JP4347030B2 - Wind power generation output prediction method - Google Patents
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- JP4347030B2 JP4347030B2 JP2003402573A JP2003402573A JP4347030B2 JP 4347030 B2 JP4347030 B2 JP 4347030B2 JP 2003402573 A JP2003402573 A JP 2003402573A JP 2003402573 A JP2003402573 A JP 2003402573A JP 4347030 B2 JP4347030 B2 JP 4347030B2
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Description
本発明は、風力発電装置の風力による発電出力を予想する風力発電出力予測方法に関する。 The present invention relates to a wind power generation output prediction method for predicting a power generation output by wind power of a wind power generator.
従来から、風力を利用して発電する風力発電装置が知られている（例えば、特許文献１参照。）。
ところで、この風力発電装置の場合、風力発電装置が施設されているサイトの地形に依存して発電出力が決定されるので、かつ、建設費用がかなりの高額となるため、建設前には風況調査を綿密に行っている。 By the way, in the case of this wind turbine generator, the power generation output is determined depending on the terrain of the site where the wind turbine generator is installed, and the construction cost is considerably high. We are conducting a thorough investigation.
しかしながら、風力発電装置による発電出力値が時々刻々どのように変化するかの予測は、電力系統の需給バランスを知るうえで重要な情報であるにもかかわらず、ほとんど行われていない。 However, prediction of how the power generation output value of the wind power generator changes from moment to moment is rarely performed despite being important information for knowing the supply and demand balance of the power system.
というのは、風力発電の発電出力がサイト周辺の地形の影響を大きく受けるため、単純に気象予報データから予測できないからである。 This is because the power output of wind power generation is greatly affected by the topography around the site, and cannot simply be predicted from weather forecast data.
風力発電は、今後、ますます増加する傾向にあり、風力発電の普及が進めば、その風力発電による発電出力の予想が正確でないと、軽負荷時の火力機の起動停止時期の判断を誤ったり、変動要因の増加により系統運用に支障をきたすおそれがある。 Wind power generation tends to increase in the future, and if wind power generation becomes more widespread, if the prediction of the power generation output by the wind power generation is not accurate, it may be wrong to determine the start and stop timing of a thermal power plant at light load There is a risk that system operation will be hindered due to an increase in fluctuation factors.
本発明は、上記の事情に基づいて為されたもので、その目的とするところは、気象予報データから風力発電装置の発電出力を予想できるようにした風力発電出力予測方法を提供することにある。 The present invention has been made based on the above circumstances, and an object of the present invention is to provide a wind power generation output prediction method capable of predicting the power generation output of a wind power generator from weather forecast data. .
請求項１に記載の風力発電出力予測方法は、風力発電装置のサイトの近隣の気象予報箇所からの現在時刻から所定時間経過後の気象予報データを複数個のファジー集合に区分し、各要素のそのファジー集合に属する割合を表すメンバーシップ関数を定義し、該メンバーシップ関数と当該メンバーシップ関数の重み付けとに基づき前記所定時間経過後の発電出力予想値を示す特性式を定義し、該特性式により得られた発電出力予想値と前記所定時間経過後に前記風力発電装置により実際に得られた発電出力実際値との誤差を求め、その誤差が最小に漸近するように前記メンバーシップ関数の重み付けを修正することにより前記特性式の学習を行い現在時刻から所定時間経過後の発電出力予想値を求めることを特徴とする。 The wind power generation output prediction method according to claim 1 divides weather forecast data after a predetermined time from a current time from a weather forecast location in the vicinity of a wind power generator site into a plurality of fuzzy sets, Defining a membership function representing a ratio belonging to the fuzzy set, defining a characteristic formula indicating a power generation output expected value after the predetermined time based on the membership function and a weight of the membership function, and the characteristic formula An error between the predicted power generation output value obtained by the above and the actual power generation output value actually obtained by the wind power generator after the predetermined time has elapsed, and the membership function is weighted so that the error gradually approaches a minimum. The characteristic expression is learned by correction, and a predicted power generation output value after a predetermined time has elapsed from the current time is obtained.
