JP2016220494A - Support system for power generation amount estimation learning in photovoltaic power generation - Google Patents
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この発明は、太陽光発電システムにおける発電量推定学習の支援システムに関するものである。 The present invention relates to a power generation amount estimation learning support system in a solar power generation system.
大規模太陽光発電所の運用において、安全を確保し、事業収益を向上させるためには、発電所のモニタリングによって運転状況を把握することが必要であるが、発電所の発電量は日射や外気温といった周囲環境条件、及び発電所の広さや構成といった固有の条件に大きく左右されるため、単にモニタリングを行うだけでは運転状況を適切に把握することは困難である。 In order to ensure safety and improve business profits in the operation of large-scale solar power plants, it is necessary to grasp the operating status through power plant monitoring. Since it is greatly affected by the surrounding environmental conditions such as the temperature and the specific conditions such as the size and configuration of the power plant, it is difficult to properly grasp the driving situation simply by performing monitoring.
そこで、日射量や外気温といった周囲環境データ、及び発電所の設備情報から発電量を推定する手法が多数考案されているが、十分な精度が得られない。あるいは精度を得るためにできるだけ多くの計測設備を設けて細かく計測することや専門家による分析が必要、といった問題点があった。 Thus, many methods have been devised for estimating the amount of power generation from ambient environment data such as the amount of solar radiation and outside temperature, and facility information of the power plant, but sufficient accuracy cannot be obtained. Or, in order to obtain accuracy, there are problems such as providing as many measuring facilities as possible and performing detailed measurement and analysis by specialists.
特許文献１の「発電量予測装置およびその方法」では、太陽光発電システムのそれぞれについて、システム係数と発電実験データから前記太陽光発電システムの設置されたサイトにおける気象状況を推定することにより、推定気象状況値を得る気象状況推定部と、前記推定気象状況値を、各前記太陽光発電システムの設置位置に応じて補正処理することにより補正気象状況値を得る気象状況空間補正部と、前記太陽光発電システム毎に、前記補正気象状況値と前記発電実績データに基づいて、前記システム係数を更新するパラメータ学習部と、前記太陽光発電システム毎に、前記参照気象データと前記システム係数に基づき、発電量予測を行う発電予測部とを備えるものである。 In the “power generation amount prediction device and method” of Patent Document 1, estimation is performed by estimating the weather condition at the site where the solar power generation system is installed from the system coefficient and the power generation experiment data for each of the solar power generation systems. A weather condition estimation unit for obtaining a weather condition value, a weather condition space correction unit for obtaining a corrected weather condition value by correcting the estimated weather condition value according to an installation position of each of the solar power generation systems, and the sun For each photovoltaic power generation system, based on the corrected weather condition value and the actual power generation data, a parameter learning unit that updates the system coefficient, and for each photovoltaic power generation system, based on the reference weather data and the system coefficient, A power generation prediction unit that performs power generation amount prediction.
特許文献１のものでは、推定を行おうとする発電所の過去の実績データ（日射・外気温・発電量等）から所定の発電量推定モデルのパラメータを学習し、そのパラメータを用いて以降の発電量を推定することにより自律的に推定精度の向上を図るものである。しかし、これを実際に運用するには、種々の問題点がある。 In Patent Document 1, the parameters of a predetermined power generation amount estimation model are learned from past performance data (sunlight, outside temperature, power generation amount, etc.) of a power plant to be estimated, and subsequent power generation is performed using the parameters. The estimation accuracy is autonomously improved by estimating the quantity. However, there are various problems in actually using this.
第１に、図１３に示すように、太陽光発電所では、積雪・電力系統事故・点検作業等による部分停止、あるいは発電所周辺の構造物・隣接する太陽電池アレイにより影の影響などによって、一定の日射強度があるにもかかわらずそれに見合った発電量が出力されない状況が発生することがある。 First, as shown in FIG. 13, in a solar power plant, due to partial suspension due to snow, power system accidents, inspection work, etc., or due to the influence of shadows by structures around the power plant and adjacent solar cell arrays, There may be a situation in which the amount of power generated is not output even though there is a certain amount of solar radiation.
発電量推定モデルのパラメータを用いる手法においては、そのような状況下のデータは学習に不適当として除外する必要があるが、除外条件の設定次第では、不適当なデータを除外しきれない、あるいは過剰にデータを除外してしまい学習精度が落ちるおそれがある。そのため、どのデータが学習に不適当であるかについて正しく判定を行い、過不足なく除外する必要がある。 In the method using the parameters of the power generation estimation model, it is necessary to exclude the data under such circumstances as inappropriate for learning, but depending on the setting of the exclusion condition, the inappropriate data cannot be excluded, or Data may be excluded excessively and learning accuracy may be reduced. Therefore, it is necessary to correctly determine which data is unsuitable for learning and exclude it without excess or deficiency.
