CN104485681B - A kind of monitoring method of wind energy turbine set energy-storage system - Google Patents

Abstract

The present invention provides a kind of monitoring method of wind energy turbine set energy-storage system, the method can predict the generated output of wind energy turbine set, the situation of change of prediction load, the battery module battery capacity of detection in real time and the ruuning situation of the real-time power distribution network obtained, formulate and implement optimum control strategy, ensure the steady output of wind energy turbine set, promote safety and the service life of energy-storage system.

Description

A kind of monitoring method of wind energy turbine set energy-storage system

Art

The present invention relates to a kind of monitoring method of wind energy turbine set energy-storage system.

Background technology

In recent years, wind-power electricity generation relies on its advantage such as environmental protection, aboundresources, has obtained the attention of countries in the world, becomes Important sources for non-fossil fuel generating.But wind energy has randomness and intermittent feature, independent wind generator system Being difficult to provide stable, continuous print power output, undulatory property is relatively big, and being directly incorporated into electrical network will necessarily affect the most steady of power system Fixed operation.Therefore, consider from power grid security angle, introduce energy storage device for wind energy turbine set and stabilize its power swing, set up wind storage Combined generating system is the inexorable trend of following wind-power electricity generation.

Wind storage system is to be absorbed dump energy rapidly by energy storage or supplemental capacity vacancy stabilizes the power of wind energy turbine set Fluctuation, so the power swing utilizing energy-storage system to stabilize wind energy turbine set when, it is impossible to ensure to carry out regular to it , easily there is super-charge super-discharge in discharge and recharge, and this not only can affect its service life, cost of increasing input, and acute at power swing Its charging and discharging capabilities may be made time strong not enough, affect the safety that wind-electricity integration runs.

Wind storage system is to be absorbed dump energy rapidly by energy storage or supplemental capacity vacancy stabilizes the power of wind energy turbine set Fluctuation, so the power swing utilizing energy-storage system to stabilize wind energy turbine set when, it is impossible to ensure to carry out regular to it , easily there is super-charge super-discharge in discharge and recharge, and this not only can affect its service life, cost of increasing input, and acute at power swing Its charging and discharging capabilities may be made time strong not enough, affect the safety that wind-electricity integration runs.If formulating energy-storage system discharge and recharge During strategy, add the extra control to its SOC (State of Charge, state-of-charge), it is possible to stabilizing wind power While fluctuation, it is to avoid the super-charge super-discharge of energy-storage system so that it is can the output of smooth wind power field for a long time.

Summary of the invention

The present invention provides a kind of monitoring method of wind energy turbine set energy-storage system, and the method can predict the generating merit of wind energy turbine set Rate, it was predicted that the situation of change of load, the battery module battery capacity of detection in real time and the operation feelings of the real-time power distribution network obtained Condition, formulates and implements optimum control strategy, ensures the steady output of wind energy turbine set, promotes the safety of energy-storage system and makes Use the life-span.

To achieve these goals, the present invention provides a kind of monitoring method of wind energy turbine set energy-storage system, and the method is based on such as Lower supervising device realizes, and this supervising device includes:

Wind-powered electricity generation monitoring module, for monitoring wind-powered electricity generation module in real time, and is predicted the generated output of wind-powered electricity generation module；

Battery monitor module, monitors battery module in real time；

Load monitoring module, the load in monitoring wind energy turbine set energy-storage system in real time, and the changed power feelings to load Condition is predicted；

Power distribution network contact module, knows the ruuning situation of power distribution network and relevant tune in real time from power distribution network regulation and control center Degree information；

Be incorporated into the power networks monitoring module, is used for controlling wind energy turbine set energy-storage system and connects or isolation power distribution network；

Middle control module, for determining the operation reserve of wind energy turbine set energy-storage system, and each module in above-mentioned supervising device Send instruction, to perform this operation reserve；

Bus module, for the liaison of the modules of this supervising device；

This monitoring method comprises the steps:

(1) wind-powered electricity generation monitoring module obtains the service data of wind-powered electricity generation module in real time, and stores data, and load monitoring module is real-time Obtain the load variations situation of load；

(2) according to the service data of wind-powered electricity generation module, the output of the wind-powered electricity generation module in following predetermined instant is carried out pre- Survey, according to the load variations situation of wind energy turbine set load, the workload demand of load is predicted；

(3) detection obtains the SOC of battery module in real time, obtains parameter and the schedule information of power distribution network in real time；

(4) with the schedule information of power distribution network, the SOC of current batteries to store energy, following wind-powered electricity generation module output and right The change of future load demand is as constraints, it is achieved the optimal control of battery module SOC.

Preferably, predict that in step (2) output of wind-powered electricity generation module, described wind-powered electricity generation module include wind in the following way Power generator and SVG:

(201) in collection wind-powered electricity generation module, current all kinds of electricity measured values are as the initial value of the predictive value of all kinds of electricity, in advance Measured value includes: blower fan is gained merit predictive valueBlower fan is idle predictive valueBlower fan set end voltage predictive valueIt is pre-that SVG is idle Measured valueSVG set end voltage predictive valueWind-powered electricity generation module site (PCC) prediction of busbar voltage value

(202) the MPC optimizing control models being made up of optimization object function and constraints is set up according to described predictive value, And solve the predictive value of the meritorious of wind-powered electricity generation module and idle output:

Shown in the object function of MPC optimizing control models such as formula (1):

$\underset{Q_{\mathrm{WTG}}^{\mathrm{set}},V_{\mathrm{SVG}}^{\mathrm{set}}}{\min }(\underset{i=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{j=0}{\overset{M-1}{\mathrm{\Σ}}}{\mathrm{\ρ}}^{t_{i,j}}{F}_1,\underset{i=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{j=0}{\overset{M-1}{\mathrm{\Σ}}}{\mathrm{\ρ}}^{t_{i,j}}{F}_2)---\left(1\right)$

