JP2014174113A - Method for predicting probability density distribution of repeated load caused to strung wire implement due to natural wind - Google Patents
Method for predicting probability density distribution of repeated load caused to strung wire implement due to natural wind Download PDFInfo
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Abstract
Description
本発明は、自然風により架渉線機材に生じる繰り返し荷重の確率密度分布を予測する方法に関する。 The present invention relates to a method for predicting the probability density distribution of repetitive loads generated on a wire-line equipment by natural wind.
配電線の支持機材における強度評価は、機材が経験しうる最悪状況を想定し、その状況下において発生する終極荷重について、機材の応力評価を行うことが一般的である。 The strength evaluation of the supporting equipment for the distribution line is generally performed by assuming the worst situation that the equipment can experience, and evaluating the stress of the equipment for the ultimate load that occurs under that situation.
詳しくは、この終極荷重での発生応力に対して機材の材料の機械的特性が満足するか否かにより機材の強度評価がなされる。このような設計を耐力設計という。この耐力設計では、大きな力に対して材料が塑性破壊する現象について、機材の安全性を評価する手法として用いられる。 Specifically, the strength of the equipment is evaluated based on whether or not the mechanical characteristics of the equipment material satisfy the generated stress at the ultimate load. Such a design is called a strength design. In this proof stress design, it is used as a method for evaluating the safety of equipment for the phenomenon that a material undergoes plastic fracture against a large force.
一方、機材・構造物の破壊現象として挙げられるのが、疲労損傷破壊である。この破壊は、材料の耐力を超過しない範囲で生じる繰り返し応力により部材に小さな損傷が蓄積し、最終的に部材の破壊に至るものである。従来から、繰り返し応力によって機材等に疲労損傷破壊が生じることは知られており、この疲労損傷破壊に対向すべく種々の方策が講じられてきた。 On the other hand, fatigue damage destruction is cited as a phenomenon of destruction of equipment and structures. This destruction is caused by repeated stress generated within a range not exceeding the proof stress of the material, so that small damage is accumulated in the member, and eventually the member is destroyed. Conventionally, it has been known that fatigue damage breakage occurs in equipment due to repeated stress, and various measures have been taken to counter this fatigue damage breakage.
例えば、特許文献１では、溶接により製造された鋼構造物の溶接部と母材との境界面が疲労損傷に弱く、疲労破壊の起点となる点に着目し、鋼構造物における溶接箇所の溶接止端部に対し、当該溶接止端部の温度が１００℃以上４００℃未満で超音波ピーニング処理を行い、かつピーニング処理部を徐冷することによって、塑性加工による硬度上昇に伴う疲労強度の上昇効果を、より向上させることができる鋼構造物の高耐久化処理方法が開示されている。 For example, in Patent Document 1, focusing on the fact that the interface between the welded portion of the steel structure manufactured by welding and the base metal is vulnerable to fatigue damage and becomes the starting point of fatigue failure, welding of the welded portion in the steel structure is performed. An increase in fatigue strength due to an increase in hardness due to plastic working by performing ultrasonic peening treatment on the toe portion at a temperature of the weld toe portion of 100 ° C. or more and less than 400 ° C. and gradually cooling the peening treatment portion. A high durability treatment method of a steel structure that can further improve the effect is disclosed.
しかしながら、機材等の疲労損傷破壊に対する評価を正確に行うことはできていない。疲労損傷破壊に対する評価を正確に行うためには、機材に作用する繰り返し荷重を的確に把握する必要があるが、配電線の支持機材に作用する疲労損傷に影響を及ぼすこの繰り返し荷重について的確に把握できておらず、正確な評価が行えていないのが現状である。 However, it has not been possible to accurately evaluate fatigue damage destruction of equipment. In order to accurately evaluate fatigue damage fracture, it is necessary to accurately grasp the repeated load acting on the equipment, but accurately grasp this repeated load affecting the fatigue damage acting on the supporting equipment of the distribution line. The current situation is that it has not been made and accurate evaluation has not been performed.
そこで本発明は、上記問題点に対処するため、機材等に作用する繰り返し荷重を的確に把握することが可能な繰り返し荷重の確率密度分布予測方法を提供することを目的とする。 Accordingly, an object of the present invention is to provide a method for predicting the probability density distribution of a repeated load that can accurately grasp the repeated load acting on the equipment and the like in order to cope with the above-described problems.
前記目的を達成するため、請求項１の発明では、
柱に架渉された配電線を当該柱に支持する支持機材において、
電線の線路方向に作用する繰り返し変動荷重と風速との相関関係を利用すること、
さらに既知の風速観測データから任意の地点の自然風の確率密度分布を予測することにより、
任意の地点に架渉された前記支持機材に対して電線の線路方向に作用する繰り返し荷重の確率密度分布を予測する、繰り返し荷重の確率密度分布予測方法とした。
In order to achieve the object, in the invention of claim 1,
In the support equipment that supports the distribution line routed on the pillar to the pillar,
Utilizing the correlation between the repeated fluctuating load acting on the wire direction of the wire and the wind speed,
Furthermore, by predicting the probability density distribution of natural wind at any point from known wind speed observation data,
The probability density distribution prediction method of the repeated load that predicts the probability density distribution of the repeated load acting in the line direction of the electric wire with respect to the support equipment negotiated at an arbitrary point.
請求項２の発明では、
電線の線路方向に作用する繰り返し変動荷重について、風速との相関関係に基づき、風速に相当する圧力である風圧荷重の係数として導出し、当該係数の確率密度分布を算出することにより特定する、請求項１に記載の繰り返し荷重の確率密度分布予測方法とした。
In the invention of claim 2,
The repetitive variable load acting in the line direction of the electric wire is identified as a coefficient of wind pressure load, which is a pressure corresponding to the wind speed, based on the correlation with the wind speed, and specified by calculating the probability density distribution of the coefficient. The probability density distribution prediction method for the repeated load described in Item 1 was used.
