TWI418119B - Wind generation system with pmsg using intelligent maximum power tracking controller - Google Patents
Wind generation system with pmsg using intelligent maximum power tracking controller Download PDFInfo
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本發明係關於一種永磁同步風力發電系統，詳言之，係關於一種利用智慧型最大功率追蹤器之永磁同步風力發電系統。The present invention relates to a permanent magnet synchronous wind power generation system, and more particularly to a permanent magnet synchronous wind power generation system utilizing a smart maximum power tracker.
習知風力發電系統可利用功率電子轉換器以定速或可變速度操作。由於可變速發電可以在所有風速下達到最大效率，以增進輸出能量及減少電壓閃爍問題，故可變速發電常為業界使用。許多發電機之研究及實務上風力發電機為具有繞組式轉子或鼠籠式轉子之感應機。最近，永磁同步發電機之應用逐漸增加。具有高效率及高可控性之高功能及可變速發電可利用永磁同步發電機達成。習知的研究專注在三種最大風力控制方法，其為：尖端速度比控制(tip-speed ratio,TSR)、功率信號迴授控制(power signal feedback,PSF)及爬坡法控制(hill-climb searching,HCS)。Conventional wind power systems can operate at constant speed or variable speed using power electronic converters. Since variable speed power generation can achieve maximum efficiency at all wind speeds to increase output energy and reduce voltage flicker, variable speed power generation is often used in the industry. The research and practice of many generators is that wind turbines are induction machines with winding rotors or squirrel cage rotors. Recently, the application of permanent magnet synchronous generators has gradually increased. High-performance and variable-speed power generation with high efficiency and high controllability can be achieved with permanent magnet synchronous generators. The well-known research focuses on three maximum wind control methods: tip-speed ratio (TSR), power signal feedback (PSF), and hill-climb searching. , HCS).
參考圖1，其顯示尖端速度比與功率係數之關係示意圖。尖端速度比控制在於控制風力機轉子速度以保持一最佳尖端速度比。功率信號迴授需要知道風力機的最大功率曲線及經由其控制機制追蹤其曲線。在習知的風力發電最大功率點追蹤策略中，尖端速度比控制方法因難以取得風速及風力機速度，故其使用受到限制。許多習知的風力發電最大功率點追蹤策略係藉由使用風力機最大功率曲線以減少測量，但仍須知道風力機的特性。習知爬坡法控制係連續地搜尋風力機的尖端輸出功率。比較上，因簡單性及系統特性的獨立性，爬坡法控制之風力發電最大功率點追蹤方法較受歡迎。Referring to Figure 1, there is shown a schematic diagram of the relationship between tip speed ratio and power factor. The tip speed ratio control is to control the wind turbine rotor speed to maintain an optimum tip speed ratio. Power signal feedback requires knowledge of the wind turbine's maximum power curve and tracking its curve via its control mechanism. In the conventional wind power maximum power point tracking strategy, the tip speed ratio control method is limited in its use because it is difficult to obtain wind speed and wind turbine speed. Many conventional wind power maximum power point tracking strategies reduce the measurement by using the wind turbine maximum power curve, but the characteristics of the wind turbine must still be known. The conventional hill climbing control system continuously searches for the tip output power of the wind turbine. In comparison, due to the simplicity and independence of system characteristics, the maximum power point tracking method for wind power generation controlled by the hill climbing method is popular.
因此，有必要提供一種創新且具進步性的利用智慧型最大功率追蹤器之永磁同步風力發電系統，以解決上述問題。Therefore, it is necessary to provide an innovative and progressive permanent magnet synchronous wind power generation system utilizing a smart maximum power tracker to solve the above problems.
