JP6053866B2 - Wind power generation system and bird flight prediction device - Google Patents

Description

  The present invention relates to a wind power generation system and bird flight prediction device that can reduce bird strikes in an area where there are many birds.

  One method of converting renewable energy that exists in nature to electric energy without using fossil energy that can be depleted is a wind power generator. Since this power generation method is a clean energy source that hardly generates carbon dioxide, which is a major cause of global warming, it has been rapidly introduced all over the world as a process for solving global environmental problems.

  The output of this wind power generation is influenced by natural phenomena, and when the output is large, there is a concern that the influence on the system becomes large when a large number of units are connected. For this reason, in order to increase the introduction amount, it is essential to take measures against frequency fluctuations, voltage fluctuations, etc. to the connected power system. In the electric power companies in Hokkaido and Tohoku regions where wind power generation is popular, as a countermeasure against frequency fluctuations for power grid interconnection in wind power plants with an output of 2 kW or more, for example, as shown in Non-Patent Document 1, output fluctuation mitigation control And technical requirements for two types of frequency fluctuations: output stabilization control.

  On the other hand, the introduction of wind power generation equipment has an impact on the surrounding environment. In November 2011, the Enforcement Order for the Environmental Impact Assessment Act was revised, and more than 10,000 kW of wind power generation equipment was classified as Type 1 project, 7,500 kW or more. The facility was certified as a target project under the Environmental Impact Assessment Law as a Type 2 project. In October 2012, the Enforcement Order for the Environmental Impact Assessment Law was enacted. Since then, environmental impact assessment based on the law, so-called legal assessment procedures have become necessary. There are three administrative opinions issued during the legal assessment process: Minister of Economy, Trade and Industry Recommendation, Minister of the Environment, and Governor Governor's Opinion. It is an opinion to the bird strike. Therefore, in order to increase the amount of wind power generation facilities installed in the future, a wind power plant that considers environmental conservation measures against bird strikes of birds will be necessary.

  The following are known as conventional technologies for environmental protection measures against bird strikes of birds. Patent Document 1 describes a system in which a radar is installed between wind power generation facilities and the wind power generation facilities are stopped when an obstacle (birds) enters the radar detection range. Patent Document 2 describes a wind power generation facility provided with a bird sensor. Furthermore, Patent Document 3 describes a device that avoids birds from wind power generation facilities.

JP 2006-125266 A Japanese Patent Laid-Open No. 2003-21046 JP 2012-19750 A

Tohoku Electric Co., Ltd., “About efforts to expand wind power generation”, [online], [Search on April 30, 2015], Internet <URL: http://www.tohoku-epco.co.jp/oshirase/ newene / 04>

  As described above, in order to expand the introduction of wind power generation facilities, it is necessary to prevent bird strikes. As countermeasures, Patent Documents 1-3 and Non-Patent Document 1 described above have been studied. However, there are the following problems at present.

  The method disclosed in Patent Document 1 is a method of stopping a wind power generation facility for the first time when a bird approaches the periphery of the wind power generation facility. For this reason, it is hard to say that it is a drastic measure against bird strike. In addition, because the sensor is installed on the ground, the wind power generation equipment is stopped even if it senses birds flying outside the blade rotation area of the wind power generation equipment, which has less impact. In addition, the amount of lost power generation increases, which adversely affects the wind power business of power generators. Furthermore, when the approach is detected by the system, the wind power generation equipment is suddenly stopped, so the load applied to the wind power generation equipment is large, and a large output fluctuation is given to the system, causing problems from the viewpoint of system stabilization. .

  On the other hand, the method disclosed in Patent Document 2 is a method of stopping a wind power generation facility for the first time when a bird approaches the periphery of the wind power generation facility, as in Patent Document 1, and does not sense unless it approaches. For this reason, birds will fly near the wind power generation facility. Therefore, it is difficult to say that this is a drastic measure against bird strikes, and a sudden load is imposed on the wind power generation equipment and system. However, since the sensor is installed at the tip of the blade, the unnecessary stop of the wind power generation equipment seen in Patent Document 1 can be avoided in that respect, but the tip of the blade is about 300 km / h, which has a great influence on the sensor. As a result, sensor malfunctions and failures increase. As a result, since the wind power generation facilities are stopped for the repair, unnecessary wind power generation facilities are stopped and the amount of lost power generation is increased, which adversely affects the wind power business of the power generation company.

  Although the method disclosed in Patent Document 3 is a device for avoiding birds from wind power generation facilities, it is desirable to prevent birds from coming close to the environment because it greatly changes the local ecosystem. It is not a conservation measure.

  From the above, current technology cannot take sufficient measures to avoid bird strikes. Moreover, since a system with sufficient measures is used, a wind power generation company's wind power business is adversely affected, and a large load is imposed on the wind power generation equipment and system. For this reason, it is a very difficult situation to put into practical use.