請求項３の発明では、
前記既知の風速観測データとして、複数の気象観測所に係る代表的な風速の頻度分布に基づいて、
統計処理により、土地利用状況により影響される架渉された配電線高さにおける任意の地点の自然風の確率密度分布を算出する、請求項１に記載の繰り返し荷重の確率密度分布予測方法とした。
In the invention of claim 3,
As the known wind speed observation data, based on the frequency distribution of typical wind speeds related to a plurality of weather stations,
The probability density distribution prediction method of repeated loads according to claim 1, wherein the probability density distribution of natural wind at any point in the negotiated distribution line height affected by land use status is calculated by statistical processing. .
本発明によれば、繰り返し荷重の確率密度分布を的確に予測することができるようになるため、架渉線機材の疲労損傷破壊に対する評価を正確に行うことができ、機材の長期信頼性設計を合理的に行うことができる。 According to the present invention, it becomes possible to accurately predict the probability density distribution of repeated loads, so it is possible to accurately evaluate the fatigue damage fracture of the wireline equipment, and to design the long-term reliability of the equipment. Can be done reasonably.
本発明は、柱に架渉された配電線を当該柱に支持する支持機材において、電線の線路方向に作用する繰り返し変動荷重と風速との相関関係を利用すること、さらに既知の風速観測データから任意の地点の自然風の確率密度分布を予測することにより、任意の地点に架渉された前記支持機材に対して電線の線路方向に作用する繰り返し荷重の確率密度分布を予測する構成とすることにより、架渉線機材の疲労損傷破壊に対する評価を正確に行うことができ、機材の長期信頼性設計を合理的に行うことができる。
なお、本発明中の風速とは、人工的な風ではなく自然風の速度を意味する。また、本発明中の風速は、所定の期間における自然風の速度の平均成分を扱う。この所定の期間の代表的な期間として、気象観測所のデータでは、「１０分間」とするのが一般的である。
The present invention uses a correlation between a repeatedly varying load acting in the line direction of the electric wire and the wind speed in the supporting equipment that supports the distribution line hung on the column to the column, and further, from known wind speed observation data. By predicting the probability density distribution of natural wind at an arbitrary point, the probability density distribution of repeated loads acting in the line direction of the wire on the support equipment negotiated at an arbitrary point shall be predicted. Therefore, it is possible to accurately evaluate the fatigue damage destruction of the wireline equipment and rationally design the long-term reliability of the equipment.
In addition, the wind speed in this invention means the speed of natural wind instead of artificial wind. Further, the wind speed in the present invention deals with an average component of the natural wind speed in a predetermined period. As a typical period of this predetermined period, “10 minutes” is generally used in meteorological observation data.
以下、添付図面を参照して本発明に係る実施例を詳細に説明する。ただし、この実施例に記載されている構成要素はあくまでも例示であり、本発明の範囲をそれらのみに限定する趣旨のものではない。 Hereinafter, embodiments according to the present invention will be described in detail with reference to the accompanying drawings. However, the components described in this embodiment are merely examples, and are not intended to limit the scope of the present invention only to them.
＜情報処理装置１００の構成＞
情報処理装置１００は、任意の地点に架渉された前記支持機材に対して電線の線路方向に作用する繰り返し荷重の確率密度分布を予測するものである。情報処理装置１００は、例えば、パーソナルコンピュータ（ＰＣ）やワークステーション（ＷＳ）等で実現される。
<Configuration of Information Processing Device 100>
The information processing apparatus 100 predicts a probability density distribution of a repetitive load that acts in the line direction of the electric wire on the support equipment negotiated at an arbitrary point. The information processing apparatus 100 is realized by, for example, a personal computer (PC) or a workstation (WS).
次に、情報処理装置１００のハードウェア構成について、図１を参照して説明する。図１は、情報処理装置１００のハードウェア構成を模式的に示したブロック図である。 Next, the hardware configuration of the information processing apparatus 100 will be described with reference to FIG. FIG. 1 is a block diagram schematically illustrating the hardware configuration of the information processing apparatus 100.
外部記録媒体接続手段１０３は、メディアへのアクセスを実現するためのものであり、メディア（記録媒体）１０４に記憶されたプログラム等を情報処理装置１００にロードすることができる。 The external recording medium connection unit 103 is for realizing access to a medium, and can load a program or the like stored in the medium (recording medium) 104 into the information processing apparatus 100.
記憶手段１０５は、大容量メモリとして機能する、例えばＨＤＤ（ハードディスクドライブ）を有しており、記憶手段１０５には、アプリケーションプログラム、ＯＳ、制御プログラム、関連プログラム等が記憶される。 The storage unit 105 includes, for example, an HDD (Hard Disk Drive) that functions as a large-capacity memory. The storage unit 105 stores an application program, an OS, a control program, a related program, and the like.
また、この記憶手段１０５には形状係数・尺度係数記憶領域１５１、乱れ強さ関連情報記憶領域１５２、風力係数の確率密度分布式記憶領域１５３、風速の確率密度分布記憶領域1５４、風力係数の確率密度分布記憶領域１５５、繰り返し荷重の確率密度分布式記憶領域１５６、繰り返し荷重の確率密度分布記憶領域１５７、架線条件情報記憶領域１５８が設けられている。ただし、この構成に限定されるわけではなく、例えば、形状係数・尺度係数記憶領域１５１、乱れ強さ関連情報記憶領域１５２、風力係数の確率密度分布式記憶領域１５３、風速の確率密度分布記憶領域1５４、風力係数の確率密度分布記憶領域１５５、繰り返し荷重の確率密度分布式記憶領域１５６、繰り返し荷重の確率密度分布記憶領域１５７、架線条件情報記憶領域１５８をメディア１０４に設ける構成としてもよい。 The storage means 105 includes a shape factor / scale coefficient storage area 151, a turbulence intensity related information storage area 152, a wind power probability density distribution expression storage area 153, a wind speed probability density distribution storage area 154, and a wind coefficient probability. A density distribution storage area 155, a repetitive load probability density distribution expression storage area 156, a repetitive load probability density distribution storage area 157, and an overhead wire condition information storage area 158 are provided. However, the present invention is not limited to this configuration. For example, the shape factor / scale coefficient storage area 151, the turbulence intensity related information storage area 152, the wind power coefficient probability density distribution expression storage area 153, and the wind speed probability density distribution storage area. The medium 104 may be provided with a wind power coefficient probability density distribution storage area 155, a repetitive load probability density distribution expression storage area 156, a repetitive load probability density distribution storage area 157, and an overhead line condition information storage area 158.
