TWI781850B - Intelligent networked wind power generation fault diagnosis and detection system and detection method - Google Patents
Intelligent networked wind power generation fault diagnosis and detection system and detection method Download PDFInfo
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Abstract
一種智慧聯網風力發電故障診斷檢測方法，係為利用三軸振動感測器來感測多葉片風機組轉動時的三軸方向的振動狀態，再透過高速資料擷取卡來擷取該三軸訊號，然後再將該三軸訊號透過一訊號處理單元進行濾波處理，最後將該已濾波處理後的三軸訊號透過一電腦運算系統進行分析故障的類型。 A fault diagnosis and detection method for smart networked wind power generation, which uses a three-axis vibration sensor to sense the vibration state of the three-axis direction when the multi-blade wind turbine rotates, and then captures the three-axis signal through a high-speed data acquisition card , and then the three-axis signal is filtered through a signal processing unit, and finally the filtered three-axis signal is analyzed through a computer operation system to analyze the type of fault.
Description
本發明是關於一種智慧聯網風力發電故障診斷檢測系統及其方法，特別涉及一種葉片旋角異常、葉片表面受損與葉片斷裂所引發的不同振動訊號來進行故障診斷識別的一種智慧聯網風力發電故障診斷檢測系統及其檢測方法。 The present invention relates to a smart networked wind power generation fault diagnosis and detection system and its method, in particular to a smart networked wind power generation fault diagnosis and recognition based on different vibration signals caused by blade rotation angle abnormality, blade surface damage and blade breakage Diagnostic detection system and detection method thereof.
風力機長期暴露在戶外，並且經長時間運轉，造成零件老化與機械磨損等故障發生的機率也相對提高。並且從全球風能協會(Global Wind Energy Council，GWEC)的過去數據統計，風力機長期運轉下，因外在天氣環境惡劣造成葉片表面受損與斷裂，或因長期運轉下葉片旋角系統因機械疲勞造成異常的故障問題最高。 The wind turbine is exposed to the outdoors for a long time, and after a long time of operation, the probability of failures such as component aging and mechanical wear is relatively high. And according to the past data statistics of the Global Wind Energy Council (GWEC), under long-term operation of wind turbines, the surface of the blades is damaged and broken due to the harsh external weather environment, or the blade rotation angle system is mechanically damaged due to long-term operation. Fatigue caused the highest failure problems caused by abnormalities.
而風力機通常架設於空曠地區，因此風力機面臨較大的機率會遭受到雷擊，並且依據全球風能協會的統計，該風力機較常遭受到雷擊的主要零組件為葉片，依據統計報告指出，該葉片的損壞位置以葉片的前緣與後緣的損壞機率最高。 However, wind turbines are usually erected in open areas, so wind turbines face a greater probability of being struck by lightning, and according to the statistics of the Global Wind Energy Association, the main component of the wind turbine that is more likely to be struck by lightning is the blade, according to the statistical report. , the damage probability of the leading edge and the trailing edge of the blade is the highest.
因此當風力機的葉片旋角系統出現異常或是葉片表面損壞或斷裂，都會嚴重損壞葉片結構與表面材料，造成昂貴的維修成本，還必須停機很長的時間，才能找到問題 進行維修，導致綠能發電的效率降低。因此如何在風力機的葉片與旋角系統出現異常時，就能儘早發現故障類型的原因，以提早進行更換與維修保養的工作，讓風力機能安全運轉與穩定供電，此乃為業界與學界亟於解決的問題之一。 Therefore, when the blade rotation angle system of the wind turbine is abnormal or the blade surface is damaged or broken, it will seriously damage the blade structure and surface materials, resulting in expensive maintenance costs, and it must be shut down for a long time to find the problem Repairs will result in a reduction in the efficiency of green energy power generation. Therefore, when there is an abnormality in the blade and rotation angle system of the wind turbine, how to find out the cause of the fault type as early as possible, so as to perform replacement and maintenance work early, so that the wind turbine can operate safely and provide stable power supply. This is an urgent need for the industry and academia. one of the problems to be solved.
本發明目的在於提供一種智慧聯網風力發電故障診斷檢測系統及其檢測方法，藉由葉片旋角異常、葉片表面受損與葉片斷裂所引發的不同振動訊號來診斷識別出葉片故障的類型。 The purpose of the present invention is to provide a smart networked wind power generation fault diagnosis and detection system and its detection method, which can diagnose and identify the types of blade faults by means of different vibration signals caused by blade rotation angle abnormality, blade surface damage and blade fracture.
