Equipment cluster energy consumption intelligent balance scheduling method for wind power plant

Abstract

An equipment cluster energy consumption intelligent balance scheduling method for a wind power plant relates to the technical field of wind power generation control, and comprises the following steps: dividing a fan cluster into four roles including a pilot machine, a main machine, a peak shaving machine and a trimming machine; collecting electrical parameters, mechanical parameters, environmental parameters and operating parameters of each fan, and respectively calculating an electrical loss coefficient, a mechanical loss coefficient and an environmental loss coefficient of each fan; calculating an energy loss value of each fan based on the calculated coefficients; constructing a fan wake flow intensity model; the output power of the main machine is determined according to the wake flow intensity, the self-loss condition of the fan and the output power; respectively setting an alternating condition based on the fixed operation time and the fan loss value change; when any alternating condition is met, fan composition in the four roles is adjusted; if all the rotation conditions are not met, all the draught fans keep corresponding role operation; the wind power plant group is used for solving the problems that a traditional wind power plant group is low in power generation efficiency and poor in emergency handling capacity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power generation control, in particular to a device cluster energy consumption intelligent balance scheduling method for a wind power plant. BACKGROUND

[0002] A wind power plant is a cluster power station composed of multiple wind turbines. The power generation of each wind turbine is closely related to wind direction, wind power and device health, but the distance between the positions of the wind turbines in the wind power plant is far, so the power generation of each wind turbine is not the same. When the power of some wind turbines exceeds the rated power, the gear box will overheat, and the generator will also cause unstable power generation due to voltage fluctuation, resulting in loss of power generation energy and affecting subsequent wind power grid connection operation.

[0003] Currently, the health of each wind turbine is detected to determine whether the wind turbine has excessive power generation or excessively high safety risk in power generation operation. When the health of a wind turbine is determined to be lower than a threshold, the power generation function of the wind turbine is immediately cut off to avoid unstable wind power output of the entire wind power plant. However, this method also reduces the overall wind power output of the wind power plant, resulting in low power generation efficiency.

[0004] Chinese patent CN109658006A discloses a large-scale wind power plant group auxiliary scheduling method and device. The daily switching frequency of each wind turbine is counted using a statistical model, and each wind turbine is regarded as a virtual wind power plant group for unified scheduling. However, using daily switching frequency as a scheduling condition lacks fairness in the scheduling process of each wind turbine and cannot solve the problem of the impact of sudden failure of a wind turbine on power generation efficiency.

[0005] Therefore, we propose a scheduling method that can ensure the power generation efficiency of a wind power plant group. SUMMARY

[0006] The present application aims to provide a device cluster energy consumption intelligent balance scheduling method for a wind power plant, which solves the problems of low power generation efficiency of a traditional wind power plant group and poor ability to cope with sudden situations.

[0007] The present application is achieved by the following technical solutions:

[0008] The device cluster energy consumption intelligent balance scheduling method for a wind power plant specifically includes:

[0009] The wind turbine cluster is divided into four types of roles, including a lead aircraft, a main aircraft, a peak-shaving aircraft and a rest aircraft.

[0010] Collecting electrical parameters, mechanical parameters, environmental parameters and operating parameters of each fan, and calculating electrical loss coefficient, mechanical loss coefficient and environmental loss coefficient of each fan respectively;

[0011] Based on the calculated coefficients, the energy loss value of each fan is calculated;

[0012] A fan wake intensity model is constructed;

[0013] According to the wake intensity and the fan's own loss, the output power of the main fan;

[0014] Based on the fixed running time and the change of fan loss value, a rotation condition is set;

[0015] When any rotation condition is met, the fan composition in the four types of roles is adjusted;

[0016] If all rotation conditions are not met, each fan maintains the corresponding role operation.

[0017] Further, the electrical parameters include stator current I s , DC bus voltage V dc and power factor cosφ;

[0018] The mechanical parameters include bearing temperature T b , vibration speed effective value v and gear box oil temperature T o ;

[0019] The environmental parameters include wind speed u, air density p and wake flag w f ;

[0020] The operating parameters include output power P out , pitch angle β and yaw angle γ.

