毕业外文翻译、中英文翻译、外文文献翻译#太阳能汽车通信工具的人工神经网络最大能量点的跟踪仪

附录 A 太阳能汽车通信工具的人工神经网络 最大能量点的跟踪仪 摘要 : 本文提出的是一台太阳能汽车的人工神经网络最大力量的跟踪仪器 (MPPT)。 MPPT 是根据一台高效率的升压变频器和绝缘阀双极晶体管 (IGBT) 电源开关为基础的。 MPPT的参考电压是以梯度下降动量算法通过人工神经网络获得 (ANN)。跟踪的算法是通过更改变频器的任务周期，以便使 PV 模块电压等于对应的 MPPT 在所有指定的日射能量、温度 , 和负荷状态下的电压。为快速的回应 , 系统被实施使用数字信号处理器 (DSP) 。整个系统的稳定性是通过一 种固有的积分导数调节控制器按比例经过包括改进按比例进行改善的，并且这种调节器也被使用在观察参考电压和控制电压的高低。调节控制器 , 以 ANN 提供的信息为依据 , 形成升压变频器的工作周期。被获得的大量能量被使用在太阳能汽车的充电锂离子电池上。实验和模拟结果表示 , 提出的计划是高效率的。 关键词 : 人工神经网络 ； 最大能量点跟踪仪 (MPPT)； 光致电压模块 ； 数字信号处理器 ； 太阳能汽车通信工具。 简介 光致电压的 (PV) 生成获取增加的重要性作为可延续能源。太阳能的不受欢迎得到了迅速的更改 , 通常发生在一个 连续通信工具上 , 由于树荫从、大厦、大树 , 和多云等情况的出现，在常规 PV 系统有困难的回应迅速差异是由于树荫产生的。 PV 系统主要缺点是 , 最初安装费用是相当地高并且能量转换效率 (从 12% 到 29%) 是相对比较低的。此外 , 在许多情况下 , PV 系统要求一台动力调节器(直流电 直流电 或 直流电 交流电 的交换器 ) 为负荷界面。所以 , 整个系统费用能被猛烈地减少，通过使用高效率的动力调节器 , 譬如最大能量点跟踪仪(MPPT), 提取和维护峰值功率从 PV 模块既使当上述不赞同的条件发生。 各种各样的最大能 量跟踪仪的方法，已经被考虑了在 PV 力量应用中1-12 。在小山之中攀登的方式 1-5 ,扰乱和观察跟踪最大能量点 (MPP) 由增加或一再减少输出电压在 PV 模块的 MPP 上。这个方法要求 P 与 V 的计算来确定 MPP1,2,4 。但当辐照度迅速的更改时它无法跟踪到 MPP值，并且 , 方法是在 MPP 附近摆动而代替直接地跟踪它。递增导率技术 (ICT) 其它方法之中是最精确的。这个方法在迅速更改的情况下可得到很好的性能。但是 , dI/dV 的复杂计算和复杂的算法要求对每一个数字信号进行处理 (DSP), 通常将增加总系统费用。 MPP 跟踪的方法使用 PV 模块的短路在当前运用情况运行下当前 PV 模块的 MPP短路电流是成线性比例的 。在迅速地更改大气情况之下 , 这个方法有跟踪 MPP 的快速的回应速度 , 但控制电路是复杂的。使用太阳能仪表板的开放电路电压的 MPP 的跟踪方法利用工作电压在 MPP 与开放电路电压几乎线性的比例在 PV 模块的 MPP中 (使用 76% 开放电路电压作为 MPP 电压 ) 。这个方法是非常简单和有效的 , 但参考电压不能更改在采样之间。 MPPTs 使用模糊的逻辑性 13,14 和人工神经网 络 (ANN) 15have 被报导出来。这些研究表示 , 像这样的现代控制算法是能改进跟踪的性能与比较常规方法。 在本文里 , 我们提出 ANN MPPT 为太阳电通信工具 PV 系统。跟踪的算法更改交换器的义务比率以便使 PV 模块电压等于电压对应于 MPP 在那个大气情况。这个调整由使用执行传播 ANN 。参考电压对 MPP 由脱机被培训的 ANN 获得。管理器生成升压交换器的工作周期。 1 .PV 模块特性 太阳列阵特性极大影响了交换器和控制系统的设计 , 因此 PV 特性简要将被复核得这里。太阳列阵是一个非线性设备 , 可能代表作为一个当前来源设计 , 依照被显示在图 1 。太阳列阵的传统 I-V 特性 , 当忽略内部分流器抵抗 , 由以下等式给 : Io 和 Vo 是太阳列阵的输出电流和输出电压 ； Ig 是被生成的当前在指定的日射能量之下 ； Isat 是反向饱和电流 ； q 是电子的电量 ； k 是伯磁曼常数 ； A 是P-N 连接点的理想系数 ； T 是列阵温度 ； Rs 和 Rsh 是太阳列阵的内在电阻和分流器抵抗电阻。 太阳列阵的饱和电流随温度变化根据以下等式 : Tr 是参考温度 ； 被使用在太阳列阵上的 Ior 是在 Tr 下的饱和 电流 ； Ego是半导体的范围空白能量 ； B 并且是理想系数 ； Isc 是在 C25 下的短路 ； iK 是短路电流的温度系数 是 1 21cm 内的日射能量。 等式 (1)-(3) 被使用在计算机模拟的发展中。 Matlab 编程语言被使用。图 2 显示被模拟的电流 电压和力量电压弯曲为太阳列阵在不同的日射能量和不同的温度。这些曲线表示 , 太阳列阵的输出特性是非线 性和受太阳辐射、温度 , 和负荷状态很大地影响。各曲线有最大力量点 (Pmax), 是优选的运行的点有利于 太阳列阵的高效率的使用。 2. 人工神经网络 图 2 Current-voltage 和力量电压弯曲为太阳列阵在不同的日射能量和不同的温度 (S 是太阳辐射 ) ANN 技术成功被申请了解决非常复杂问题。最近 , 其申请以各种各样的域增加速度 16,17 。误差的平方或误差能量的瞬间总和在迭代 n 次被测量 那里神经元 j 位于同一层的神经元 i 右边 , 和神经元 k位于同一层的神经元右边j 当神 经元 j 是一个被隐藏的部件 ； ej (n) 是误差信号在神经元 j 输出为迭代 n； 并且集 C 包括所有神经元在网络的外面层。jiw(n) 对突触神经的重量jiw ( 被给出： 是动量常数 ； 是反向增值算法的学习率参变量 ； j (n) 是局部梯 差信号在输出被定义 jd (n) 是渴望的回应或想要的目标和 yj (n) 是输出信号。重量的调整为这些层被给 从 Eqs 。 (4) 和 (6), 平均值的平方误差信息标准值可写出，为： 网络培训一再执行直到性能标准 2Aref 下跌在一个指定值之下 , 理想的到零。然后被连接的网络的重量以这样的方式被调整，即下列阵电压 VA 与最大力量点电压 Vmp 完全相等。参考电压 Vref 在这个状况下变得相等与最大力量点电压 Vmp 。 被提出的三层哺养转接神经网络功能的近似值被显示在 图 3 。神经网络被使用获得最大力量 Vmp (n) 的电压的太阳盘区。网络有三块层 : 输入 , 隐藏 , 和输出层。节点的数量是二 , 四 , 和一个在输入 , 被隐藏的 , 和输出层 , 各自地。参考电池开放电路电压 Voc (n) 和时间参数 T (n) 被提供给神经网络的输入层。这些信号直接地通过对节点在下块被隐藏的层。节点在输出层提供辨认的最大力量点电压 Vmp (n) 。节点在被隐藏的层得到信号从输入分层堆积和寄发他们的输出到节点在输出层。标准差的放射性功能被运用在网络的层。训练计划计算连接的重量 WI1,1 以偏心 b1 为输入到被隐藏的层映射 , 连接的重量 2,1偏心倾斜 b2 为被隐藏的层被输出层映射。