CN103161670B - Output control system and output control method of wind power plant - Google Patents
Output control system and output control method of wind power plant Download PDFInfo
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- CN103161670B CN103161670B CN201310098971.XA CN201310098971A CN103161670B CN 103161670 B CN103161670 B CN 103161670B CN 201310098971 A CN201310098971 A CN 201310098971A CN 103161670 B CN103161670 B CN 103161670B
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- 238000013459 approach Methods 0.000 claims description 23
- 238000013528 artificial neural network Methods 0.000 claims description 23
- 238000012549 training Methods 0.000 claims description 15
- 230000001174 ascending effect Effects 0.000 claims description 14
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 abstract 3
- 230000001276 controlling effect Effects 0.000 description 21
- 230000005684 electric field Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000012423 maintenance Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 2
- 230000000926 neurological effect Effects 0.000 description 2
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- 238000006243 chemical reaction Methods 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
Provided are a power output control system and a power output control method for a wind power plant. A contribution control system of a wind farm includes: the wind power plant operation parameter acquisition module is used for continuously acquiring operation parameters of each fan; the wind power plant operation parameter acquisition module is used for acquiring operation parameters of each wind power plant; and the fan control command output module is used for respectively sending the shutdown command to the fan which is determined by the fan running state evaluation module and is in the state needing shutdown. When the running state of any fan is determined by the fan running state evaluation module, if the temperature difference of each variable pitch system of the fan exceeds a preset temperature difference range within preset time or the vibration of each main component of the fan exceeds a preset vibration limit value, the fan running state evaluation module determines that the fan is in a state needing to be stopped.
Description
Technical field
The application relates to going out Force control system and going out force control method of a kind of wind energy turbine set, particularly relate to a kind of intellectual analysis that adopts and control of exerting oneself is performed, to reach going out Force control system and going out force control method of wind energy turbine set overall control requirement to the blower fan in wind energy turbine set.
Background technique
Along with the continuous increase of electric motor power, the ratio of wind-power electricity generation in electrical network is increasing.Because wind power generating set is exerted oneself, randomness, intermittence have impact on the receiving ability of electrical network to wind-powered electricity generation, so general dispatching of power netwoks according to the electric motor power of wind energy turbine set and power prediction situation, can give upper limit control to exerting oneself of wind energy turbine set.
Wind energy turbine set is made up of multiple stage wind power generating set, and controls in fact to be exactly the control to every Fans to exerting oneself of wind energy turbine set.The safe and stable operation of every Fans, is related to the stable operation of whole wind energy turbine set.So-called blower fan team control, namely by realizing wind energy turbine set overall planning target to the control adjustment of separate unit blower fan.For this reason, not only need to consider separate unit blower fan self-condition, also will consider the mutual cooperation between the whole blower fan of wind energy turbine set.
Traditional blower fan automation strategy generally only considered the control of separate unit blower fan in control procedure, and does not consider the cooperation factor between blower fan, therefore can not ensure the rationality that wind power generation blower fan runs, and easily produces fan trouble hidden danger.Current, the blower fan quantity of wind energy turbine set gets more and more, and competition requires the cost constantly reducing blower fan maintenance.Appropriate application blower fan, ensures blower fan stable operation, is reduced the maintenance cost of blower fan by rational blower fan protection mechanism, has become the development trend that following wind energy turbine set is safeguarded.
In one existing wind farm group prosecutor case; dispatching of power netwoks issues the upper limit of totally exerting oneself to wind energy turbine set; when the service capacity upper limit be less than wind energy turbine set general theory exert oneself time; manually the power upper limit of blower fan controlled or manual shut-down is carried out to part fan, exerting oneself requirement to meet wind energy turbine set entirety.The overall power efficiency of manual tune wind energy turbine set is low, and regulating time is long, and regulating power calculates also cumbersome, and power division is according to the role of subjective intentions of people.
In another kind existing wind farm group prosecutor case, according to the output of wind electric field upper limit of dispatching down distributing, carry out automatic power distribution adjustment.Limit power proportions as required, automated randomized schedule of apportionment power proportions that Fans is limit, or when limitting power ratio larger, at random blower fan is shut down, to meet the control of totally exerting oneself to wind energy turbine set.The random mode of this employing is to separate unit blower fan limit power or shutdown; running state not for blower fan itself carries out power division; the blower fan continuous service of potential faults may be made in irrational situation; or make a healthy blower fan long time continuous working and damage is shone into the hardware of blower fan; thus reduce the availability of blower fan, improve blower fan maintenance cost.
Summary of the invention
The object of the present invention is to provide going out Force control system and going out force control method of a kind of wind energy turbine set, adopt intellectual analysis to perform control of exerting oneself, to reach the overall control requirement of wind energy turbine set to the blower fan in wind energy turbine set.
Another object of the present invention is to provide going out Force control system and going out force control method of a kind of wind energy turbine set, the running state of intellectual analysis to the blower fan in wind energy turbine set is adopted to assess, and consider that the running state of each blower fan controls exerting oneself of wind energy turbine set, thus blower fan is run healthily, improve the reliability of wind energy turbine set overall operation.
According to an aspect of the present invention, what provide a kind of wind energy turbine set goes out Force control system, comprise: wind energy turbine set Operational Limits acquisition module, for from each its Operational Limits of blower fan continuous collecting, described Operational Limits comprises the current operate power of the temperature parameter of each critical piece and vibration parameters and blower fan, wind speed and ambient temperature; Fan operation state estimation module, the Operational Limits for each blower fan gathered within a period of time according to wind energy turbine set Operational Limits acquisition module determines the running state of each blower fan, and described running state is can running state and need one of outage state; Air-blower control order output module, that determines for halt command being sent to respectively fan operation state estimation module is in the blower fan needing outage state.Wherein, fan operation state estimation module is when determining the running state of arbitrary blower fan; if in the given time; the temperature difference of each pitch-controlled system of described blower fan exceeds predetermined temperature range; or the vibration of each critical piece of described blower fan exceedes predetermined vibration limit value, then fan operation state estimation module determines that described blower fan is in needs outage state.