本發明提供一種利用智慧型最大功率追蹤器之永磁同步風力發電系統，包括：一風力機、一永磁同步發電機、一轉換器(Converter)、一反流器(Inverter)及一智慧型最大功率追蹤器。該永磁同步發電機用以接收該風力機之機械能，並輸出三相交流電能。該轉換器用以將該三相交流電能轉換為直流電。該反流器用以將該直流電轉換為交流電。該智慧型最大功率追蹤器包括一爬坡控制電路、一Wilcoxon徑向基底類神經網路及一電流控制器。該爬坡控制電路用以依據該直流電之一實際直流電壓及一實際直流電流，於一最大功率曲線對應計算一設定直流電壓。該Wilcoxon徑向基底類神經網路用以依據該實際直流電壓及該設定直流電壓，計算一命令電流。該電流控制器用以依據該交流電之一實際交流電流及該命令電流，輸出一控制值至該反流器。The invention provides a permanent magnet synchronous wind power generation system using a smart maximum power tracker, comprising: a wind turbine, a permanent magnet synchronous generator, a converter, an inverter and a smart type. Maximum power tracker. The permanent magnet synchronous generator is configured to receive mechanical energy of the wind turbine and output three-phase alternating current energy. The converter is used to convert the three-phase alternating current electrical energy into direct current. The inverter is used to convert the direct current into alternating current. The intelligent maximum power tracker includes a hill climbing control circuit, a Wilcoxon radial base-like neural network, and a current controller. The climbing control circuit is configured to calculate a set DC voltage corresponding to a maximum power curve according to an actual DC voltage of the DC power and an actual DC current. The Wilcoxon radial base-like neural network is configured to calculate a command current based on the actual DC voltage and the set DC voltage. The current controller is configured to output a control value to the inverter according to an actual alternating current of the alternating current and the command current.
本發明利用該爬坡控制電路及該Wilcoxon徑向基底類神經網路，可達到良好控制效果，並且本發明之系統不需升壓型(Boost)轉換器及偵測發電機之轉速，可降低系統成本。本發明之永磁同步風力發電系統可實現變速運轉，及控制風力機保持在最佳尖端速度比及最大功率係數附近運行，以使風能獲得較高能量轉換效率，明顯提高發電量。The invention utilizes the climbing control circuit and the Wilcoxon radial base type neural network to achieve good control effect, and the system of the invention does not need a boost converter and detects the speed of the generator, which can be reduced System cost. The permanent magnet synchronous wind power generation system of the invention can realize the shifting operation, and control the wind turbine to maintain the operation near the optimal tip speed ratio and the maximum power coefficient, so that the wind energy can obtain higher energy conversion efficiency and significantly increase the power generation amount.
參考圖2，其顯示本發明利用智慧型最大功率追蹤器之永磁同步風力發電系統之電路方塊示意圖。本發明利用智慧型最大功率追蹤器之永磁同步風力發電系統20包括：一風力機21、一永磁同步發電機22(PMSG)、一轉換器23(Converter)、一反流器25(Inverter)及一智慧型最大功率追蹤器26。該永磁同步發電機22用以接收該風力機21之機械能，並輸出三相交流電能。Referring to Figure 2, there is shown a block diagram of a circuit of a permanent magnet synchronous wind power generation system utilizing a smart maximum power tracker of the present invention. The permanent magnet synchronous wind power generation system 20 utilizing the intelligent maximum power tracker of the present invention comprises: a wind turbine 21, a permanent magnet synchronous generator 22 (PMSG), a converter 23 (Converter), and a inverter 25 (Inverter). And a smart maximum power tracker 26. The permanent magnet synchronous generator 22 is configured to receive the mechanical energy of the wind turbine 21 and output three-phase alternating current energy.
該轉換器23用以將該三相交流電能轉換為直流電。在本實施例中，該轉換器23包括複數個二極體(例如：六個二極體)，組成為一個三相全波整流電路。該反流器25用以將該直流電轉換為交流電。在本實施例中，該反流器25包括複數個反流單元(例如：四個反流單元)，每一反流單元具有一電晶體及一個二極體。The converter 23 is used to convert the three-phase alternating current electrical energy into direct current. In this embodiment, the converter 23 includes a plurality of diodes (for example, six diodes) and is composed of a three-phase full-wave rectifier circuit. The inverter 25 is used to convert the direct current into alternating current. In the present embodiment, the inverter 25 includes a plurality of reverse flow units (for example, four reverse flow units), each of the reverse flow units having a transistor and a diode.