為了達成上述目的，本發明實施例所揭露之一種智慧聯網風力發電故障診斷檢測系統，至少包括：多葉片風機組，至少具有一馬達來帶動該多葉片風機組轉動；一三軸振動感測器，電性連接該葉片風機組，用以感測該多葉片風機組轉動時的三軸方向的振動狀態，並產生出三軸訊號；一高速資料擷取卡，電性連接該三軸振動感測器，用以擷取該三軸訊號；一訊號處理單元，電性連接該高速資料擷取卡，並將該高速資料擷取卡擷取到的三軸訊號進行濾波處理；一電腦運算系統，電性連接或網路連接該訊號處理單元，用以將該已濾波處理的三軸訊號進行分析故障的類型。 In order to achieve the above purpose, a smart networked wind power generation fault diagnosis and detection system disclosed in an embodiment of the present invention at least includes: a multi-bladed wind turbine, at least having a motor to drive the multi-bladed wind turbine to rotate; a three-axis vibration sensor , electrically connected to the blade fan unit, to sense the vibration state of the three-axis direction when the multi-blade fan unit rotates, and generate a three-axis signal; a high-speed data acquisition card, electrically connected to the three-axis vibration sensor A detector for capturing the three-axis signal; a signal processing unit electrically connected to the high-speed data acquisition card, and filtering the three-axis signal captured by the high-speed data acquisition card; a computer computing system , electrically or network-connected to the signal processing unit for analyzing the type of the fault on the filtered triaxial signal.
其中該電腦運算系統，具有：一數據可視化平台，將已濾波處理過的三軸訊號轉換成不同種類數量的多組訊號特徵圖；多層卷積神經網路，將該多組訊號特徵圖各別透過對應的多層卷積神經網路來選擇不同的網路層數、卷積 核大小、激活函數或池化方法來辦識出多個辨識結果；一共振理論類神經網路，再將該多個辨識結果進行一邊緣抑制運算，再從一風力發電故障瑕疵模型來選擇決定最終輸出辨識結果。 Among them, the computer computing system has: a data visualization platform, which converts the filtered three-axis signal into multiple sets of signal feature maps of different types and quantities; a multi-layer convolutional neural network, which separates the multiple sets of signal feature maps Through the corresponding multi-layer convolutional neural network to select different network layers, convolution Kernel size, activation function or pooling method to identify multiple identification results; a resonance theory-like neural network, and then perform an edge suppression operation on the multiple identification results, and then select and decide from a wind power fault defect model Finally output the recognition result.
為了達成上述目的，本發明實施例所揭露之一種智慧聯網風力發電故障診斷檢測方法，包括如下步驟：第一步驟：利用一三軸振動感測器來感測多葉片風機組轉動時的三軸方向的振動狀態，並產生出三軸訊號。第二步驟：透過一高速資料擷取卡來擷取該三軸訊號。第三步驟：將該三軸訊號透過一訊號處理單元進行濾波處理。第四步驟：將該已濾波處理後的三軸訊號透過一電腦運算系統進行分析故障的類型。 In order to achieve the above purpose, a fault diagnosis and detection method for smart networked wind power generation disclosed by an embodiment of the present invention includes the following steps: Step 1: Use a three-axis vibration sensor to sense the three-axis vibration of the multi-blade wind turbine when it rotates Direction of the vibration state, and generate a three-axis signal. Step 2: Acquire the three-axis signals through a high-speed data acquisition card. Step 3: filtering the three-axis signal through a signal processing unit. The fourth step: analyzing the type of the fault through a computer operation system through the filtered three-axis signal.
其中該電腦運算系統，包括如下步驟：第一步驟：利用一數據可視化平台將已濾波處理過的三軸訊號轉換成不同種類數量的多組訊號特徵圖。第二步驟：再將該多組訊號特徵圖各別透過對應的多層卷積神經網路來選擇不同的網路層數、卷積核大小、激活函數或池化方法來辦識出多個辨識結果。第三步驟：再將該多個辨識結果透過一共振理論類神經網路進行一邊緣抑制運算，最後再從一風力發電故障瑕疵模型來選擇決定最終輸出辦識結果。 The computer computing system includes the following steps: first step: using a data visualization platform to convert the filtered three-axis signal into multiple sets of signal feature maps of different types and quantities. The second step: through the corresponding multi-layer convolutional neural network to select different network layers, convolution kernel size, activation function or pooling method to identify multiple sets of signal feature maps result. The third step: performing an edge suppression operation on the plurality of identification results through a resonance theory-like neural network, and finally selecting and determining the final output identification result from a fault model of wind power generation.