第２に、パラメータの学習を行うためには、過去（例：発電所開始から一定期間）計測データを用い、前記の不適当データを除外した上で、重回帰分析を行って発電量推定モデルのパラメータを算出する必要がある。 Secondly, in order to learn the parameters, past measurement data (eg, a fixed period from the start of the power plant) is used, the inappropriate data is excluded, and multiple regression analysis is performed to generate a power generation amount estimation model. It is necessary to calculate the parameters.
そのため、ある程度時間が経過した後、学習対象期間の変更を行おうとした場合、図１４に示すように、過去の必要な計測データを全て保持した上で一から除外及び学習処理を行わなければならず、データ保持及び処理の負担が大きくなる。その負担を軽減するために、何らかの形で必要なデータを抽出・集約しておく必要がある。 For this reason, when the learning target period is to be changed after a certain amount of time has passed, as shown in FIG. 14, all the necessary measurement data in the past must be retained and the learning process must be performed from the beginning. Therefore, the burden of data retention and processing increases. In order to reduce the burden, it is necessary to extract and aggregate necessary data in some form.
第３に、不適当データの除外及びパラメータ学習を行い、高精度で発電量の推定ができたとしても、天候が急激に変化した際の日射計と発電所の太陽電池全体への日射のかかり方の違いや、太陽高度が低い早朝や夕方における発電所周辺の障害物や多数並んでいる前方の太陽電池アレイなどの影の影響、あるいは作業による運転停止など、瞬時又は一定期間にわたって推定発電量と実測発電量との間に誤差が発生するケースがある。 Thirdly, even if the inappropriate data is excluded and parameters are learned, and the power generation amount can be estimated with high accuracy, the solar radiation meter and the entire solar cell of the power plant will be exposed to solar radiation when the weather changes suddenly. Estimated power generation over a certain period of time, such as the difference in the direction, the influence of shadows such as obstacles around the power plant in the early morning and evening when the solar altitude is low, and a large array of solar cells in front, or operation shutdown There is a case where an error occurs between the measured power generation amount and the measured power generation amount.
これらの対策としては、（１）発電設備に不具合があるか否かの良否判定の閾値を広くとる。（２）瞬時判定ではなく、一定期間（例：１日）の総発電量で比較する。といった方法が考えられるが、（１）は判定条件の緩和により、（２）は作業停止など比較的長時間にわたる停止と発電設備の故障等の不具合による発電量低下の区別がつかないことにより、それぞれ判定精度が落ちるため、判定精度を高く（判定閾値を狭く）保ちながら、瞬時のずれや作業停止などを除外した上で適切に良否判定を行える方法が必要である。 As these countermeasures, (1) a wide threshold for determining whether or not there is a malfunction in the power generation facility is taken. (2) Compare with the total amount of power generated for a certain period (eg, 1 day), not instantaneous determination. However, (1) is due to the relaxation of judgment conditions, and (2) is indistinguishable from a relatively long stoppage such as a work stoppage and a decrease in power generation due to a malfunction such as a failure of a power generation facility. Since each determination accuracy is lowered, there is a need for a method capable of appropriately determining pass / fail while excluding instantaneous deviation and work stoppage while keeping the determination accuracy high (decision threshold is narrow).
そこで、この発明は、太陽光発電において、発電に関係する天候条件値、例えば、日射強度や外気温、及び発電量を一定時間ごとに一定期間にわたり計測してこれらをデータとして蓄積し、当該蓄積データから推定パラメータを学習し、発電量を推定するが、前記パラメータ学習における多数の分析対象データを集約されたデータとして保持でき、当該集約データから容易に新たなモデル係数を算出可能な支援システムを提供することを目的としたものである。 Therefore, in the present invention, in solar power generation, weather condition values related to power generation, for example, solar radiation intensity, outside air temperature, and power generation amount are measured over a certain period of time, and these are accumulated as data. An estimation system is learned from data to estimate the amount of power generation. A support system that can hold a large number of analysis target data in the parameter learning as aggregated data and can easily calculate new model coefficients from the aggregated data. It is intended to provide.
請求項１の発明は、太陽光発電の発電量の推定に係る重回帰分析のモデル式に用いる推定パラメータを算出する方法において、発電に係るデータを行列式を用いて所定の単位毎に集約し、集約後のデータを保持する、太陽光発電における発電量推定学習の支援システムとした。 The invention according to claim 1 is a method for calculating an estimation parameter used in a model expression of multiple regression analysis related to estimation of power generation amount of photovoltaic power generation, and aggregates data relating to power generation for each predetermined unit using a determinant. This is a support system for learning to estimate the amount of power generation in solar power generation that holds the aggregated data.
また、請求項２の発明は、前記重回帰分析のモデル式は、確率変数ｘに対してｙ＝ｆ（ｘ）で与えられる統計量を与えるモデル（例：ｙ=推定発電量、ｘ１＝日射強度、ｘ２＝外気温、ｘ３＝風速とした場合に、ｙ=ａ１・ｘ１＋ａ２・ｘ２＋ａ３・ｘ３）である、請求項１に記載の太陽光発電における発電量推定学習の支援システムとした。 In the invention of claim 2, the model expression of the multiple regression analysis is a model that gives a statistical quantity given by y = f (x) for a random variable x (eg, y = estimated power generation amount, x1 = solar radiation). The power generation amount estimation learning support system for solar power generation according to claim 1, wherein y = a1 · x1 + a2 · x2 + a3 · x3) when intensity, x2 = outside air temperature, and x3 = wind speed.
