### 统计代写|贝叶斯网络代写Bayesian network代考|IMC012

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|贝叶斯网络代写Bayesian network代考|DBN Structure Modeling

A BN is generally constructed through two major procedures, namely the construction of structure models and the definition of parameter models [46]. In the first step, a set of relevant variables and their possible values should be decided. A network structure can then be set up by connecting these variables into a directed acyclic graph. In the second step, the conditional probability table for each network variable should be defined.

The DBN structure models for the PV systems with centralized, string, and multistring configurations in the presence of intermittent faults are constructed (Fig. 3) according to the PV system configurations given in Fig. 1. Figure 3a demonstrates that the failure of any PV component in a PV system with centralized configuration will cause the failure of the entire PV system. This case signifies that the PV components, including four PV modules #1, #2, #3, and #4 (i.e., PV1, PV2, PV3, and PV4), two DC combiners (Comb1 and Comb2), a DC/DC converter (Conv), and a DC/AC inverter (Inve), are considered a series. Therefore, the network structure is built with two layers using the Netica software tool. The first layer consists of eight nodes representing the status of eight PV components. Each node has three states, i.e., the fault not existing state (NF), intermittent faulty state (IF), and permanent faulty state (PF). The second layer includes one node that depicts the status of PV system. This node has two states, i.e., work and fail, which indicate whether the whole PV system is working or not.

DBNs are an extension of the general BNs that allow the explicit modeling of changes over time. In this process, each time step is called a time slice. Figure 3a indicates that the DBNs of the PV system with centralized configuration consist of two time slices, that is, from $t=0$ to $t=\Delta t$. The nodes PV1, PV2, PV3, PV4, Comb1, Comb2, Conv, and Inve at $t=0$ are extended to PV5, PV6, PV 7, PV8, Comb3, Comb4, Conv1, and Inve1 at $t=\Delta t$, respectively. The number of time slice and the value of $\Delta t$ are determined by the purpose of research and the time the Netica runs. A great number of time slices correspond to a smaller value of $\Delta t$, and, hence, a longer time at which Netica runs. The DBN structure models for the PV systems with string and multistring configurations are similar to that for the PV system with centralized configuration and are produced based on the series and parallel relationship of the PV components, as shown in Fig. 3b, c. The DBN structure model of the complex PV system is given in Fig. 4. The series and parallel relationship among the PV components establishes the conditional probability tables of nodes, which are described in the subsequent section.

## 统计代写|贝叶斯网络代写Bayesian network代考|Intermittent Fault Modeling

Intermittent faults can hardly be modeled using a directed DBN structural modeling directed. Therefore, this study proposes a method that fuses the Markov model into a DBN model. The developed method has four basic assumptions specified as follows [47-50]:
(1) The PV systems begin with a perfect operation, in which all PV components are functioning correctly.
(2) The transition rates of the PV components, including the failure and repair rates are different, but constant. The lifetimes of these components are exponentially distributed because they are mainly electronic products.
(3) The states of all components are statistically independent.
(4) The PV systems are considered “as good as new” after repairs.
The idea of intermittent and permanent faults can be incorporated in terms of the three-state Markov models as shown in Fig. $5[25,26]$. The model stipulates that the NF state can be converted into a PF and IF states with a failure rate $\lambda_{1}$ and $\lambda_{2}$, respectively. An intermittent fault can lead the components into PF and NF states. Therefore, the IF state can become a PF state with a failure rate of $\lambda_{3}$ and to an NF state with a repair rate of $\mu_{1}$ (autorecovery), as shown in Fig. 5a. If a failed component is repaired once permanent fault occurs, then a repair arc should be added to the state transition diagram. In this case, the PF state can become an NF state with a repair rate of $\mu_{2}$ (manual repair), as shown in Fig. $5 \mathrm{~b}$. When the repair action is not considered, the reliability of the PV system can be calculated. When the repair action is considered, the availability of the PV system can be calculated using the proposed DBN model.

## 统计代写|贝叶斯网络代写Bayesian network代考|DBN Structure Modeling

BN通常通过两个主要程序构建，即结构模型的构建和参数模型的定义[46]。第一步，应确定一组相关变量及其可能值。然后可以通过将这些变量连接到有向无环图中来建立网络结构。第二步，定义每个网络变量的条件概率表。

DBN 是通用 BN 的扩展，允许对随时间的变化进行显式建模。在这个过程中，每个时间步称为一个时间片。图 3a 表明集中配置光伏系统的 DBN 由两个时间片组成，即从吨=0至吨=D吨. 节点 PV1、PV2、PV3、PV4、Comb1、Comb2、Conv 和 Inve 在吨=0扩展到 PV5、PV6、PV 7、PV8、Comb3、Comb4、Conv1 和 Inve1吨=D吨， 分别。时间片的数量和值D吨由研究目的和 Netica 运行时间决定。大量的时间片对应于较小的值D吨，因此，Netica 的运行时间更长。组串和多串配置光伏系统的DBN结构模型与集中配置光伏系统相似，是根据光伏组件的串联和并联关系生成的，如图3b、c所示。复杂光伏系统的DBN结构模型如图4所示。光伏组件之间的串联和并联关系建立了节点的条件概率表，将在下一节中描述。

## 统计代写|贝叶斯网络代写Bayesian network代考|Intermittent Fault Modeling

1）光伏系统从完美运行开始，其中所有光伏组件都正常运行。
(2) 光伏组件的转换率，包括故障率和修复率是不同的，但是是恒定的。这些组件的寿命呈指数分布，因为它们主要是电子产品。
(3) 所有组件的状态在统计上是独立的。
(4) 光伏系统在维修后被认为“和新的一样好”。

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## MATLAB代写

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