  The present invention is an invention for solving the above-described problems, and an object thereof is to provide a wind power generation system and a bird flight prediction device capable of reducing a bird strike in an area where there are many birds.

In order to achieve the above object, a wind power generation system according to the present invention determines whether or not a bird will fly based on a wind power generator (for example, a wind power generator 111) and weather data, and wind power based on the determination result. and bird flying prediction device which determines the operation schedule of the power generation apparatus, a control device for controlling the wind turbine generator based on the operation schedule (for example, host controller 3) have a bird flying prediction apparatus includes wind speed, wind direction Based on the meteorological data acquisition unit that acquires the weather forecast data, a database that correlates each parameter of the number of arrivals, time, wind speed, and wind direction of the birds, the acquired weather forecast data, and the correlation And a determination unit for determining flying . Other aspects of the present invention will be described in the embodiments described later.

  ADVANTAGE OF THE INVENTION According to this invention, the bird strike in the area with many birds can be reduced.

It is a figure which shows the structure of the wind farm to which the wind power generation system which concerns on Embodiment 1 is applied. It is a figure which shows the structure of the bird flight prediction apparatus which concerns on Embodiment 1. FIG. It is a figure which shows the driving schedule which concerns on Embodiment 1. FIG. It is a figure which shows the relationship between the sound frequency which measured the bird at each time, a radar frequency, and a wind speed. It is a figure which shows the correlation of the number of times of bird migration counted visually, and a wind speed and a wind direction. In FIG. 5, it is a figure which shows the relationship between the bird's time counted visually, and the frequency | count of migratory on the conditions of northeast, east-northeast, east, east-southeast, southeast, and south-southeast. It is a figure which shows the correlation of the frequency | count of bird migration according to time counted by visual observation, and a wind speed. It is the figure which compared the bird flight prediction by a bird flight prediction apparatus, and an observation result. It is a figure which shows the relationship between the time of the year when the frequency | count which makes the bird arrival prediction ON state the least, and a windmill output, (a) is a conventional example, (b) applied the bird arrival prediction method of this embodiment. Is the case. It is a figure which shows the structure of the wind farm to which the wind power generation system which concerns on Embodiment 2 is applied. It is a figure which shows the pilot plant arrangement structure which concerns on Embodiment 2. FIG. It is a figure which shows the structure of the bird flight prediction apparatus which concerns on Embodiment 3. FIG. It is the image figure which showed carrying out the moderate output suppression, charging the storage battery installed side by side from the time of the arrival prediction of a bird, (a) shows a windmill output, (b) shows storage battery charge amount. FIG.

DESCRIPTION OF EMBODIMENTS Embodiments for carrying out the present invention will be described in detail with reference to the drawings as appropriate.
(Embodiment 1)
FIG. 1 is a diagram illustrating a configuration of a wind farm to which the wind power generation system according to the first embodiment is applied. A wind farm 100 shown in FIG. 1 is connected to the power system 5 at one location via the interconnection transformer 4 and supplies power. The wind farm 100 includes a wind power generation system 1 (wind power generation device group), a power storage system 2, a host controller 3, a power meter 6 that measures output power PW and the like of the wind power generation system 1 and transmits them to the host controller 3. And a wattmeter 7 that measures charging power and discharging power PB that are charging power and discharging power of the power storage system 2 and transmits them to the host controller 3. In addition, the relationship of Formula (1) is established between the output power PS of the wind farm 100 and PW and PB.
PS = PW + PB (1)

  The bird arrival prediction device 8 determines whether or not a bird flies (flies) to the wind farm 100 based on the weather data to the host controller 3 of the wind farm 100, and the wind farm based on the determination result. The operation schedule of 100 wind power generation systems 1 is determined.

  Birds flying prediction device 8, when the advance what conditions on the basis of meteorological data, birds flying database 871 of the analysis of whether birds flying has (see FIG. 2), the condition of the birds flying database 871 If they do not match, the wind power generation system 1 after a predetermined time (for example, 1 hour later) from the present time continues normal operation (hereinafter referred to as a flying prediction OFF (off) setting). On the other hand, when the conditions are met, the arrival of a bird is predicted, so control for suppressing the output after a predetermined time from the current time is performed on the wind power generation system 1 (hereinafter referred to as the flight prediction ON (ON) setting). Called). This makes it possible to prevent bird strikes.

  Meteorological data are the results of weather forecasts including GSM (Global Spectral Model), MSM (Meso Scale Model), and LSM (Land Surface Model) distributed by the Japan Meteorological Agency, and the wind speed and direction created by each company. Etc. The bird flight prediction device 8 acquires the result of the weather prediction via the network NW and stores it in the storage unit 87 (see FIG. 2) as weather prediction data 872 (see FIG. 2).