In formula (1)WithFor optimized variable,WithSetting value that implication respectively blower fan is idle and SVG voltage set Definite value；N is the number in time window Coverage Control cycle；M is the number under the single control cycle containing future position；ρ is attenuation quotient, Value ρ ＜ 1；Time variable ti, j=(Mi+j) Δ t meaning is the jth future position that current time plays in the i-th control cycle, Δ t is future position interval, and Δ t is determined by wind-powered electricity generation modular power predicted time interval；

F1 is the variance level of wind-powered electricity generation module site busbar voltage and setting value, F1 expression such as formula (2):

$F_1\left(t_{i,j}\right)={[V_{\mathrm{PCC}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{PCC}}^{\mathrm{ref}}]}^2---\left(2\right)$

In formula (2)Represent the reference value of PCC voltage, set after extracting from main website control instruction；

F2 is SVG reactive reserve level, F2 expression such as formula (3):

$F_2\left(t_{i,j}\right)={[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{opr}}]}^2---\left(3\right)$

In formula (3)For the idle best operating point of SVG；

The constraints of MPC optimizing control models, specifically includes:

Blower fan is gained merit prediction-constraint condition:

$P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\underset{k=1}{\overset{N_a}{\mathrm{\Σ}}}{\mathrm{\φ}}_k{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)+{\mathrm{\ϵ}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-\underset{k-1}{\overset{N_m}{\mathrm{\Σ}}}{\mathrm{\θ}}_k{\mathrm{\ϵ}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)---\left(4\right)$

In formula (4)Gain merit forecast error for blower fan；It is phase that Na and Nm is respectively the exponent number of AR and MA model, φ k and θ k Closing weight, exponent number and weight determine all in accordance with blower fan history value of gaining merit；Ti, j-k (include for participating in calculating data in prediction) the corresponding moment, subscript k pushes away the k Δ t time before characterizing prediction time, works as ti, and during j-k≤0, meritorious predictive value should take Corresponding moment history value；

Prediction-constraint condition that blower fan is idle:

Blower fan is idle reaches setting value before lower secondary control:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)=Q_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i-\mathrm{1,0}}\right)---\left(5\right)$

Each future position in the i-th control cycle, the change procedure of blower fan reactive power is with exponential function matching:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\frac{1-e^{-(t_{i,j}-t_{i,0})/T_s}}{1-e^{-\mathrm{M\Δt}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i,0}\right)+\frac{e^{-(t_{i,j}-t_{i,0})/T_s}-e^{-\mathrm{M\Δt}/T_s}}{1-e^{-\mathrm{M\Δt}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)---\left(6\right)$

In formula (6), Ts is blower fan Reactive-power control time constant, can obtain according to blower fan Reactive-power control testing experiment.

Prediction-constraint condition that SVG is idle:

Reference value that SVG is idleAs shown in formula (7):

$\begin{array}{c}{Q}_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j}\right)=K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,0}\right)]+K_I\mathrm{\Δt}\underset{k=0}{\overset{i\×M+j}{\mathrm{\Σ}}}[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,-k}\right)]\\ +Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{\mathrm{0,0}}\right)]\end{array}---\left(7\right)$

In formula (7), KI and KP is respectively the coefficient of proportional component and integral element；

SVG is idle shown in predictive value such as formula (8):

$Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)=Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)+{[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-1}\right)-Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)]e}^{-(t_{i,j}-t_{i,j-1})/T_d}---\left(8\right)$

In formula (8), time constant Td is SVG power electronic equipment action delay；

Voltage prediction constraints:

$V^{\mathrm{pre}}\left(t_{i,j}\right)-V^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)=S\left[\begin{array}{c}{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\end{array}\right]---\left(9\right)$

V in formula (9)^preThe vector constituted for blower fan machine end, SVG machine end and PCC prediction of busbar voltage value, S is sensitivity Matrix；

The constraints that system voltage, generator operation and SVG run:

$\left\{\begin{array}{c}{V}^{\min }\≤V^{\mathrm{pre}}\left(t_{i,j}\right)\≤V^{\max }\\ Q_{\mathrm{WTG}}^{\min }\≤Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)\≤Q_{\mathrm{WTG}}^{\max }\\ Q_{\mathrm{SVG}}^{\min }\≤Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)\≤Q_{\mathrm{SVG}}^{\max }\\ {\mathrm{\ΔQ}}_{\mathrm{WTG}}^{\min }\≤Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right)\≤{\mathrm{\ΔQ}}_{\mathrm{WTG}}^{\max }\\ {\mathrm{\ΔQ}}_{\mathrm{SVG}}^{\min }\≤Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right){\mathrm{\ΔQ}}_{\mathrm{SVG}}^{\max }\end{array}\right.---\left(10\right)$

V in formula (11)^maxAnd V^minIt is respectively the upper limit being made up of system voltage vector PCC, blower fan and SVG voltage prediction value And lower limit, wherein PCC voltage limits is given by power distribution network control centre, and blower fan and SVG voltage limits are according to equipment factory The normal range of operation that business is given determines；WithIt is respectively blower fan idle operation bound,WithWei SVG Idle operation bound, the normal range of operation be all given according to equipment production firm determines；WithIt is respectively wind Climbing bound that machine is idle,WithIt is respectively SVG idle climbing bound, all needs to tie through reactive speed experiment test Fruit determines.