在培训期间 , 连接的重量递归地被修改直到最佳的适应值，直达到输入 - 输出模式在培训中的数据。最后的培训是成功脱机使用 Matlab 。 图 3 哺养转接神经网络功能近似值 3. 实验模拟结果 PV 列阵被使用为实验数据的收集是 JDG-M-45 (德国 )型模块。模块有最大功率输出 45 W 和 20-V 开放电路电压在 1000瓦 /平方米。的辐照区域年和 25.C 温度条件下。 PV 模块说明提供了由制造商在表 1 。 （上面的表） PV 模块 (JDG-M-45) 最大力量的表 1 电子说明 , Pm (w) 45短路当前 , Isc (a) 2.93 开放电路电压 , Voc (v) 20 电压在最大力量点 , Vmp (v) 17.1 当前在最大力量点 , 淘气鬼 (a) 2.64 范围 (mm m m) 971 41 8 质量 (公斤 ) 5 哺养转接传播 ANN, 依照被显示在图 3 被培训了以值被获得从参考电池的实验数据而获得的值。梯度下降算法被使用在培训如同改进 ANN 的性能 , 减少总误差可由沿其梯度更改重量。培训参数是如 下 : 学习的费率参数 = 0.1； 动量系数 = 0.9； 培训 iterations=10 000 的编号 ； 误差 goal=0.000 001 。汇合误差为培训进程被显示在图 4 。 图 4 汇合误差为神经网络培训进程 ANN 培训的使用系数和输出值的培训由 ANN 给在培训之后可能被查找在表 2 之后。各种各样套参考电池开放电路电压 Voc 和时间参数 T (依照被显示在表 2) 被提供作为输入给 ANN 。为了验证 ANN 的学习能力 , 其它 Voc 与那个不同在表 2 并且被提供给 ANN, 依照被预计给在 Vmp 之外的值。软件 Matlab 被使用了在 ANN 的培训中。 从输入 1到隐藏层的重量如下 : 重量对输出层 2 是 WI2,1 = ?0.2039 0.0065 0.0561?0.3029 。偏心对层 1 是 b1 = -60.0887 12.996 -54.4753 -1.5969 。偏心到层 2 是 b2=-0.0397 。 4. 结论 人工神经网络 MPPT 为充电太阳 (合成 ) 通信工具的电池已经 被提议在本文里。脱机 ANN,以梯度下降动量算法被培训使用传播 , 被运用为参考电压的联机估计为哺养转接循环。实验数据被使用为 ANN 的脱机培训 , 并且软件 Matlab 被使用在最后的培训中。估计的精确度由汇合误差的图形验证了。提出的方法有几个好处在常规方法中 , 特别由于没有对电压和当前传感器的需要 , 和因为它避免力量的一个复杂计算。实验和模拟结果表示 , 提出的计划是高效率的。 参考文献： 1 Hua C, Lin J, Shen C. Implementation of a DSP-controlled photovoltaic system with peak power tracking. IEEE Trans. Ind. Electron., 1998, 45(1): 99-107. 2 Koutroulis E, Kalaitzakis K, Voulgaris N C. Development of a microcontroller-based photovoltaic maximum power point tracking control system. IEEE Trans. Power Elec-tron., 2001, 16(1): 46-54. 3 Kuo Y C, Liang T J, Chen J F. Novel maximum power point tracking controller for photovoltaic energy conver-sion system. IEEE Trans. Ind. Electron., 2001, 48(3): 594-601. 4 Sullivan C R, Powers M J. A high-efficiency maximum power point tracker for photovoltaic arrays in a solar-powered race vehicle. In: Proc. IEEE Power Electron. Spec.Conf., Seattle, WA, USA, 1993: 574-580. 5 Simoes M G, Franceschetti N N. A risc-microcontroller based photovoltaic system for illumination applications. In: Proc. IEEE Applied Power Electron. Conf., New Orleans, LA, USA, 2000: 115-1156. 6 Tse K K, Ho M T, Chung H S, Hui S Y. A novel maximum power point tracker for PV panels using switching fre-quency modulation. IEEE Trans. Power Electron., 2002, 17(6): 980-989. 7 Brambilla A, Gambarra M, Garutti A, Ronchi F. New ap-proach to photovoltaic arrays maximum power point track-ing. In: Proc. IEEE Power Electron. Spec. Conf., Charles-ton, SC, USA, 1999: 632-637. 8 Mutoh N, Matuo T, Okada K, Sakai M. Prediction-data-based maximum power point tracking method for photo-voltaic power generation systems. In: Proc. IEEE Power Electron. Spec. Conf., Caims, Austrilia, 2002: 1489-1494. 9 Noguchi T, Togashi S, Nakamoto R. Short-current pulse-based maximum power point tracking method for multiple photovoltaic and converter module system. IEEE Trans.Ind. Electron., 2002, 49(1): 217-223. 10 Lee D Y, Noh H J, Hyun D S, Choy I. An improved MPPT converter using current compensation method for small scaled PV-applications. In: Proc. IEEE Applied Power Electron. Conf., Dalas, TX, USA, 2002: 540-545. 