また、請求項３の発明は、前記所定の単位とは、時間軸、所定の日射強度、所定の発電量、所定の外気温、所定の風速、所定の日射強度の範囲、所定の外気温の範囲、所定の風速の範囲のいずれか、あるいは複数である、請求項１又は２に記載の太陽光発電における発電量推定学習の支援システムとした。 According to a third aspect of the present invention, the predetermined unit includes a time axis, a predetermined solar radiation intensity, a predetermined power generation amount, a predetermined outside air temperature, a predetermined wind speed, a predetermined solar radiation intensity range, and a predetermined outside air temperature. The power generation amount estimation learning support system for solar power generation according to claim 1 or 2, wherein the system is one of a range, a range of predetermined wind speeds, or a plurality of ranges.
請求項１〜３の発明によれば、行列式を用いて集約したデータは任意の単位で設定することが可能であり、時間単位、日単位、月単位等の時間軸で設定したり、外気温の１０°Ｃ以下、１０°〜２０°Ｃ、２０°Ｃ以上というようなデータ値の範囲や特定値を基にして設定したりと、種々の単位で設定することができる。そして、前記集約したデータに、新たなデータが加えられた時、上記新たな集約したデータを加算することにより、容易に新たなモデル式の係数の推定値を算出することができる。 According to the first to third aspects of the invention, the data aggregated using the determinant can be set in arbitrary units, and can be set on the time axis such as hourly, daily, monthly, etc. It can be set in various units, for example, based on a range of data values such as 10 ° C. or lower, 10 ° to 20 ° C., or 20 ° C. or higher, or a specific value. When new data is added to the aggregated data, the estimated value of the coefficient of the new model formula can be easily calculated by adding the new aggregated data.
この様に、当該発明を用いれば、前記モデル式の係数を容易にかつきめ細かく分析できる（学習支援できる）。太陽光発電による地域内融通、電力市場におけるリアルタイム市場では、時間帯別の供給量の精度が重要であるが、当該発明によれば、精度の高い発電量推定に大きく寄与できるものである。 As described above, by using the present invention, the coefficients of the model formula can be easily and finely analyzed (learning support can be performed). In the regional interchange by solar power generation and the real-time market in the electric power market, the accuracy of the supply amount by time zone is important, but according to the present invention, it can greatly contribute to the estimation of the power generation amount with high accuracy.
（実施の形態例１）
以下、この発明の実施の形態例１の太陽光発電システムの発電状況診断装置及び方法を図１に基づいて説明する。
(Embodiment 1)
Hereinafter, a power generation state diagnosis apparatus and method for a solar power generation system according to Embodiment 1 of the present invention will be described with reference to FIG.
まず、太陽光発電所の運転開始後、最初の１ヶ月間、当該発電所に設けた日射計や温度計、電流電圧計等により、日射強度、外気温等の発電に関係する天候条件値及び発電量を１分間ごとに一定時間にわたり計測する（ステップＳ−１）。そしてこれらの計測値をデータとしてコンピュータの記憶部に蓄積する。また、その際、設備情報として当該発電所の太陽光電池のアレイ容量やＰＣＳ容量も記憶部に入力する。 First, for the first month after the start of the operation of the solar power plant, the weather condition values related to power generation such as solar radiation intensity, outside temperature, etc. by using a pyranometer, thermometer, current voltmeter, etc. installed in the power plant The amount of power generation is measured over a fixed time every one minute (step S-1). These measured values are stored as data in the storage unit of the computer. At that time, the array capacity and PCS capacity of the photovoltaic cells of the power plant are also input to the storage unit as the facility information.
次に、これらの蓄積データの内の発電量データのうち、後述のパラメータ学習に不適当なデータを除外する（ステップＳ−２）。これは予め定めた時刻や閾値等と比較して選別する。この処理により残ったデータを基にパラメータの学習を行う（ステップＳ−３）。これは上記の計測、当該計測によるデータ蓄積、データ除外等を例えば１年間行い、その間にデータ等の更新を逐次行ってパラメータ学習を行う。 Next, data inappropriate for parameter learning to be described later is excluded from the power generation amount data in the accumulated data (step S-2). This is selected by comparison with a predetermined time, threshold value or the like. Parameter learning is performed based on the data remaining by this processing (step S-3). For example, the above-described measurement, data accumulation by the measurement, data exclusion, etc. are performed for one year, for example, during which data is updated sequentially to perform parameter learning.