該智慧型最大功率追蹤器26包括一爬坡控制電路261、一Wilcoxon徑向基底類神經網路262及一電流控制器263。該爬坡控制電路261用以依據該直流電之一實際直流電壓Vdc 及一實際直流電流Idc ，於一最大功率曲線對應計算一設定直流電壓。該Wilcoxon徑向基底類神經網路262用以依據該實際直流電壓Vdc 及該設定直流電壓，計算一命令電流Id 。該電流控制器263用以依據該交流電之一實際交流電流I及該命令電流Id ，輸出一控制值至該反流器25。The intelligent maximum power tracker 26 includes a hill climbing control circuit 261, a Wilcoxon radial base-like neural network 262, and a current controller 263. The climbing control circuit 261 is configured to calculate a set DC voltage corresponding to a maximum power curve according to an actual DC voltage V dc and an actual DC current I dc of the DC power. . The Wilcoxon radial base-like neural network 262 is configured to use the actual DC voltage V dc and the set DC voltage , calculate a command current I d . The current controller 263 is configured to output a control value to the inverter 25 according to the actual alternating current I and the command current I d of the alternating current.
在本實施例中，該爬坡控制電路261依據該實際直流電壓Vdc 及該實際直流電流Idc 計算得一直流功率Pdc ，該直流功率Pdc 近似於該最大功率曲線之一機械功率Pm 。參考圖3，其顯示複數個最大功率曲線及其對應之最佳操作點之示意圖，其中風速u1<u2<u3<u4。依據該最大功率曲線之該機械功率Pm 與該設定直流電壓之關係，對應計算該設定直流電壓。為得到最大功率，最佳的該設定直流電壓必須利用爬坡法即時搜尋。利用該爬坡控制電路261，若該設定直流電壓是隨著該機械功率Pm 之增加而增加，則該設定直流電壓之搜尋方向與該機械功率Pm 之增加方向相同；反之，則搜尋方向為相反，例如：若風速之改變為u 3→u 4→u 2，則該設定直流電壓之搜尋為A →B →C →D →E 。且該機械功率Pm 之增加量近似於該直流功率Pdc 之增加量，故可在該直流功率Pdc 近似等於該機械功率Pm 及風力機慣量可降至最低之動態平衡操作點情形下，執行該設定直流電壓之搜尋。在動態情形下，該設定直流電壓可保持且該Wilcoxon徑向基底類神經網路262可即時調整負載電流，使得系統盡快達到其平衡點。In the present embodiment, the ramp control circuit 261 according to the actual DC voltage V dc and the actual DC current I dc calculated DC power P dc, the DC power P dc power curve similar to the one of the maximum mechanical power P m . Referring to Figure 3, there is shown a schematic diagram of a plurality of maximum power curves and their corresponding optimal operating points, wherein the wind speed u1 < u2 < u3 < u4. The mechanical power P m according to the maximum power curve and the set DC voltage Relationship, corresponding to calculating the set DC voltage . The best setting of the DC voltage for maximum power You must use the hill climbing method to search instantly. Using the climbing control circuit 261, if the DC voltage is set The DC voltage is increased as the mechanical power P m increases. The search direction is the same as the increase direction of the mechanical power P m ; otherwise, the search direction is opposite, for example, if the wind speed changes to u 3 → u 4 → u 2, the set DC voltage The search is A → B → C → D → E. And the increase of the mechanical power P m is similar to the increase of the DC power P dc , so that the DC power P dc can be approximately equal to the mechanical power P m and the wind turbine inertia can be minimized. , execute the set DC voltage Search. In the dynamic case, the set DC voltage The Wilcoxon radial substrate-based neural network 262 can be maintained and the load current can be adjusted immediately so that the system reaches its equilibrium point as quickly as possible.