11 Enslin J H R, Wolf M S, Snyman D B, Sweigers W. Inte-grated photovoltaic maximum power point tracking con-verter. IEEE Trans. Ind. Electron., 1997, 44(6): 769-773. 12 Chen Y, Smedley K, Vacher F, Brouwer J. A new maxi-mum power point tracking controller for photovoltaic power generation. In: Proc. IEEE Applied Power Electron.Conf., Miami Beach, FL, USA, 2003: 56-62. 13 Nafeh A, Fahmy F H, El-Zahab E M A. Evaluation of a proper controller performance for maximum power point tracking of a stand-alone PV system. International Journal of Numerical Modelling, 2002, 15(4): 385-398. 14 Veerachary M, Senjyu T S, Uezato K. Feedforward maximum power point tracking of PV systems using fuzzy controller. IEEE Trans. Aerosp. and Electron. Syst., 2002,38(3): 969-981. 15 Hiyama T, Kouzuma S, Ortmeyer T. Evaluation of neural network based real time maximum power tracking control-ler for PV system. IEEE Trans. on Energy Conv., 1995,10(3): 543-548. 16 Bose B K. Artificial neural network applications in power electronics. In: Proc. IEEE Ind. Electron. Conf., Denver, CO, USA, 2001: 1631-1638. 17 Haykin S. Neural Networks A Comprehensive Founda-tion, 2nd Edition. New York: Prentice Hall Inc., 1999. 附录 B Artificial Neural Network Maximum Power Point Trackerfor Solar Electric Vehicle Abstract: This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transis-tor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, tempera-ture, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information sup-plied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lith-ium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient. Key words: artificial neural network； maximum power point tracker (MPPT)； photovoltaic module； digital signal processor； solar electric vehicle Introduction Photovoltaic (PV) generation is gaining increased im-portance as a renewable source of energy. The undesir-able rapid changes of solar power, which usually occur in a running vehicle, arise as a result of shade from buildings, large trees, and clouds in the sky. Conven-tional PV systems have difficulties in responding to rapid variations due to shade. The main drawbacks of PV systems are that the initial installation cost is con-siderably high and the energy conversion efficiency (from 12% to 29%) is relatively low. Furthermore, in most cases, PV systems require a power conditioner (dc-dc or dc-ac converter) for load interface. Therefore, the overall system cost could be reduced drastically by using highly efficient power conditioners, such as the maximum power point tracker (MPPT), to extract and maintain the peak power from the PV module even when the above-mentioned unfavorable conditions occur. Various methods of maximum power tracking have been considered in PV power applications1-12. Among the hill climbing methods1-5, the perturb and observe (P&O) method tracks maximum power point (MPP) by repeatedly increasing or decreasing the output voltage at MPP of the PV module. This method requires calcu-lation of dP/dV to determine the MPP1,2,4. Though the method is relatively simple