＜形状係数・尺度係数記憶領域１５１の構成＞
形状係数・尺度係数記憶領域１５１には、図２に示すように、各観測地点の緯度・経度、形状係数k、及び尺度係数cが記憶されている。なお、各観測地点とは、既存の各気象観測所が設置されている地点であって、図２では各観測所名が表示されている。従って、形状係数・尺度係数記憶領域１５１には、各気象観測所における緯度・経度、形状係数k、及び尺度係数cが記憶されていることとなる。なお、本発明での対象となる機材は配電線支持物であり、当該機材は自然風が土地の利用状況によって大きく影響される地上高２０m未満に設置されている。このような高さでの風の頻度分布を予測するために既存の観測データは地上高２０m未満で観測されているものを用いる。例えば、気象庁所有の観測データなどは地上高２０m未満の観測データが多く含まれている。また、「形状係数k」は、分布の起伏の変化に影響を及ぼすパラメータであり、「尺度係数c」は、分布の累積頻度が６３．２％になるときの確率変数の値（風速）を示したものであり、分布の平均的な値を示すパラメータである。
<Configuration of Shape Factor / Scale Factor Storage Area 151>
As shown in FIG. 2, the shape factor / scale factor storage area 151 stores the latitude / longitude of each observation point, the shape factor k, and the scale factor c. Note that each observation point is a point where each existing weather station is installed, and the name of each station is displayed in FIG. Therefore, the latitude / longitude, the shape factor k, and the scale factor c at each weather station are stored in the shape factor / scale factor storage area 151. In addition, the target equipment in the present invention is a distribution line support, and the equipment is installed at a ground height of less than 20 m, where natural wind is greatly affected by land use conditions. In order to predict the wind frequency distribution at such a height, existing observation data is used that is observed at a ground height of less than 20 m. For example, observation data owned by the Japan Meteorological Agency contains a lot of observation data with a ground height of less than 20 m. The “shape factor k” is a parameter that affects the change in the undulation of the distribution, and the “scale factor c” is the value (wind speed) of the random variable when the cumulative frequency of the distribution is 63.2%. This is a parameter indicating the average value of the distribution.
＜乱れ強さ関連情報記憶領域１５２の構成＞
乱れ強さ関連情報記憶領域１５２には、乱れ強さ関連情報として、図３に示すように、地表面粗度区分１〜５に対応して、べき指数が記憶されている。この粗度区分、べき指数は、例えば、日本建築学会出版の建築物荷重指針・同解説（２００４）によって公開されているものを流用する。また、乱れ強さ関連情報として、機材の設置場所の地上高Z（m）の風速の乱れ強さIzを算出するための数１及び数２が記憶されている。この数１及び数２は、例えば、日本建築学会出版の建築物荷重指針・同解説（２００４）によって公開されているものを流用する。なお、「乱れ強さIz」とは、風速の乱れ強さ（＝所定の時間で平均値に対してどれだけ風がばらつくか）を示したものである。
<Configuration of Disturbance Strength Related Information Storage Area 152>
In the turbulence intensity related information storage area 152, exponents corresponding to the ground surface roughness categories 1 to 5 are stored as turbulence intensity related information as shown in FIG. For this roughness classification and power index, for example, those disclosed by the Architectural Institute of Japan published by the Building Load Guidelines / Comment (2004) are used. Further, as the turbulence intensity related information, Formula 1 and Formula 2 for calculating the turbulence intensity Iz of the wind speed at the ground height Z (m) of the installation location of the equipment are stored. For these formulas 1 and 2, for example, those disclosed by the Architectural Institute of Japan published by the Building Load Guidelines / Comments (2004) are used. The “turbulence intensity Iz” indicates the turbulence intensity of the wind speed (= how much the wind varies with respect to the average value in a predetermined time).
＜風力係数の確率密度分布式記憶領域１５３の構成＞
風力係数の確率密度分布式記憶領域１５３には、風力係数の確率密度分布fcuを算出するための数３が記憶されている。
<Configuration of Probability Density Distribution Type Storage Area 153 of Wind Coefficient>
The wind power coefficient probability density distribution equation storage area 153 stores Formula 3 for calculating the wind power coefficient probability density distribution fcu .
なお、数３の中で示される風力係数Cuは、数４にて導出される。また、風力係数Cuは、電線の線路方向に作用する繰り返し荷重の変動分に対するもの（＝電線の線路方向に作用する繰り返し変動荷重）であって、風速との相関関係に基づき、風速に相当する圧力である風圧荷重の係数として導出される。
The wind force coefficient Cu shown in Equation 3 is derived from Equation 4. The wind force coefficient Cu is for the fluctuation of the repeated load acting in the line direction of the electric wire (= the repeated fluctuation load acting in the line direction of the electric wire), and corresponds to the wind speed based on the correlation with the wind speed. It is derived as a coefficient of wind pressure load which is pressure.
なお、実際の架線状態の概念図である図４に示すように、数4中の「Tu」は、「所定の不平衡張力の値（N）」であり、「Ｄ」は、「電線の弛みの長さを示す弛度（m）」であり、「S」は、「電柱の間隔を示す径間（m）」であり、「ρ」は、「空気密度（kg/m3）」であり、「B」は、「電線の外径（m）」であり、「v」は、「10分間平均風速（m/s）」である。なお、空気密度のρの値としては、標準大気密度の「１．２２５（kg/m3）」の値を用いる。 In addition, as shown in FIG. 4 which is a conceptual diagram of an actual overhead wire state, “Tu” in Equation 4 is a “predetermined unbalance tension value (N)”, and “D” is “ “Sag” indicating the length of the slack (m), “S” is “the span (m) indicating the distance between the power poles”, and “ρ” is the “air density (kg / m 3 )”. “B” is “the outer diameter of the electric wire (m)”, and “v” is “the average wind speed (m / s) for 10 minutes”. As the value of ρ of the air density, the value of “1.225 (kg / m 3 )” of the standard atmospheric density is used.