Preferably, in step (4), the optimal control of above-mentioned battery module SOC comprises the following steps:

(41) solve optimum SOC scope, concretely comprise the following steps:

The object function of optimum SOC scope Optimized model is:

$\begin{array}{c}\min F={\mathrm{\λ}}_1\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{u}_{\mathrm{optSOC}\min }\left(t_i\right)\mathrm{\Δt}+{\mathrm{\λ}}_2\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{u}_{\mathrm{optSOC}\max }\left(t_i\right)\mathrm{\Δt}\\ +{\mathrm{\λ}}_3|{\mathrm{SOC}}_{\mathrm{opt}\_\min }-{\mathrm{SOC}}_{\min }|+{\mathrm{\λ}}_4|{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\max }|\end{array}$

Constraints is:

$\begin{array}{c}\underset{j=\mathrm{1,2},...,N_k}{\max }{P}_{\mathrm{out}}\left(t_{i-j}\right)-\underset{j=\mathrm{1,2},...,N_k}{\min }{P}_{\mathrm{out}}\left(t_{i-j}\right)\≤{\mathrm{\γ}}_k,k=\mathrm{1,2},...,K\\ -P_{\mathrm{ch}\_\max }\≤P_{B\_\mathrm{ref}}\left(t_i\right)\≤P_{\mathrm{disch}\_\max }\\ {\mathrm{SOC}}_{\min }\≤{\mathrm{SOC}}_{\mathrm{opt}\_\min }\≤{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\min }\≤{\mathrm{SOC}}_{\mathrm{opt}\_\max }\≤{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\left(t_i\right)}={\mathrm{SOC}}_{\left(t_{i-1}\right)}-\frac{P_{B\_\mathrm{ref}}\left(t_i\right)\mathrm{\Δt}}{E_{\mathrm{cap}}}\\ P_{\mathrm{out}}\left(t_i\right)=P_{B\_\mathrm{ref}}\left(t_i\right)+P_{w\_\mathrm{pre}}\left(t_i\right)\end{array}---\left(12\right)$

Wherein, SOC_{opt_min}Represent the lower limit of energy-storage system optimum working range, SOC_{opt_max}Represent energy-storage system optimum work Make the upper limit of scope, SOC_minRepresent the lower limit of energy-storage system normal range of operation, SOC_maxRepresent that energy-storage system normally works model The upper limit enclosed, λ₁、λ₂、λ₃、λ₄It is respectively corresponding weight coefficient, is positive number and weight coefficient and is 1, SOC (t_i) and SOC (t_i-1) it is respectively t_iMoment and t_i-1The energy-storage system state-of-charge in moment, P_{B_ref}(t_i) it is that energy-storage system is at t_iThe setting in moment Power, E_capFor the capacity of energy-storage system, P_out(t_i) it is the wind energy turbine set grid-connected power after energy-storage system is stabilized, u_optSOCmin (t_i) represent initial time state-of-charge SOC (t₀) it is SOC_{opt_min}Time, t_iWhether moment energy-storage system there is super-charge super-discharge； u_optSOCmax(t_i) represent initial time state-of-charge SOC (t₀) it is SOC_{opt_max}Time, t_iWhether moment energy-storage system occurs overcharges Cross and put；P_{ch_max}The maximum charge power allowed by energy-storage system, P_{disch_max}The maximum electric discharge merit allowed by energy-storage system Rate；N_kThe number of time step Δ t in expression kth undulated control time range, K represents the number of undulated control time range Amount, γ_kThe power maximum variable quantity allowed in representing kth undulated control time range；

Set the attribute of particle as SOC_{opt_min}、SOC_{opt_max}And P_{B_ref(ti)}, use particle cluster algorithm to optimum SOC scope Optimized model solves i.e. available optimum SOCopt_min, SOCopt_max；

(42) wind storage system is in running, according to real-time state-of-charge shift ratio and the setting merit of energy-storage system Rate, periodically regulates time constant filter, regulates every time method particularly includes:

(421) shift ratio calculates:

According to the optimum SOC scope (SOC obtained_{opt_min},SOC_{opt_max}) and the real-time SOC charged shape of calculating of energy-storage system State shift ratio pro_ΔSOC, computing formula is:

${\mathrm{pro}}_{\mathrm{\ΔSOC}}=\frac{\mathrm{SOC}-\frac{1}{2}({\mathrm{SOC}}_{\mathrm{opt}\_\min }+{\mathrm{SOC}}_{\mathrm{opt}\_\max })}{{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\mathrm{opt}\_\min }}---\left(13\right)$

(422) by state-of-charge shift ratio pro Δ SOC and the setting power P of energy-storage system current time_{B_ref}As defeated Entering, time constant filter T is as output, according to default fuzzy control rule, uses fuzzy control strategy to obtain filtering time Constant T；

(423) in this regulating cycle, defeated to the reality of wind energy turbine set according to the time constant filter T that step (422) obtains Go out power P_wCarrying out low-pass filtering, the grid-connected power of expectation after stabilizing is designated as P_{out_exp}, it is calculated the goal setting of energy-storage system PowerAnd according to below equation, goal setting power is carried out limit value process, obtain final setting Power P_{B_ref}, restriction processes formula and is:

$\begin{array}{c}{\tilde{P}}_{B\_\mathrm{ref}}\≤\frac{(\mathrm{SOC}-{\mathrm{SOC}}_{\mathrm{protect}})*E_{\mathrm{cap}}}{\mathrm{\Δk}}\\ -P_{\mathrm{ch}\_\max }\≤{\tilde{P}}_{B\_\mathrm{ref}}\≤P_{\mathrm{disch}\_\max }\end{array}---\left(14\right)$

Wherein, SOC_protectRepresenting the state-of-charge protection set, Δ k represents the control week that time constant filter regulates Phase.

The monitoring method of the present invention has the advantage that the changed power situation of (1) Accurate Prediction wind energy turbine set；(2) control Strategy takes into account power distribution network scheduling requirement, energy-storage system ruuning situation and the workload demand of load, meets user simultaneously, has taken into account confession Electricity reliability, ensures the safety of energy-storage system, extends the service life of system stored energy system.