[0021] Further, in the four types of roles of the fan cluster, the number of lead aircraft accounts for 10%-20%, the number of main aircraft accounts for 50%-70%, the number of peak regulation aircraft accounts for 20%-30%, and the number of rest aircraft accounts for 0%-15%; and the output power of the lead aircraft is 0.8} out ~P out , the output power of the main aircraft is 0.6P out ~0.9P out , the output power of the peak regulation aircraft is 0.3P oot ~0.8P out , and the output power of the rest aircraft is 0~0.4P out .

[0022] Further, the expression of the electrical loss coefficient K e is:

[0023]

[0024] wherein R s is the stator resistance, k sw is the switching loss coefficient, unit kW / V 2 , V mom is the reference voltage.

[0025] Further, the expression of the mechanical loss coefficient K m is:

[0026]

[0027] wherein a is the mechanical loss weight coefficient, T ref is the reference temperature, ΔT max is the maximum deviation range of the bearing temperature allowance.

[0028] Further, the expression of the environmental loss coefficient K env is:

[0029]

[0030] wherein δ is the environmental loss weight coefficient, ρ0 is the standard air density, k1 is the load loss coefficient, u base is the rated wind speed.

[0031] Further, the energy loss value L i of each wind turbine is calculated, and the expression is:

[0032] L i = K e + L base (K m + K env )

[0033] wherein L base is the reference loss under rated working condition, unit kW, and N is the number of wind turbines.

[0034] Further, the wind turbine wake intensity model is constructed, and the expression is:

[0035]

[0036] wherein d i is the distance from the wind turbine i to the nearest upwind wind turbine, D is the impeller diameter, N u is the number of upwind wind turbines, and θ k is the yaw angle deviation of the kth upwind wind turbine.

[0037] Further, the output power of the wind turbine is adjusted according to the wake intensity and the self-loss of the wind turbine, and the specific process is:

[0038] Calculate the average loss value η of the fan cluster L :

[0039]

[0040] When L i >η L , adjust the output power of the fan, that is:

[0041]

[0042] In the formula, P i is the adjusted fan output power, ξ is the control parameter, L min is the smallest fan loss value in the fan cluster, and L max is the largest fan loss value in the fan cluster.

[0043] Further, the fixed running time, fan loss value change and fan output power threshold are set to set a rotation condition respectively, and the expression is:

[0044]

[0045] In the formula, t0 is the initial time, t base is the fixed running period;

[0046] When any rotation condition is met, the fan group in the four types of roles is adjusted, and the specific steps are:

[0047] First, determine the leader, and the expression is:

[0048]

[0049] In the formula, t lead,i is the cumulative time of fan i as a leader; and T max is the maximum continuous leadership time

[0050] Then determine the trimmer, and the expression is:

[0051]

[0052] In the formula, t rest,i is the time since the last end of the rest role of fan i;

[0053] The remaining fans are sorted according to the loss value, and the first 70% are main machines, and the rest are peak shaving machines.

[0054] The technical scheme of the present application has at least the following advantages and beneficial effects:

[0055] The application discloses a wind farm-oriented equipment cluster energy consumption intelligent balance scheduling method, which classifies wind turbine clusters according to functions, sets rotation conditions, and timely adjusts various wind turbines, so that the overall wind turbine cluster can operate stably.

[0056] In addition, the role classification and rotation of the wind turbine cluster are based on the loss value of each wind turbine, which can avoid the output power fluctuation of the wind turbine, thereby ensuring the stability of the overall output power of the wind turbine cluster and improving the overall power generation efficiency of the wind turbine cluster. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 A method flowchart of the application;

[0058] Figure 2 A system structure diagram of the application;

[0059] Figure 3 An electronic device structure diagram in the application. DETAILED DESCRIPTION

[0060] To make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. The components of the embodiments of the application described and shown in the drawings can be arranged and designed in various different configurations.