＜風速の確率密度分布記憶領域１５４の構成＞
風速の確率密度分布記憶領域１５４には、ユーザによって入力された任意の地点に係る形状係数k及び尺度係数cに基づいて算出された風速の確率密度分布fv（＝ワイブル分布関数）が記憶される。
<Configuration of Wind Speed Probability Density Distribution Storage Area 154>
The wind speed probability density distribution storage area 154 stores a wind speed probability density distribution f v (= Weibull distribution function) calculated based on the shape factor k and the scale factor c of an arbitrary point input by the user. The
＜風力係数の確率密度分布記憶領域１５５の構成＞
風力係数の確率密度分布記憶領域１５５には、上述した数３によって算出された風力係数の確率密度分布fcuが記憶される。
<Configuration of Probability Density Distribution Storage Area 155 of Wind Coefficient>
The wind power coefficient probability density distribution storage area 155 stores the wind power coefficient probability density distribution f cu calculated by Equation 3 described above.
＜繰り返し荷重の確率密度分布式記憶領域１５６の構成＞
繰り返し荷重の確率密度分布式記憶領域１５６には、繰り返し荷重の確率密度分布fTuを算出するための数５が記憶されている。
<Configuration of Repeated Load Probability Density Distribution Expression Storage Area 156>
In the repeated load probability density distribution equation storage area 156, the number 5 for calculating the repeated load probability density distribution f Tu is stored.
＜繰り返し荷重の確率密度分布記憶領域１５７の構成＞
繰り返し荷重の確率密度分布記憶領域１５７には、上述した数５によって算出された繰り返し荷重の確率密度分布fTuが、機材の設置場所に関連付けて記憶される。
<Configuration of Repeated Load Probability Density Distribution Storage Area 157>
The probability density distribution storage area 157 of the repeated load stores the probability density distribution f Tu of the repeated load calculated by Equation 5 described above in association with the installation location of the equipment.
＜架線条件情報記憶領域１５８の構成＞
架線条件情報記憶領域１５８には、ユーザによって入力された弛度D（m）、径間S（m）、電線外径Ｂ（m）といった架線条件が記憶される。
<Configuration of overhead line condition information storage area 158>
The overhead wire condition information storage area 158 stores overhead wire conditions such as the sag D (m), span S (m), and wire outer diameter B (m) input by the user.
なお、記憶手段１０５に、基本Ｉ／Ｏプログラム等のプログラム、基本処理において使用する各種の情報を記憶する、ＲＯＭ（Read Only Memory）等を有する構成としても良い。また、記憶手段１０５に、各種の情報を一時記憶するための制御手段１１０の主メモリ、ワークエリア等として機能する、ＲＡＭ（Random Access Memory）等を有する構成としても良い。 Note that the storage unit 105 may include a program such as a basic I / O program and various information used in basic processing, such as a ROM (Read Only Memory). The storage unit 105 may include a RAM (Random Access Memory) that functions as a main memory, a work area, and the like of the control unit 110 for temporarily storing various types of information.
入力手段１０６は、例えば、キーボードやポインティングデバイス（マウス等）、タッチパネルである。この入力手段１０６を用いて、ユーザは、情報処理装置１００に対して情報処理装置１００を制御するコマンド等を入力指示する。特に、ユーザは入力手段１０６を用いて、機材の設置場所（＝機材の設置場所の緯度・経度）や、機材の設置場所の地上高Z（m）や、１〜５のいずれかの粗度区分や、弛度D（m）、径間S（m）、電線外径Ｂ（m）といった架線条件を入力する。 The input unit 106 is, for example, a keyboard, a pointing device (such as a mouse), or a touch panel. Using this input unit 106, the user instructs the information processing apparatus 100 to input a command or the like for controlling the information processing apparatus 100. In particular, the user uses the input means 106 to install the equipment (= latitude / longitude of the equipment installation), the ground height Z (m) of the equipment installation, and any roughness 1-5. Enter the overhead wire conditions such as classification, sag D (m), span S (m), and wire outer diameter B (m).
表示手段１０７は、例えば液晶ディスプレイ、有機ELディスプレイ、ＣＲＴであり、入力手段１０６から入力されたコマンドや、それに対する管理装置６００の応答出力等を表示するものである。例えば、表示手段１０７は、算出された風速の確率密度分布fvや、算出された風力係数の確率密度分布fcuや、算出された繰り返し荷重の確率密度分布fTuを表示する。 The display unit 107 is, for example, a liquid crystal display, an organic EL display, or a CRT, and displays a command input from the input unit 106, a response output of the management apparatus 600 for the command, and the like. For example, the display unit 107 displays the calculated wind speed probability density distribution f v , the calculated wind power coefficient probability density distribution f cu, and the calculated repeated load probability density distribution f Tu .
バス１０９は、情報処理装置１００内の情報の流れを司るものであり、情報処理装置１００内の制御手段１１０や記憶手段１０５等の各装置を接続する信号線である。１０８は通信手段であり、この通信手段１０８を介して外部装置との情報のやり取りを行う。 The bus 109 controls the flow of information in the information processing apparatus 100, and is a signal line that connects each device such as the control unit 110 and the storage unit 105 in the information processing apparatus 100. Reference numeral 108 denotes a communication unit, which exchanges information with an external device via the communication unit 108.
制御手段１１０は、例えばＣＰＵ（Central Processing Unit）であって、記憶手段１０５に記憶されているアプリケーションプログラム、オペレーティングシステム（ＯＳ）や制御プログラム等を実行し、記憶手段１０５にプログラムの実行に必要な情報、ファイル等を一時的に記憶する制御を行う。 The control unit 110 is, for example, a CPU (Central Processing Unit), and executes an application program, an operating system (OS), a control program, and the like stored in the storage unit 105, and is necessary for executing the program in the storage unit 105. Control to temporarily store information, files, etc.