Accompanying drawing explanation

Fig. 1 shows a kind of wind energy turbine set energy-storage system and the block diagram of supervising device thereof that the inventive method used；

Fig. 2 shows the flow chart of the inventive method.

Detailed description of the invention

Fig. 1 shows a kind of wind energy turbine set energy-storage system supervising device 11 of the present invention, and this device 11 includes: wind-powered electricity generation monitors Module 114, the wind-powered electricity generation module 12 in monitoring wind energy turbine set energy-storage system 10 in real time, and the generated output of wind-powered electricity generation module 12 is entered Row prediction；Battery monitor module 115, the battery module 13 in monitoring wind energy turbine set energy-storage system 10 in real time；Load prison Control module 116, the load 17 in monitoring wind energy turbine set energy-storage system 10 in real time, and the changed power situation of load 17 is carried out Prediction；Power distribution network contact module 112, in real time, regulating and controlling center from power distribution network 20 knows ruuning situation and the phase of power distribution network 20 Close schedule information；Parallel control module 113, connects or isolates power distribution network 20 for wind energy turbine set energy-storage system 10；Middle control module 117, for determining the operation reserve of wind energy turbine set energy-storage system 10, and send instruction to above-mentioned each module, to perform this power supply plan Slightly；Bus module 111, for the liaison of the modules of this supervising device 11.

Communication module 111, the communication between above-mentioned modules, described bus communication module 111 is double by redundancy CAN is connected with other modules.

Wind-powered electricity generation module includes multiple wind-driven generator and SVG equipment.Wind-powered electricity generation monitoring module 114 at least includes wind-driven generator Level pressure, electric current, frequency detection equipment, wind speed measurement equipment, and SVG voltage and current detection equipment.Wind-driven generator defeated Go out power to be determined by wind speed, wind direction and the unique characteristics of wind-driven generator site.

Battery monitor module 116 at least includes that accumulator voltage, electric current, SOC detection equipment and temperature detection set Standby.SOC for monitoring battery module in real time.

Middle control module 117 at least includes CPU element, data storage cell and display unit.

Power distribution network contact module 112 at least includes Wireless Telecom Equipment.This Wireless Telecom Equipment can be wireline equipment or Wireless device.

Parallel control module 113 at least includes for detecting power distribution network and wind energy turbine set energy-storage system voltage, electric current and frequency Detection equipment, data acquisition unit and data processing unit.Data acquisition unit comprises collection pretreatment and A/D modulus of conversion Block, gathers eight tunnel telemetered signal amounts, comprises grid side A phase voltage, electric current, the three-phase voltage of wind energy turbine set energy-storage system side, electric current. Strong ac signal (5A/110V) can be changed without distortion by remote measurement amount by the high-precision current in terminal and voltage transformer For internal weak electric signal, enter A/D chip after filtered process and carry out analog digital conversion, converted after digital signal at data Reason unit calculates, it is thus achieved that the three-phase voltage current value of wind energy turbine set energy-storage system 10 side and power distribution network 20 side phase voltage current value.This Telemetered signal amount processes and have employed high-speed and high-density synchronized sampling, automatic frequency tracking technology also has the fft algorithm improved, so Precision is fully guaranteed, it is possible to complete that wind energy turbine set energy-storage system 10 side is meritorious, idle and electric energy divides from first-harmonic to higher hamonic wave The measurement of amount and process.

Seeing accompanying drawing 2, the method for the present invention comprises the steps:

S1. wind-powered electricity generation monitoring module obtains the service data of wind-powered electricity generation module in real time, and stores data, and load monitoring module is real-time Obtain the load variations situation of load；

S2. according to the service data of wind-powered electricity generation module, the output of the wind-powered electricity generation module in following predetermined instant is carried out pre- Survey, according to the load variations situation of wind energy turbine set load, the workload demand of load is predicted；

S3. detection obtains the SOC of battery module in real time, obtains parameter and the schedule information of power distribution network in real time；

S4. with the schedule information of power distribution network, the SOC of current batteries to store energy, following wind-powered electricity generation module output and right The change of future load demand is as constraints, it is achieved the optimal control of battery module SOC.

Preferably, predict that in step S2. the output of wind-powered electricity generation module, described wind-powered electricity generation module include wind in the following way Power generator and SVG:

S201. in collection wind-powered electricity generation module, current all kinds of electricity measured values are as the initial value of the predictive value of all kinds of electricity, in advance Measured value includes: blower fan is gained merit predictive valueBlower fan is idle predictive valueBlower fan set end voltage predictive valueIt is pre-that SVG is idle Measured valueSVG set end voltage predictive valueWind-powered electricity generation module site (PCC) prediction of busbar voltage value

S202. the MPC optimizing control models being made up of optimization object function and constraints is set up according to described predictive value, And solve the predictive value of the meritorious of wind-powered electricity generation module and idle output:

Shown in the object function of MPC optimizing control models such as formula (1):

$\underset{Q_{\mathrm{WTG}}^{\mathrm{set}},V_{\mathrm{SVG}}^{\mathrm{set}}}{\min }(\underset{i=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{j=0}{\overset{M-1}{\mathrm{\Σ}}}{\mathrm{\ρ}}^{t_{i,j}}{F}_1,\underset{i=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{j=0}{\overset{M-1}{\mathrm{\Σ}}}{\mathrm{\ρ}}^{t_{i,j}}{F}_2)---\left(1\right)$