以上關於本發明內容的說明及以下實施方式的說明係用以示範與解釋本發明的原理，並且提供本發明的專利申請範圍更進一步的解釋。 The above description of the content of the present invention and the following description of the implementation are used to demonstrate and explain the principle of the present invention, and provide further explanation of the patent application scope of the present invention.
1:智慧聯網風力發電故障診斷檢測系統 1: Fault diagnosis and detection system for intelligent networked wind power generation
11:葉片風機組 11: Blade fan unit
111:風機葉片 111: fan blade
112:葉片表面受損 112: The surface of the blade is damaged
113:葉片斷裂 113: blade break
12:三軸振動感測器 12: Three-axis vibration sensor
13:高速資料擷取卡 13: High-speed data acquisition card
14:訊號處理單元 14: Signal processing unit
15:電腦運算系統 15:Computer computing system
151:數據可視化平台 151: Data visualization platform
152:卷積神經網路 152: Convolutional Neural Networks
1521:第一卷積神經網路 1521: The first convolutional neural network
1522:第二卷積神經網路 1522: The second convolutional neural network
1523:第三卷積神經網路 1523: The third convolutional neural network
153:共振理論類神經網路 153: Resonance Theory Neural Networks
154:風力發電故障瑕疵模型 154:Wind Power Fault Model
S10~S13:步驟 S10~S13: Steps
S20~S22:步驟 S20~S22: Steps
圖1為本發明的智慧聯網風力發電故障診斷檢測系統的整體方塊示意圖； Fig. 1 is the overall block schematic diagram of the fault diagnosis and detection system for intelligent networked wind power generation of the present invention;
圖2為本發明的智慧聯網風力發電故障診斷檢測方法的步驟流程圖； Fig. 2 is a flow chart of the steps of the intelligent networked wind power generation fault diagnosis and detection method of the present invention;
圖3為本發明的電腦運算系統的步驟流程圖； Fig. 3 is the flow chart of the steps of the computer computing system of the present invention;
圖4A為本發明之X軸與Y軸的各種類型的訊號特徵圖； FIG. 4A is a signal characteristic diagram of various types of X-axis and Y-axis of the present invention;
圖4B為本發明之X軸與Z軸的各種類型的訊號特徵圖； FIG. 4B is a characteristic diagram of various types of signals of the X-axis and the Z-axis of the present invention;
圖4C為本發明之Y軸與Z軸的各種類型的訊號特徵圖； FIG. 4C is a characteristic diagram of various types of signals of the Y axis and the Z axis of the present invention;
圖5A為本發明之發力發電故障瑕疵模型中的風機葉片正常狀態模型示意圖； Fig. 5A is a schematic diagram of the normal state model of the fan blade in the power generation fault defect model of the present invention;
圖5B為本發明之發力發電故障瑕疵模型中的風機葉片旋角異常之瑕疵模型示意圖； Fig. 5B is a schematic diagram of a defect model of an abnormal fan blade rotation angle in the power generation fault defect model of the present invention;
圖5C為本發明之發力發電故障瑕疵模型中的風機葉片表面受損之瑕疵模型示意圖； Fig. 5C is a schematic diagram of a flaw model of a damaged fan blade surface in the power generation fault flaw model of the present invention;
圖5D為本發明之發力發電故障瑕疵模型中的風機葉片斷裂之瑕疵模型示意圖； Fig. 5D is a schematic diagram of a defect model of fan blade fracture in the power generation fault defect model of the present invention;
圖6A為本發明之發力發電故障瑕疵模型中的風機葉片正常狀態模型所引發出的三軸的原始訊號與濾波後的訊號之振動訊號示意圖； Fig. 6A is a schematic diagram of the vibration signals of the three-axis original signal and the filtered signal caused by the fan blade normal state model in the power generation fault defect model of the present invention;
圖6B為本發明之發力發電故障瑕疵模型中的風機葉片旋角異常之瑕疵模型所引發出的三軸的原始訊號與濾波後的訊號之振動訊號示意圖； Fig. 6B is a schematic diagram of the vibration signals of the three-axis original signal and the filtered signal caused by the defect model of the fan blade rotation angle abnormality in the power generation fault defect model of the present invention;
圖6C為本發明之發力發電故障瑕疵模型中的風機葉片表面受損之瑕疵模型所引發出的三軸的原始訊號與濾波後的訊號之振動訊號示意圖； Fig. 6C is a schematic diagram of the vibration signals of the three-axis original signal and the filtered signal caused by the defect model of the fan blade surface damage in the power generation fault defect model of the present invention;
圖6D為本發明之發力發電故障瑕疵模型中的風機葉片斷裂之瑕疵模型所引發出的三軸的原始訊號與濾波後的訊號之振動訊號示意圖。 6D is a schematic diagram of vibration signals of three-axis original signals and filtered signals caused by the fault model of fan blade fracture in the power generation fault fault model of the present invention.