參考圖4，其顯示本發明之該Wilcoxon徑向基底類神經網路之階層示意圖。該Wilcoxon徑向基底類神經網路262包括一輸入層、一隱藏層及一輸出層。其中該輸入層計算該實際直流電壓及該設定直流電壓之一誤差函數，在該輸入層之輸入為及。其中=V dc -=e 及。在該輸入層之節點(nodes)用以直接傳送輸入至下一層。亦即，對於該輸入層之第i個節點，其輸入及輸出可如式(1)表示。Referring to Figure 4, there is shown a hierarchical diagram of the Wilcoxon radial substrate-like neural network of the present invention. The Wilcoxon radial substrate-like neural network 262 includes an input layer, a hidden layer, and an output layer. The input layer calculates an error function of the actual DC voltage and the set DC voltage, and the input at the input layer is and . among them = V dc - = e and . The nodes at the input layer are used to directly transfer input to the next layer. That is, for the i-th node of the input layer, its input and output can be expressed as equation (1).
該隱藏層依據該誤差函數進行一高斯函數運算，以計算得一高斯函數運算結果。在本實施例中，在該隱藏層之每一節點進行一高斯函數運算(Gaussian basis function)，該高斯運算(幅狀基底函數(radial basis function)之一特殊例)於此處用以做為一隸屬函數(membership function)，如下式(2)所示。The hidden layer performs a Gaussian function operation according to the error function to calculate a Gaussian function operation result. In this embodiment, a Gaussian basis function is performed at each node of the hidden layer, and the Gaussian operation (a special example of a radial basis function) is used here as A membership function is shown in the following equation (2).
其中，c j =[c 1 j c 2 j …c ij ] T 及v ij 分別表示為該高斯函數之平均值及標準偏差值。Where c j =[ c 1 j c 2 j ... c ij ] T and v ij are respectively expressed as the mean value and standard deviation value of the Gaussian function.
該輸出層對該高斯函數運算結果進行一權重值運算，以計算得該命令電流Id 。在該輸出層之單一節點k表示為計算所有輸出為所有輸入訊號之總和，如下式(3)所示。The output layer performs a weight value operation on the Gaussian function operation result to calculate the command current I d . A single node k at the output layer is represented as calculating the sum of all outputs for all input signals, as shown in equation (3) below.
其中，w jk 為隱藏層及輸出層間之權重值。Where w jk is the weight value between the hidden layer and the output layer.
該Wilcoxon徑向基底類神經網路262另包括一訓練及學習裝置264，用以調整該誤差函數，更新該輸出層之複數個權重值，及更新該隱藏層之該高斯函數之複數個平均值及標準偏差值。首先誤差函數可被最小化，如下式(4)所示。The Wilcoxon radial base-like neural network 262 further includes a training and learning device 264 for adjusting the error function, updating a plurality of weight values of the output layer, and updating a plurality of average values of the Gaussian function of the hidden layer. And standard deviation values. First, the error function can be minimized as shown in the following equation (4).
在輸出層中，誤差項被展開為如下式(5)所示。In the output layer, the error term is expanded as shown in the following equation (5).
權重值調整為如下式(6)所示。The weight value is adjusted as shown in the following equation (6).
因此，如下式(7)所示。Therefore, it is shown by the following formula (7).
w jk (N +1 )=w jk (N )+η w Δw jk (N ) (7) w jk ( N + 1 )= w jk ( N )+η w Δ w jk ( N ) (7)
其中，η w 為用以調整權重值w jk 之學習比。Where η w is a learning ratio for adjusting the weight value w jk .
在該隱藏層中再進行乘法運算，對於平均值c ij 之法則為如下式(8)所示。The multiplication operation is performed again in the hidden layer, and the law for the average value c ij is as shown in the following formula (8).
對於標準偏差值v ij 之法則為如下式(9)所示。The law for the standard deviation value v ij is as shown in the following formula (9).