In formula (1)WithFor optimized variable,WithSetting value that implication respectively blower fan is idle and SVG voltage set Definite value；N is the number in time window Coverage Control cycle；M is the number under the single control cycle containing future position；ρ is attenuation quotient, Value ρ ＜ 1；Time variable ti, j=(Mi+j) Δ t meaning is the jth future position that current time plays in the i-th control cycle, Δ t is future position interval, and Δ t is determined by wind-powered electricity generation modular power predicted time interval；

F1 is the variance level of wind-powered electricity generation module site busbar voltage and setting value, F1 expression such as formula (2):

$F_1\left(t_{i,j}\right)={[V_{\mathrm{PCC}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{PCC}}^{\mathrm{ref}}]}^2---\left(2\right)$

In formula (2)Represent the reference value of PCC voltage, set after extracting from main website control instruction；

F2 is SVG reactive reserve level, F2 expression such as formula (3):

$F_2\left(t_{i,j}\right)={[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{opr}}]}^2---\left(3\right)$

In formula (3)For the idle best operating point of SVG；

The constraints of MPC optimizing control models, specifically includes:

Blower fan is gained merit prediction-constraint condition:

$P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\underset{k=1}{\overset{N_a}{\mathrm{\Σ}}}{\mathrm{\φ}}_k{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)+{\mathrm{\ϵ}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-\underset{k-1}{\overset{N_m}{\mathrm{\Σ}}}{\mathrm{\θ}}_k{\mathrm{\ϵ}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)---\left(4\right)$

In formula (4)Gain merit forecast error for blower fan；It is phase that Na and Nm is respectively the exponent number of AR and MA model, φ k and θ k Closing weight, exponent number and weight determine all in accordance with blower fan history value of gaining merit；Ti, j-k (include for participating in calculating data in prediction) the corresponding moment, subscript k pushes away the k Δ t time before characterizing prediction time, works as ti, and during j-k≤0, meritorious predictive value should take Corresponding moment history value；

Prediction-constraint condition that blower fan is idle:

Blower fan is idle reaches setting value before lower secondary control:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)=Q_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i-\mathrm{1,0}}\right)---\left(5\right)$

Each future position in the i-th control cycle, the change procedure of blower fan reactive power is with exponential function matching:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\frac{1-e^{-(t_{i,j}-t_{i,0})/T_s}}{1-e^{-\mathrm{M\Δt}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i,0}\right)+\frac{e^{-(t_{i,j}-t_{i,0})/T_s}-e^{-\mathrm{M\Δt}/T_s}}{1-e^{-\mathrm{M\Δt}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)---\left(6\right)$

In formula (6), Ts is blower fan Reactive-power control time constant, can obtain according to blower fan Reactive-power control testing experiment.

Prediction-constraint condition that SVG is idle:

Reference value that SVG is idleAs shown in formula (7):

$\begin{array}{c}{Q}_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j}\right)=K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,0}\right)]+K_I\mathrm{\Δt}\underset{k=0}{\overset{i\×M+j}{\mathrm{\Σ}}}[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,-k}\right)]\\ +Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{\mathrm{0,0}}\right)]\end{array}---\left(7\right)$

In formula (7), KI and KP is respectively the coefficient of proportional component and integral element；

SVG is idle shown in predictive value such as formula (8):

$Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)=Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)+{[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-1}\right)-Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)]e}^{-(t_{i,j}-t_{i,j-1})/T_d}---\left(8\right)$

In formula (8), time constant Td is SVG power electronic equipment action delay；

Voltage prediction constraints:

$V^{\mathrm{pre}}\left(t_{i,j}\right)-V^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)=S\left[\begin{array}{c}{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\end{array}\right]---\left(9\right)$

V in formula (9)^preThe vector constituted for blower fan machine end, SVG machine end and PCC prediction of busbar voltage value, S is sensitivity Matrix；

The constraints that system voltage, generator operation and SVG run:

$\left\{\begin{array}{c}{V}^{\min }\≤V^{\mathrm{pre}}\left(t_{i,j}\right)\≤V^{\max }\\ Q_{\mathrm{WTG}}^{\min }\≤Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)\≤Q_{\mathrm{WTG}}^{\max }\\ Q_{\mathrm{SVG}}^{\min }\≤Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)\≤Q_{\mathrm{SVG}}^{\max }\\ {\mathrm{\ΔQ}}_{\mathrm{WTG}}^{\min }\≤Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right)\≤{\mathrm{\ΔQ}}_{\mathrm{WTG}}^{\max }\\ {\mathrm{\ΔQ}}_{\mathrm{SVG}}^{\min }\≤Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right){\mathrm{\ΔQ}}_{\mathrm{SVG}}^{\max }\end{array}\right.---\left(10\right)$

V in formula (11)^maxAnd V^minIt is respectively the upper limit being made up of system voltage vector PCC, blower fan and SVG voltage prediction value And lower limit, wherein PCC voltage limits is given by power distribution network control centre, and blower fan and SVG voltage limits are according to equipment factory The normal range of operation that business is given determines；WithIt is respectively blower fan idle operation bound,WithWei SVG Idle operation bound, the normal range of operation be all given according to equipment production firm determines；WithIt is respectively wind Climbing bound that machine is idle,WithIt is respectively SVG idle climbing bound, all needs to tie through reactive speed experiment test Fruit determines.