為使本發明實施例的目的、技術方案和優點更加清楚，下面將結合本發明實施例中的附圖，對本發明實施例中的技術方案進行清楚、完整地說明，顯然，所描述的實施例是本發明一部分實施例，而不是全部的實施例。基於本發明中的實施例，本領域普通技術人員在沒有做出創造性勞動前提下所獲得的所有其他實施例，都屬本發明保護的範圍。 In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.
請參閱圖1，圖1為本發明的智慧聯網風力發電故障診斷檢測系統的整體方塊示意圖。本發明實施例之智慧聯網風力發電故障診斷檢測系統1，至少包括多葉片風機組11、一三軸振動感測器12、一高速資料擷取卡13、一訊號處理單元14及一電腦運算系統15。其中該葉片風機組11電性連
接該三軸振動感測器12，該三軸振動感測器12電性連接該高速資料擷取卡13，該高速資料擷取卡13電性連接該訊號處理單元14，該訊號處理單元14電性連接或網路連接該電腦運算系統15。
Please refer to FIG. 1 . FIG. 1 is an overall block diagram of a fault diagnosis and detection system for smart networked wind power generation according to the present invention. The fault diagnosis and
前述該多葉片風機組11至少包括有一馬達(圖未顯示)來帶動該多葉片風機組11轉動；其中該多葉片風機組11在本發明實施例中係為三組葉片來做為實施說明。
The aforementioned
前述該電腦運算系統15還包括有一數據可視化平台151、多層卷積神經網路152、一共振理論(Adaptive Resonance Theory,ART)類神經網路153及一風力發電故障瑕疵模型154。其中該數據可視化平台151在本發明實施例中係利用一實驗室虛擬儀器工程平台(Laboratory Virtual Instrumentation Engineering Workbench，簡稱LabVIEW)之圖形化程式語言與一矩陣實驗室(Matrix Laboratory，簡稱MATLAB)演算法開發軟體所組成。其中該多層卷積神經網路(Convolutional Neural Networks，簡稱CNN)152在本發明實施例中包括有一第一卷積神經網路1521、一第二卷積神經網路1522和一第三卷積神經網路1523。其中該共振理論類神經網路153在本發明實施例中係使用能夠抑制反饋結果的一最大輸出網路(MAXNET)。其中該發力發電故障瑕疵模型154在本發明實施例中至少有四種瑕疵模型：一風機葉片正常狀態模
型、一風機葉片旋角異常之瑕疵模型、一葉片表面受損之瑕疵模型及一葉片斷裂之瑕疵模型。
The aforementioned
以上，需要特別說明的是：本發明實施例之智慧聯網風力發電故障診斷檢測系統1係用於感測該葉片風機組11的葉片與旋角狀態來發現故障類型，因此可利用一三軸振動感測器12來感測該葉片風機組11上的多葉片轉動時的三軸方向(例如X軸、Y軸及Z軸)的振動狀態，並產生出三軸訊號(例如一X軸訊號、一Y軸訊號及一Z軸訊號)，再透過一高速資料擷取卡13來擷取該三軸訊號，再將該三軸訊號透過一訊號處理單元14進行濾波處理，然後將該已濾波處理的該三軸訊號再透過一電腦運算系統15進行分析故障的類型。該電腦運算系統15先利用一數據可視化平台151將三軸訊號各別轉換成不同種類數量的多組訊號特徵圖(例如一第一訊號特徵圖IF1、一第二訊號特徵圖IF2及一第三訊號特徵圖IF3)，然後再將該多組訊號特徵圖各別透過對應的多層卷積神經網路152(例如一第一卷積神經網路1521、一第二卷積神經網路1522和一第三卷積神經網路1523)來選擇不同的網路層數、卷積核大小、激活函數或池化方法來各別辦識出多個辨識結果(例如一第一辨識結果CNN1、一第二辨識結果CNN2和一第三辨識結果CNN3)，然後再將該多個辨識結果透過一共振理
論類神經網路153進行邊緣抑制運算，最後從一風力發電故障瑕疵模型154來選擇決定最終輸出辨識結果。
Above, what needs to be specially explained is that the intelligent networked wind power generation fault diagnosis and