to implement, it cannot track the MPP when the irradiance changes rapidly. Also, the method oscillates around the MPP instead of directly tracking it. The incremental conductance technique (ICT) is the most accurate6,7 among the other methods. This method gives good performance under rapidly changing conditions. However, the com-plex calculation of dI/dV and the complicated algo-rithm require use of a digital signal processor (DSP), which will usually increase the total system cost. The MPP tracking method using the short circuit current of the PV module utilizes the fact that the op-erating current at MPP of the PV module is linearly proportional to the short circuit current of the PV mod-ule9. Under rapidly changing atmospheric conditions,this method has a fast response speed of tracking the MPP, but the control circuit is complicated. The MPP tracking method using open circuit voltage of the solar panel11 utilizes the fact that the operating voltage at MPP is almost linearly proportional to open circuit voltage at MPP of the PV module (using 76% of open circuit voltage as the MPP voltage). This method is very simple and cost-effective, but the reference volt-age does not change between samplings. MPPTs using the fuzzy logic13,14 and the artificial neural network (ANN)15have been reported. These studies show that such modern control algorithms are capable of improv-ing the tracking performance as compared tothe conventional methods. In this paper, we propose an ANN MPPT for solar electric vehicle PV systems. The tracking algorithm changes the duty ratio of the converter so that the PV module voltage equals the voltage corresponding to the MPP at that atmospheric condition. This adjustment is carried out by using the back-propagation ANN. The reference voltage to the MPP is obtained by an offline trained ANN. The controller generates the boost converter duty-cycle. 1 PV Module Characteristics The solar array characteristics significantly influence the design of the converter and the control system, so the PV characteristics will be briefly reviewed here. The solar array is a nonlinear device and can be repre-sented as a current source model, as shown in Fig. 1. The traditional I-V characteristics of a solar array, when neglecting the internal shunt resistance, are given by the following equation: where Io and Vo are the output current and output voltage of the solar array； Ig is the generated current under a given insolation； Isat is the reverse saturation current； q is the charge of an electron； k is the Boltzmann constant； A is the ideality factor for a P- N junction； T is the array temperature； Rs and Rsh are the intrinsic series and shunt resistances of the solar array. The saturation current of the solar array varies withtemperature according to the following equation: where Tr is the reference temperature； Ior is the saturation current at Tr ； EGO is the band-gap en-ergy of the semiconductor used in the solar array； B is also an ideality factor； Isc is the short circuit current at 25C； KI is the short-circuit current