Preferably, in step S4, the optimal control of above-mentioned battery module SOC comprises the following steps:

S41. solve optimum SOC scope, concretely comprise the following steps:

The object function of optimum SOC scope Optimized model is:

$\begin{array}{c}\min F={\mathrm{\λ}}_1\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{u}_{\mathrm{optSOC}\min }\left(t_i\right)\mathrm{\Δt}+{\mathrm{\λ}}_2\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{u}_{\mathrm{optSOC}\max }\left(t_i\right)\mathrm{\Δt}\\ +{\mathrm{\λ}}_3|{\mathrm{SOC}}_{\mathrm{opt}\_\min }-{\mathrm{SOC}}_{\min }|+{\mathrm{\λ}}_4|{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\max }|\end{array}$

Constraints is:

$\begin{array}{c}\underset{j=\mathrm{1,2},...,N_k}{\max }{P}_{\mathrm{out}}\left(t_{i-j}\right)-\underset{j=\mathrm{1,2},...,N_k}{\min }{P}_{\mathrm{out}}\left(t_{i-j}\right)\≤{\mathrm{\γ}}_k,k=\mathrm{1,2},...,K\\ -P_{\mathrm{ch}\_\max }\≤P_{B\_\mathrm{ref}}\left(t_i\right)\≤P_{\mathrm{disch}\_\max }\\ {\mathrm{SOC}}_{\min }\≤{\mathrm{SOC}}_{\mathrm{opt}\_\min }\≤{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\min }\≤{\mathrm{SOC}}_{\mathrm{opt}\_\max }\≤{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\left(t_i\right)}={\mathrm{SOC}}_{\left(t_{i-1}\right)}-\frac{P_{B\_\mathrm{ref}}\left(t_i\right)\mathrm{\Δt}}{E_{\mathrm{cap}}}\\ P_{\mathrm{out}}\left(t_i\right)=P_{B\_\mathrm{ref}}\left(t_i\right)+P_{w\_\mathrm{pre}}\left(t_i\right)\end{array}---\left(12\right)$

Wherein, SOC_{opt_min}Represent the lower limit of energy-storage system optimum working range, SOC_{opt_max}Represent energy-storage system optimum work Make the upper limit of scope, SOC_minRepresent the lower limit of energy-storage system normal range of operation, SOC_maxRepresent that energy-storage system normally works model The upper limit enclosed, λ₁、λ₂、λ₃、λ₄It is respectively corresponding weight coefficient, is positive number and weight coefficient and is 1, SOC (t_i) and SOC (t_i-1) it is respectively t_iMoment and t_i-1The energy-storage system state-of-charge in moment, P_{B_ref}(t_i) it is that energy-storage system is at t_iThe setting in moment Power, E_capFor the capacity of energy-storage system, P_out(t_i) it is the wind energy turbine set grid-connected power after energy-storage system is stabilized, u_optSOCmin (t_i) represent initial time state-of-charge SOC (t₀) it is SOC_{opt_min}Time, t_iWhether moment energy-storage system there is super-charge super-discharge； u_optSOCmax(t_i) represent initial time state-of-charge SOC (t₀) it is SOC_{opt_max}Time, t_iWhether moment energy-storage system occurs overcharges Cross and put；P_{ch_max}The maximum charge power allowed by energy-storage system, P_{disch_max}The maximum electric discharge merit allowed by energy-storage system Rate；N_kThe number of time step Δ t in expression kth undulated control time range, K represents the number of undulated control time range Amount, γ_kThe power maximum variable quantity allowed in representing kth undulated control time range；

Set the attribute of particle as SOC_{opt_min}、SOC_{opt_max}And P_{B_ref(ti)}, use particle cluster algorithm to optimum SOC scope Optimized model solves i.e. available optimum SOCopt_min, SOCopt_max；

S42. wind storage system is in running, according to real-time state-of-charge shift ratio and the setting merit of energy-storage system Rate, periodically regulates time constant filter, regulates every time method particularly includes:

S421. shift ratio calculates:

According to the optimum SOC scope (SOC obtained_{opt_min},SOC_{opt_max}) and the real-time SOC charged shape of calculating of energy-storage system State shift ratio pro_ΔSOC, computing formula is:

${\mathrm{pro}}_{\mathrm{\ΔSOC}}=\frac{\mathrm{SOC}-\frac{1}{2}({\mathrm{SOC}}_{\mathrm{opt}\_\min }+{\mathrm{SOC}}_{\mathrm{opt}\_\max })}{{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\mathrm{opt}\_\min }}---\left(13\right)$

S422. by state-of-charge shift ratio pro Δ SOC and the setting power P of energy-storage system current time_{B_ref}As defeated Entering, time constant filter T is as output, according to default fuzzy control rule, uses fuzzy control strategy to obtain filtering time Constant T；

S423. in this regulating cycle, defeated to the reality of wind energy turbine set according to the time constant filter T that step (422) obtains Go out power P_wCarrying out low-pass filtering, the grid-connected power of expectation after stabilizing is designated as P_{out_exp}, it is calculated the goal setting of energy-storage system PowerAnd according to below equation, goal setting power is carried out limit value process, obtain final setting Power P_{B_ref}, restriction processes formula and is:

$\begin{array}{c}{\tilde{P}}_{B\_\mathrm{ref}}\≤\frac{(\mathrm{SOC}-{\mathrm{SOC}}_{\mathrm{protect}})*E_{\mathrm{cap}}}{\mathrm{\Δk}}\\ -P_{\mathrm{ch}\_\max }\≤{\tilde{P}}_{B\_\mathrm{ref}}\≤P_{\mathrm{disch}\_\max }\end{array}---\left(14\right)$

Wherein, SOC_protectRepresenting the state-of-charge protection set, Δ k represents the control week that time constant filter regulates Phase.

In S2, use Neural Network model predictive workload demand, specifically comprise the following steps that

S211. gather 12 groups of active power and reactive power, co-continuous collection 8 days every day, so have 96 groups of data P (k) and Q (k), k=1,2 ..., 96.