その後パラメータ学習に基づいて推定パラメータを決定し（ステップＳ−４）、当該推定パラメータと前記の日射強度や外気温等の発電に関係する天候条件値のデータを勘案して推定発電量を算定する（ステップＳ−５）。そして、実測の発電量と推定発電量を比較する（ステップＳ−６）。当該比較の結果、良否判定を行う（ステップＳ−７）。そして否の場合は警報を出力する。 Thereafter, an estimated parameter is determined based on parameter learning (step S-4), and an estimated power generation amount is calculated in consideration of the estimated parameter and data on weather condition values related to power generation such as solar radiation intensity and outside temperature. (Step S-5). Then, the measured power generation amount is compared with the estimated power generation amount (step S-6). As a result of the comparison, pass / fail determination is performed (step S-7). If not, an alarm is output.
上記の各ステップは計測手段、当該各計測値をデータとして記憶する記憶手段、不適当なデータを除外するデータ除外手段、パラメータ学習手段、推定パラメータ決定手段、推定発電量算出手段、比較手段、良否判定手段及び警報手段とから成るコンピュータシステムを使用して実現できる。そして、これらの各構成手段による上記ステップ作用は、たとえばコンピュータプログラムモジュールとして実現することができ、各プログラムモジュールを含むプログラムをコンピュータシステムにおいて各機能を実現することができる。 Each of the above steps includes measuring means, storage means for storing each measured value as data, data excluding means for excluding inappropriate data, parameter learning means, estimated parameter determining means, estimated power generation amount calculating means, comparing means, pass / fail This can be realized by using a computer system comprising determination means and alarm means. And the said step effect | action by each of these structure means can be implement | achieved as a computer program module, for example, and each function can be implement | achieved in a computer system by the program containing each program module.
このコンピュータシステムには、図２に示すように、プログラム命令を実行するＣＰＵ１１、メモリ等の主記憶装置１２、ハードディスク、磁気ディスク装置又は光磁気ディスク装置等の外部記憶装置１３、データ入力装置１４、表示装置１５及びこれらを相互に接続するバス１６を具備している。プログラムは外部記憶装置１３に保存されており、ＣＰＵ１１がこのプログラムを主記憶装置１２に展開し、展開したプログラムを逐次読み出し実行する。 As shown in FIG. 2, the computer system includes a CPU 11 for executing program instructions, a main storage device 12 such as a memory, an external storage device 13 such as a hard disk, a magnetic disk device or a magneto-optical disk device, a data input device 14, A display device 15 and a bus 16 for connecting them to each other are provided. The program is stored in the external storage device 13, and the CPU 11 expands the program in the main storage device 12, and sequentially reads and executes the expanded program.
次に、前記不適当なデータを過不足なく除外するために、以下の処理を行う。これを図３〜図６に基づいて説明する。 Next, in order to exclude the inappropriate data without excess or deficiency, the following processing is performed. This will be described with reference to FIGS.
（１）太陽高度又は時刻による除外
太陽高度が低く、周辺構造物や隣接する太陽電池アレイが作る影によって発電量の低下が起こっている時刻の発電量データはパラメータ学習には不適当であるため、太陽高度が一定以下の時間帯のデータを除外する。太陽高度は日時情報から算出することも可能であるが、ここでは簡易的は方法として、太陽高度が低い早朝や夕方の時間帯の発電量データを除外することとした。
(1) Excluded by solar altitude or time The amount of generated power at the time when the solar altitude is low and the power generation is reduced due to shadows created by surrounding structures and adjacent solar cell arrays is not suitable for parameter learning. Excludes data for times when the solar altitude is below a certain level. The solar altitude can be calculated from the date and time information, but here, as a simple method, the power generation amount data in the early morning and evening hours when the solar altitude is low is excluded.
時刻による除外の例を図３に示す。図３のグラフの左の縦軸は日射強度（ｋＷ／ｍ２）を、右縦軸が発電量（ｋＷ）を表し、横軸は１日の時刻を示す。ここでは、０時から９時未満、１５時以降から２４までのデータを除外し、９時〜１５時のデータのみを残し、パラメータ学習に使用する。 An example of exclusion by time is shown in FIG. The vertical axis on the left of the graph of FIG. 3 represents the solar radiation intensity (kW / m 2 ), the right vertical axis represents the amount of power generation (kW), and the horizontal axis represents the time of the day. Here, the data from 0 o'clock to less than 9 o'clock and from 15 o'clock to 24 o'clock is excluded, and only the data from 9 o'clock to 15 o'clock is left and used for parameter learning.
（２）日射強度閾値による除外
上記（１）の場合に当てはまらない場合であっても、極端に日射強度が低いと、ＰＣＳ（パワーコンディショナ）の変換効率が大きく下がる。ＰＣＳの空調システム負荷が軽くなるといった理由により、一定以上の日射強度がある場合と異なる条件下での運転となり、パラメータ学習には不適当である。そこで、日射強度閾値（例：０．２ｋＷ／ｍ２）を設け、計測した日射強度が閾値以下の時刻の発電量データを除外することとした。
(2) Exclusion by the solar radiation intensity threshold Even if the above case (1) does not apply, if the solar radiation intensity is extremely low, the conversion efficiency of the PCS (power conditioner) is greatly reduced. Because the load on the air conditioning system of the PCS is reduced, the operation is performed under conditions different from the case where the solar radiation intensity exceeds a certain level, which is inappropriate for parameter learning. Therefore, a solar radiation intensity threshold value (for example, 0.2 kW / m 2 ) is provided, and power generation amount data at a time when the measured solar radiation intensity is equal to or less than the threshold value is excluded.