請參閱圖2，圖2為本發明的智慧聯網風力發電故障診斷檢測方法的步驟流程圖。本發明實施例的智慧聯網風力發電故障診斷檢測方法，包括如下步驟： Please refer to FIG. 2 . FIG. 2 is a flow chart of the steps of the method for fault diagnosis and detection of smart networked wind power generation according to the present invention. The intelligent networked wind power generation fault diagnosis and detection method of the embodiment of the present invention includes the following steps:
第一步驟S10：利用一三軸振動感測器來感測葉片風機組轉動時的三軸方向(例如X軸、Y軸及Z軸)的振動狀態，並產生出三軸訊號(例如一X軸訊號、一Y軸訊號及一Z軸訊號)； The first step S10: use a three-axis vibration sensor to sense the vibration state of the three-axis directions (such as X-axis, Y-axis and Z-axis) when the blade fan unit rotates, and generate a three-axis signal (such as an X Axis signal, a Y-axis signal and a Z-axis signal);
第二步驟S11：再透過一高速資料擷取卡來擷取該三軸訊號； The second step S11: capturing the three-axis signal through a high-speed data acquisition card;
第三步驟S12：再將該三軸訊號透過一訊號處理單元進行濾波處理； The third step S12: filtering the three-axis signal through a signal processing unit;
第四步驟S13：最後將該已濾波處理後的三軸訊號透過一電腦運算系統進行分析故障的類型。 The fourth step S13: Finally, analyze the type of the fault through a computer operation system through the filtered three-axis signal.
請參閱圖3，圖3為本發明的電腦運算系統的步驟流程圖。本發明實施例的電腦運算系統15，包括如下步驟：
Please refer to FIG. 3 . FIG. 3 is a flowchart of steps of the computer computing system of the present invention. The
第一步驟S20：利用一數據可視化平台將三軸訊號各別轉換成不同種類數量的多組訊號特徵圖，如圖4A、圖4B、圖4C所示。其中該訊號特徵圖的種類數量N，可由如下數學方程式來計算
出兩兩相對的三軸的振動訊號(例如X軸與Y軸、X軸與Z軸、Y軸與Z軸)：
其中，N為可生成訊號特徵圖之種類數量，W為輸入資料之數目。 Among them, N is the number of types that can generate signal feature maps, and W is the number of input data.
第二步驟S21：再將該多組訊號特徵圖各別透過對應的多層卷積神經網路來選擇不同的網路層數、卷積核大小、激活函數或池化方法來各別辦識出多個辨識結果；
第三步驟S22：再將該多個辨識結果透過一共振理論類神經網路進行邊緣抑制運算，最後從一風力發電故障瑕疵模型來選擇決定最終輸出辦識結果。其中該邊緣抑制運算的數學方程式為：
其中Wij為MAXNET權重，ε為節點間的權重，i與j為MAXNET節點，M為類數，每個節點到自身的權重均為1。 Where W ij is the MAXNET weight, ε is the weight between nodes, i and j are MAXNET nodes, M is the number of classes, and the weight of each node to itself is 1.