因此，如下式(10)所示。Therefore, it is shown by the following formula (10).
c ij (k +1)=c ij (k )+η m Δc ij c ij ( k +1)= c ij ( k )+η m Δ c ij
v ij (k +1)=v ij (k )+ησ Δv ij (10) v ij ( k +1)= v ij ( k )+η σ Δ v ij (10)
其中，η m 及ησ 分別為用以調整平均值c ij 及標準偏差值v ij 之學習比。Where η m and η σ are learning ratios for adjusting the average value c ij and the standard deviation value v ij , respectively.
利用該訓練及學習裝置264可使該Wilcoxon徑向基底類神經網路262之基底逐漸降低，以降低計算複雜度。The training and learning device 264 can be used to gradually reduce the base of the Wilcoxon radial basal neural network 262 to reduce computational complexity.
再參考圖2，本發明之該永磁同步風力發電系統20另包括一直流鏈結電路24(DC link)，其包括一直流電容器241及一個二極體242。本發明之該永磁同步風力發電系統20另包括一負載電路27，連接至該反流器25，該負載電路27包括一負載電感器271及一負載電容器272。Referring again to FIG. 2, the permanent magnet synchronous wind power generation system 20 of the present invention further includes a DC link circuit including a DC capacitor 241 and a diode 242. The permanent magnet synchronous wind power generation system 20 of the present invention further includes a load circuit 27 connected to the inverter 25, the load circuit 27 including a load inductor 271 and a load capacitor 272.
在本實施例中，該電流控制器263係為一比較器，用以比較該交流電之該實際交流電流I及該命令電流Id ，且該控制值係為一脈波寬度調變訊號(PWM)。In this embodiment, the current controller 263 is a comparator for comparing the actual alternating current I and the command current I d of the alternating current, and the control value is a pulse width modulation signal (PWM). ).
本發明利用該爬坡控制電路及該Wilcoxon徑向基底類神經網路，可達到良好控制效果，並且本發明之系統不需升壓型(Boost)轉換器及偵測發電機之轉速，可降低系統成本。本發明之永磁同步風力發電系統可實現變速運轉，及控制風力機保持在最佳尖端速度比及最大功率係數附近運行，以使風能獲得較高能量轉換效率，明顯提高發電量。The invention utilizes the climbing control circuit and the Wilcoxon radial base type neural network to achieve good control effect, and the system of the invention does not need a boost converter and detects the speed of the generator, which can be reduced System cost. The permanent magnet synchronous wind power generation system of the invention can realize the shifting operation, and control the wind turbine to maintain the operation near the optimal tip speed ratio and the maximum power coefficient, so that the wind energy can obtain higher energy conversion efficiency and significantly increase the power generation amount.
上述實施例僅為說明本發明之原理及其功效，並非限制本發明。因此習於此技術之人士對上述實施例進行修改及變化仍不脫本發明之精神。本發明之權利範圍應如後述之申請專利範圍所列。The above embodiments are merely illustrative of the principles and effects of the invention and are not intended to limit the invention. Therefore, those skilled in the art can make modifications and changes to the above embodiments without departing from the spirit of the invention. The scope of the invention should be as set forth in the appended claims.
20．．．本發明之永磁同步風力發電系統20. . . Permanent magnet synchronous wind power generation system of the invention
21．．．風力機twenty one. . . Wind turbine
22．．．永磁同步發電機twenty two. . . Permanent magnet synchronous generator
23．．．轉換器twenty three. . . converter
24．．．直流鏈結電路twenty four. . . DC link circuit
25．．．反流器25. . . Reflux
26．．．智慧型最大功率追蹤器26. . . Smart maximum power tracker
27．．．負載電路27. . . Load circuit
241．．．直流電容器241. . . DC capacitor
242．．．二極體242. . . Dipole
261．．．爬坡控制電路261. . . Climbing control circuit
262．．．Wilcoxon徑向基底類神經網路262. . . Wilcoxon radial basement neural network
263．．．電流控制器263. . . Current controller
264．．．訓練及學習裝置264. . . Training and learning device
271．．．負載電感器271. . . Load inductor
272．．．負載電容器272. . . Load capacitor
圖1顯示尖端速度比與功率係數之關係示意圖。；Figure 1 shows a schematic diagram of the relationship between the tip speed ratio and the power factor. ;
圖2顯示本發明利用智慧型最大功率追蹤器之永磁同步風力發電系統之電路方塊示意圖；2 is a block diagram showing the circuit of a permanent magnet synchronous wind power generation system using a smart maximum power tracker according to the present invention;
圖3顯示複數個最大功率曲線及其對應之最佳操作點之示意圖；及Figure 3 shows a schematic diagram of a plurality of maximum power curves and their corresponding optimal operating points; and
圖4顯示本發明之該Wilcoxon徑向基底類神經網路之階層示意圖。Figure 4 is a schematic diagram showing the hierarchy of the Wilcoxon radial substrate-like neural network of the present invention.