Preferably, described can running state comprise normal operating condition and fall power can running state, and fan operation state estimation module is when determining the running state of arbitrary blower fan, if in the given time, the temperature of each critical piece of described blower fan is in normal range (NR) and its vibration is no more than predetermined vibration limit value, then fan operation state estimation module determines that described blower fan is in normal operating condition, if in the given time, the temperature rise of the temperature of each critical piece of described blower fan exceedes predetermined temperature limit, the temperature difference of its each pitch-controlled system is in predetermined temperature range, and the vibration of each critical piece of described blower fan is no more than predetermined vibration limit value, then fan operation state estimation module determine described blower fan be in power falls and can running state.
Preferably, the described Force control system that goes out also comprises: limit power decision module, the running state of each blower fan that Operational Limits and fan operation state estimation module for each blower fan according to the collection of wind energy turbine set Operational Limits acquisition module are determined determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.
Preferably, limit power decision module determine the current blower fan and being in being in normal operating condition fall power can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state time, the Operational Limits of each blower fan gathered within a period of time using wind energy turbine set Operational Limits acquisition module is as training sample, use neural network modeling approach to fall power for being in of determining of fan operation state estimation module and each blower fan of running state can set up the power module of its maximum operate power, calculate the current maximum output and being in being in every Fans of normal operating condition fall power can the summation of maximum operate power of every Fans of running state as wind energy turbine set maximum output total output P
max, power falls in each the current each blower fan and being in being in normal operating condition determined according to fan operation state estimation module can current operate power, the wind energy turbine set maximum output total output P of each blower fan of running state
maxand predetermined wind energy turbine set plan is exerted oneself upper limit P
plan, determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.
Preferably, limit power decision module determine the current blower fan and being in being in normal operating condition fall power can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state time, if determine P
max> P
plan, then power decision module is limit preferentially to fall power can determine that one or more blower fan is as performing the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in.
Preferably; limit power decision module is fallen power preferential can determine that one or more blower fan is as when need perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in, and preferentially falls power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in.Wherein, the blower fan that the needs that halt command also sends to limit power decision module to determine by air-blower control order output module are respectively shut down.
Preferably, limit power decision module is fallen power preferential can determine that one or more blower fan is as when need perform the blower fan of exerting oneself and controlling, and calculates wind power deviation value P in the middle of the blower fan of running state from being in
excess=P
max-P
planif, P
excess≤ α × P
maxwherein, 0 < α < 1, power decision module is then limit preferentially to fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in, wherein, air-blower control order output module also sends to falling power command the blower fan needing downrating limitting power decision module to determine respectively.
Preferably, if limit power decision module determines P
excess> α × P
maxpower decision module is then limit preferentially to fall power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in; and by the blower fan that halt command to send to the needs determined to shut down by air-blower control order output module respectively, falling power with the blower fan making to be in normal operating condition according to remaining and being in can the P that again calculates of the blower fan of running state
excess≤ α × P
maxand then preferentially fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in remaining, and send to falling power command the blower fan needing downrating determined by air-blower control order output module respectively.
Preferably, limit power decision module is fallen power preferential can determine that one or more blower fan is as when needing the blower fan shutting down or need downrating in the middle of the blower fan of running state from being in, for current not being in needs every Fans of outage state to calculate rationing the power supply the time in the time cycle of described blower fan predetermined length in the past, the mark not being in each blower fan needing outage state is carried out ascending sort according to the time of rationing the power supply of blower fan, according to by the order of time ascending order of rationing the power supply, select and determine that one or more blower fan is as the blower fan needing to shut down or need downrating.
Preferably, limit power decision module is the mean value Tp that each critical piece of blower fan calculates the temperature upper limit of described critical piece, and using the part of the Tp of each critical piece of calculating as the input of neural network modeling approach, the Operational Limits of each blower fan gathered within a period of time using wind energy turbine set Operational Limits acquisition module, as training sample, uses neural network modeling approach to fall power for being in of determining of fan operation state estimation module and each blower fan of running state can set up the power module of its maximum operate power.
According to a further aspect in the invention, what provide a kind of wind energy turbine set goes out force control method, comprise, following steps: A is performed at wind energy turbine set central monitoring system) from each its Operational Limits of blower fan continuous collecting, described Operational Limits comprises the current operate power of the temperature parameter of each critical piece and vibration parameters and blower fan, wind speed and ambient temperature; B) determine the running state of each blower fan according to the Operational Limits of each blower fan gathered within a period of time, described running state is can running state and need one of outage state; C) halt command is sent to step B respectively) in determine be in the blower fan needing outage state.Wherein, in step B); when determining the running state of arbitrary blower fan; if in the given time; the temperature difference of each pitch-controlled system of described blower fan exceeds described predetermined temperature range; or the vibration of each critical piece of described blower fan exceedes predetermined vibration limit value, then determining that described blower fan is in needs outage state.
Preferably, described can running state comprise normal operating condition and fall power can running state, and in step B), when determining the running state of arbitrary blower fan, if in the given time, the temperature of each critical piece of described blower fan is in normal range (NR) and its vibration is no more than predetermined vibration limit value, then determine that described blower fan is in normal operating condition, if in the given time, the temperature rise of the temperature of each critical piece of described blower fan exceedes predetermined temperature limit, the temperature difference of its each pitch-controlled system is in predetermined temperature range, and the vibration of each critical piece of described blower fan is no more than predetermined vibration limit value, then determine described blower fan be in power falls and can running state.
Preferably, the described force control method that goes out also comprises: D) according in steps A) Operational Limits of each blower fan that gathers and in step B) running state of each blower fan determined determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.