[0061] Embodiment 1

[0062] As shown in the wind farm-oriented equipment cluster energy consumption intelligent balance scheduling method, the method specifically comprises the following steps. Figure 1

[0063] The wind turbine cluster is divided into four types of roles, including a lead aircraft, a main aircraft, a peak-shaving aircraft and a rest aircraft.

[0064] The lead aircraft is generally the first aircraft to contact the wind in the wind turbine cluster, so that it can generate wind power through a wire and is not disturbed by the wake of the remaining wind turbines. The main aircraft is a wind power generation device that mainly generates power in the wind turbine cluster. The peak-shaving aircraft is a flexible adjustment unit in the wind turbine cluster, which can eliminate the overall fluctuation of the wind turbine cluster by adjusting its output power when the output power of the wind turbine cluster fluctuates, so as to make the overall output power of the wind turbine cluster more stable. The rest aircraft is a wind turbine with a high loss value, which can restore the loss value by reducing its output power. ​

[0065] In addition, in the four types of roles of the fan cluster, the number of the lead aircraft accounts for 10%-20%, the number of the main aircraft accounts for 50%-70%, the number of the peak-shaving aircraft accounts for 20%-30%, and the number of the rest aircraft accounts for 0%-15%; the specific number of the lead aircraft, the main aircraft, the peak-shaving aircraft and the rest aircraft is allocated by the operator according to the field situation;

[0066] The output power of the lead aircraft is 0.8P out ~P out , the output power of the main aircraft is 0.6P out ~0.9P out , the output power of the peak-shaving aircraft is 0.3P out ~0.8P out , and the output power of the rest aircraft is 0~0.4P out . These output power ranges are required to be maintained by the fans of each role type during operation, so as to keep the output power of the fan cluster in a balanced state, reduce the number of enabled fans, and avoid affecting the overall power generation efficiency of the fan cluster.

[0067] The electrical parameters, mechanical parameters, environmental parameters and operating parameters of each fan are collected, and the electrical loss coefficient, mechanical loss coefficient and environmental loss coefficient of each fan are calculated respectively;

[0068] In addition, the electrical parameters include stator current I s , DC bus voltage V dc and power factor cosφ;

[0069] The mechanical parameters include bearing temperature T b , vibration speed effective value v and gear box oil temperature T o ;

[0070] The environmental parameters include wind speed u, air density p and wake flag w f ;

[0071] The operating parameters include output power P out , pitch angle β and yaw angle γ;

[0072] The electrical loss coefficient is used to reflect the energy loss in the process of electrical energy conversion, including copper loss generated by current flowing through the winding, and switching loss generated by IGBT high-frequency switching, and the specific expression is:

[0073]

[0074] In the formula, R s is the stator resistance, k sw is the switching loss coefficient, with the unit of kW / V 2 , and V momis a reference voltage; and represents a copper loss part, k sw ·(V dc -V mom ) 2 represents a switching loss part.

[0075] The mechanical loss coefficient is used to characterize the mechanical friction and vibration loss of the fan transmission system, and its expression is:

[0076]

[0077] In the formula, a is a mechanical loss weight coefficient, T ref is a reference temperature, and ΔT max is the maximum deviation range of the bearing temperature allowed, represents mechanical friction, represents vibration loss;

[0078] The environmental loss coefficient is used to quantify the additional effects of external environment, such as wind speed, air density and wake, on the equipment loss, and its expression is:

[0079]

[0080] In the formula, δ is an environmental loss weight coefficient, ρ0 is a standard air density, k1 is a load loss coefficient, u base is a rated wind speed.

[0081] Based on the calculated various coefficients, the energy loss value of each fan is calculated; wherein the energy loss value of each fan is a core index reflecting the energy efficiency state of the equipment, and the specific expression is:

[0082] L i = K e + L base (K m + K env )

[0083] In the formula, L base is a rated operating condition reference loss, unit: kW, and N is the number of fans.