また、制御手段１１０は、入力手段１０６を通じて、機材の設置場所（＝機材の設置場所の緯度・経度）の入力を受け付けると、形状係数・尺度係数記憶領域１５１に記憶されている各気象観測所の緯度・経度と比較して、当該機材の設置場所に最も近い２つの地点に係る形状係数k及び尺度係数cを夫々特定する制御を行う。例えば、制御手段１１０は、機材の設置場所を基準として、上下あるいは左右方向に最も近い２つの地点に係る形状係数k及び尺度係数cを夫々特定する。あるいは、例えば、制御手段１１０は、主に風が吹く方向に対して、機材の設置場所を基準として、最も近い上流側の地点と、下流側の地点に係る形状係数k及び尺度係数cを夫々特定する。 Further, when the control unit 110 receives input of the installation location of the equipment (= latitude / longitude of the installation location of the equipment) through the input means 106, each weather station stored in the shape factor / scale coefficient storage area 151. Compared to the latitude / longitude of each, control is performed to identify the shape factor k and the scale factor c relating to the two points closest to the installation location of the equipment. For example, the control unit 110 specifies the shape factor k and the scale factor c relating to the two points closest to the vertical and horizontal directions, respectively, based on the installation location of the equipment. Alternatively, for example, the control unit 110 mainly sets the shape factor k and the scale factor c related to the nearest upstream point and the downstream point, with respect to the direction in which the wind blows, based on the installation location of the equipment. Identify.
なお、本実施例では、ユーザによって入力された機材の設置場所の緯度・経度と、各気象観測所の緯度・経度とを比較して、当該機材の設置場所に最も近い２つの地点に係る形状係数k及び尺度係数cを夫々特定する構成を示したが、この構成に限定されるものではない。例えば、形状係数kと尺度係数cの分布をコンター（等値線）図化し、あらかじめ記憶手段１０５等に記憶させておく。そして、機材の設置場所に最も近い２つの地点に係る形状係数k及び尺度係数cを特定する際には、制御手段１１０は、表示手段１０７上に当該コンター図を表示させ、入力手段１０６を通じてユーザから機材の設置場所の入力を受け付けると、入力された機材の設置場所と同一の等値線上にある２つの地点に係る形状係数k及び尺度係数cを夫々特定する制御を行う。 In this embodiment, the latitude / longitude of the installation location of the equipment input by the user and the latitude / longitude of each weather station are compared, and the shape relating to the two points closest to the installation location of the equipment Although the configuration in which the coefficient k and the scale coefficient c are specified is shown, the configuration is not limited to this configuration. For example, the distribution of the shape factor k and the scale factor c is made into a contour (isoline) diagram and stored in advance in the storage means 105 or the like. When specifying the shape factor k and the scale factor c relating to the two points closest to the installation location of the equipment, the control unit 110 displays the contour diagram on the display unit 107 and the user through the input unit 106. When the input of the installation location of the equipment is received from, the control is performed to specify the shape factor k and the scale coefficient c related to two points on the same isoline as the input installation location of the equipment.
また、制御手段１１０は、２つの地点に係る形状係数kと尺度係数cについて線形補間を行い、機材の設置場所に係る形状係数kおよび尺度係数ｃを算出する制御を行う。また、制御手段１１０は、機材の設置場所に係る形状係数k及び尺度係数cに基づいて、機材の設置場所に係る風速の確率密度分布fv（＝ワイブル分布関数）を算出する制御を行う。そして、制御手段１１０は、算出した機材の設置場所の風速の確率密度分布fvを風速の確率密度分布記憶領域１５４に記憶させる制御を行う。 The control unit 110 performs linear interpolation on the shape factor k and the scale factor c for the two points, and performs control to calculate the shape factor k and the scale factor c for the installation location of the equipment. Further, the control means 110 performs control to calculate the probability density distribution f v (= Weibull distribution function) of the wind speed related to the installation location of the equipment based on the shape factor k and the scale factor c related to the installation location of the equipment. Then, the control means 110 performs control to store the calculated wind speed probability density distribution f v at the installation location of the equipment in the wind speed probability density distribution storage area 154.
また、制御手段１１０は、乱れ強さ関連情報記憶領域１５２を参照して、風速の乱れ強さIzを算出する制御を行う。詳しくは、制御手段１１０は、乱れ強さ関連情報記憶領域１５２を参照し、ユーザによって入力された粗度区分に対応するべき指数を特定し、特定されたべき指数と、ユーザによって入力された機材の設置場所の地上高Z（m）を用いて風速の乱れ強さIzを算出する。 Further, the control unit 110 refers to the turbulence intensity related information storage area 152 and performs control for calculating the turbulence intensity Iz of the wind speed. Specifically, the control unit 110 refers to the turbulence intensity related information storage area 152, specifies an index to be associated with the roughness classification input by the user, and specifies the index to be specified and the equipment input by the user. The wind speed turbulence intensity Iz is calculated using the ground height Z (m) at the installation location of.
また、制御手段１１０は、入力手段１０６を通じて、弛度D（m）、径間S（m）、電線外径Ｂ（m）といった架線条件情報の入力を受け付けると、当該情報を架線条件情報記憶領域１５８に記憶させる制御を行う。 When the control unit 110 receives input of overhead line condition information such as the sag degree D (m), the span S (m), and the wire outer diameter B (m) through the input unit 106, the control unit 110 stores the information in the overhead line condition information. Control to be stored in the area 158 is performed.
また、制御手段１１０は、風力係数の確率密度分布式記憶領域１５３を参照して、風力係数の確率密度分布fcuを算出する制御を行う。詳しくは、制御手段１１０は、風速の乱れ強さIzを、数３に代入して風力係数の確率密度分布fcuを算出する。そして、制御手段１１０は、算出した風力係数の確率密度分布fcuを風力係数の確率密度分布記憶領域１５５に記憶させる制御を行う。 Further, the control unit 110 refers to the wind power coefficient probability density distribution equation storage area 153 and performs control to calculate the wind power coefficient probability density distribution f cu . Specifically, the control means 110 calculates the probability density distribution f cu of the wind power coefficient by substituting the wind speed turbulence intensity Iz into Equation 3. Then, the control unit 110 performs control to store the calculated probability density distribution f cu of the wind power coefficient in the probability density distribution storage area 155 of the wind power coefficient.