Preferably, in step D) determine the current blower fan and being in being in normal operating condition fall power can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state time, D-1) with within a period of time, in steps A) Operational Limits of each blower fan that gathers is as training sample, using neural network modeling approach in step B) being in of determining fall power and each blower fan of running state can set up the power module of its maximum operate power, D-2) calculate the current maximum output and being in being in every Fans of normal operating condition fall power can the summation of maximum operate power of every Fans of running state as wind energy turbine set maximum output total output P
max, D-3) and falling power according to each the current each blower fan and being in being in normal operating condition determined can current operate power, the wind energy turbine set maximum output total output P of each blower fan of running state
maxand predetermined wind energy turbine set plan is exerted oneself upper limit P
plan, determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.
Preferably, step D-3) comprising: if P
max> P
plan, then preferentially fall power can determine that one or more blower fan is as performing the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in.
Preferably; fall power preferential can determine that one or more blower fan is as when need perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in; preferentially fall power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in, and the blower fan being sent to by halt command the needs determined to shut down respectively.
Preferably, step D-3) comprising: if determine P
max> P
plan, then wind power deviation value P is calculated
excess=P
max-P
planand if, P
excess≤ × P
max, wherein, 0 < α < 1, then preferentially fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in.Wherein, step C) also comprise: send to falling power command the blower fan needing downrating determined respectively.
Preferably, if P
excess> α × P
maxthen preferentially fall power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in; and halt command is sent to respectively the blower fan that the needs determined are shut down, falling power with the blower fan making to be in normal operating condition according to remaining and being in can the P that again calculates of the blower fan of running state
excess≤ α × P
max, and then preferentially fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in remaining, and send to falling power command the blower fan needing downrating determined respectively.
Preferably; fall power preferential can determine that one or more blower fan is as when needing the blower fan shutting down or need downrating in the middle of the blower fan of running state from being in; for current not being in needs every Fans of outage state to calculate rationing the power supply the time in the time cycle of described blower fan predetermined length in the past; the mark not being in each blower fan needing outage state is carried out ascending sort according to the time of rationing the power supply of blower fan; according to by the order of time ascending order of rationing the power supply, select and determine that one or more blower fan is as the blower fan needing to shut down or need downrating.
Preferably, step D-1) comprising: for each critical piece of blower fan calculates the mean value Tp of the temperature upper limit of described critical piece, and using the part of the Tp of each critical piece of calculating as the input of neural network modeling approach, using the Operational Limits of each blower fan gathered within a period of time as training sample, use neural network modeling approach to fall power for being in of determining and each blower fan of running state can set up the power module of its maximum operate power.
Accompanying drawing explanation
By the description carried out below in conjunction with accompanying drawing, above and other object of the present invention and feature will become apparent, wherein:
Fig. 1 is the logic diagram Force control system of the wind energy turbine set illustrated according to exemplary embodiment of the present invention;
Fig. 2 ~ Fig. 6 is the flow chart force control method of the wind energy turbine set illustrated according to exemplary embodiment of the present invention;
Fig. 7 illustrates to set up the schematic diagram of power module according to exemplary embodiment of the present invention for blower fan.
Embodiment
Below, embodiments of the invention are described in detail with reference to the accompanying drawings.
Fig. 1 is the logic diagram Force control system of the wind energy turbine set illustrated according to exemplary embodiment of the present invention.Wind energy turbine set of the present invention go out the part that Force control system can be embodied as the central monitoring system of wind energy turbine set.
With reference to Fig. 1, comprise wind energy turbine set Operational Limits acquisition module 110, fan operation state estimation module 120 and air-blower control order output module 130 according to the output of wind electric field control system of exemplary embodiment of the present invention.
Wind energy turbine set Operational Limits acquisition module 110 is for from each its Operational Limits of blower fan continuous collecting, described Operational Limits comprises the current operate power of the temperature parameter of each critical piece (critical piece refers to the main large parts of unit, includes but not limited to generator, current transformer, pitch motor, change oar inverter, yaw motor etc.) and vibration parameters and blower fan, wind speed and ambient temperature.
Fan operation state estimation module 120 determines the running state of each blower fan for the Operational Limits of each blower fan gathered within a period of time according to wind energy turbine set Operational Limits acquisition module 110, and described running state is can running state and need one of outage state.According to a preferred embodiment of the invention, fan operation state estimation module 120 also further by be in can the blower fan of running state be defined as being in normal operating condition or falling power can running state.
Fan operation state estimation module 120 is when determining the running state of arbitrary blower fan; if in the given time; the temperature difference of each pitch-controlled system of described blower fan exceeds described predetermined temperature range; or the vibration of each critical piece of described blower fan exceedes predetermined vibration limit value, then fan operation state estimation module 120 determines that described blower fan is in needs outage state.
In addition, fan operation state estimation module 120 is when determining the running state of arbitrary blower fan, if in the given time, in normal range (NR), (normal temperature mainly refers to temperature upper limit to the temperature of each critical piece of described blower fan, the temperature upper limit of each zones of different with the environment residing for it about) and its vibration is no more than predetermined vibration limit value, then fan operation state estimation module 120 determines that described blower fan is in normal operating condition; If in the given time, the temperature rise of the temperature of each critical piece of described blower fan exceedes predetermined temperature limit, the temperature difference of its each pitch-controlled system is in predetermined temperature range, and the vibration of each critical piece of described blower fan is no more than predetermined vibration limit value, then fan operation state estimation module 120 determine described blower fan be in power falls and can running state.
The temperature range of above-mentioned predetermined temperature range, vibration limit value and each pitch-controlled system is the empirical value pre-set.The situation different according to wind energy turbine set, can adjust described empirical value.
Air-blower control order output module 130 is in for what sent to by halt command fan operation state estimation module 120 to determine respectively the blower fan needing outage state.