  The wind power generation system 1 includes a plurality of wind power generation apparatuses 111, 112,..., 11 n and SCADA 11. The wind power generators 111, 112,..., 11n are wind power generators that are variable in rotation speed and pitch and can be controlled. SCADA is an abbreviation for Supervisory Control And Data Acquisition.

  The SCADA 11 plays a role of collecting operating information such as the operating status of the wind power generation system and generated power. At the same time, the SCADA 11 receives the generated power limit command PLC of the wind power generation system 1 from the host controller 3, and the sum of the generated power of the individual wind power generators 111, 112,. In this manner, the generated wind power limit devices PLC1, PLC2,..., PLCn are given to the individual wind power generators 111, 112,.

  The power storage system 2 includes one or more power storage devices 211, 212, ..., 21m. The power storage devices 211, 212,..., 21 m can charge the generated power of the wind power generation system 1 or discharge the stored power in accordance with the charge / discharge power command from the host controller 3. In addition, each of the power storage devices 211, 212,..., 21m measures a state of charge (SOC) of the storage battery, and transmits the SOC value to the host controller 3. The power storage devices 211, 212,..., 21 m are configured by any one of a lead storage battery, a sodium sulfur battery, a redox flow battery, a lithium ion battery, a nickel hydrogen battery, and a lithium ion capacitor, or a combination thereof.

  The display device 10 is installed in a monitoring room (not shown) of the wind farm 100 or a facility (not shown) for monitoring the wind farm outside the wind farm 100 and is processed by the host controller 3. Any one or more of information such as the operating state of the system 1, the power generation output, the output restriction command, the charging rate of the power storage system 2, the charge / discharge limit value, the output fluctuation record of the wind farm 100, the compliance rate with respect to the predetermined output fluctuation, etc. It has a function to display combined information. Further, the display device 10 has a function of displaying the determination result of the operation schedule of the wind power generation system 1 in the bird arrival prediction device 8.

  FIG. 2 is a diagram illustrating a configuration of the bird flight prediction apparatus according to the first embodiment. The bird flight prediction device 8 includes a transmission / reception unit 88 (weather data acquisition unit), a determination unit 81 that determines whether or not bird flight is predicted, and an operation schedule of the wind power generation system 1 based on the determination result of the determination unit 81. It has the operation schedule setting part 82 to set, and the memory | storage part 87 (database).

  In the storage unit 87, a bird flying database 871, which is a correlation database related to bird flight based on FIGS. 4 to 7 to be described later, weather forecast data 872 received via the transmission / reception unit 88, and driving set by the driving schedule setting unit 82 A schedule 873 and the like are stored.

  The determination unit 81 determines whether or not bird flight is predicted based on the bird flight database 871 and weather prediction data 872 such as GSM. Next, based on the determination result, the operation schedule setting unit 82 determines an operation schedule. Specifically, when it is predicted that a bird will fly, the output schedule of the wind power generator of the wind power generation system 1 is suppressed, while when it is predicted that a bird will not fly, the output schedule is not performed. Then, the operation schedule setting unit 82 transmits the determined operation schedule to the host controller 3 via the transmission / reception unit 88.

  FIG. 3 is a diagram illustrating an operation schedule according to the first embodiment. The input data is weather forecast data such as GSM, and the output is an operation schedule. In the figure, the time unit on the horizontal axis is arbitrary. For example, when one scale is one hour, one hour ahead (after a predetermined time) from the current weather forecast data 872 and the bird flight database 871 In the 60 minutes two hours ahead, the determination unit 81 determines whether or not to suppress the output of the wind power generator of the wind power generation system 1 (whether or not the bird will fly), and the operation schedule is determined accordingly. It is to do.

[Torabi database]
In this embodiment, in order to set the flight conditions and the like of the bird flight database 871, whether or not a correct prediction result is obtained at the time when the bird is predicted to fly is actually verified. The verification methods include visual verification, verification by sound collection using a recording device, and verification by video using a radar device.

  Here, verification by a recording device and verification by a radar device will be described. The recording device includes a microphone, a sound level meter main body, and a WAV recorder. To mitigate the effects of wind noise, the microphone was equipped with a windscreen. The frequency weighting characteristic of the sound level meter is “A”. Next, an outline of the system will be described. The frequency analysis was performed at intervals of 0.2 seconds, the sound pressure level for each of the all-pass value and the frequency band was obtained, and the sound pressure level fluctuation diagram was displayed on the computer screen. After that, the WAV file was reproduced from an arbitrary time position so that birds could sing. The survey area was G city in Aomori Prefecture, where there were relatively many birds flying. Four recorders were installed. In addition, in order to clarify the certainty of the recording survey in advance, visual inspection and aerial photography were also performed at the same time, and as a result of evaluating the validity, 90% or more was obtained by extracting voices of 45 db or more as birds crying The probability is consistent with the visual inspection. However, at present, it is not possible to specify the species of birds, but only the specification of “~”.

