贝叶斯网络及其在发电系统可靠性评估中的应用
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摘要
发电系统可靠性评估是电力系统可靠性评估中最重要环节之一，对其进行可靠性研究具有非常重要的理论意义和巨大的社会经济价值。在常规可靠性计算中，仅计算系统各项可靠性指标，很少涉及可靠性最薄弱环节的识别，而识别电力系统可靠性最薄弱环节，具有非常重要的意义。
文中简要介绍了贝叶斯网络的基本概念，深入研究了常用的推理算法，并在此基础上编写了基于桶消元的贝叶斯网络推理程序。在分析常规电力系统可靠性评估方法的基础上，提出了基于贝叶斯网络的发电系统可靠性评估方法。该方法首先建立发电系统可靠性评估的贝叶斯网络模型，然后利用贝叶斯网络推理对发电系统进行可靠性分析。考虑到发电系统规模很大，编写了发电系统可靠性评估的贝叶斯网络自动生成工具，从而极大地减少了贝叶斯网络的建立时间，提高了模型的准确性。
将贝叶斯网络方法应用于发电系统的可靠性评估中，利用贝叶斯网络对不确定性知识的灵活表示，较好地解决了系统负荷预测的不确定性问题。运用贝叶斯网络灵活的因果推理和诊断推理，不但能够计算电力不足概率(LOLP)，还可以识别系统的薄弱环节。首次采用基于贝叶斯网络的元件灵敏度分析方法，解决了常规灵敏度分析中解析表达式及其偏导表达式难求的问题。
最后，用IEEE可靠性测试系统(IEEE-RTS)验证了此方法的正确性。并把此方法应用到更为复杂的互连发电系统，取得了不错的效果。因此，基于贝叶斯网络理的发电系统可靠性评估是一种较好的方法。
The generation systems reliability assessment is one of the most important part of power systems reliability assessment. .The reliability assessment study to generation systems is of critical theoretic significance and great social economic worth. In general reliability assessment computation, only are the reliability indices computated, little has been done to identify the weak components. However, identifying the weak components is very important.
The paper briefly introduces the basal conception of Bayesian networks, then further studys the general inference algorithm, and the Bayesian networks inference program based on bucket elimination is developed. After analysing the usual reliability assessment methods, a new reliability assessment method based on Bayesian networks is presented. The process of this method is as follows: Firstly, build the Bayesian networks models for generation systems reliability assessment; Secondly, anayse the generation systems reliability by using Bayesian networks inference program. Considering the actual generation systems scale is very large, the Bayesian networks automatic generator tool of generation systems reliability assessment is developed. Using this tool, the building-time of Bayesian networks is remarkably reduced and the reliability assessment model's accuracy is improved.
Due to Bayesian networks provide a flexible representing method for uncertainty knowledge. The uncertainty of load forecast is absolved easily by using Bayesian networks method. Utilizing efficient and flexible probabilistic inference of Bayesian networks, not only can loss of load probability be computated , but also the weak components of the systems be identified. For the first time, the parametric sensitivity analysis method based on Bayesian networks is adopted. By using this method, the difficulties of forming the formulation of parametric sensitivity analysis and its partial derivative formulation are overcome.
The test results in the IEEE reliability test systems (IEEE-RTS) have verified the effectiveness of this new method and the software,also the results of more complex multi-area generation test systems are also reported. The results show that the new method is valid and available.
文中简要介绍了贝叶斯网络的基本概念，深入研究了常用的推理算法，并在此基础上编写了基于桶消元的贝叶斯网络推理程序。在分析常规电力系统可靠性评估方法的基础上，提出了基于贝叶斯网络的发电系统可靠性评估方法。该方法首先建立发电系统可靠性评估的贝叶斯网络模型，然后利用贝叶斯网络推理对发电系统进行可靠性分析。考虑到发电系统规模很大，编写了发电系统可靠性评估的贝叶斯网络自动生成工具，从而极大地减少了贝叶斯网络的建立时间，提高了模型的准确性。
将贝叶斯网络方法应用于发电系统的可靠性评估中，利用贝叶斯网络对不确定性知识的灵活表示，较好地解决了系统负荷预测的不确定性问题。运用贝叶斯网络灵活的因果推理和诊断推理，不但能够计算电力不足概率(LOLP)，还可以识别系统的薄弱环节。首次采用基于贝叶斯网络的元件灵敏度分析方法，解决了常规灵敏度分析中解析表达式及其偏导表达式难求的问题。
最后，用IEEE可靠性测试系统(IEEE-RTS)验证了此方法的正确性。并把此方法应用到更为复杂的互连发电系统，取得了不错的效果。因此，基于贝叶斯网络理的发电系统可靠性评估是一种较好的方法。
The generation systems reliability assessment is one of the most important part of power systems reliability assessment. .The reliability assessment study to generation systems is of critical theoretic significance and great social economic worth. In general reliability assessment computation, only are the reliability indices computated, little has been done to identify the weak components. However, identifying the weak components is very important.
The paper briefly introduces the basal conception of Bayesian networks, then further studys the general inference algorithm, and the Bayesian networks inference program based on bucket elimination is developed. After analysing the usual reliability assessment methods, a new reliability assessment method based on Bayesian networks is presented. The process of this method is as follows: Firstly, build the Bayesian networks models for generation systems reliability assessment; Secondly, anayse the generation systems reliability by using Bayesian networks inference program. Considering the actual generation systems scale is very large, the Bayesian networks automatic generator tool of generation systems reliability assessment is developed. Using this tool, the building-time of Bayesian networks is remarkably reduced and the reliability assessment model's accuracy is improved.
Due to Bayesian networks provide a flexible representing method for uncertainty knowledge. The uncertainty of load forecast is absolved easily by using Bayesian networks method. Utilizing efficient and flexible probabilistic inference of Bayesian networks, not only can loss of load probability be computated , but also the weak components of the systems be identified. For the first time, the parametric sensitivity analysis method based on Bayesian networks is adopted. By using this method, the difficulties of forming the formulation of parametric sensitivity analysis and its partial derivative formulation are overcome.
The test results in the IEEE reliability test systems (IEEE-RTS) have verified the effectiveness of this new method and the software,also the results of more complex multi-area generation test systems are also reported. The results show that the new method is valid and available.
引文
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