20．．．本發明之永磁同步風力發電系統20. . . Permanent magnet synchronous wind power generation system of the invention
21．．．風力機twenty one. . . Wind turbine
22．．．永磁同步發電機twenty two. . . Permanent magnet synchronous generator
23．．．轉換器twenty three. . . converter
24．．．直流鏈結電路twenty four. . . DC link circuit
25．．．反流器25. . . Reflux
26．．．智慧型最大功率追蹤器26. . . Smart maximum power tracker
27．．．負載電路27. . . Load circuit
241．．．直流電容器241. . . DC capacitor
242．．．二極體242. . . Dipole
261．．．爬坡控制電路261. . . Climbing control circuit
262．．．Wilcoxon徑向基底類神經網路262. . . Wilcoxon radial basement neural network
263．．．電流控制器263. . . Current controller
271．．．負載電感器271. . . Load inductor
272．．．負載電容器272. . . Load capacitor
Claims (10)
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| TW99133462A TWI418119B (en) | 2010-10-01 | 2010-10-01 | Wind generation system with pmsg using intelligent maximum power tracking controller |
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| TW99133462A TWI418119B (en) | 2010-10-01 | 2010-10-01 | Wind generation system with pmsg using intelligent maximum power tracking controller |
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| TW201216594A TW201216594A (en) | 2012-04-16 |
| TWI418119B true TWI418119B (en) | 2013-12-01 |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200801328A (en) * | 2006-06-02 | 2008-01-01 | Univ Yuan Ze | Grid-connected wind generation system and its maximum-power-extraction control method |
| TW200843293A (en) * | 2007-03-23 | 2008-11-01 | Shinetsu Chemical Co | Permanent magnet generator and wind power generator using the same |
| TWI312030B (en) * | 2006-12-13 | 2009-07-11 | Nat Kaohsiung University Of Applied Science | A maximum power point tracking method and device for a wind power generator |
| TW200931783A (en) * | 2008-01-04 | 2009-07-16 | Univ Nat Kaohsiung Marine | An AC/DC power conversion equipment for wind power generation system is proposed |
| TW201031822A (en) * | 2009-02-20 | 2010-09-01 | Hitachi Engineering & Services Co Ltd | Incorporated electric power storage system type wind power generation system |
-
2010
- 2010-10-01 TW TW99133462A patent/TWI418119B/en not_active IP Right Cessation
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200801328A (en) * | 2006-06-02 | 2008-01-01 | Univ Yuan Ze | Grid-connected wind generation system and its maximum-power-extraction control method |
| TWI312030B (en) * | 2006-12-13 | 2009-07-11 | Nat Kaohsiung University Of Applied Science | A maximum power point tracking method and device for a wind power generator |
| TW200843293A (en) * | 2007-03-23 | 2008-11-01 | Shinetsu Chemical Co | Permanent magnet generator and wind power generator using the same |
| TW200931783A (en) * | 2008-01-04 | 2009-07-16 | Univ Nat Kaohsiung Marine | An AC/DC power conversion equipment for wind power generation system is proposed |
| TW201031822A (en) * | 2009-02-20 | 2010-09-01 | Hitachi Engineering & Services Co Ltd | Incorporated electric power storage system type wind power generation system |
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| TW201216594A (en) | 2012-04-16 |
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