  With regard to the radar apparatus, images observed by the radar are continuously recorded as image files on a personal computer, and moving objects that are determined to be birds are extracted from the image data group by moving object monitoring software. A total of four places were installed near the recording device. However, since the observation range of the radar is 20 degrees, not all are captured. This is an issue for the future. In addition, we surveyed small birds in consideration of the fact that their size and flying altitude are low, so they are unlikely to be identified by radar surveys.

  The survey period was mid to late March 2014, and the same period was used for both recording and radar. In the case of visual bird flight surveys that have been generally used so far, it has been difficult to see bird flight at night. However, in the investigation by the recording or the radar device as in the present embodiment, it is possible to investigate the flight all the time. This is a great advantage.

  In the present embodiment, as shown in FIG. 3, in the wind turbine generator installed at the place where birds fly, whether or not the birds fly from the weather data at the location where the wind turbine generator is installed and the surrounding points. When such a prediction is carried out and it is predicted that a bird will fly, the output of the wind turbine generator is suppressed in advance.

  The above-mentioned flight prediction is based on GSM, MSM, LSM data distributed by the Meteorological Agency, or weather prediction data (wind speed data, wind direction data, atmospheric pressure) predicted based on them at or around the point where the wind power generator is installed. Data, weather data, temperature data, humidity data, and the like) and the meteorological data when the birds stored in the bird flying database 871 fly.

  In addition, the flight prediction is based on meteorological observation data of the meteorological observation device 9 (see FIG. 10) installed at or around the point where the wind power generator is installed, or meteorological observation data (wind speed) of the regional meteorological observation system (Amedas). (Data, wind direction data, atmospheric pressure data, weather data, temperature data, humidity data, etc.) and the degree of coincidence with the meteorological data when a bird stored in the bird flying database 871 is flying.

The actual bird survey results will be described below.
FIG. 4 is a diagram illustrating the relationship between sound frequency, radar frequency, and wind speed at each time. Four recording devices and radar devices as described above were installed in G city, Aomori Prefecture, where there are relatively many birds flying. The measurement was carried out for 10 days in the middle of March 2014, but what is shown is one day. The measurement time is 0: 0 to 23:59. The horizontal axis represents the time every 30 minutes, the left vertical axis represents the frequency of sound and radar, and the right vertical axis represents the average wind speed of 30 minutes obtained by measuring the wind speed every 10 minutes. The unit of frequency is arbitrary.

As a result of the investigation, it was found that both sound frequency and radar frequency had one peak each in the morning and afternoon. It was also found that the frequency was low from night to early morning. As a trend from the results of recording survey and radar survey over 10 days,
(1) Increase in frequency during sunrise and after sunset
(2) Do not fly when the wind speed exceeds 6m / sec.
(3) It became clear that midnight would not fly even at low wind speeds. Next, we decided to conduct a visual survey with high data reliability to verify the validity of the sound and radar surveys. The results are shown in FIG. 5, FIG. 6, and FIG.

  FIG. 5 is a diagram showing the correlation between the number of bird migrations counted visually and the wind speed and direction. The measurement was carried out for 10 days in the middle of March 2014, but what is shown is one day. The measurement time was set to 4:00 to 18:59 because of visual observation. The horizontal axis is the wind direction, and here it has 16 directions. The wind direction 17 in FIG. 5 is a calm state as comparison data of 16 directions, and indicates that the wind is weak enough to be unidentifiable when the wind is blowing in any direction or no wind. Table 1 shows the azimuth number (No.) and wind direction attached on the horizontal axis of FIG. When the azimuth number is 1, the wind direction is from north to north-northeast, and when the azimuth number is 2, the north-north-to-north direction and azimuth number 16 is from north-northwest to north. Since 360 ° is divided into 16 equal parts, each angle is 22.5 °. The vertical axis indicates the wind speed. The number of flying is represented by the size of the plot points, and the size of the plot points is changed by dividing into 4 groups of 1-9, 10-99, 100-999, 1000 or more respectively. .

Trends from the results of a 10-day visual survey and an aerial photography survey include:
(1) Wind from east to southeast, and wind speed is 3.5m / sec or less and there are many crossings,
(2) When the wind speed exceeds 6m / sec, it will not fly (flying will obviously be reduced)
Became clear.

  FIG. 6 is a diagram illustrating the relationship between the bird's time and the number of crossings counted visually under the six conditions of FIG. 5 in which the wind direction is northeast, east-northeast, east, east-southeast, southeast, and southeast-south (azimuth number 3 to 8). Similar to FIG. 5, the size of the plot points is changed by dividing into four groups of 1-9, 10-99, 100-999, 1000 or more. As a result, it was clarified that the wind speed was east, east-southeast, and southeast (azimuth number 5-7), and the number of flights increased in the morning and evening.