また、制御手段１１０は、繰り返し荷重の確率密度分布式記憶領域１５６を参照して、繰り返し荷重の確率密度分布fTuを算出する制御を行う。詳しくは、制御手段１１０は、架線条件情報記憶領域１５８から弛度D（m）、径間S（m）、電線外径Ｂ（m）を呼び出して、風力係数Cuと不平衡張力Tuの関係を示す数４から由来される下記の数６に代入し、当該算出結果に、風速の確率密度分布記憶領域１５４から呼び出した機材の設置場所の風速の確率密度分布fvと、風力係数の確率密度分布記憶領域１５５から呼び出した風力係数の確率密度分布fcuを乗算して、乗算結果を積分して、繰り返し荷重の確率密度分布fTuを算出する。そして、制御手段１１０は、算出した繰り返し荷重の確率密度分布fTuを繰り返し荷重の確率密度分布記憶領域１５７に記憶させる制御を行う。 Further, the control unit 110 refers to the probability density distribution equation storage area 156 of the repeated load and performs control to calculate the probability density distribution f Tu of the repeated load. Specifically, the control unit 110 calls the sag D (m), the span S (m), and the wire outer diameter B (m) from the overhead line condition information storage area 158, and the relationship between the wind force coefficient Cu and the unbalanced tension Tu. Substituting into the following equation 6 derived from equation 4 indicating the probability density distribution f v of the wind speed at the installation location of the equipment called from the probability density distribution storage area 154 of the wind speed and the probability of the wind coefficient The probability density distribution f cu of the wind power coefficient called from the density distribution storage area 155 is multiplied, and the multiplication result is integrated to calculate the probability density distribution f Tu of the repeated load. The control unit 110 performs control to store the calculated probability density distribution f Tu of the repeated load in the probability density distribution storage area 157 of the repeated load.
また、制御手段１１０は、繰り返し荷重の確率密度分布記憶領域１５７に記憶されている繰り返し荷重の確率密度分布fTuを機材の設置場所に関連付けて表示させる制御を行う。制御手段１１０は、図１０に示すように、このfTuに基づいて、指定の階級幅に区分された各Tu(不平衡張力)に対して指定した期間中の出現頻度を表示させることができる。 Further, the control unit 110 performs control to display the repeated load probability density distribution f Tu stored in the repeated load probability density distribution storage area 157 in association with the installation location of the equipment. As shown in FIG. 10, the control means 110 can display the appearance frequency during the designated period for each Tu (unbalanced tension) divided into the designated class width based on this f Tu. .
以上の各手段と同等の機能を実現するソフトウェアにより、ハードウェア装置の代替として構成することもできる。 It can also be configured as an alternative to a hardware device by software that realizes functions equivalent to those of the above means.
本実施例では、メディア１０４から本実施例に係るプログラム及び関連する情報を直接記憶手段１０５のＲＡＭ等にロードして実行させる例を示しているが、これ以外にも、本実施例に係るプログラムを動作させる度に、既にプログラムがインストールされている記憶手段１０５のＨＤＤ等から記憶手段１０５のＲＡＭ等にロードするようにしてもよい。加えて、本実施例に係るプログラムを記憶手段１０５のＲＯＭ等に記憶しておき、これをメモリマップの一部をなすように構成し、直接制御手段１１０で実行することも可能である。 In the present embodiment, an example is shown in which the program according to the present embodiment and related information are directly loaded from the medium 104 into the RAM or the like of the storage unit 105 and executed. However, the program according to the present embodiment is not limited thereto. May be loaded into the RAM or the like of the storage means 105 from the HDD or the like of the storage means 105 in which the program is already installed. In addition, the program according to the present embodiment can be stored in the ROM or the like of the storage unit 105, configured so as to form a part of the memory map, and directly executed by the control unit 110.
さらに、本実施例では、説明の便宜のため、情報処理装置１００を１つの装置で実現した構成について述べているが、複数の装置にリソースを分散した構成によって実現してもよい。例えば、記憶や演算のリソースを複数の装置に分散した形に構成してもよい。あるいは、情報処理装置１００上で仮想的に実現される構成要素毎にリソースを分散し、並列処理を行うようにしてもよい。 Further, in the present embodiment, for convenience of explanation, a configuration in which the information processing apparatus 100 is realized by a single device is described, but may be realized by a configuration in which resources are distributed to a plurality of devices. For example, storage and calculation resources may be distributed in a plurality of devices. Alternatively, resources may be distributed for each component virtually realized on the information processing apparatus 100 to perform parallel processing.
＜情報処理装置１００が実行する処理の流れ＞
次に、情報処理装置１００が実行する処理の流れを、図５を用いて説明する。情報処理装置１００は、入力手段１０６を通じて、機材の設置場所の入力を受け付けると、形状係数・尺度係数記憶領域１５１を参照して、当該機材の設置場所に最も近い２つの地点に係る形状係数k及び尺度係数cを夫々特定する。情報処理装置１００は、特定された２つの地点に係る形状係数k及び尺度係数cを線形補間し、機材の設置場所に係る形状係数kおよび尺度係数ｃを算出する（ステップＳ５０１）。そして、情報処理装置１００は、機材の設置場所に係る形状係数k及び尺度係数cに基づいて、機材の設置場所の風速の確率密度分布fv（＝ワイブル分布関数）を算出する（ステップＳ５０２）。情報処理装置１００は、算出した機材の設置場所の風速の確率密度分布fvを風速の確率密度分布記憶領域１５４に記憶させる。
<Flow of processing executed by information processing apparatus 100>
Next, the flow of processing executed by the information processing apparatus 100 will be described with reference to FIG. When the information processing apparatus 100 receives input of the installation location of the equipment through the input unit 106, the information processing apparatus 100 refers to the shape factor / scale coefficient storage area 151 and refers to the shape factor k related to the two points closest to the installation location of the equipment. And a scale factor c are specified. The information processing apparatus 100 linearly interpolates the shape factor k and the scale factor c related to the two specified points, and calculates the shape factor k and the scale factor c related to the installation location of the equipment (step S501). Then, the information processing apparatus 100 calculates the probability density distribution f v (= Weibull distribution function) of the wind speed at the installation location of the equipment based on the shape factor k and the scale factor c related to the installation location of the equipment (step S502). . The information processing apparatus 100 stores the calculated probability density distribution f v of the wind speed at the installation location of the equipment in the probability density distribution storage area 154 of the wind speed.