According to a preferred embodiment of the invention, output of wind electric field control system also comprises limit power decision module 140.The running state of each blower fan that limit power decision module 140 is determined for the Operational Limits of each blower fan that gathers according to wind energy turbine set Operational Limits acquisition module 110 and fan operation state estimation module 120, determines that the current blower fan and being in being in normal operating condition falls power and can need to perform the blower fan of control of exerting oneself in the middle of the blower fan of running state.Describedly need perform exert oneself the blower fan blower fan that is divided into needs to shut down controlled and the blower fan needing downrating.The blower fan that the needs that halt command also sends to limit power decision module to determine by air-blower control order output module 130 are respectively shut down, and/or send to falling power command the blower fan needing downrating.
Determine that the current blower fan and being in being in normal operating condition falls power and can need to perform the process of the blower fan controlled of exerting oneself in the middle of the blower fan of running state by describing limit power decision module 140 in detail below.
According to exemplary embodiment of the present invention, in order to calculate the maximum processing power of wind energy turbine set, first calculate the peak output of falling each blower fan that power can run and can send in theory within the scope of reasonable terms.For this reason, the present invention proposes to set up by neural network modeling approach the power module falling each blower fan that power can run.
First, the Operational Limits of each blower fan that limit power decision module 140 gathers within a period of time using wind energy turbine set Operational Limits acquisition module 110, as training sample, uses neural network modeling approach (such as RBF) to fall power for being in of determining of fan operation state estimation module 120 and each blower fan of running state can set up the power module of its maximum operate power.Fig. 7 illustrates and sets up the schematic diagram of power module according to exemplary embodiment of the present invention for blower fan.As shown in Figure 7, limit power decision module 140 with the normal temperature upper limit Tp of wind speed V, the ambient temperature t of each blower fan, current operate power P and predetermined each parts for input, trained by RBF neural training method, thus obtain each be in power falls and can the power module of maximum operate power of blower fan of running state.
According to a preferred embodiment of the invention, limit power decision module 140 is the mean value Tp that each critical piece of blower fan calculates the temperature upper limit of described critical piece, and using the part of the Tp of each critical piece of calculating as the input of neural network modeling approach, the Operational Limits of each blower fan gathered within a period of time using wind energy turbine set Operational Limits acquisition module 110, as training sample, uses neural network modeling approach to fall power for being in of determining of fan operation state estimation module 120 and each blower fan of running state can set up the power module of its maximum operate power.
Secondly, limit power decision module 140 calculate the current maximum output and being in being in every Fans of normal operating condition fall power can the summation of maximum operate power of every Fans of running state as wind energy turbine set maximum output total output P
max.For the current blower fan being in normal state, its maximum operate power can be calculated according to the current wind speed of blower fan and operate power curvimeter; And according to aforementioned neurological network modelling can obtain be in power falls and can the maximum operate power of each blower fan of running state.
Then, power falls in each the current each blower fan and being in being in normal operating condition limitting power decision module 140 to determine according to fan operation state estimation module 120 can current operate power, the wind energy turbine set maximum output total output P of each blower fan of running state
maxand predetermined wind energy turbine set plan is exerted oneself upper limit P
plan, determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.Some unit is due to long-play in good condition, and wish to allow Wind turbines generating dutation distribute relatively more even, in order to the operation protecting unit safety stable, the unit preferentially choosing long operational time of can trying one's best is shut down.In addition; because unit is rationed the power supply by becoming oar realization; the words pitch-controlled system duty ratio of rationing the power supply for a long time is larger; pitch-controlled system is the fragile link of unit one; good multiple faults is had to be all that pitch-controlled system causes; in order to protect pitch-controlled system, time of rationing the power supply before preferentially choosing of also can trying one's best short unit limit Power operation controls.
According to exemplary embodiment of the present invention, limit power decision module 140 when determining to perform the blower fan of exerting oneself and controlling, if determine P
max> P
plan, then power decision module 140 is limit preferentially to fall power can determine that one or more blower fan is as performing the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in.
According to a preferred embodiment of the invention, limit power decision module 140 is fallen power preferential can determine that one or more blower fan is as when need perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in, for current not being in needs every Fans of outage state to calculate rationing the power supply the time in the time cycle of described blower fan predetermined length in the past, the mark not being in each blower fan needing outage state is carried out ascending sort according to the time of rationing the power supply of blower fan, then according to by the order of time ascending order of rationing the power supply, select and determine that one or more blower fan is as performing the blower fan of exerting oneself and controlling.
According to an alternative embodiment of the invention; limit power decision module 140 is fallen power preferential can determine that one or more blower fan is as when need perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in, and preferentially falls power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in.Now, the blower fan that the needs that halt command also sends to limit power decision module 140 to determine by air-blower control order output module 130 are respectively shut down.
Table 1 illustrates the example of the power of one group of blower fan according to time of the rationing the power supply ascending sort in past one week:
| Blower fan sequence number (mark) | 5 | 4 | 3 | 2 | 1 |
| Ration the power supply the time (hour) | 2 | 3 | 4 | 5 | 6 |
| Power of fan (KW) | 800 | 800 | 700 | 600 | 500 |
Suppose the wind energy turbine set maximum output total output P calculated
max3800KW, the upper limit P and wind energy turbine set plan is exerted oneself
plan2500KW.Due to P
max> P
plan, therefore, blower fan 5 and blower fan 4, from the blower fan of shortest time of rationing the power supply, are defined as the blower fan needing to shut down by limit power decision module 140, thus the current operate power sum of current 3 remaining Fans is 1800KW, lower than P
plan.
According to another exemplary embodiment of the present invention, if limit power decision module 140 determines P
max> P
plan, then power decision module 140 is limit first to calculate wind power deviation value P
excess=P
max-P
plan.