日射強度による除外の例を図４に示す。図４のグラフの左の縦軸は日射強度（ｋＷ／ｍ２）を、右縦軸が発電量（ｋＷ）を表し、横軸は１日の時刻を示す。ここでは日射強度０．２ｋＷ／ｍ２以上の時のデータのみを残し、パラメータ学習に使用する。この日射強度閾値は、０．２ｋＷ／ｍ２に限らず、０．１ｋＷ／ｍ２又は０．０５ｋＷ／ｍ２等任意の閾値で良い。 An example of exclusion by solar radiation intensity is shown in FIG. The vertical axis on the left of the graph in FIG. 4 represents solar radiation intensity (kW / m 2 ), the vertical axis on the right represents the amount of power generation (kW), and the horizontal axis represents the time of the day. Here, only the data when the solar radiation intensity is 0.2 kW / m 2 or more is left and used for parameter learning. The solar radiation intensity threshold is not limited to 0.2 kW / m 2, it may be a 0.1 kW / m 2 or 0.05 kW / m 2, such as arbitrary threshold.
（３）該当日のみを対象とした学習後推定量との比較による除外
上記（１）、（２）の条件に当てはまらない場合であっても、電力系統事故や作業停止によって発電が停止することがあり、その時刻のデータはパラメータ学習には不適当である。
(3) Exclusion by comparison with estimated amount after learning only for the relevant day Even if the conditions of (1) and (2) above are not met, power generation stops due to power system accident or work stoppage The data at that time is inappropriate for parameter learning.
そこで、ある一定期間（例：１日）の計測データを対象として、（１）、（２）の条件によって不適当データを除外した上で重回帰分析を行い、算出した発電量推定パラメータを用いて該当期間の発電量を推定し、推定した発電量と実測データとの誤差が一定値（例：推定値の１０％）以上乖離していた場合、発電所の運転状態に異常があったものとして該当時刻のデータを除外することとした。 Therefore, for the measurement data for a certain period (example: 1 day), multiple regression analysis is performed after removing inappropriate data under the conditions (1) and (2), and the calculated power generation amount estimation parameter is used. If the error between the estimated power generation amount and the measured data is more than a certain value (eg 10% of the estimated value), the operating condition of the power plant is abnormal. It was decided to exclude the data at the corresponding time.
当日学習による除外の例を図５に示す。図５のグラフの左の縦軸は日射強度（ｋＷ／ｍ２）を、右縦軸が発電量（ｋＷ）を表し、横軸は１日の時刻を示す（ただし、右のグラフでは発電量の目盛は省略）。ここでは、右のグラフ図に示すように、推定した発電量と実測した発電量との差が一定値以上乖離しているため、発電所の運転状況に異常があったものとして、１４時から１６時のデータを除外した。 An example of exclusion by learning on the day is shown in FIG. The vertical axis on the left of the graph in FIG. 5 represents the solar radiation intensity (kW / m 2 ), the vertical axis on the right represents the power generation amount (kW), and the horizontal axis represents the time of the day (however, in the right graph, the power generation amount). Is omitted). Here, as shown in the graph on the right, the difference between the estimated power generation amount and the actually measured power generation amount deviates by more than a certain value. The data at 16:00 was excluded.
（４）類似の発電所の実績に基づく学習後の推定量との比較による除外
上記（３）の条件によって、一定の期間内において、一時的に発電所の運転に異常があった場合はその時間帯のデータを除外することが可能であるが、太陽電池パネルの初期不良や直流回路の投入忘れ、ＰＣＳ停止等により、該当期間全体にわたって一様に発電量が低下している場合は、（３）の方法では適不適を判定することができない。
(4) Exclusion by comparison with estimated amount after learning based on the results of similar power plants If the operation of the power plant is temporarily abnormal within a certain period due to the condition of (3) above, It is possible to exclude the data of the time zone, but if the power generation amount decreases uniformly over the entire period due to the initial failure of the solar cell panel, forgetting to turn on the DC circuit, PCS stop, etc., ( The method 3) cannot determine suitability.
そこで、構成的に類似している他の発電所の実績データから算出した推定モデル・パラメータを用いて発電量を推定し、推定した発電量と実測データの誤差が一定値（例：推定値の１０％）以上乖離していた場合、発電所の初期状態あるいは運転状態に異常があるものとして、該当データを除外することとした。 Therefore, the power generation amount is estimated using estimated model parameters calculated from the actual data of other power plants that are structurally similar, and the error between the estimated power generation amount and the measured data is a constant value (eg, the estimated value 10%) or more, it was decided to exclude the relevant data because there was an abnormality in the initial state or operation state of the power plant.