例えば、図６に示すように、予測地点Ｅの緯度、経度を入力すると、その地点の近傍にある既存の観測所の中から、予測地点Ｅを基準として、上下あるいは左右方向に最も近い地点に在る２つの観測所を選択する。なお、図６では、予測地点Ｅを基準として、左右方向に最も近い地点に在る観測所Ａ及び観測所Ｂを選択する。この２カ所のデータから求めたワイブル係数（形状係数kと尺度係数c）について線形補間を行うことによって、予測地点Ｅの形状係数k「０．９５」の値および尺度係数ｃ「１．７」の値が算出され、この形状係数k及び尺度係数cの値に基づいて、図７に示すように、風速の確率密度分布fvが算出される。 For example, as shown in FIG. 6, when the latitude and longitude of the predicted point E are input, from among existing observation stations in the vicinity of the point, the point closest to the vertical or horizontal direction with respect to the predicted point E is used. Select two existing stations. In FIG. 6, an observation station A and an observation station B that are closest to the left-right direction are selected with the predicted point E as a reference. By performing linear interpolation on the Weibull coefficients (shape factor k and scale factor c) obtained from these two data points, the value of the shape factor k “0.95” and the scale factor c “1.7” at the predicted point E are obtained. As shown in FIG. 7, the probability density distribution f v of the wind speed is calculated based on the values of the shape factor k and the scale factor c.
情報処理装置１００は、乱れ強さ関連情報記憶領域１５２を参照し、ユーザによって入力された粗度区分に対応するべき指数を特定し、特定されたべき指数と、ユーザによって入力された機材の設置場所の地上高Z（m）を用いて風速の乱れ強さIzを算出する（ステップＳ５０３）。情報処理装置１００は、風力係数の確率密度分布式記憶領域１５３を参照して、風力係数の確率密度分布fcuを算出する（ステップＳ５０４）。 The information processing apparatus 100 refers to the turbulence intensity related information storage area 152, specifies an index that should correspond to the roughness classification input by the user, and sets the index to be specified and the equipment input by the user The wind speed turbulence intensity Iz is calculated using the ground height Z (m) of the place (step S503). The information processing apparatus 100 refers to the wind power coefficient probability density distribution equation storage area 153 to calculate the wind power coefficient probability density distribution fcu (step S504).
例えば、ユーザによって入力された予測地点Ｅの粗度区分が「３」(α＝０．２)であり、ユーザによって入力された予測地点Ｅの地上高Ｚの値が「10ｍ」の場合は、数１と数２の関係から、風速の乱れ強さIzは「０．２６」と算出され、数３により風力係数の確率密度分布fcuが図８に示すように算出される。なお、図８では、ユーザによって入力された予測地点Ｅの粗度区分は、ギリシャ文字の「３」で表示されている。情報処理装置１００は、算出した風力係数の確率密度分布fcuを風力係数の確率密度分布記憶領域１５５に記憶させる。 For example, when the roughness classification of the predicted point E input by the user is “3” (α = 0.2) and the value of the ground height Z of the predicted point E input by the user is “10 m”, From the relationship between Equations 1 and 2, the wind speed turbulence intensity Iz is calculated as “0.26”, and the probability density distribution f cu of the wind power coefficient is calculated as shown in FIG. In FIG. 8, the roughness classification of the predicted point E input by the user is displayed with the Greek letter “3”. The information processing apparatus 100 stores the calculated probability density distribution f cu of the wind power coefficient in the probability density distribution storage area 155 of the wind power coefficient.
情報処理装置１００は、入力手段１０６を通じて、弛度D（m）、径間S（m）、電線外径Ｂ（m）といった架線条件情報の入力を受け付けると、当該情報を架線条件情報記憶領域１５８に記憶させる。そして、情報処理装置１００は、繰り返し荷重の確率密度分布式記憶領域１５６と、架線条件情報記憶領域１５８を参照して、繰り返し荷重の確率密度分布fTuを算出する（ステップＳ５０５）。例えば、図７の風速の確率密度分布fvおよび図８の風力係数の確率密度分布fcuから、数５により繰り返し荷重の確率密度分布fTuが、図９で示されるように算出される。 When the information processing apparatus 100 receives input of overhead line condition information such as the sag D (m), the span S (m), and the wire outer diameter B (m) through the input unit 106, the information is stored in the overhead line condition information storage area. 158 to store. Then, the information processing apparatus 100 refers to the probability density distribution equation storage area 156 of the repeated load and the overhead line condition information storage area 158 to calculate the probability density distribution f Tu of the repeated load (step S505). For example, the probability density distribution f Tu of the repeated load is calculated as shown in FIG. 9 from the wind speed probability density distribution f v of FIG. 7 and the wind power coefficient probability density distribution f cu of FIG.
このように、繰り返し荷重の確率密度分布fTuを算出（＝予測）する構成としたことによって、架渉線機材の疲労損傷破壊に対する評価を正確に行うことができ、機材の長期信頼性設計を合理的に行うことができる。 In this way, the probability density distribution f Tu of repeated loads is calculated (= predicted), so that it is possible to accurately evaluate the fatigue damage fracture of the wireline equipment and to design the long-term reliability of the equipment. Can be done reasonably.
本実施例では、形状係数・尺度係数記憶領域１５１に記憶させた形状係数kと尺度係数cとを利用する構成を示したが、この構成に限定されるわけではない。 In the present embodiment, the configuration using the shape factor k and the scale factor c stored in the shape factor / scale factor storage area 151 is shown, but the present invention is not limited to this configuration.
例えば、前述のワイブル分布関数による風況予測においても、観測所間の形状係数k及び尺度係数cから補間する構成だけでなく、粗度区分や地形情報等を因子として、形状係数kや尺度係数cを補正する構成等も当該実施例として含まれる。 For example, in the wind condition prediction using the above-described Weibull distribution function, not only the configuration interpolating from the shape factor k and the scale factor c between the stations, but also the shape factor k and the scale factor using the roughness classification and topographic information as factors. A configuration for correcting c is also included as the embodiment.