  FIG. 7 is a diagram showing the correlation between the number of bird migrations by time and the wind speed counted visually. The measurement was carried out for 10 days in the middle of March 2014, but what is shown is one day. The measurement time zone was set to 4:30 to 12:59 because of visual observation. The horizontal axis shows the time every 30 minutes, the left vertical axis shows the number of bird migrations counted visually, and the right vertical axis shows the average wind speed of 30 minutes obtained by measuring the wind speed every 10 minutes. In addition, the wind direction is also noted in the figure.

As a tendency from the measurement result of 10 days,
(1) The wind direction from 5:30 to 7:30, where the number of crossings is large, is east to south,
(2) It became clear that the wind speed was 3 m / sec or less and the number of crossings increased after sunrise.

  Although the results in FIGS. 5 to 7 are visual inspections, a survey by aerial photography is also performed in parallel with the visual observation, and it has been confirmed that the results of the visual observation and the aerial photography are almost the same. The aerial shot was taken from above with a motorized hang glider.

From the above results, the facts revealed by the survey using the recording device and the radar device of the present embodiment, the visual survey and the aerial photography survey are as follows.
(I) the frequency increases during sunrise and after sunset
(Ii) Do not fly when the wind speed exceeds 6m / second,
(Iii) Do not fly even at low wind speeds at midnight,
(Iv) Wind from east to southeast, with a wind speed of 3.5m / sec or less and a large number of crossings

  Previously, it was unclear when and when the bird would fly, so when a windmill was installed on the route where the bird came, it was forced to suppress output for several days as a countermeasure for bird strike, the worst case Then, the windmill was stopped completely. From the perspective of wind power companies, this has had the problem of greatly affecting business profitability. Further, when the conventional techniques described in Patent Documents 1 to 3 are applied, there is a problem that mechanical damage is caused because the windmill must be stopped suddenly.

  On the other hand, if it can be predicted in advance that the above four conditions ((i) to (iv)) clarified in this embodiment can be predicted, it is only necessary to suppress the output of the windmill at that time. .

  FIG. 8 is a diagram comparing the bird arrival prediction by the bird arrival prediction device and the observation result. In this embodiment, when it is predicted that the above four conditions will be met using GSM data periodically announced by the Japan Meteorological Agency, the flight prediction is set to the ON state, and 100 birds are set within 6 hours after the ON state is set. A verification experiment was conducted with the above flight in place, with the other being off. Specifically, the case where the observation result and the prediction period overlap in FIG. 8 is appropriate. In FIG. 8, the horizontal axis represents the date and time, and the vertical axis represents the number of crossings. The evaluated period was 3/20 to 3/27. In the meantime, it was turned on 12 times as a prediction of flight, but 8 of them were observed to fly more than 100 birds within 6 hours and were able to be in the middle. On the other hand, four predictions were wrong, and the appropriate rate was 67%. Further, in FIG. 8, 8 times are appropriate and 9 times are not, and 47% is calculated when the appropriate ratio is calculated from that viewpoint.

  Here, prediction is performed for six hours from the present time, but the accuracy of the weather data is improved by shortening the interval. For this reason, it has been clarified through experiments that the predictive value can be improved. However, since a windmill requires several minutes to several tens of minutes to stop and start, it is desirable to predict the arrival of birds at least about an hour ago.

  FIG. 9 is a diagram illustrating a relationship between the time of the year when the number of times of setting the bird arrival prediction on state is the smallest and the wind turbine output. FIG. 9A is a conventional windmill output pattern at the time of bird flight, and FIG. 9B is a simulation result when it is assumed that flight prediction is performed in this embodiment. Conventionally, measures have been taken in view of bird behavior patterns, but in the worst case as a measure against bird strike, a windmill was stopped for several days. FIG. 9 (a) shows the windmill output when it is carried out for 5 days (120 hours). It shows that the windmill has been stopped all day because we do not know when the birds will fly in a five-day period.

  On the other hand, if the flight prediction according to the present embodiment is applied, as shown in FIG. 9B, the wind turbine stop time can be set to 30 hours in total, which greatly contributes from the viewpoint of efficient wind turbine operation. We have demonstrated that we can do it.

  Based on the above, the conditions under which birds fly or do not fly are analyzed and grasped, and when it is assumed that the conditions for flying will be met by predictions from various weather data, the output is suppressed only during that time period. By doing so, it is possible to generate power efficiently as a wind power plant and to achieve both environmental conservation.

  In addition, although GSM data was used in the present Example, it confirmed that the same result was obtained also by flight prediction using MSM data and LSM data.