If P
excess≤ × P
max, then power decision module 140 is limit preferentially to fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in; Now, air-blower control order output module 130 also sends to falling power command the blower fan needing downrating limitting power decision module 140 to determine respectively.Here, α value to be chosen according to the real needs of the adjustment range of power of fan and wind energy turbine set, 0 < α < 1; Preferably, 0.3≤α≤0.6.That is, as wind energy turbine set maximum output P
maxexceed wind energy turbine set plan to exert oneself upper limit P
plannot too many (power offset value P
excesstime within the specific limits), limit power decision module 140 can select downrating part fan.Can refer to the aforementioned select progressively based on time ascending order of rationing the power supply in a period of time and determine described downrating blower fan.
Same for the example shown in table 1, suppose the wind energy turbine set maximum output total output P calculated
max3800KW, the upper limit P and wind energy turbine set plan is exerted oneself
plan2500KW, P
max> P
plan.Here, P
excess=1300, suppose α=0.5, P
excess< α × P
max.Therefore, limit power decision module 140 only can be selected to fall power to part fan and control, and not to the fan parking in wind energy turbine set.Such as, the blower fan of shortest time of certainly rationing the power supply, according to predetermined weights, ratio or algorithm, blower fan 5 and blower fan 4 can be determined respectively to fall power 650KW and 650KW; Or blower fan 5, blower fan 4 and blower fan 3 can be determined respectively to fall power 500KW, 400KW and 400KW; Or blower fan 5, blower fan 4, blower fan 3 and blower fan 2 can be determined respectively to fall power 400KW, 400KW, 250KW and 250KW, and the selection of falling power Fan is not limited to above-mentioned example.
On the other hand, if P
excess> × P
max, then power decision module 140 is limit preferentially to fall power can determine that one or more blower fan is as the blower fan needing to shut down, to make the gross output of the blower fan run according to residue lower than (1-α) × P in the middle of the blower fan of running state from being in
maxor it is lower.Then, after determining to need the blower fan of shutting down to be shut down, determine that the mode of the blower fan needing downrating is carried out limit power further and controlled according to aforementioned limit power decision module 140.That is, as wind energy turbine set maximum output P
maxexceed wind energy turbine set plan to exert oneself upper limit P
planquite a lot of (power offset value P
excessexceed in certain limit) time, limit power decision module 140 first can be selected to carry out shutdown to part fan and control, and drops in certain limit, then, then select to carry out downrating control to part fan with the output power making wind energy turbine set total.
Still for the example shown in table 1, suppose the wind energy turbine set maximum output total output P calculated
max3800KW, the upper limit P and wind energy turbine set plan is exerted oneself
plan2500KW, P
max> P
plan.Here, P
excess=1300, suppose α=0.2, P
excess> α × P
max.Therefore, the optional selection of limit power decision module 140 is shut down part fan, and wind energy turbine set gross output is dropped to certain limit.Such as, the blower fan of shortest time of certainly rationing the power supply, first determine blower fan 5 to shut down, wind energy turbine set gross output is dropped to about 3000KW.After this, power decision module 140 is limit can to determine that the mode of the blower fan needing downrating is carried out limit power further and controlled according to aforementioned limit power decision module 140.
Describe in detail hereinafter with reference to Fig. 2 ~ Fig. 6 and go out force control method according to the wind energy turbine set of exemplary embodiment of the present invention.
Fig. 2 ~ Fig. 6 is the flow chart force control method of the wind energy turbine set illustrated according to exemplary embodiment of the present invention.
Fig. 2 is the flow chart force control method of the wind energy turbine set illustrated according to exemplary embodiment of the present invention.With reference to Fig. 2, in step S210, the wind energy turbine set Operational Limits acquisition module 110 going out Force control system of wind energy turbine set is from each its Operational Limits of blower fan continuous collecting, and described Operational Limits comprises the current operate power of the temperature parameter of each critical piece and vibration parameters and blower fan, wind speed and ambient temperature.
In step S220, fan operation state estimation module 120 determines the running state of each blower fan according to the Operational Limits of each blower fan gathered within a period of time, and described running state is can running state and need one of outage state.
According to exemplary embodiment of the present invention; when determining the running state of arbitrary blower fan; if in the given time; the temperature difference of each pitch-controlled system of described blower fan exceeds described predetermined temperature range; or the vibration of each critical piece of described blower fan exceedes predetermined vibration limit value, then determining that described blower fan is in needs outage state.
According to a preferred embodiment of the invention, described can running state comprise normal operating condition and power falls and can running state.When determining the running state of arbitrary blower fan, if in the given time, the temperature of each critical piece of described blower fan is in normal range (NR) and its vibration is no more than predetermined vibration limit value, then fan operation state estimation module determines that described blower fan is in normal operating condition; If in the given time, the temperature rise of the temperature of each critical piece of described blower fan exceedes predetermined temperature limit, the temperature difference of its each pitch-controlled system is in predetermined temperature range, and the vibration of each critical piece of described blower fan is no more than predetermined vibration limit value, then fan operation state estimation module determine described blower fan be in power falls and can running state.
After this, in step S230, what halt command sent to fan operation state estimation module 120 to determine in step S220 by air-blower control order output module 130 respectively is in the blower fan needing outage state.
Go out force control method according to above-described embodiment, the present invention by gathering its Operational Limits from each blower fan in wind energy turbine set, and carries out intellectual analysis to determine its running state, thus carries out the control of limit power according to the result analyzed to problem blower fan.
Fig. 3 is the flow chart force control method of the wind energy turbine set illustrated according to another exemplary embodiment of the present invention.Step S310 in Fig. 3 is identical with S210 with S220 in Fig. 2 respectively with the operation of S320, is not therefore specifically described step S310 and S320 at this.
With reference to Fig. 3, in step S330, the running state of each blower fan that the Operational Limits of each blower fan that limit power decision module 140 gathers in step S310 according to wind energy turbine set Operational Limits acquisition module 110 and fan operation state estimation module 120 are determined in step S320, determines that the current blower fan and being in being in normal operating condition falls power and can need to perform the blower fan of control of exerting oneself in the middle of the blower fan of running state.Fig. 4 illustrates the handling process of the step S330 in Fig. 3.The process of step S330 is described in detail hereinafter with reference to Fig. 4.