他の発電所のパラメータ使用による除外の例を図６に示す。図６の左のグラフは当該発電所の発電実績であり、ＰＣＳ停止により、一日中発電量が低下した。
右縦軸が日射強度（ｋＷ／ｍ２）を、右縦軸が発電量（ｋＷ）を表し、横軸は１日の時刻を示す。日射量は実線で表し、矢印の上の箇所に頂点を置いた曲線が、日射量に相応して本来発電すべき発電量を示し、矢印の下の箇所に頂点を置いた曲線は実測の発電量を示す。また、図６の右のグラフは、一番大きい山の曲線が、他の発電所のパラメータを使用した推定発電量、下の重なった山の曲線が当該発電所の実測発電量及び当日学習の推定発電量を示す。
An example of exclusion by using parameters of other power plants is shown in FIG. The graph on the left of FIG. 6 shows the power generation performance of the power plant, and the power generation amount decreased all day due to the PCS stoppage.
The right vertical axis represents the solar radiation intensity (kW / m 2 ), the right vertical axis represents the power generation amount (kW), and the horizontal axis represents the time of the day. The amount of solar radiation is represented by a solid line, and the curve with the apex at the location above the arrow indicates the amount of power that should be generated according to the amount of solar radiation. The curve with the apex at the location below the arrow indicates the actual power generation Indicates the amount. The graph on the right side of FIG. 6 shows that the largest peak curve is the estimated power generation using the parameters of the other power plant, and the lower peak curve is the actual power generation and the same day learning of the power plant. Indicates the estimated power generation amount.
以上の対策により、発電所が本来の性能を発揮している時の計測データのみを用い、パラメータ学習を行うことが可能となった。上記（３）及び（４）の除外条件は、良否判定の閾値を調整することによって、予備的な発電状況診断を行い、異常があった場合に警報を出力する機能として使用することも可能である。 With the above measures, it has become possible to perform parameter learning using only the measurement data when the power plant exhibits its original performance. The exclusion conditions (3) and (4) above can also be used as a function to perform preliminary power generation status diagnosis by adjusting the pass / fail judgment threshold and to output an alarm when there is an abnormality. is there.
また、ある発電所（又はその一部）において、「どの時刻のデータがどの条件によって不適当として除外されたか」、「除外条件を変更した時の発電量推定精度の変化」等に関してシステム上で分析を行うことにより、該当箇所におけるデータ除外方法それ自体を自律的に最適化することができる。 In addition, in a certain power plant (or part of it) on the system regarding “what time data was excluded as inappropriate due to which conditions”, “change in power generation estimation accuracy when the exclusion conditions were changed”, etc. By performing the analysis, it is possible to autonomously optimize the data exclusion method itself at the corresponding location.
これは、例えば、「どの時刻のデータが上記（３）又は（４）の条件によって除外されたか」を集計し、その結果に基づいて該当箇所における上記（１）の条件（除外対象となる時刻）を決定する。 This is, for example, totaling “which time data was excluded by the above condition (3) or (4)”, and based on the result, the above condition (1) (the time to be excluded) ).
また、上記（２）の条件（日射強度閾値）を変更した時の発電量推定精度を比較し、もっとも精度の高い結果が出た条件を採用する、等の最適化調整ができる。 In addition, optimization adjustments such as comparing the power generation amount estimation accuracy when the condition (2) (irradiance intensity threshold) is changed and adopting the condition with the highest accuracy result can be performed.
次に、図１のパラメータ学習のための過去データへの遡及について説明する。
学習対象期間の変更等に必要な過去データへの遡及に伴う負担を軽減するために、一定期間（例：１日）ごとに前記の不適当データ除外を行った上で、後から遡ってのパラメータ学習が可能な形でデータを集約する。
Next, a retrospective review of past data for parameter learning in FIG. 1 will be described.
In order to reduce the burden associated with retroactive retrospective data required for changing the learning period, etc., the above inappropriate data is excluded every certain period (eg, 1 day) and then retroactively Aggregate data in a form that allows parameter learning.
具体的には、通常、データ入力から学習後推定パラメータ出力まで一工程で行われる重回帰分析処理を分割し、処理の途中で生成される行列（これを中間行列と呼ぶ）を保持する。この中間行列は、前記の条件によって不適当データを除外した上で重回帰分析に必要な前処理を施した集約データであり、後から学習期間の変更を行う際には、対象期間内の中間行列を加算した上で行列の割り算を１回行うだけで新規に推定パラメータを出力することが可能である。 Specifically, a multiple regression analysis process that is normally performed in one process from data input to post-learning estimation parameter output is divided, and a matrix generated during the process (this is called an intermediate matrix) is held. This intermediate matrix is aggregated data that has been subjected to preprocessing necessary for multiple regression analysis after excluding inappropriate data according to the above conditions, and when changing the learning period later, the intermediate matrix within the target period It is possible to newly output an estimation parameter by adding a matrix and dividing the matrix once.
これにより保持すべきデータ量を大幅に圧縮し（例：１４４０×３データ→５×６データ）、学習処理の負担を軽減することが可能となった。 As a result, the amount of data to be held can be greatly compressed (eg, 1440 × 3 data → 5 × 6 data), and the burden of learning processing can be reduced.
