JP2016220493A - Power generation situation discrimination method for photovoltaic power generation system, and device therefor - Google Patents
Power generation situation discrimination method for photovoltaic power generation system, and device therefor Download PDFInfo
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Abstract
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この発明は、太陽光発電システムの発電状況診断方法及びその装置に関するものである。 The present invention relates to a power generation state diagnosis method and apparatus for 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７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. 7, in a photovoltaic power plant, due to partial suspension due to snow, power system accidents, inspection work, etc., or due to the influence of shadows by the structure around the power plant and the adjacent solar cell array, 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、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。 Secondly, even if the inappropriate amount of data is excluded and parameters are learned, and the amount of power generation can be estimated with high accuracy, it will be necessary to apply solar radiation to the solar cell and the entire solar cell of the power plant 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、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てパラメータの学習を行うことができ、より精度の高い推定発電量と実測発電量の比較により、太陽電池パネルの故障等による性能劣化をできるだけ早く且つ確実に検出できる発電状況診断方法及びその装置を提供することを目的としたものである。 Therefore, the present invention can perform parameter learning using only measurement data when the power plant is performing its original performance, and by comparing the estimated power generation amount with the measured power generation amount with higher accuracy, It is an object of the present invention to provide a power generation state diagnosis method and apparatus capable of detecting performance degradation due to a battery panel failure or the like as quickly and reliably as possible.
請求項１の発明は、太陽光発電システムの発電状況診断方法において、当該太陽光発電システムにおける発電に関係する天候条件値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、これらの中から、事前に定めた不適当な発電量データを除外し、残った発電量データを用いてこれを逐次更新して推定パラメータを学習し、これにより推定パラメータを決定し、当該推定パラメータから前記発電に関係する気象条件値を勘案して推定発電量を算出し、当該推定発電量と実測の発電量とを比較して、実測発電量が推定発電量の一定値以下の場合に、当該太陽光発電システムに異常があると判定してこれを警報する、発電状況診断方法とした。 The invention of claim 1 is a method for diagnosing a power generation state of a solar power generation system, and measures weather condition values and power generation related to power generation in the solar power generation system over a certain period of time and accumulates them as data. From these, the inappropriate power generation amount data determined in advance is excluded, the remaining power generation data is used to update this sequentially to learn the estimation parameter, thereby determining the estimation parameter, When the estimated power generation amount is calculated from the estimated parameters in consideration of the weather condition values related to the power generation, and the estimated power generation amount is compared with the actually measured power generation amount. In addition, it was determined that there is an abnormality in the solar power generation system, and this is a power generation status diagnosis method for warning this.
また、請求項２の発明は、前記不適当な発電量データであると事前に定めた発電量データは、太陽高度の低い時刻の発電量データ、日射強度の閾値以下における発電量データ、ある一定期間の発電量の計測データを対象として上記太陽高度の低い時刻の発電量データ及び日射強度の閾値以下における発電量データを除外した残りのデータから算出した推定パラメータを用いて前記期間の発電量を推定し、推定した発電量と実測データの誤差が一定値以上乖離した時刻の発電量データ、及び構成的に類似した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との誤差が一定値以上乖離した時刻の発電量データの一つ又は複数である、請求項１に記載の発電状況診断方法とした。 Further, in the invention of claim 2, the power generation amount data determined in advance as the inappropriate power generation amount data is the power generation amount data at a time when the solar altitude is low, the power generation amount data below the solar radiation intensity threshold, Using the estimation parameters calculated from the remaining data excluding the power generation data at the time when the solar altitude is low and the power generation data below the solar radiation intensity threshold for the measurement data of the power generation during the period, Estimate the power generation amount using the estimated model parameters calculated from the power generation amount data at the time when the estimated power generation amount and the error of the measured data deviate more than a certain value, and the actual data of other structurally similar power plants The power generation state diagnosis method according to claim 1, wherein the power generation state diagnosis method according to claim 1 is one or a plurality of power generation amount data at a time when the error between the estimated power generation amount and the measured data deviates by a predetermined value or more.