With reference to Fig. 4, in step S3310, the Operational Limits of each blower fan that limit power decision module 140 gathers within a period of time using wind energy turbine set Operational Limits acquisition module 110, as training sample, uses neural network modeling approach to fall power for being in of determining of fan operation state estimation module and each blower fan of running state can set up the power module of its maximum operate power.
According to a preferred embodiment of the invention, limit power decision module 140 is the mean value Tp that each critical piece of blower fan calculates the temperature upper limit of described critical piece, and using the part of the Tp of each critical piece of calculating as the input of neural network modeling approach, the Operational Limits of each blower fan gathered within a period of time using wind energy turbine set Operational Limits acquisition module, as training sample, uses neural network modeling approach to fall power for being in of determining of fan operation state estimation module and each blower fan of running state can set up the power module of its maximum operate power.
In step S3330, limit power decision module 140 calculate the current maximum output and being in being in every Fans of normal operating condition fall power can the summation of maximum operate power of every Fans of running state as wind energy turbine set maximum output total output P
max.For the current blower fan being in normal state, its maximum operate power can be calculated according to the current wind speed of blower fan and operate power curvimeter; And according to aforementioned neurological network modelling can obtain be in power falls and can the maximum operate power of each blower fan of running state.
In step S3350, power falls in each current each blower fan and being in being in normal operating condition that limit power decision module 140 is determined according to fan operation state estimation module can current operate power, the wind energy turbine set maximum output total output P of each blower fan of running state
maxand predetermined wind energy turbine set plan is exerted oneself upper limit P
plan, determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.Fig. 5 and Fig. 6 illustrates the process of exemplary embodiment according to the present invention in step S3350 respectively.Hereinafter with reference to the process of Fig. 5 and Fig. 6 difference interpretation procedure S3350.
With reference to Fig. 5, in step S3352, limit power decision module 140 determines P
maxwhether be greater than P
plan.If determine P
max> P
plan, then power decision module 140 is limit preferentially to fall power can determine that one or more blower fan is as performing the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in.
According to exemplary embodiment of the present invention, in step S3354, limit power decision module 140 is that current not being in needs every Fans of outage state to calculate rationing the power supply the time in the time cycle of described blower fan predetermined length in the past.In step S3356, the mark not being in each blower fan needing outage state is carried out ascending sort according to the time of rationing the power supply of blower fan by limit power decision module 140.Then, in step S3358, according to the order of time ascending order of rationing the power supply, select and determine that one or more blower fan is as the blower fan needing to shut down.
On the other hand, if in step S3352, limit power decision module 140 determines P
maxbe not more than P
plan, then power decision module 140 is limit not need to select need perform the blower fan of exerting oneself and controlling, the process of end step S3350.
Fig. 6 illustrates the process of the step S3350 according to another exemplary embodiment of the present invention.With reference to Fig. 6, in step S3352, limit power decision module 140 determines P
maxwhether be greater than P
plan.If determine P
max> P
plan, then in step S3355, limit power decision module 140 calculates wind power deviation value P
excess=P
max-P
plan, and by P
excesswith α × P
maxcompare, wherein, 0 < α < 1, is preferably 0.3≤α≤0.6.
If in step S3355, limit power decision module 140 determines P
excess≤ α × P
max, then in step S3357, limit power decision module 140 is preferentially fallen power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in.In this case, in step S350, air-blower control order output module 130 sends to falling power command the blower fan needing downrating limitting power decision module 140 to determine in step S3357 respectively.Can refer to step S3354, S3356 and S3358 select and determine to need the mode of blower fan of shutting down to determine to need the blower fan of downrating.
If in step S3355, limit power decision module 140 determines P
excess> α × P
max, then in step S3359, limit power decision module 140 is preferentially fallen power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in.Can refer to step S3354, S3356 and S3358 select and determine need shut down blower fan.According to a preferred embodiment of the invention; in step S3359; limit power decision module 140 is preferentially fallen power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in; and in step S350; by the blower fan that halt command to send to the needs determined to shut down by air-blower control order output module 130 respectively, falling power with the blower fan making to be in normal operating condition according to remaining and being in can the P that again calculates of the blower fan of running state
excess≤ α × P
max.After this, again according to step S3355 and S3357, preferentially fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in remaining, and in step S350, air-blower control order output module 130 sends to falling power command the blower fan needing downrating determined respectively.
Return Fig. 3, as previously mentioned, in step S350, air-blower control order output module 130 sends and exerts oneself control command to corresponding blower fan.Such as; if in step S330 or step S3358; limit power decision module 140 determines one or more blower fan needing to shut down, then in step S350, and the blower fan that halt command sends to the needs determined in step S330 or step S3358 to shut down by air-blower control order output module 130 respectively.If step S3357 in figure 6, limit power decision module 140 determines one or more blower fan needing downrating, then in step S350, air-blower control order output module 130 sends to falling power command the blower fan needing downrating limitting power decision module 140 to determine in step S3357 respectively.If step S3359 in figure 6; limit power decision module 140 determines one or more blower fan needing to shut down; then in step S350, the blower fan that halt command sends to limit power decision module 140 to shut down in the needs that step S3359 determines by air-blower control order output module 130 respectively.
Can find out the description of exemplary embodiment of the present invention by referring to accompanying drawing above, going out Force control system and going out force control method not only by assessing the running state of separate unit blower fan to carry out the control of limit power according to wind energy turbine set of the present invention, and can the comprehensively running state of wind turbine and requirement of rationing the power supply in wind energy turbine set, reasonably carry out limit power to multiple stage blower fan to control, comprise and preferentially fall power the ventilator selection blower fan of running state can carry out downrating control from being in, and/or preferentially fall power the ventilator selection blower fan of running state can carry out shutdowns control from being in.Therefore, output of wind electric field control system of the present invention and method realize the team control of exerting oneself to wind electric field blower, optimize output of wind electric field control program, and the health being conducive to blower fan is run, and improves the reliability of wind energy turbine set entirety.