これをより具体的に示すと、たとえば、図７に示すように、１年分のデータを用いてパラメータ学習を行う場合、ｙ：発電量、ｘ１：日射強度、 ｘ２：外気温、 ｘ３：風速とした場合、
モデル式：ｙ＝ａ１・ｘ１＋ａ２・ｘ２＋ａ３・ｘ３ となる。
通常の重回帰分析処理では、対象期間の全データ（例：１４４０分×３６５日×４データ＝約２００万データ）を入力し、重回帰分析処理を行い、推定パラメータａ１〜ａ３を出力する。
More specifically, for example, as shown in FIG. 7, when parameter learning is performed using data for one year, y: power generation amount, x1: solar radiation intensity, x2: outside air temperature, x3: wind speed If
Model formula: y = a1 · x1 + a2 · x2 + a3 · x3
In the normal multiple regression analysis process, all the data for the target period (eg, 1440 minutes × 365 days × 4 data = about 2 million data) is input, the multiple regression analysis process is performed, and the estimated parameters a1 to a3 are output.
しかしながら、これでは約２００万データの中から不適当データを除外したり、また、日射計の故障による発電量の異常低下データを除いたりの学習作業は煩雑となる。そこで、この発明では、図８に示すように、１日ごとに、処理工程の途中で生成される行列（これを「中間行列」と呼ぶ）を出力して、この形で保持しておく。これにより保持データ量：３行×（３＋１）列×３６５日＝４３８０データとなる。 However, this makes the learning work of excluding inappropriate data out of about 2 million data and excluding abnormally reduced data of power generation due to a failure of the pyranometer. Therefore, in the present invention, as shown in FIG. 8, a matrix generated in the middle of the processing step (referred to as an “intermediate matrix”) is output every day and held in this form. As a result, the amount of retained data is 3 rows × (3 + 1) columns × 365 days = 4380 data.
そして、対象期間の中間行列を加算しておき、逆行列計算で、推定パラメータａ１〜ａ３を出力する。出力された推定パラメータａ１〜ａ３は前記の推定パラメータと同じ値になる。 Then, the intermediate matrix of the target period is added, and the estimation parameters a1 to a3 are output by inverse matrix calculation. The output estimation parameters a1 to a3 have the same values as the above estimation parameters.
この応用例として、図９に示すように、推定パラメータの季節変動の分析に使用することができる。これは例えば、１月の中間行列のみを加算して、１月の推定パラメータを算出し、これを２月〜１２月について同様な処理を行う。 As an application example, as shown in FIG. 9, it can be used for analysis of seasonal variation of estimated parameters. For example, by adding only the intermediate matrix for January, an estimated parameter for January is calculated, and the same processing is performed for February to December.
これらの算出された推定パラメータを用い、図１０の（ａ）図は１月〜１２月までの日射強度の推定パラメータの変動推移、（ｂ）図は１月〜１２月までの外気温の推定パラメータの変動推移を表示する。これにより季節による推定パラメータの変動推移等の特徴分析を容易に行うことができる。 Using these calculated estimated parameters, (a) in FIG. 10 shows changes in the estimated parameter of solar radiation intensity from January to December, and (b) shows the estimation of outside air temperature from January to December. Displays the change of parameter. As a result, it is possible to easily perform feature analysis such as a change transition of the estimated parameter according to the season.
また、図１１に示すように、上記データを天候（１日ごとの発電量）毎に分けて推定パタメータを算出することができる。図１１では、好天日（１日の総発電量が一定以上の日）の中間行列の実を加算して、推定パラメータを算出し、また、曇天・雨天日（１日の総発電量が一定未満の日）の中間行列のみを加算して推定パラメータを算出する。これにより、日射強度の影響度は好天時の方が大きく、温度や風速は好天時と曇天・雨天時とで影響の与え方が反対になる傾向があることが分かった。 In addition, as shown in FIG. 11, the estimated data can be calculated by dividing the above data for each weather (power generation amount per day). In FIG. 11, the estimated parameters are calculated by adding the actual values of the intermediate matrix of the good weather day (the day when the total power generation amount per day is above a certain level), and the cloudy and rainy days (the total power generation amount per day is The estimated parameter is calculated by adding only the intermediate matrix of days less than a certain day). As a result, it was found that the degree of influence of solar radiation intensity was greater in good weather, and that temperature and wind speed tended to have opposite effects in good weather and in cloudy / rainy weather.
さらに、図１２に示すように、１月〜６月までのデータを用いて推定パラメータを学習する。その後、２月〜７月までのデータを用いて推定パラメータを学習したい場合、２月〜７月までの中間行列のみを加算して推定パラメータを算出する。また、対象期間中の何日かのデータを除外した形でパラメータを学習したい場合、除外したい日の中間行列を除いて、対象期間中の各日のデータを中間行列で加算し、推定パラメータを算出する。この様に、学習期間の変更、対象日の選択除外等を容易に行うことができる。 Furthermore, as shown in FIG. 12, the estimation parameter is learned using data from January to June. Thereafter, when it is desired to learn the estimation parameter using data from February to July, only the intermediate matrix from February to July is added to calculate the estimation parameter. In addition, if you want to learn parameters in a form that excludes data for several days during the target period, add the data for each day in the target period with the intermediate matrix, excluding the intermediate matrix for the days you want to exclude, calculate. In this way, it is possible to easily change the learning period, exclude the selection of the target date, and the like.