temperature coefficient and is the insolation in mW/cm2. Equations (1)-(3) are used in the development of computer simulations for the solar array. The Matlab programming language is used. Figure 2 show the simulated current-voltage and power-voltage curves for the solar array at different insolations and different temperatures. These curves show that the output char-acteristics of the solar array are nonlinear and greatly affected by the solar radiation, temperature, and load condition. Each curve has a maximum power point (Pmax), which is the optimal operating point for the ef-ficient use of the solar array. 2 Artificial Neural Network ANN technology has been successfully applied to solve very complex problems. Recently, its application in various fields is increasing rapidly16,17. The instantaneous sum of error squares or error en-ergy at iteration n is given by where neuron j lies in a layer to the right of neuroni , and neuron k lies in a layer to the right of neuronj when neuron j is a hidden unit； ej (n) is the er-ror signal at the output of neuron j for iteration n ； and the set C includes all the neurons in the outer layer of the network. The correction ?wji (n) to the synaptic weight wji (n) is given by where is the momentum constant； is the learn-ing-rate parameter of the back-propagation algorithm； j(n) is the local gradient. The error signal at the out-put is defined as where dj (n) is the desired response or wanted tar-gets and yj (n) is the output signal. Adjustment of the weights for these layers is given by From Eqs. (4) and (6), the mean squared error per-formance index can be rewritten as The network training is performed repeatedly until the performance index = (Vref ?VA )2 falls below a specified value, ideally to zero. In other words 0 implies (Vref ?VA )2 0, and then the connecting weights of the network are adjusted in such a way that the array voltage VA is identically equal to the maxi-mum power point voltage Vmp . At this stage the refer-ence voltage Vref becomes equal to the maximum power point voltage Vmp . The configuration of the proposed three-layer feed-forward neural network function approximator is shown in Fig. 3. The neural network is used to obtain the voltage of the maximum power Vmp (n) of the so-lar panel. The network has three layers: an input, a hidden, and an output layer. The numbers of nodes are two, four, and one in the input, the hidden, and output layers, respectively. The reference-cell open circuit voltage Voc (n) and the time parameter T (n) are supplied to the input layer of the neural network. These signals are directly passed to the nodes in the next hid-den layer. The node in the output layer provides the identified maximum power point voltage Vmp (n) . The nodes in the hidden layer get signals from the input layer and send their output to the node in the output layer. The sigmoid activation function is utilized in the layers of the network. The training program calculates the connecting weights WI1,1 with the bias b1 for the input to hidden layer mapping, the connecting weightsWL2,1with bias b2 for the hidden layer to output layer mapping. During the training, the con-necting weights are modified recursively until the best fit is achieved for the input-output patterns in the train-ing data. The training of the net was accomplished off-line using Matlab. 