またワイブル分布関数による予測以外にも、「風速比解析」などがある。この解析は基準位置の風速と対象地域の風速との同時刻歴での風速観測データの比をとり、その比をエリア的にまとめるものである。その場合、形状係数・尺度係数記憶領域１５１の代わりに、記憶手段１０５内等に風速比記憶領域（図示省略）を設け、情報処理装置１００は、当該風速比記憶領域に各観測所の風速比を記憶させる。そして、情報処理装置１００は、風速の確率密度分布領域１５４に風速の確率密度分布fvを記憶させる。 In addition to prediction using the Weibull distribution function, there is “wind speed ratio analysis”. This analysis takes the ratio of the wind speed observation data in the same time history between the wind speed of the reference position and the wind speed of the target area, and summarizes the ratio area by area. In that case, instead of the shape factor / scale coefficient storage area 151, a wind speed ratio storage area (not shown) is provided in the storage means 105 or the like, and the information processing apparatus 100 has the wind speed ratio of each station in the wind speed ratio storage area. Remember. Then, the information processing apparatus 100 stores the wind speed probability density distribution f v in the wind speed probability density distribution region 154.
またさらには、年間の風速頻度をワイブル分布関数以外の他の分布関数（例えば対数正規分布など）で示し、当該関数の係数について補間する手法なども考えられる。その場合、形状係数・尺度係数記憶領域１５１の代わりに、記憶手段１０５内等に当該他の分布関数の係数を記憶する記憶領域を設ける。そして、情報処理装置１００は、風速の確率密度分布領域１５４に風速の確率密度分布fvを記憶させる。 Furthermore, a method is also conceivable in which the annual wind speed frequency is indicated by a distribution function other than the Weibull distribution function (for example, a lognormal distribution) and the coefficients of the function are interpolated. In that case, instead of the shape coefficient / scale coefficient storage area 151, a storage area for storing the coefficient of the other distribution function is provided in the storage means 105 or the like. Then, the information processing apparatus 100 stores the wind speed probability density distribution f v in the wind speed probability density distribution region 154.
以上、本発明の実施例について詳述したが、本発明は、例えば、システム、装置、方法、プログラムもしくは記憶媒体等としての実施態様を取ることが可能である。 Although the embodiments of the present invention have been described in detail above, the present invention can take an embodiment as a system, apparatus, method, program, storage medium, or the like.
また、本発明は、上述した実施例の機能を実現するプログラムを、システムあるいは装置に、直接あるいは遠隔から供給する。そして、そのシステムあるいは装置のコンピュータが供給された当該プログラムからプログラムコードを読み出して実行することによって達成される場合を含む。 Further, the present invention supplies a program for realizing the functions of the above-described embodiments directly or remotely to a system or apparatus. And the case where it is achieved by reading and executing the program code from the program supplied by the computer of the system or apparatus is included.
したがって、本発明の機能処理をコンピュータで実現するために、このコンピュータにインストールされるプログラム自体も本発明の技術的範囲に含まれる。つまり、本発明は、本発明の機能処理を実現するためのコンピュータプログラム自体も含む。この場合、プログラムの提供方法としては、ＣＤ−ＲＯＭ、ＤＶＤ等の記憶媒体を用いて提供する方法や、電気通信回線を介して提供する方法等が含まれる。 Therefore, in order to implement the functional processing of the present invention on a computer, the program itself installed in this computer is also included in the technical scope of the present invention. That is, the present invention includes a computer program itself for realizing the functional processing of the present invention. In this case, the program providing method includes a method of providing using a storage medium such as a CD-ROM and a DVD, a method of providing via a telecommunication line, and the like.
１００：情報処理装置、１０３：外部記憶媒体接続手段、１０４：メディア、１０５：記憶手段、１０６：入力手段、１０７：表示手段、１０８：通信手段、１０９：バス、１１０：制御手段、１５１：形状係数・尺度係数記憶領域、１５２：乱れ強さ関連情報記憶領域、１５３：風力係数の確率密度分布式記憶領域、１５４：風速の確率密度分布記憶領域、１５５：風力係数の確率密度分布記憶領域、１５６：繰り返し荷重の確率密度分布式記憶領域、１５７：繰り返し荷重の確率密度分布記憶領域、１５８：架線条件情報記憶領域 DESCRIPTION OF SYMBOLS 100: Information processing apparatus, 103: External storage medium connection means, 104: Media, 105: Storage means, 106: Input means, 107: Display means, 108: Communication means, 109: Bus, 110: Control means, 151: Shape Coefficient / scale coefficient storage area, 152: Turbulence intensity related information storage area, 153: Probability density distribution equation storage area of wind power coefficient, 154: Probability density distribution storage area of wind speed, 155: Probability density distribution storage area of wind coefficient, 156: Probability density distribution formula storage area of repeated load, 157: Probability density distribution storage area of repeated load, 158: Overhead condition information storage area
Claims (3)
電線の線路方向に作用する繰り返し変動荷重と風速との相関関係を利用すること、
さらに既知の風速観測データから任意の地点の風速の確率密度分布を予測することにより、
任意の地点に架渉された前記支持機材に対して電線の線路方向に作用する繰り返し荷重の確率密度分布を予測することを特徴とする、繰り返し荷重の確率密度分布予測方法。 In the support equipment that supports the distribution line routed on the pillar to the pillar,
Utilizing the correlation between the repeated fluctuating load acting on the wire direction of the wire and the wind speed,
Furthermore, by predicting the probability density distribution of wind speed at any point from known wind speed observation data,
A method for predicting the probability density distribution of a repeated load, which predicts the probability density distribution of a repeated load acting in the line direction of the electric wire with respect to the support equipment negotiated at an arbitrary point.
統計処理により、土地利用状況により影響される架渉された配電線高さにおける任意の地点の自然風の確率密度分布を算出することを特徴とする、請求項１に記載の繰り返し荷重の確率密度分布予測方法。
As the known wind speed observation data, based on the frequency distribution of typical wind speeds related to a plurality of weather stations,
The probability density of repetitive load according to claim 1, characterized in that the probability density distribution of natural wind at any point in the negotiated distribution line height affected by land use status is calculated by statistical processing. Distribution prediction method.
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