また、請求項３の発明は、前記太陽光発電システムに異常があるとの判定は、ある時刻の前記発電に関係する天候条件値から、学習した推定パラメータを用いて発電量を推定し、事前に定めた一日の時間帯の範囲内であって、事前に定めた日射強度閾値以上の日射強度であれば「推定外れ判定対象サンプル数」に１を加算し、実測発電量が推定発電量よりも判定閾値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「推定外れカウント」に１を加算し、これを一定期間にわたって上記の処理を行い、前記「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って、かつ、「推定外れ判定対象サンプル数」に対する「推定外れカウント」の割合が事前に定めた値以上である場合、当該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する、請求項１又は２に記載の発電状況診断方法とした。 In the invention of claim 3, the determination that there is an abnormality in the photovoltaic power generation system is performed by estimating the power generation amount using a learned estimation parameter from a weather condition value related to the power generation at a certain time, If the solar radiation intensity is within the range of the daily time zone specified in the above and is equal to or greater than the predetermined solar radiation intensity threshold, 1 is added to the “estimated outage determination target sample number”, and the measured power generation amount is the estimated power generation amount. If the difference is greater than the determination threshold, 1 is added to the “estimated outage count” if there is a deviation, and the above processing is performed for a certain period of time, and the “number of estimation outage determination target samples” Is equal to or greater than a predetermined threshold and the ratio of the “estimated out-of-estimation count” to the “number of samples to be estimated out-of-estimation determination” is equal to or greater than a predetermined value, a decrease in the amount of power generated due to a malfunction occurs during the period. Determines that the and the power generation state diagnosis method according to claim 1 or 2.
また、請求項４の発明は、太陽光発電システムの発電状況診断装置において、当該太陽光発電システムにおける発電に関係する天候条件値及び発電量を一定時間ごとに一定期間にわたり計測する計測手段と、当該計測手段により計測したデータを記憶する記憶手段と、これらの中から、事前に定めた不適当な発電量データを除外するデータ除外手段と、データ除外手段によって残った発電量データを逐次更新して推定パラメータを学習するパラメータ学習手段と、当該パラメータ学習手段により推定パラメータを決定する推定パラメータ決定手段と、当該推定パラメータから前記発電に関係する天候条件値を勘案して推定発電量を演算する推定発電量算出手段と、当該推定発電量と実測の発電量とを比較する比較手段と、当該比較手段により比較した実測発電量が推定発電量の一定値以下の場合に、当該太陽光発電システムに異常があると判定する良否判定手段と、良否判定手段による結果により発電システムの異常を警報する警報手段から構成された、発電状況診断装置とした。 Further, the invention of claim 4 is a power generation condition diagnosis device for a solar power generation system, wherein the weather condition value and the power generation amount related to power generation in the solar power generation system are measured over a predetermined period every fixed time; A storage means for storing data measured by the measurement means, a data exclusion means for excluding predetermined inappropriate power generation amount data from these, and a power generation amount data remaining by the data exclusion means are sequentially updated. A parameter learning means for learning an estimated parameter, an estimation parameter determining means for determining an estimated parameter by the parameter learning means, and an estimation for calculating an estimated power generation amount in consideration of a weather condition value related to the power generation from the estimated parameter A power generation amount calculation means, a comparison means for comparing the estimated power generation amount and the actually measured power generation amount, and the comparison means When the compared actual measured power generation amount is less than or equal to the estimated power generation amount, a pass / fail judgment means for judging that the solar power generation system is abnormal, and an alarm means for warning the abnormality of the power generation system based on the result of the pass / fail judgment means The power generation status diagnosis device is configured.