3 Experimental/Simulation Results A PV array used for the collection of the experimental data is JDG-M-45 (Germany) type modules. The mod-ule has a maximum power output of 45 W and a 20-V open-circuit voltage at an irradiation of 1000 W/ m2 and a 25C temperature. The PV module specifica-tions provided by the manufacturer in Table 1. The feed-forward back-propagation ANN, as shown in Fig. 3 was trained with values obtained from ex-perimental data of the reference cell. Gradient descent algorithm was used in training as it improves the per-formance of the ANN, reducing the total error by changing the weights along its gradient. The training parameters are as follows: learning rate parameter =0.1； momentum factor =0.9； number of training iterations=10 000； error goal=0.000 001. The conver-gence error for the training process is shown in Fig. 4. The parameters used for the training of the ANN and the output values given by the ANN after the training can be found in Table 2. Various sets of reference cell open circuit voltage Voc and a time parameter T (as shown in Table 2) are supplied as the input to the ANN. In order to validate the learning capability of the ANN, other sets of Voc different from the one in Table 2 were also supplied to the ANN, which gave out values of Vmp as expected. The software Matlab was used in the training of the ANN. The weights to the hidden layer 1 from input 1 are as follows: The weights to the output layer 2 are W2,1 =-0.2039 0.0065 0.0561 ?0.3029. The bias to layer 1 is b1 = -60.0887 12.996 -54.4753 -1.5969. The bias to layer 2 is b2=-0.0397. 4 Conclusions An artificial neural network MPPT for charging the battery stack of a solar (hybrid) vehicle has been pro-posed in this paper. An off-line ANN, trained using a back-propagation with gradient descent momentum al-gorithm, is utilized for online estimation of reference voltage for the feed-forward loop. Experimental data is used for the offline training of the ANN, and software Matlab is used in the training of the net. The precision of the estimation has been verified by the graph of the convergence error. The proposed method has several advantages over the conventional methods, particularly in that there is no need for voltage and current sensors, and in that it avoids a complex calculation of power. The experimental and simulation results show that the proposed scheme is highly efficient. References 1 Hua C, Lin J, Shen C. Implementation of a DSP-controlled photovoltaic system with peak power tracking. IEEE Trans. Ind. Electron., 1998, 45(1): 99-107. 2 Koutroulis E, Kalaitzakis K, Voulgaris N C. Development of a microcontroller-based photovoltaic maximum power point tracking control system. IEEE Trans. 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