請求項２に記載の風力発電出力予測方法は、請求項１に記載の風力発電出力予測方法において、前記発電出力実際値を自動的にフィードバックして前記誤差を求めると共に前記メンバーシップ関数の重み付けを修正することを特徴とする。
The wind power generation output prediction method according to
請求項１または請求項２に記載の風力発電出力予測方法によれば、ファジィ推論方法を用いて気象予報データから発電出力を予想できるので、気象予報データに基づき簡便に正確な風力発電の発電出力を予想できるという効果を奏する。
According to the wind power output prediction method according to claim 1 or
以下に本発明に係わる風力発電出力予測方法を図面を参照しつつ説明する。 A wind power generation output prediction method according to the present invention will be described below with reference to the drawings.
図１は本発明に係わる風力発電出力予測方法を説明するための説明図であって、１は風力発電装置の設備施設箇所であるサイト、２は気象予報データが得られる気象予報箇所である。風力発電出力の予測には、そのサイト１を囲む気象予報箇所２における気象予報データが用いられる。
FIG. 1 is an explanatory diagram for explaining a wind power generation output prediction method according to the present invention, wherein 1 is a site that is a facility facility location of a wind power generator, and 2 is a weather forecast location from which weather forecast data is obtained. For forecasting wind power generation output, weather forecast data at a
いま、時刻ｔにおいて、サイト１を囲むその近傍のＫ個の箇所からそれぞれａ個の種類の気象予報データが得られるものとする。 Now, at time t, it is assumed that a type of weather forecast data is obtained from each of K locations in the vicinity surrounding the site 1.
ここで、ａ個の種類の気象予報データとは、風速Ｖi、風向θi、大気圧Ｓi、風速の３乗値Ｖi3の４個であるが、これらに限られるものではなく、大気密度ρに関係する気温、湿度、運動エネルギーに比例するＶi2をこれらの気象予報データに加えても良い。 Here, “a” types of weather forecast data are four of wind speed Vi, wind direction θi, atmospheric pressure Si, and the cube of the wind speed Vi3, but are not limited to these, and are related to the atmospheric density ρ. Vi2 proportional to the temperature, humidity, and kinetic energy to be added may be added to these weather forecast data.
というのは、風力発電の発電出力は一般に、風速Ｖiの３乗値に大気密度ρを積算して得られた値に比例するからである。 This is because the power generation output of wind power generation is generally proportional to the value obtained by adding the atmospheric density ρ to the cube of the wind speed Vi.
ここでは、気象予報データとして、風速Ｖi、風向θi、大気圧Ｓi、風速の３乗値Ｖi3の４個を用いて、時刻ｔから所定時間Ｔ経過後の気象予報データのかたまりＸ（ｔ,Ｔ）が得られたものとする。ここで、Ｘ（ｔ,Ｔ）の要素数はａ×Ｋ個である。 Here, as weather forecast data, the wind speed Vi, the wind direction θi, the atmospheric pressure Si, and the cubed value Vi3 of the wind speed are used, and a block X (t, T) of the weather forecast data after a predetermined time T has elapsed from the time t. ) Is obtained. Here, the number of elements of X (t, T) is a × K.
その気象予報データのかたまりを下記に（１）式として示す。
この気象予報データのかたまりＸ（ｔ,Ｔ）の各要素それぞれをｍi個（添え字iはＸ（ｔ,Ｔ）のi番目の要素）に区分して（２）式を定義する。
この要素の区分毎にファジー集合Ｍn（ｎ＝１〜ａＫ）を定義する。要素ｎごとのファジー集合を以下の（３）式で定義する。
各要素ｎのi番目のファジー集合Ｍ（ｎ,i）（i＝１〜ｍn）に属する割合（グレード）を表すメンバーシップ関数をＧ（ｎ,i）とする。 A membership function representing a ratio (grade) belonging to the i-th fuzzy set M (n, i) (i = 1 to mn) of each element n is defined as G (n, i).
このメンバーシップ関数Ｇ（ｎ,i）には、各種のものを用いることができ、例えば、図２に示す三角形関数があるが、ここでは、下記（４）式で示すものを用いる。
第１番目の気象予報データ箇所からＫ番目の気象予報データ箇所までの気象予報データを（４）式の右辺に代入して、下記の（５）式に基づきグレードを計算する。
ただし、i1＝１〜ｍ1 However, i1 = 1 to m1
…
iak＝１〜ｍak
（５）式を用いて風力発電の予想発電出力式としての特性式Ｐs（ｔ,Ｔ）を以下の（６）式で定義する。
iak = 1-mak
The characteristic formula Ps (t, T) as an expected power generation output formula of wind power generation is defined by the following formula (6) using formula (5).