最後に、前記図１の良否判定について説明する。
天候急変時や早朝・夕方において不可避的に発生する推定発電量と実測発電量の誤差、及び作業停止等による発電量の低下の影響を除去し、高い精度で発電所の運転状況の良否判定を行うために、以下の処理を行う。
Finally, the quality determination in FIG. 1 will be described.
Eliminates the effects of errors in estimated and measured power generation that are unavoidably generated during sudden weather changes, early mornings and evenings, and the effects of power generation decline due to work stoppage, etc. In order to do so, the following processing is performed.
（１）ある時刻の周囲環境データから、学習した推定パラメータを用いて発電量を推定する。（２）事前に定めた時間帯の範囲内であり、かつ事前に定めた日射強度閾値以上の日射強度があれば「推定外れ判定対象サンプル数」に１を加算する。 (1) A power generation amount is estimated from ambient environment data at a certain time using a learned estimation parameter. (2) If the solar radiation intensity is within the range of the predetermined time zone and is equal to or greater than the predetermined solar radiation intensity threshold value, 1 is added to the “estimated outlier determination target sample number”.
（３）実測発電量が推定発電量よりも判定閾値（例：推定値の−３％）以上乖離しているかどうかをチェックし、乖離していれば、「判定外れカウント」に１を追加する。（４）一定期間（例：１日）にわたって前記（１）〜（３）の処理を行い、
・「推定外れ判定対象サンプル数」が事前に定めた閾値以上であること。
・「推定外れカウント」÷「推定外れ判定対象サンプル数」が事前に定めた値以上である。
(3) Check whether the measured power generation amount deviates from the estimated power generation amount by more than the judgment threshold (eg, -3% of the estimated value), and if it deviates, 1 is added to the “determination count” . (4) The processes (1) to (3) are performed over a certain period (eg, 1 day),
・ The “number of samples to be estimated out of judgment” is not less than a predetermined threshold.
“Estimated outlier count” ÷ “number of estimated outlier determination target samples” is equal to or greater than a predetermined value.
これらの両方が成立した場合、該当期間において不具合による発電量低下が発生したと判定し、警報を出力する。 When both of these are established, it is determined that a decrease in the amount of power generation due to a malfunction has occurred in the corresponding period, and an alarm is output.
前者の閾値を１００とした場合、例えば、１分ごとのデータを６時間とり、各発電量データを１サンプルとした時、最高で３６０サンプルあるが、実際は１００サンプル未満であった。その場合は、前者に該当せず、不具合があったとは判定されない。また、後者の値を５割とした場合、「推定外れカウント」が１００、「推定外れ判定対象サンプル数」が３００の場合、後者に該当せず、不具合があったとは判定されない。 When the former threshold is 100, for example, when taking data every minute for 6 hours and each power generation amount data as 1 sample, there are 360 samples at the maximum, but actually it was less than 100 samples. In that case, it does not fall under the former, and it is not determined that there was a problem. Further, when the latter value is 50%, when the “estimated out-count” is 100 and the “estimated out-judgment determination target sample number” is 300, the latter does not fall under the latter and it is not determined that there is a problem.
これにより、早朝・夕方における推定誤差や作業停止等による一時的な発電量の低下の影響を除去し、不具合による継続的な発電量低下のみを検出できるようになった。 As a result, the effects of temporary power generation reduction due to estimation errors in early morning and evening, work stoppage, etc. can be removed, and only continuous power generation reduction due to malfunctions can be detected.
Ｓ−１ データ計測 Ｓ−２ データ除外処理 Ｓ−３ パラメータ学習 Ｓ−４ 推定パラメータ
Ｓ−５ 推定発電量 Ｓ−６ 比較
Ｓ−７ 良否判定
１１ ＣＰＵ １２ 主記憶装置
１３ 外部記憶装置 １４ 入力装置
１５ 表示装置 １６ バス
S-1 Data measurement S-2 Data exclusion process S-3 Parameter learning S-4 Estimated parameter S-5 Estimated power generation S-6 Comparison S-7 Pass / fail judgment
11 CPU 12 Main storage device 13 External storage device 14 Input device 15 Display device 16 Bus
Claims (3)
発電に係るデータを行列式を用いて所定の単位毎に集約し、集約後のデータを保持することを特徴とする、太陽光発電における発電量推定学習の支援システム。 In a method for calculating an estimation parameter used in a model equation for multiple regression analysis related to estimation of power generation amount of solar power generation,
A power generation amount estimation learning support system in solar power generation, wherein data related to power generation is aggregated for each predetermined unit using a determinant and the data after aggregation is retained.
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