(Embodiment 2)
FIG. 10 is a diagram illustrating a configuration of a wind farm to which the wind power generation system according to the second embodiment is applied. FIG. 10 has a pilot plant PP. In order to improve the predictive value of flight prediction, it is important to comprehensively analyze more parameters. Therefore, when performing the same flight prediction as in the first embodiment, weather data (for example, wind speed data, wind direction data, atmospheric pressure data, weather data, temperature data) is analyzed, and a combination of a plurality of parameters is used to determine any past conditions. At that time, we investigated in advance whether birds were flying. We also considered incorporating wind condition data of pilot plant PP, which is one or more meteorological observation devices installed at or around the site where the wind power generation equipment (wind power generation equipment) was installed.

  The pilot plant PP described in the present embodiment is a device installed on a steel tower or the like at a location several km to several tens km away from the actual wind farm 100 installation location, and measures wind speed, wind direction, temperature, and the like. is there. Thereby, it can apply to the wind condition prediction from the present which the wind power generator of the actual wind farm 100 receives. This pilot plant PP is installed on the windward side of the prevailing wind direction received by the wind farm 100.

  The meteorological observation device 9 includes one or more types of meteorological data such as wind speed data, wind direction data, temperature data, humidity data, and atmospheric pressure data, and the time when the meteorological data is measured. The location data such as the latitude, longitude, altitude, etc. of the installed points are stored sequentially or for a fixed time from the meteorological measurement value processing / transmitting device 91, and are collected to the bird arrival prediction device 8 via the network NW. To send.

  The weather observation device 9 and the weather measurement value processing / transmission device 91 may be provided as a configuration of the wind farm 100, or may be configured using an external device. Moreover, the installation position of the weather observation device 9 is not limited to the windward direction, and may be located where the wind speed in the wind farm 100 can be measured satisfactorily. Details will be described with reference to FIG.

  FIG. 11 is a diagram illustrating a pilot plant arrangement configuration according to the second embodiment. In FIG. 11, the wind farm 100 is installed at intervals of 90 degrees, but in actuality, when the wind farm 100 is constructed, the pilot plant PP is located at a position where the probability of windward is high considering the topography and the like. It is desirable to stand up. When the wind direction received by the wind power generator matches the wind direction received by the pilot plant PP, and the pilot plant PP is on the windward side, the data close to the measured data of the pilot plant PP reaches the wind power generator with a time delay. Has been empirically revealed.

  Also in this embodiment, as a result of evaluating the appropriateness ratio in the same manner as in the first embodiment, when compared under the same conditions, the appropriateness ratio is 67% to 83% by applying the wind condition data obtained from the pilot plant PP to the bird flight prediction. % Was found to improve.

  Further, by installing a bird detection sensor 92 that detects when a bird flies in the pilot plant PP, the predictive value has been improved from 83% to 86%. This result suggests that birds often fly from the leeward side to the leeward side. The bird detection sensor 92 is an infrared sensor, for example, and detects when a flying object enters within a predetermined distance above the pilot plant PP.

  From the above, meteorological data (for example, wind speed data, wind direction data, barometric pressure data, weather data, temperature data) is comprehensively analyzed, and by combining multiple parameters, under what conditions in the past the bird is flying In addition, it is possible to apply wind condition data of one or a plurality of pilot plants PP installed at a point where a wind power generation facility is installed or a surrounding point, and further, a bird detection sensor which detects the arrival of birds in the pilot plant PP By installing 92, it is possible to predict the arrival of birds with high prediction accuracy, enabling efficient power generation as a wind power plant and achieving both environmental conservation.

(Embodiment 3)
FIG. 12 is a diagram illustrating a configuration of a bird flight prediction apparatus according to the third embodiment. In the third embodiment, in order to further improve the predictability of flight prediction, the accuracy of prediction is improved by weighting the accumulated wind speed data, wind direction data, and time data for the methods used in the first and second embodiments. Tried. Compared with FIG. 2, FIG. 12 has a reasonable rate calculation unit 83 that calculates a reasonable rate of bird flight prediction. The storage unit 87 stores weather observation data 874 acquired from the weather observation device 9, a bird flight result 875 from the bird detection sensor 92, and an appropriate rate calculation result 876 calculated by the appropriate rate calculation unit 83. .

The appropriate rate calculation unit 83 calculates an appropriate rate based on the evaluation value E based on a plurality of parameters and the bird flight result 875 in the weather data. For example, the wind speed step function is S (W), the wind direction step function is S (D), the time step function is S (T), the wind speed weighting coefficient is k1, the wind direction weighting coefficient is k2, and the time weighting. When the coefficient is k3, the evaluation value E is calculated by equation (2).
E = k1 · S (W) + k2 · S (D) + k3 · S (T) (2)
S (W) is 0 if it is less than a predetermined wind speed threshold, and 1 if it is greater than or equal to a predetermined threshold. S (D) is 0 if it is outside the predetermined wind direction range, and 1 if it is within the predetermined wind direction range. S (T) is 0 if it is outside the predetermined time, and 1 if it is within the predetermined time.