ここで、ｑ組の数値予測データと各組に対応した風力発電の発電出力実際値として対象時刻を中心にした一定時間幅内の平均値としてＰ（ｔ＋Ｔ）を与えたとき、（６）式で得られた発電出力予想値との誤差Ｅを下記の（７）式で定義する。
この（７）式で示す誤差を最小にする重み付けＷ（i1,…iak）を（７）式を用いてＷ（i1,…iak）で偏微分し、下記（８）式の重み付け修正式Ｗ（i1,…iak）を得る。
ただし、Ｗ’（i1,…iak）は修正前の重み付け、γは定数である。 However, W ′ (i1,... Iak) is a weight before correction, and γ is a constant.
（７）式で示す誤差Ｅが最小に漸近するように（８）式で定義される重み付け修正式Ｗ（i1,…iak）を用いて重み付けＷ（i1,…iak）を修正する。 The weighting W (i1,..., Iak) is corrected using the weighting correction formula W (i1,..., Iak) defined by the expression (8) so that the error E indicated by the expression (7) gradually approaches the minimum.
このように本発明の風力発電出力予測方法によれば、風力発電装置１のサイトを囲む気象予報箇所２からの現在時刻Ｔから所定時間Ｔ経過後の気象予報データを複数個のファジー集合Mnに区分し、各要素のそのファジー集合Mnに属する割合を表すメンバーシップ関数Ｇ（ｎ,i）を定義し、メンバーシップ関数Ｇ（ｎ,i）とメンバーシップ関数Ｇ（ｎ,i）の重み付けＷ（i1,…iak）とに基づき所定時間Ｔ経過後の発電出力予想値を示す特性式Ｐs（ｔ,Ｔ）を定義し、この特性式Ｐs（ｔ,Ｔ）により得られた発電出力予想値と所定時間Ｔ経過後に風力発電装置により実際に得られた発電出力実際値との誤差Ｅを求め、その誤差Ｅが最小となるようにメンバーシップ関数Ｇ（ｎ,i）の重み付けＷ（i1,…iak）を修正することにより特性式Ｐs（ｔ,Ｔ）の学習を行い、現在時刻ｔから所定時間Ｔ経過後の発電出力予想値を求める。
As described above, according to the wind power generation output prediction method of the present invention, weather forecast data after a predetermined time T has elapsed from the current time T from the
このファジィ推論方法を用いれば、重み付け係数を時間Ｔ経過後に得られた実際の発電出力実際値とＴ時間前の予想した発電出力予想値との差に基づいてその誤差が自動的に最小に漸近するように演算できるので、気象予報データに基づき簡便に正確な風力発電の発電出力を予想できる。 If this fuzzy inference method is used, the error is automatically asymptotically minimized to the minimum based on the difference between the actual power output actual value obtained after the lapse of time T and the predicted power output predicted before T time. because be calculated as, Ru can predict the power output of the simple accurate wind power on the basis of the weather forecast data.
以上、実施例では、風力発電の実際の発電電力出力値として平均値Ｐ（ｔ＋Ｔ）を用いたが、出力変動特性（標準偏差、フーリエ変換、ウエーブレット変換）を与えても良い。 As described above, in the embodiment, the average value P (t + T) is used as the actual generated power output value of wind power generation, but output fluctuation characteristics (standard deviation, Fourier transform, wavelet transform) may be given.
本発明による風力発電出力予測方法は、電力系統の運用分野や電力取引分野に利用できる。 The wind power generation output prediction method according to the present invention can be used in the power system operation field and the power trading field.
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| CN102705158A (en) * | 2012-05-25 | 2012-10-03 | 江南大学 | Feedback control method of wind energy converting system based on fuzzy performance estimator |
| CN102938093A (en) * | 2012-10-18 | 2013-02-20 | 安徽工程大学 | Wind power forecasting method |
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