本發明實施例的風力發電故障瑕疵模型為建立至少4種瑕疵模型： The fault defect model of wind power generation in the embodiment of the present invention is to establish at least 4 kinds of defect models:
第一種為風機葉片111正常狀態模型，如圖5A所示。該正常狀態模型的三軸振動訊號(X軸、Y軸、Z軸)不管是原始三軸振動訊號或是濾波後的三軸振動訊號都還是會有些微的微幅振動，因此該瑕疵模型所引發出的振動訊號示意圖，為如圖6A所示。
The first type is the normal state model of the
第二種為風機葉片111旋角異常之瑕疵模型，如圖5B所示。當風機三組葉片的旋角長時間運轉於不同角度情況下，易造成風機機構不平衡，輕則造成機構受損，重則造成風機倒塌。因此本實施例為了詳細說明本發明的技術特徵，將三組風機葉片的其中一組旋角角度調整為45°其它二組風機葉片的旋角角度為30°，用以模擬風機於旋角變槳時，其中一組旋角未同步所引發之振動訊號。從檢測的振動訊號中可以發現X軸之振動相對其他兩軸之振動訊號差異較大，測得之最高振動幅度約為0.15G，如圖6B所示。
The second type is the defect model of the abnormal rotation angle of the
第三種為葉片表面受損112之瑕疵模型，如圖5C所示。當該葉片表面受損112之模型所測得之三軸振動訊號，從中可發現Z軸之振動相對其他兩軸之振動訊號差異較大，測得之最高振動幅度約為0.38G，如圖6C所示。
The third type is a defect model of
第四種為葉片斷裂113之瑕疵模型，如圖5D所示。當該葉片斷裂113之模型所測得之三軸振動訊號，從中 可發現Z軸之振動相對其他兩軸之振動訊號差異較大，測得之最高振動幅度約為0.48G，如圖6D所示。 The fourth type is the defect model of blade fracture 113, as shown in FIG. 5D. When the three-axis vibration signal measured by the model of the blade fracture 113, from which It can be found that the vibration signal of the Z axis is quite different from the vibration signals of the other two axes, and the highest measured vibration amplitude is about 0.48G, as shown in Figure 6D.
雖然本發明以前述的諸項實施例揭露如上，然其並非用以限定本發明，任何熟習相像技藝者，在不脫離本發明的精神和範圍內，當可作些許的更動與潤飾，因此本發明的專利保護範圍須視本說明書所附的權利要求的保護範圍所界定者為准。 Although the present invention has been disclosed above with the foregoing embodiments, it is not intended to limit the present invention. Any person familiar with similar skills may make some changes and modifications without departing from the spirit and scope of the present invention. Therefore, this The scope of patent protection for inventions shall be defined by the scope of protection of the claims attached to this specification.
1:智慧聯網風力發電故障診斷檢測系統 1: Fault diagnosis and detection system for intelligent networked wind power generation
11:葉片風機組 11: Blade fan unit
12:三軸振動感測器 12: Three-axis vibration sensor
13:高速資料擷取卡 13: High-speed data acquisition card
14:訊號處理單元 14: Signal processing unit
15:電腦運算系統 15:Computer computing system
151:數據可視化平台 151: Data visualization platform
152:卷積神經網路 152: Convolutional Neural Networks
1521:第一卷積神經網路 1521: The first convolutional neural network
1522:第二卷積神經網路 1522: The second convolutional neural network
1523:第三卷積神經網路 1523: The third convolutional neural network
153:共振理論類神經網路 153: Resonance Theory Neural Networks
154:風力發電故障瑕疵模型 154:Wind Power Generation Fault Defect Model
Claims (10)
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW202016428A (en) * | 2018-10-24 | 2020-05-01 | 日商日立製作所股份有限公司 | Wind-based power generating system characterized in that the operation controlling portion can improve blade aerodynamic performance by considering the wing deformation and suppress the reduction of the power generating efficiency by considering the real machine operation |
| CN111173687A (en) * | 2019-12-30 | 2020-05-19 | 国核信息科技有限公司 | On-line monitoring device and method for crack damage of wind power fan blade |
| TWI732660B (en) * | 2020-08-20 | 2021-07-01 | 國立勤益科技大學 | Wind power generator fault diagnosis system and method |
| CN113738595A (en) * | 2021-07-09 | 2021-12-03 | 国电和风风电开发有限公司 | Method and system for monitoring state of blade of wind generating set |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW202016428A (en) * | 2018-10-24 | 2020-05-01 | 日商日立製作所股份有限公司 | Wind-based power generating system characterized in that the operation controlling portion can improve blade aerodynamic performance by considering the wing deformation and suppress the reduction of the power generating efficiency by considering the real machine operation |
| CN111173687A (en) * | 2019-12-30 | 2020-05-19 | 国核信息科技有限公司 | On-line monitoring device and method for crack damage of wind power fan blade |
| TWI732660B (en) * | 2020-08-20 | 2021-07-01 | 國立勤益科技大學 | Wind power generator fault diagnosis system and method |
| CN113738595A (en) * | 2021-07-09 | 2021-12-03 | 国电和风风电开发有限公司 | Method and system for monitoring state of blade of wind generating set |
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