S212. 96 groups of data P (k) and Q (k) are normalized so that N=1,2 ..., 96；First using the 12 of every day active-power Ps (k) as one group of input vector R (m), 12 reactive power Qs (k) as one group of input vector S (m), m=1,2 ..., 8, m represent the frequency of training of neutral net； Pre-supposing that 12 active-power Ps ' (k) of the 9th day output vector R ' as pre-power scale, 12 of the 9th day idle simultaneously Power Q ' (k) is as the output vector S ' of pre-power scale；The active power input vector of the most first 8 days is just

R (1), R (2), R (3), R (4), R (5), R (6), R (7), R (8), the output vector of the 9th day prediction active power is R′；The reactive power input vector of first 8 days is just

S (1), S (2), S (3), S (4), S (5), S (6), S (7), S (8), the output vector of the 9th day prediction active power is S′。

S213. using 8 groups of input vectors R (m) and S (m) as the input layer of neutral net, the transmission letter of hidden layer neuron Number uses S type tan tansig, and the transmission function of output layer neuron uses S type logarithmic function logsig, such as Fig. 2 institute Show, so after 8 neural metwork trainings, determined that the weights of each connection weight in neutral net.

S214. for 8 active power input vector R (m), there is a in hidden layer neuron¹=tansig (IW¹R+b¹), Wherein a¹Export for hidden layer neuron, IW¹For the weights of hidden layer neuron, b¹Threshold value for hidden layer neuron；In output Layer neuron has a²=log sig (LW²a¹+b²), wherein a²Export for output layer neuron, IW²Power for output layer neuron Value, b²Threshold value for output layer neuron.

S215. for 8 active power input vector S (m), there is c in hidden layer neuron¹=tansig (IW¹S+b¹), Wherein c¹Export for hidden layer neuron, IW¹For the weights of hidden layer neuron, b¹Threshold value for hidden layer neuron；In output Layer neuron has c²=log sig (LW²c1+b²), wherein c²Export for output layer neuron, IW²Power for output layer neuron Value, b²Threshold value for output layer neuron.

S216. using the input vector R (8) and S (8) of the 8th day again as the input layer of neutral net, now neutral net The output vector R ' and S ' of the pre-power scale of middle output are the power prediction normalized value of the 9th day, then calculate with renormalization Method, i.e.K=1,2 ..., 96, the vector value R (9) and S (9) of output are just It is 12 active-power Ps ' (k) of the 9th day pre-power scale and 12 reactive power Qs ' (k).The most by that analogy, can repeat Above step utilizes the power of the data prediction to the tenth day of second day to the 9th day, and the power of every day all may be used so below With predicted out.

In step s 4, being constrained to of wind energy turbine set energy-storage system general power Pg:

At non-response scheduling slot 1 time, P_g,min≤P_g(l)≤P_g,max, P_g,minCan be from joining for wind energy turbine set energy-storage system 10 The peak power that electrical network 20 absorbs, P_g,maxThe peak power of power can be carried to power distribution network 20 for wind energy turbine set energy-storage system 10；

Response scheduling period 2 times, P_g(2)=P_set, P_setThe dominant eigenvalues required for 2 times for the response scheduling period.

Above content is to combine concrete preferred implementation further description made for the present invention, it is impossible to assert Being embodied as of the present invention is confined to these explanations.For general technical staff of the technical field of the invention, On the premise of present inventive concept, make some equivalents and substitute or obvious modification, and performance or purposes are identical, all should It is considered as belonging to protection scope of the present invention.

Claims (1)

1. a monitoring method for wind energy turbine set energy-storage system, the method realizes based on following supervising device, this supervising device bag Include:

Wind-powered electricity generation monitoring module, for monitoring wind-powered electricity generation module in real time, and is predicted the generated output of wind-powered electricity generation module；

Battery monitor module, monitors battery module in real time；

Load monitoring module, the load in monitoring wind energy turbine set energy-storage system in real time, and the changed power situation of load is entered Row prediction；

Power distribution network contact module, knows the ruuning situation of power distribution network and relevant scheduling letter in real time from power distribution network regulation and control center Breath；

Be incorporated into the power networks monitoring module, is used for controlling wind energy turbine set energy-storage system and connects or isolation power distribution network；

Middle control module, for determining the operation reserve of wind energy turbine set energy-storage system, and each module in above-mentioned supervising device sends Instruction, to perform this operation reserve；

Bus module, for the liaison of the modules of this supervising device；

This monitoring method comprises the steps:

(1) wind-powered electricity generation monitoring module obtains the service data of wind-powered electricity generation module in real time, and stores data, and load monitoring module obtains in real time The load variations situation of load；

(2) according to the service data of wind-powered electricity generation module, the output of the wind-powered electricity generation module in following predetermined instant is predicted, root According to the load variations situation of wind energy turbine set load, the workload demand of load is predicted；

(3) detection obtains the SOC of battery module in real time, obtains parameter and the schedule information of power distribution network in real time；

(4) with the schedule information of power distribution network, the SOC of current batteries to store energy, following wind-powered electricity generation module output and to future The change of workload demand is as constraints, it is achieved the optimal control of battery module SOC；

In step (2), predicting the output of wind-powered electricity generation module in the following way, described wind-powered electricity generation module includes wind-driven generator And SVG:

(201) current all kinds of electricity measured values are gathered in wind-powered electricity generation module as the initial value of the predictive value of all kinds of electricity, it was predicted that value Gain merit predictive value including: blower fanBlower fan is idle predictive valueBlower fan set end voltage predictive valueSVG is idle predictive valueSVG set end voltage predictive valueWind-powered electricity generation module site (PCC) prediction of busbar voltage value

(202) set up the MPC optimizing control models being made up of optimization object function and constraints according to described predictive value, and ask Meritorious and the predictive value of idle output of solution wind-powered electricity generation module:

Shown in the object function of MPC optimizing control models such as formula (1):