請求項５の発明は、前記データ除外手段は、太陽高度の低い時刻の発電量データ、日射強度の閾値以下における発電量データ、ある一定期間の発電量の計測データを対象として上記太陽高度の低い時刻の発電量データ及び日射強度の閾値以下における発電量データを除外した残りのデータから算出した推定パラメータを用いて前記期間の発電量を推定し、推定した発電量と実測データの誤差が一定値以上乖離した時刻の発電量データ、及び構成的に類似した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との誤差が一定値以上乖離した時刻の発電量データの一つ又は複数である場合に当該データを除外する構成である、請求項４に記載の発電状況診断装置とした。 According to a fifth aspect of the present invention, the data excluding means includes a low solar altitude with respect to power generation data at a time when the solar altitude is low, power generation data below a threshold of solar radiation intensity, and measurement data of a power generation over a certain period. Estimate the power generation amount during the period using the estimated parameters calculated from the remaining power data excluding the power generation amount data at the time and the power generation amount data below the solar radiation intensity threshold, and the error between the estimated power generation amount and the measured data is a constant value The power generation amount is estimated using estimated model parameters calculated from the power generation data at the time of divergence and other structurally similar power plant data, and the error between the estimated power generation and the measured data is constant. The power generation state diagnosis apparatus according to claim 4, wherein the power generation amount diagnosis device is configured to exclude the data when one or more of the power generation amount data at a time deviated by a value or more.
請求項６の発明は、前記良否判定手段において、ある時刻の前記発電に関係する天候条件値から、学習した推定パラメータを用いて発電量を推定し、事前に定めた一日の時間帯の範囲内であって、事前に定めた日射強度閾値以上の日射強度であれば「推定外れ判定対象サンプル数」に１を加算し、実測発電量が推定発電量よりも判定閾値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「推定外れカウント」に１を加算し、これを一定期間にわたって上記の処理を行い、前記「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って、かつ、「推定外れ判定対象サンプル数」に対する「推定外れカウント」の割合が事前に定めた値以上である場合、当該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する構成から成る、請求項４又は５に記載の発電状況診断装置とした。 In the invention according to claim 6, in the quality determination means, a power generation amount is estimated using a learned estimation parameter from a weather condition value related to the power generation at a certain time, and a predetermined time range of a day is determined. If the solar radiation intensity is within the predetermined solar radiation intensity threshold value, 1 is added to the “estimated outlier determination target sample number”, and the measured power generation amount is more than the determination threshold value than the estimated power generation amount. If there is a discrepancy, 1 is added to the “estimated out-of-count”, and the above processing is performed over a certain period, and the “number of samples to be estimated out-of-judgment determination” is equal to or greater than a predetermined threshold value. In addition, when the ratio of the “estimated outage count” to the “estimated outage determination target sample number” is equal to or greater than a predetermined value, it is determined that a decrease in power generation amount due to a malfunction has occurred during the period. And a power generation state diagnostic apparatus according to claim 4 or 5.
請求項１〜６の発明によれば、太陽光発電システムの発電状況診断方法において、当該太陽光発電システムにおける発電に関係する天候条件値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、これらの中から、事前に定めた不適当な天候条件値データ及び発電量データを除外し、残った天候条件値データ及び発電量データを用いてこれを逐次更新して推定パラメータを学習し、これにより推定パラメータを決定するため、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てパラメータの学習を行うことができる。また、不適当データの除外状況及び推定パラメータの学習状況の分析を行うことにより、パラメータ学習におけるデータ除外条件自体の最適化を図ることが可能である。これによって、より精度の高い推定発電量を得ることができる。そして、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。 According to the first to sixth aspects of the invention, in the method for diagnosing the power generation state of the solar power generation system, the weather condition value and the power generation amount related to the power generation in the solar power generation system are measured over a predetermined period every predetermined time. Are stored as data, and from these, inappropriate weather condition value data and power generation data determined in advance are excluded, and the remaining weather condition value data and power generation data are used to update and estimate them sequentially. Since the parameter is learned and the estimated parameter is determined thereby, the parameter can be learned using only the measurement data when the power plant exhibits its original performance. In addition, by analyzing the exclusion status of inappropriate data and the learning status of estimated parameters, it is possible to optimize the data exclusion condition itself in parameter learning. As a result, a more accurate estimated power generation amount can be obtained. Then, the estimation error in the early morning and evening, and the effect of temporary decrease in power generation due to work stoppage, etc. are removed, and only continuous power generation decrease due to malfunction can be detected.