[0084] For the main mechanism to build a fan wake intensity model, its expression is:

[0085]

[0086] In the formula, d i is the distance from the fan i to the nearest upwind fan, D is the impeller diameter, N u is the number of upwind fans, and θ kis the yaw angle deviation of the kth upwind wind turbine; it is worth noting that the coefficient 0.8 is the maximum wake decay strength under the minimum spacing, which is obtained from experience, the exponential coefficient 0.3 is the wake decay rate, which is obtained from experience, the coefficient 0.6 refers to the maximum influence weight of a single upwind wind turbine, which is measured from the wind farm, 45° is the angle normalization parameter, which is the preset influence threshold of the yaw angle deviation;

[0087] wherein is used to represent a straight-line arrangement of wind farms, is used to represent a complex layout of wind farms, so that when the output power of the wind turbine is subsequently adjusted, the corresponding expression needs to be selected for calculation according to the actual layout of the wind farm;

[0088] According to the wake strength and the wind turbine's own loss, the output power of the corresponding wind turbine is adjusted. Due to the existence of the wake of the front leading aircraft, the loss of the main aircraft during actual operation will increase. By adjusting the output power, the output power of the main aircraft is compensated, so as to ensure the stability of the output power of the main aircraft and prolong the maintenance period.

[0089] In addition, the compensation is generally the downwind wind turbine in the main aircraft, while the upwind wind turbine will increase the output power due to the small wake influence, so that the overall power generation of the main aircraft is improved.

[0090] The specific process is as follows:

[0091] Calculate the average loss value η of the wind turbine cluster L :

[0092]

[0093] When L i > η L , the output power of the wind turbine is adjusted, that is:

[0094]

[0095] In the formula, P i is the adjusted output power of the wind turbine, ξ is the control parameter, L min is the minimum wind turbine loss value in the wind turbine cluster, and L max is the maximum wind turbine loss value in the wind turbine cluster.

[0096] Based on the fixed running time and the change of the wind turbine loss value, a kind of rotation condition is set, the expression is:

[0097]

[0098] In the formula, t0 is the initial time, t base is the fixed running period;

[0099] When any rotation condition is met, the fan composition in the four types of roles is adjusted, and the specific steps are as follows:

[0100] Firstly, the lead aircraft is determined, and the expression is as follows:

[0101]

[0102] In the formula, t lead,i is the cumulative time of fan i as a lead aircraft; T max is the maximum continuous lead time

[0103] Then the maintenance aircraft is determined, and the expression is as follows:

[0104]

[0105] In the formula, t rest,i is the time since the last end of the rest role of fan i;

[0106] The remaining fans are sorted according to the loss value, and the first 70% are designated as main force machines, and the rest are designated as peak shaving machines.

[0107] If all rotation conditions are not met, each fan maintains the corresponding role operation.

[0108] Embodiment 2

[0109] The wind farm-oriented device cluster energy consumption intelligent balancing scheduling system shown in Figure 2 includes a data acquisition module for collecting electrical parameters, mechanical parameters, environmental parameters and operating parameters of each fan, and calculating electrical loss coefficients, mechanical loss coefficients and environmental loss coefficients of each fan;

[0110] A loss calculation module for calculating the energy loss value of each fan based on the calculated coefficients;

[0111] A wake compensation module that writes a fan wake intensity model and can adjust the output power of the main force machine according to the wake intensity and the fan's own loss;

[0112] A rotation module, wherein rotation conditions are set, and when any rotation condition is met, the fan composition in the lead aircraft, main force machine, peak shaving machine and rest machine is adjusted.

[0113] Embodiment 3

[0114] An electronic device as shown in the accompanying Figure 3 characterized in that it comprises:

[0115] a processor, a memory, and a communication interface;

[0116] The memory is used to store executable instructions of the processor.

[0117] The processor is configured to execute the above-mentioned wind farm-oriented device cluster energy consumption intelligent balancing scheduling method via executing the executable instructions.

[0118] A readable storage medium, on which a computer program is stored, wherein the computer program is executed by a processor to implement the above-mentioned wind farm-oriented device cluster energy consumption intelligent balancing scheduling method.

