according to claim 1, characterized in that, The step of determining the black-start fuzzy state of the wind-storage system based on the membership value includes: Obtain the operating status of the wind-storage system during black start-up. Based on the operating status and state variables of the wind-storage system during black start-up, construct an expert survey questionnaire using the analytic hierarchy process (AHP). Fuzzy inference rules are constructed based on the expert knowledge corresponding to the expert survey questionnaire. Based on the fuzzy inference rules, the black-start fuzzy state of the wind-storage system corresponding to the membership value is determined by the table lookup method.

3. The method according to claim 2, characterized in that, The construction of fuzzy reasoning rules based on the expert knowledge corresponding to the expert survey questionnaire includes: The expert knowledge is used to evaluate the expert questionnaire, obtain expert evaluation parameters, and construct an expert reasoning result matrix based on the expert evaluation parameters. Obtain the expert authority evaluation matrix, and weight the expert reasoning result matrix based on the expert authority evaluation matrix to obtain the weighted expert reasoning result matrix; The fuzzy inference rules are constructed based on the weighted expert inference result matrix.

4. The method according to claim 1, characterized in that, The step of inputting the black-start fuzzy state of the wind-storage system into a pre-set fuzzy state machine to obtain the black-start control strategy of the wind-storage system includes: Obtain the current operating state of the wind-storage system during black start, and compare the fuzzy state of the wind-storage system during black start with the current operating state of the wind-storage system during black start; If the black start fuzzy state of the wind and energy storage system is consistent with the current operating state of the black start of the wind and energy storage system, then the current control strategy shall be used as the black start control strategy of the wind and energy storage system. If the fuzzy state of the black start of the wind and energy storage system is inconsistent with the current operating state of the black start of the wind and energy storage system, then the control strategy corresponding to the fuzzy state of the black start of the wind and energy storage system shall be used as the black start control strategy of the wind and energy storage system.

5. The method according to claim 4, characterized in that, The black start fuzzy states of the wind-storage system include the black start wind turbine DC capacitor charging state, the black start wind turbine low power operation state, the black start wind turbine stable operation state, the black start battery charging state, the black start battery discharging state, the other wind turbines being put into operation state, and the load recovery state.

6. The method according to claim 4, characterized in that, The black start control strategy of the wind-storage system includes a black start wind turbine DC capacitor charging control strategy, a black start wind turbine low power operation control strategy, a black start wind turbine stable operation control strategy, a black start battery charging control strategy, a black start battery discharging control strategy, other wind turbine input control strategies, and load input control strategies.

7. A black-start device for a wind-storage system based on a fuzzy state machine, characterized in that, The device includes: The acquisition module is used to acquire the state variables of the wind-storage system during black start; wherein, the state variables of the wind-storage system during black start include DC bus voltage, AC bus frequency and battery capacity; The fuzzification module is used to fuzzify the state variables of the wind-storage system during black start using membership functions to obtain membership values. Specifically, the state variables of the wind-storage system during black start include: DC bus voltage (high, medium, low) with three states; AC bus frequency (high, relatively high, medium, relatively low, low) with five states; and battery capacity (high, relatively high, medium, relatively low, low) with five states. The state variables of the wind-storage system during black start have 13 membership values: DC bus voltage (high, medium, low), AC bus frequency (high, relatively high, medium, relatively low, low), and battery capacity (high, relatively high, medium, relatively low, low). The highest membership value represents the fuzzy state of the DC bus voltage. The fuzzy inference module is used to perform fuzzy inference based on the membership value to determine the black start fuzzy state of the wind-storage system; The determination module is used to input the black start fuzzy state of the wind and storage system into a pre-set fuzzy state machine to obtain the black start control strategy of the wind and storage system. The control module is used to control the black start of the wind-storage system based on the black start control strategy of the wind-storage system. The black start process of the wind-storage system is as follows: the battery charges the DC capacitor of the black start wind turbine; after the DC capacitor is charged, the black start wind turbine is set to operate in a low-power operation mode; after the wind turbine speed stabilizes, the black start wind turbine starts to generate electricity stably in MPPT mode; if the wind turbine's power generation is greater than the load power, the battery charging is started; if the wind turbine's power generation is less than the load power, the battery discharging is started; other wind turbines are put into operation, and the load is gradually put into operation.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the black-start method for a wind-storage system based on a fuzzy state machine as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the black-start method for a wind-storage system based on a fuzzy state machine as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the black-start method for a wind-storage system based on a fuzzy state machine as described in any one of claims 1 to 6.

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