  The appropriate rate calculation unit 83 determines that the bird will fly when the evaluation value E obtained by the equation (2) is equal to or greater than a predetermined value, and determines that the bird does not fly when the value is less than the predetermined value.

  The appropriate rate calculation unit 83 calculates an appropriate rate based on the determination result and the actual bird flight result 875. The appropriateness ratio calculation unit 83 can increase the appropriateness ratio by repeating this calculation process while changing the weighting coefficient, or by changing a predetermined value used for the determination process of bird arrival.

  Further, in the present embodiment, it has been considered that past similar data is searched based on data up to about 360 minutes (6 hours) before the present time, and the data is used as a predicted value. At this time, it is effective to classify past similar data into wind speed, wind direction, atmospheric pressure, air temperature, humidity, AMeDAS, etc., and to set a higher weighting coefficient for parameters that have a higher degree of similarity in the past waveform. It is.

  In addition, the present embodiment is most effective when applied to the arrival time of migratory birds in spring, but may be used at any time. For example, we found that the wind direction was the highest weight in spring, humidity in summer, and wind speed in autumn and winter. As a result of detailed examination, it was found that it is effective to make the coefficient arbitrarily changeable, and the flying prediction accuracy is improved.

  It is preferable that the degree of matching of similar data is verified 1 minute to 360 minutes before the present, and the one with the highest prediction accuracy is adopted as the prediction ahead of the present. In addition to the weighting factor, by making it possible to arbitrarily change the application time of each data, the data update time, etc., the predictive value of the flying prediction can be further improved up to 92%.

  In the evaluation value E of equation (2), weighting coefficients are added to the parameters of wind speed data, wind direction data, and time data, but atmospheric pressure data, weather data, temperature data, humidity data, and the like may be used. It is also effective to use measured weather data of the pilot plant PP.

  In FIG. 12, the determination unit 81 may determine that a bird will fly when the evaluation value E is equal to or greater than a predetermined value, based on the evaluation value E having a high appropriateness ratio of the appropriateness ratio calculation result in the appropriateness ratio calculation unit 83.

  FIGS. 13A and 13B are image diagrams showing moderate output suppression while charging an attached storage battery from the time when a bird flight prediction is transmitted. FIG. 13A is a windmill output, and FIG. 13B is a storage battery. It is a figure which shows charge amount. In the present embodiment, in the wind farm 100 performing the output fluctuation mitigation control, when the bird flight prediction is transmitted, the power storage system 2 provided in the wind power generation system 1 is charged and the output is moderately suppressed. By doing so, a highly efficient wind farm 100 can be provided. When the flying prediction is turned on, the output is gently suppressed until the time until the start of the output suppression period, and the power storage system 2 is charged at that time. In this way, the power generation opportunity loss and the charge / discharge loss are reduced by the flight prediction and the improvement of the operation method, and efficient power generation becomes possible.

  1 and 10, the wind farm 100 has been described. However, the present invention is not limited to this. For example, in a wind power generation facility installed in a place where birds fly, the bird flight prediction device 8 includes a wind power generation facility. It is better to predict whether or not birds will fly from the weather data at the point where is installed and the surrounding points, and change the output of the wind power generation facility in advance according to the result.

  More specifically, in the Tobirai database 871 (see FIG. 2), the weather data (for example, wind speed data) acquired from the weather observation device 9 installed at the point where the wind power generation facility is installed or the surrounding points, AMeDAS, etc. , Wind direction data, air pressure data, weather data, temperature data, humidity data) and data such as data in which a bird flew or passed a predetermined value or more (for example, 100 birds or more) are stored. The determination unit 81 (see FIG. 2) determines whether or not the weather prediction data satisfies the correlation, and when the condition is satisfied, the output of the wind power generation facility may be changed in advance.

  In this embodiment, when it is determined that a bird approaches the wind power generation facility vicinity based on weather data (weather forecast data 872 or weather observation data 874), the bird strike to the windmill is stopped in order to stop the wind power generation facility. Can be avoided. In addition, it can be expected that the load applied to the wind power generation facility is small, and no large output fluctuation is given to the system. Also, by collecting information about bird flight, it is possible to predict bird flight with high accuracy without using an expensive sensor or the like. In addition, the system is easy to accept because the planned outage of wind power generation facilities does not increase unnecessary lost power generation. Therefore, it is possible to provide a wind power generation system that takes into consideration overall environmental conservation such as avoiding bird strikes and economic efficiency.