Although show and describe the present invention with reference to preferred embodiment, art technology high-ranking official should be appreciated that, when not departing from the spirit and scope of the present invention be defined by the claims, can carry out various amendment and conversion to these embodiments.
Claims (20)
1. wind energy turbine set go out a Force control system, comprising:
Wind energy turbine set Operational Limits acquisition module, for from each its Operational Limits of blower fan continuous collecting, described Operational Limits comprises the current operate power of the temperature parameter of each critical piece and vibration parameters and blower fan, wind speed and ambient temperature;
Fan operation state estimation module, the Operational Limits for each blower fan gathered within a period of time according to wind energy turbine set Operational Limits acquisition module determines the running state of each blower fan, and described running state is can running state and need one of outage state;
Air-blower control order output module, that determines for halt command being sent to respectively fan operation state estimation module is in the blower fan needing outage state,
Wherein, fan operation state estimation module is when determining the running state of arbitrary blower fan; if in the given time; the temperature difference of each pitch-controlled system of described blower fan exceeds predetermined temperature range; or the vibration of each critical piece of described blower fan exceedes predetermined vibration limit value; then fan operation state estimation module determines that described blower fan is in needs outage state
Wherein, described can running state comprise normal operating condition and fall power can running state, and fan operation state estimation module is when determining the running state of arbitrary blower fan,
If in the given time, the temperature of each critical piece of described blower fan is in normal range (NR) and its vibration is no more than predetermined vibration limit value, then fan operation state estimation module determines that described blower fan is in normal operating condition,
If in the given time, the temperature rise of the temperature of each critical piece of described blower fan exceedes predetermined temperature limit, the temperature difference of its each pitch-controlled system is in predetermined temperature range, and the vibration of each critical piece of described blower fan is no more than predetermined vibration limit value, then fan operation state estimation module determine described blower fan be in power falls and can running state.
2. go out Force control system as claimed in claim 1, also comprise: limit power decision module, the running state of each blower fan that Operational Limits and fan operation state estimation module for each blower fan according to the collection of wind energy turbine set Operational Limits acquisition module are determined determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.
3. go out Force control system as claimed in claim 2, it is characterized in that, limit power decision module determine the current blower fan and being in being in normal operating condition fall power can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state time,
The Operational Limits of each blower fan gathered within a period of time using wind energy turbine set Operational Limits acquisition module is as training sample, use neural network modeling approach to fall power for being in of determining of fan operation state estimation module and each blower fan of running state can set up the power module of its maximum operate power
Calculate the current maximum output and being in being in every Fans of normal operating condition fall power can the summation of maximum operate power of every Fans of running state as wind energy turbine set maximum output total output P
max,
Power falls in each the current each blower fan and being in being in normal operating condition determined according to fan operation state estimation module can current operate power, the wind energy turbine set maximum output total output P of each blower fan of running state
maxand predetermined wind energy turbine set plan is exerted oneself upper limit P
plan, determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.
4. go out Force control system as claimed in claim 3, it is characterized in that, limit power decision module determine the current blower fan and being in being in normal operating condition fall power can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state time, if determine P
max>P
plan, then power decision module is limit preferentially to fall power can determine that one or more blower fan is as performing the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in.
5. go out Force control system as claimed in claim 4; it is characterized in that; limit power decision module is fallen power preferential can determine that one or more blower fan is as when need perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in; preferentially fall power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in
Wherein, the blower fan that the needs that halt command also sends to limit power decision module to determine by air-blower control order output module are respectively shut down.
6. go out Force control system as claimed in claim 4, it is characterized in that, limit power decision module is fallen power preferential can determine that one or more blower fan is as when need perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in,
Calculate wind power deviation value P
excess=P
max– P
plan,
If P
excess≤ α × P
max, wherein, 0< α <1, then limit power decision module preferentially to fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in,
Wherein, air-blower control order output module also sends to falling power command the blower fan needing downrating limitting power decision module to determine respectively.
7. go out Force control system as claimed in claim 6, it is characterized in that, if limit power decision module determines P
excess> α × P
maxpower decision module is then limit preferentially to fall power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in; and by the blower fan that halt command to send to the needs determined to shut down by air-blower control order output module respectively, falling power with the blower fan making to be in normal operating condition according to remaining and being in can the P that again calculates of the blower fan of running state
excess≤ α × P
maxand then preferentially fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in remaining, and send to falling power command the blower fan needing downrating determined by air-blower control order output module respectively.
8. according to any one of claim 3-7, go out Force control system; it is characterized in that; limit power decision module is fallen power preferential can determine that one or more blower fan is as when needing the blower fan shutting down or need downrating in the middle of the blower fan of running state from being in
For current not being in needs every Fans of outage state to calculate rationing the power supply the time in the time cycle of described blower fan predetermined length in the past,
The mark not being in each blower fan needing outage state is carried out ascending sort according to the time of rationing the power supply of blower fan,
According to by the order of time ascending order of rationing the power supply, select and determine that one or more blower fan is as the blower fan needing to shut down or need downrating.
9. according to any one of claim 3-7, go out Force control system, it is characterized in that, limit power decision module is the mean value Tp that each critical piece of blower fan calculates the temperature upper limit of described critical piece, and using the part of the Tp of each critical piece of calculating as the input of neural network modeling approach, the Operational Limits of each blower fan gathered within a period of time using wind energy turbine set Operational Limits acquisition module is as training sample, use neural network modeling approach to fall power for being in of determining of fan operation state estimation module and each blower fan of running state can set up the power module of its maximum operate power.
10. go out Force control system as claimed in claim 8, it is characterized in that, limit power decision module is the mean value Tp that each critical piece of blower fan calculates the temperature upper limit of described critical piece, and using the part of the Tp of each critical piece of calculating as the input of neural network modeling approach, the Operational Limits of each blower fan gathered within a period of time using wind energy turbine set Operational Limits acquisition module is as training sample, use neural network modeling approach to fall power for being in of determining of fan operation state estimation module and each blower fan of running state can set up the power module of its maximum operate power.