[0119] The above only is the preferred embodiment of the present application, and is not used to limit the present application, for the person skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for intelligent energy balance scheduling of equipment clusters in wind power plants, characterized in that, Specifically, it includes: The wind turbine cluster is divided into four roles: navigator, main turbine, peak shaving turbine, and maintenance turbine. Collect electrical, mechanical, environmental, and operating parameters of each fan, and calculate the electrical loss coefficient, mechanical loss coefficient, and environmental loss coefficient of each fan respectively. Based on the calculated coefficients, the energy loss value of each wind turbine is calculated; Construct a wind turbine wake intensity model; Adjust the output power of the main unit according to the wake intensity and the fan's own losses; A rotation condition is set based on both fixed operating time and changes in fan loss value; If any rotation condition is met, the composition of the wind turbines in the four roles will be adjusted. If none of the rotation conditions are met, each wind turbine will continue to operate in its corresponding role.

2. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 1, characterized in that: The electrical parameters include stator current I. s DC bus voltage V dc and power factor Mechanical parameters include bearing temperature T b The effective value of vibration velocity v and gearbox oil temperature T o ; Environmental parameters include wind speed u, air density ρ, and wake indicator w. f ; Operating parameters include output power P out , pitch angle β and yaw angle γ.

3. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 2, characterized in that: Of the four roles in the wind turbine cluster, the number of lead turbines accounts for 10%-20%, the number of main turbines accounts for 50%-70%, the number of peak-shaving turbines accounts for 20%-30%, and the number of maintenance turbines accounts for 0%-15%; and the output power of the lead turbine is 0.8P. out ~P out The main unit's output power is 0.6P. out ~0.9P out The peak shaving unit has an output power of 0.3P. out ~0.8P out The output power of the refurbishing machine is 0-0.4P. out .

4. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 2, characterized in that: The electrical loss coefficient K e The expression is: In the formula, R s For stator resistance, k sw This is the switching loss factor, in kW / V. 2 V mom This is the reference voltage.

5. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 4, characterized in that: The mechanical loss coefficient K m The expression is: In the formula, α is the mechanical loss weighting coefficient, and T ref ΔT is the reference temperature. max This represents the maximum allowable deviation range for bearing temperature.

6. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 5, characterized in that: The environmental loss coefficient K env The expression is: In the formula, δ is the environmental loss weighting coefficient, ρ0 is the standard air density, k1 is the load loss coefficient, and u base This is the rated wind speed.

7. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 6, characterized in that: The calculation of the energy loss value L for each wind turbine. i Its expression is: L i =K e +L base (K m +K env ) In the formula, L base The rated operating condition baseline loss is expressed in kW, and N represents the number of fans.

8. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 7, characterized in that: The formula for constructing the wind turbine wake intensity model is as follows: In the formula, d i Let N be the distance from fan i to the nearest upwind fan, D be the impeller diameter, and N be the distance from fan i to the nearest upwind fan. u θ represents the number of wind turbines upwind. k Let yaw angle be the deviation of the k-th upwind wind turbine.

9. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 8, characterized in that: The process of adjusting the output power of the fan based on the wake intensity and the fan's own losses is as follows: Calculate the average loss value η of the wind turbine cluster. L : When L i >η L Adjust the output power of the fan, that is: In the formula, P i The adjusted output power of the fan is ξ, where ξ is the control parameter and L is the output power of the fan. min L represents the minimum wind turbine loss value in the wind turbine cluster. max This represents the maximum wind turbine loss value in the wind turbine cluster.

10. The intelligent energy balance scheduling method for equipment clusters in wind power plants according to claim 9, characterized in that: A rotation condition is set based on a fixed operating time, changes in fan loss value, and a fan output power threshold, and the expression is: In the formula, t0 is the initial time, t base For fixed operating cycles; If any rotation condition is met, the composition of the wind turbines in the four roles will be adjusted. The specific steps are as follows: First, determine the navigator, expressed as: In the formula, t lead,i The cumulative time that wind turbine i serves as the navigator; T max The expression for determining the maximum continuous navigation time and then the repair aircraft is: In the formula, t rest,i This is the time since the last time the character "Wind Machine i" finished its rest period; The remaining wind turbines are sorted according to their loss values, with the top 70% designated as main turbines and the rest as peak-shaving turbines.

Images