DESCRIPTION OF SYMBOLS 1 Wind power generation system 2 Power storage system 3 Host controller 8 Bird flight forecasting device 9 Meteorological observation device 10 Display device 81 Judgment unit 82 Operation schedule setting unit 83 Moderate rate calculation unit 87 Storage unit 88 Transmission / reception unit (meteorological data acquisition unit)
91 Meteorological measurement value processing / transmission device 92 Bird detection sensor
100 Wind Farm 871 Bird Flight Database 872 Weather Prediction Data 873 Operation Schedule 874 Weather Observation Data 875 Bird Flight Result 876 Moderate Prediction Result NW Network

Claims (8)

A wind power generator,
It is determined whether or not a bird will fly based on weather data, and a bird flight prediction device that determines an operation schedule of the wind turbine generator based on the determination result;
A control device for controlling the wind turbine generator based on the operation schedule,
The bird flying prediction device
A weather data acquisition unit for acquiring weather forecast data including wind speed and direction;
A database that correlates each parameter of the number of flying birds, time, wind speed, and wind direction;
Wind power systems that comprising: the weather forecast data that the acquired, and a determination unit for flying birds on the basis of said correlation. A wind power generator,
It is determined whether or not a bird will fly based on weather data, and a bird flight prediction device that determines an operation schedule of the wind turbine generator based on the determination result;
A control device for controlling the wind turbine generator based on the operation schedule;
Has a weather observation device for observing the previous Symbol weather data, the,
The bird flying prediction device
A weather data acquisition unit for acquiring weather observation data from the weather observation device;
A database that correlates each parameter of the number of flying birds, time, wind speed, and wind direction;
Wind power system that, comprising a meteorological data the acquired, and a determination unit for flying birds on the basis of said correlation. The bird flying prediction device
Wherein based on a determination of the determination result, the wind power generation system according to claim 1 or claim 2, characterized in that it has the operation schedule setting portion that sets the operation schedule of the wind turbine generator. The meteorological observation device further includes a sensor for detecting the arrival of birds,
The bird flight prediction device further includes:
3. An appropriate rate calculation unit that calculates an appropriate rate of whether or not a bird has come based on a result of a bird flying by the sensor and a determination result determined by the determination unit. Wind power generation system. A wind power generator,
It is determined whether or not a bird will fly based on weather data, and a bird flight prediction device that determines an operation schedule of the wind turbine generator based on the determination result;
A control device for controlling the wind turbine generator based on the operation schedule,
The bird flying prediction device
A weather data acquisition unit for acquiring weather forecast data including wind speed and direction;
A database that correlates each parameter of the number of flying birds, time, wind speed, and wind direction;
A determination unit for determining the arrival of a bird based on the acquired weather prediction data and the correlation;
The determination unit
The step function of wind speed is S (W), the step function of wind direction is S (D), the step function of time is S (T), the weighting coefficient of wind speed is k1, the weighting coefficient of wind direction is k2, When the time weighting factor is k3,
E = k1 · S (W) + k2 · S (D) + k3 · S (T)
The evaluation value E indicated by
Wind power system the evaluation value E is you characterized in that in the case of a predetermined value or more is determined that a bird is flying. A wind power generator,
It is determined whether or not a bird will fly based on weather data, and a bird flight prediction device that determines an operation schedule of the wind turbine generator based on the determination result;
A control device for controlling the wind turbine generator based on the operation schedule;
A weather observation device for observing the weather data;
The bird flying prediction device
A weather data acquisition unit for acquiring weather observation data from the weather observation device;
A database that correlates each parameter of the number of flying birds, time, wind speed, and wind direction;
A determination unit that determines the arrival of birds based on the acquired weather observation data and the correlation;
The determination unit
The step function of wind speed is S (W), the step function of wind direction is S (D), the step function of time is S (T), the weighting coefficient of wind speed is k1, the weighting coefficient of wind direction is k2, When the time weighting factor is k3,
E = k1 · S (W) + k2 · S (D) + k3 · S (T)
The evaluation value E indicated by
Wind power system the evaluation value E is you characterized in that in the case of a predetermined value or more is determined that a bird is flying. A database that correlates each parameter of the number of flying birds, time, wind speed, and wind direction;
A weather data acquisition unit for acquiring weather forecast data including wind speed and direction;
A determination unit for determining the arrival of a bird based on the acquired weather prediction data and the correlation;
The determination unit
The step function of wind speed is S (W), the step function of wind direction is S (D), the step function of time is S (T), the weighting coefficient of wind speed is k1, the weighting coefficient of wind direction is k2, When the time weighting factor is k3,
E = k1 · S (W) + k2 · S (D) + k3 · S (T)
The evaluation value E indicated by
Birds flying prediction apparatus the evaluation value E is you characterized in that in the case of a predetermined value or more is determined that a bird is flying. The bird flight prediction device further includes:
Before SL based on the determination of the determination result, birds flying prediction apparatus according to claim 7, characterized in that it comprises the operation schedule setting portion that sets the operation schedule of the wind turbine generator.