11. 1 kinds of wind energy turbine set go out force control method, comprise, wind energy turbine set central monitoring system perform following steps:
A) from each its Operational Limits of blower fan continuous collecting, described Operational Limits comprises the current operate power of the temperature parameter of each critical piece and vibration parameters and blower fan, wind speed and ambient temperature;
B) determine the running state of each blower fan according to the Operational Limits of each blower fan gathered within a period of time, described running state is can running state and need one of outage state;
C) halt command is sent to step B respectively) in determine be in the blower fan needing outage state,
Wherein, in step B); when determining the running state of arbitrary blower fan; if in the given time; the temperature difference of each pitch-controlled system of described blower fan exceeds described predetermined temperature range; or the vibration of each critical piece of described blower fan exceedes predetermined vibration limit value, then determining that described blower fan is in needs outage state
Wherein, described can running state comprise normal operating condition and fall power can running state, and in step B), when determining the running state of arbitrary blower fan,
If in the given time, the temperature of each critical piece of described blower fan is in normal range (NR) and its vibration is no more than predetermined vibration limit value, then determine that described blower fan is in normal operating condition,
If in the given time, the temperature rise of the temperature of each critical piece of described blower fan exceedes predetermined temperature limit, the temperature difference of its each pitch-controlled system is in predetermined temperature range, and the vibration of each critical piece of described blower fan is no more than predetermined vibration limit value, then determine described blower fan be in power falls and can running state.
12. go out force control method as claimed in claim 11, also comprise: D) according in steps A) Operational Limits of each blower fan that gathers and in step B) running state of each blower fan determined determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.
13. go out force control method as claimed in claim 12, it is characterized in that, in step D) determine the current blower fan and being in being in normal operating condition fall power can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state time,
D-1) with within a period of time, in steps A) Operational Limits of each blower fan that gathers is as training sample, using neural network modeling approach in step B) being in of determining fall power and each blower fan of running state can set up the power module of its maximum operate power
D-2) calculate the current maximum output and being in being in every Fans of normal operating condition fall power can the summation of maximum operate power of every Fans of running state as wind energy turbine set maximum output total output P
max,
D-3) falling power according to each the current each blower fan and being in being in normal operating condition determined can current operate power, the wind energy turbine set maximum output total output P of each blower fan of running state
maxand predetermined wind energy turbine set plan is exerted oneself upper limit P
plan, determine current be in normal operating condition blower fan and be in and fall power and can need to perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state.
14. go out force control method as claimed in claim 13, it is characterized in that, step D-3) comprising: if P
max>P
plan, then preferentially fall power can determine that one or more blower fan is as performing the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in.
15. go out force control method as claimed in claim 14; it is characterized in that; fall power preferential can determine that one or more blower fan is as when need perform the blower fan of exerting oneself and controlling in the middle of the blower fan of running state from being in; preferentially fall power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in, and the blower fan being sent to by halt command the needs determined to shut down respectively.
16. go out force control method as claimed in claim 14, it is characterized in that, step D-3) comprising: if determine P
max>P
plan, then wind power deviation value P is calculated
excess=P
max– P
planand if, P
excess≤ α × P
max, wherein, 0< α <1, then preferentially fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in,
Wherein, step C) also comprise: send to falling power command the blower fan needing downrating determined respectively.
17. go out force control method as claimed in claim 16, it is characterized in that, if P
excess> α × P
maxthen preferentially fall power can determine that one or more blower fan is as the blower fan needing to shut down in the middle of the blower fan of running state from being in; and halt command is sent to respectively the blower fan that the needs determined are shut down, falling power with the blower fan making to be in normal operating condition according to remaining and being in can the P that again calculates of the blower fan of running state
excess≤ α × P
max, and then preferentially fall power can determine that one or more blower fan is as the blower fan needing downrating in the middle of the blower fan of running state from being in remaining, and send to falling power command the blower fan needing downrating determined respectively.
18. go out force control method according to any one of claim 13-17, it is characterized in that, fall power preferential and can determine that one or more blower fan is as when needing the blower fan shutting down or need downrating in the middle of the blower fan of running state from being in,
For current not being in needs every Fans of outage state to calculate rationing the power supply the time in the time cycle of described blower fan predetermined length in the past,
The mark not being in each blower fan needing outage state is carried out ascending sort according to the time of rationing the power supply of blower fan,
According to by the order of time ascending order of rationing the power supply, select and determine that one or more blower fan is as the blower fan needing to shut down or need downrating.
19. go out force control method according to any one of claim 13-17, it is characterized in that, step D-1) comprising:
For each critical piece of blower fan calculates the mean value Tp of the temperature upper limit of described critical piece, and
Using the part of the Tp of each critical piece of calculating as the input of neural network modeling approach, using the Operational Limits of each blower fan gathered within a period of time as training sample, use neural network modeling approach to fall power for being in of determining and each blower fan of running state can set up the power module of its maximum operate power.
20. go out force control method as claimed in claim 18, it is characterized in that, step D-1) comprising:
For each critical piece of blower fan calculates the mean value Tp of the temperature upper limit of described critical piece, and
Using the part of the Tp of each critical piece of calculating as the input of neural network modeling approach, using the Operational Limits of each blower fan gathered within a period of time as training sample, use neural network modeling approach to fall power for being in of determining and each blower fan of running state can set up the power module of its maximum operate power.
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| US20110153096A1 (en) * | 2009-12-22 | 2011-06-23 | Sujan Kumar Pal | Method and system for monitoring operation of a wind farm |
| US9335748B2 (en) * | 2010-07-09 | 2016-05-10 | Emerson Process Management Power & Water Solutions, Inc. | Energy management system |
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