（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誤差が一定値以上乖離しているため、発電所の運転状態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, since the error between the estimated power generation amount and the actually measured power generation amount is more than a certain value, it is assumed that there was an abnormality in the operating state of the power plant. 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、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。
Next, 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 (6)
当該太陽光発電システムにおける発電に関係する天候条件値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、これらの中から、事前に定めた不適当な発電量データを除外し、残った発電量データを用いてこれを逐次更新して推定パラメータを学習し、これにより推定パラメータを決定し、当該推定パラメータから前記発電に関係する気象条件値を勘案して推定発電量を算出し、当該推定発電量と実測の発電量とを比較して、実測発電量が推定発電量の一定値以下の場合に、当該太陽光発電システムに異常があると判定してこれを警報することを特徴とする、発電状況診断方法。 In the power generation status diagnosis method of the solar power generation system,
The weather condition values and power generation related to power generation in the solar power generation system are measured over a certain period of time and accumulated as data, and from these, inappropriate power generation data determined in advance are stored. Excluded, use the remaining power generation data to update it sequentially to learn the estimation parameters, determine the estimation parameters, and take into account the meteorological condition values related to the power generation from the estimation parameters And the estimated power generation amount is compared with the actually measured power generation amount.If the measured power generation amount is equal to or less than a predetermined value of the estimated power generation amount, it is determined that there is an abnormality in the solar power generation system and an alarm is given. A power generation condition diagnosis method characterized by:
当該太陽光発電システムにおける発電に関係する天候条件値及び発電量を一定時間ごとに一定期間にわたり計測する計測手段と、当該計測手段により計測したデータを記憶する記憶手段と、これらの中から、事前に定めた不適当な発電量データを除外するデータ除外手段と、データ除外手段によって残った発電量データを用いてこれを逐次更新して推定パラメータを学習するパラメータ学習手段と、当該パラメータ学習手段により推定パラメータを決定する推定パラメータ決定手段と、当該推定パラメータから前記発電に関係する天候条件値を勘案して推定発電量を演算する推定発電量算出手段と、当該推定発電量と実測の発電量とを比較する比較手段と、当該比較手段により比較した実測発電量が推定発電量の一定値以下の場合に、当該太陽光発電システムに異常があると判定する良否判定手段と、良否判定手段による結果により発電システムの異常を警報する警報手段から構成されたことを特徴とする、発電状況診断装置。 In the power generation status diagnosis device of the solar power generation system,
Measurement means for measuring weather condition values and power generation related to power generation in the solar power generation system over a certain period of time, storage means for storing data measured by the measurement means, and from these in advance A data excluding means for excluding inappropriate power generation amount data determined in (2), a parameter learning means for successively updating the power generation amount data remaining by the data excluding means and learning an estimation parameter, and the parameter learning means An estimated parameter determining means for determining an estimated parameter, an estimated power generation amount calculating means for calculating an estimated power generation amount in consideration of a weather condition value related to the power generation from the estimated parameter, the estimated power generation amount and an actually measured power generation amount, If the measured power generation compared by the comparison means is less than or equal to the estimated power generation Wherein the quality determining means for determining that there is an abnormality in the photovoltaic system, that is constructed from the alarm means to alert the abnormality of the power generation system as a result by the quality determining means, the power generation state diagnostic apparatus.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPWO2019150779A1 (en) * | 2018-02-01 | 2021-01-28 | 住友電気工業株式会社 | Judgment device, judgment method and judgment program |
| JP2021145509A (en) * | 2020-03-13 | 2021-09-24 | オムロン株式会社 | Abnormality detection device, abnormality detection method, and abnormality detection program |