In formula (1)WithFor optimized variable,WithImplication is respectively the idle setting value of blower fan and SVG voltage sets Value；N is the number in time window Coverage Control cycle；M is the number under the single control cycle containing future position；ρ is attenuation quotient, takes Value ρ ＜ 1；Time variable ti, j=(Mi+j) Δ t meaning is the jth future position that current time plays in the i-th control cycle, Δ T is future position interval, and Δ t is determined by wind-powered electricity generation modular power predicted time interval；

F1 is the variance level of wind-powered electricity generation module site busbar voltage and setting value, F1 expression such as formula (2):

In formula (2)Represent the reference value of PCC voltage, set after extracting from main website control instruction；

F2 is SVG reactive reserve level, F2 expression such as formula (3):

In formula (3)For the idle best operating point of SVG；

The constraints of MPC optimizing control models, specifically includes:

Blower fan is gained merit prediction-constraint condition:

In formula (4)Gain merit forecast error for blower fan；It is correlative weight that Na and Nm is respectively the exponent number of AR and MA model, φ k and θ k Weight, exponent number and weight determine all in accordance with blower fan history value of gaining merit；Ti, j-k (include for participating in calculating data in prediction) the corresponding moment, subscript k pushes away the k Δ t time before characterizing prediction time, works as ti, and during j-k≤0, meritorious predictive value should take Corresponding moment history value；

Prediction-constraint condition that blower fan is idle:

Blower fan is idle reaches setting value before lower secondary control:

Each future position in the i-th control cycle, the change procedure of blower fan reactive power is with exponential function matching:

In formula (6), Ts is blower fan Reactive-power control time constant, can obtain according to blower fan Reactive-power control testing experiment;

Prediction-constraint condition that SVG is idle:

Reference value that SVG is idleAs shown in formula (7):

In formula (7), KI and KP is respectively the coefficient of proportional component and integral element；

SVG is idle shown in predictive value such as formula (8):

In formula (8), time constant Td is SVG power electronic equipment action delay；

Voltage prediction constraints:

V in formula (9)^preThe vector constituted for blower fan machine end, SVG machine end and PCC prediction of busbar voltage value, S is sensitivity matrix；

The constraints that system voltage, generator operation and SVG run:

V in formula (11)^maxAnd V^minBe respectively by PCC, blower fan and SVG voltage prediction value constitute system voltage vector the upper limit and under Limit, wherein PCC voltage limits is given by power distribution network control centre, and blower fan and SVG voltage limits are given according to equipment production firm The normal range of operation gone out determines；WithIt is respectively blower fan idle operation bound,WithWei the idle fortune of SVG Row bound, the normal range of operation be all given according to equipment production firm determines；WithRespectively blower fan is idle climbs Slope bound,WithIt is respectively SVG idle climbing bound, all needs to determine through reactive speed experimental results；

In step (4), the optimal control of above-mentioned battery module SOC comprises the following steps:

(41) solve optimum SOC scope, concretely comprise the following steps:

The object function of optimum SOC scope Optimized model is:

Constraints is:

Wherein, SOC_{opt_min}Represent the lower limit of energy-storage system optimum working range, SOC_{opt_max}Represent energy-storage system optimum work model The upper limit enclosed, SOC_minRepresent the lower limit of energy-storage system normal range of operation, SOC_maxRepresent energy-storage system normal range of operation The upper limit, λ₁、λ₂、λ₃、λ₄It is respectively corresponding weight coefficient, is positive number and weight coefficient and is 1, SOC (t_i) and SOC (t_i-1) It is respectively t_iMoment and t_i-1The energy-storage system state-of-charge in moment, P_{B_ref}(t_i) it is that energy-storage system is at t_iThe setting power in moment, E_capFor the capacity of energy-storage system, P_out(t_i) it is the wind energy turbine set grid-connected power after energy-storage system is stabilized, u_optSOCmin(t_i) table Show initial time state-of-charge SOC (t₀) it is SOC_{opt_min}Time, t_iWhether moment energy-storage system there is super-charge super-discharge；u_optSOCmax (t_i) represent initial time state-of-charge SOC (t₀) it is SOC_{opt_max}Time, t_iWhether moment energy-storage system there is super-charge super-discharge； P_{ch_max}The maximum charge power allowed by energy-storage system, P_{disch_max}The maximum discharge power allowed by energy-storage system；N_k The number of time step Δ t in expression kth undulated control time range, K represents the quantity of undulated control time range, γ_k The power maximum variable quantity allowed in representing kth undulated control time range；

Set the attribute of particle as SOC_{opt_min}、SOC_{opt_max}And P_{B_ref(ti)}, use particle cluster algorithm that optimum SOC scope is optimized Model solution i.e. available optimum SOCopt_min, SOCopt_max；

(42) wind storage system is in running, according to real-time state-of-charge shift ratio and the setting power of energy-storage system, Periodically regulate time constant filter, regulate every time method particularly includes:

(421) shift ratio calculates:

According to the optimum SOC scope (SOC obtained_{opt_min},SOC_{opt_max}) and energy-storage system real-time SOC calculate state-of-charge inclined Shifting ratio pro_ΔSOC, computing formula is:

(422) by state-of-charge shift ratio pro Δ SOC and the setting power P of energy-storage system current time_{B_ref}As input, Time constant filter T is as output, according to default fuzzy control rule, uses fuzzy control strategy to obtain time constant filter T；

(423) in this regulating cycle, the time constant filter T obtained according to step (422) the actual output work to wind energy turbine set Rate P_wCarrying out low-pass filtering, the grid-connected power of expectation after stabilizing is designated as P_{out_exp}, it is calculated the goal setting power of energy-storage systemAnd according to below equation, goal setting power is carried out limit value process, obtain final setting power P_{B_ref}, restriction processes formula and is:

Wherein, SOC_protectRepresenting the state-of-charge protection set, Δ k represents the control cycle that time constant filter regulates.