| JP2022008764A (en) * | 2020-03-23 | 2022-01-14 | 春禾科技股▲分▼有限公司 | Solar radiation amount estimation method for photovoltaic power plant |
| KR20220037156A (en) * | 2020-09-17 | 2022-03-24 | (주)탑인프라 | Failure detection method for photovoltaic power generating system and analysis apparatus |
| US11290056B2 (en) | 2018-02-20 | 2022-03-29 | Taiyo Yuden Co., Ltd. | Solar power generation fault diagnosis device and solar power generation fault diagnosis method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008077561A (en) * | 2006-09-25 | 2008-04-03 | Nippon Telegr & Teleph Corp <Ntt> | Energy prediction method, energy prediction device and program |
| JP2012043857A (en) * | 2010-08-16 | 2012-03-01 | Tokyo Electric Power Co Inc:The | Bright and clear time determination device and photovoltaic power generation amount prediction system |
| JP2012104750A (en) * | 2010-11-12 | 2012-05-31 | Ntt Facilities Inc | Photovoltaic power generation diagnostic apparatus |
| JP2013073537A (en) * | 2011-09-29 | 2013-04-22 | Omron Corp | Information processor, power generation amount calculating method, and program |
| JP2013099143A (en) * | 2011-11-01 | 2013-05-20 | Nippon Telegr & Teleph Corp <Ntt> | Prediction model construction device, method and program, and power generation amount prediction device and method |
| JP2014175478A (en) * | 2013-03-08 | 2014-09-22 | Shimizu Corp | Failure determination apparatus and failure determination method for photovoltaic power generation system |
| US20150142347A1 (en) * | 2013-11-15 | 2015-05-21 | Rahul Mohan | Solar Energy Disaggregation Techniques for Whole-House Energy Consumption Data |
-
2015
- 2015-05-26 JP JP2015106314A patent/JP6608619B2/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008077561A (en) * | 2006-09-25 | 2008-04-03 | Nippon Telegr & Teleph Corp <Ntt> | Energy prediction method, energy prediction device and program |
| JP2012043857A (en) * | 2010-08-16 | 2012-03-01 | Tokyo Electric Power Co Inc:The | Bright and clear time determination device and photovoltaic power generation amount prediction system |
| JP2012104750A (en) * | 2010-11-12 | 2012-05-31 | Ntt Facilities Inc | Photovoltaic power generation diagnostic apparatus |
| JP2013073537A (en) * | 2011-09-29 | 2013-04-22 | Omron Corp | Information processor, power generation amount calculating method, and program |
| JP2013099143A (en) * | 2011-11-01 | 2013-05-20 | Nippon Telegr & Teleph Corp <Ntt> | Prediction model construction device, method and program, and power generation amount prediction device and method |
| JP2014175478A (en) * | 2013-03-08 | 2014-09-22 | Shimizu Corp | Failure determination apparatus and failure determination method for photovoltaic power generation system |
| US20150142347A1 (en) * | 2013-11-15 | 2015-05-21 | Rahul Mohan | Solar Energy Disaggregation Techniques for Whole-House Energy Consumption Data |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPWO2019150779A1 (en) * | 2018-02-01 | 2021-01-28 | 住友電気工業株式会社 | Judgment device, judgment method and judgment program |
| JP7163931B2 (en) | 2018-02-01 | 2022-11-01 | 住友電気工業株式会社 | Determination device, determination method and determination program |
| US11290056B2 (en) | 2018-02-20 | 2022-03-29 | Taiyo Yuden Co., Ltd. | Solar power generation fault diagnosis device and solar power generation fault diagnosis method |
| JP2021145509A (en) * | 2020-03-13 | 2021-09-24 | オムロン株式会社 | Abnormality detection device, abnormality detection method, and abnormality detection program |
| JP7435073B2 (en) | 2020-03-13 | 2024-02-21 | オムロン株式会社 | Anomaly detection device, anomaly detection method, and anomaly detection program |
| JP2022008764A (en) * | 2020-03-23 | 2022-01-14 | 春禾科技股▲分▼有限公司 | Solar radiation amount estimation method for photovoltaic power plant |
| KR20220037156A (en) * | 2020-09-17 | 2022-03-24 | (주)탑인프라 | Failure detection method for photovoltaic power generating system and analysis apparatus |
| KR102459015B1 (en) * | 2020-09-17 | 2022-10-26 | (주)탑인프라 | Failure detection method for photovoltaic power generating system and analysis apparatus |
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