# 多因素驱动的配电变压器时变故障失效模型及应用Time-Varying Failure Model of Multi-Factor Driven Distribution Transformer and Its Application

DOI: 10.12677/SG.2019.96028, PDF, HTML, XML, 下载: 290  浏览: 746

Abstract: Most of distribution system reliability evaluation is based on the average failure rate of the long-term historical data of the equipment, which cannot respond to the dynamic changes of the operation status and the operation environment. When synthetically considering the failure model of transformer caused by thermal aging, discharge, damp and other factors, the paper presents a failure model of environmental temperature and load characteristics, with the failure model based on health status. Therefore, we can gain the short-term reliability model of transformer driven by multi factors. With the reliability evaluation index constructed of distribution network, the short-term reliability evaluation of distribution network is realized by using the forward fault diffusion method based on the short-term reliability model of transformer. Finally, the validity of the short-term reliability model of the transformer is verified by a test system. The result can realize the fault early warning, and provide the basis for the rapid troubleshooting of the distribution network.

1. 引言

2. 多因素驱动的变压器故障率模型

2.1. 负荷特性与环境温度相依的老化失效模型

${\theta }_{\text{oil}}={\left(\frac{{k}^{2}R+1}{R+1}\right)}^{\frac{1}{1+n}}{\mu }_{\text{pu}}^{\frac{n}{n+1}}\Delta {\theta }_{\text{oil,R}}+{\theta }_{\text{a}}$ (1)

${\mu }_{\text{pu}}=\frac{\mu }{{\mu }_{\text{R}}}=\mathrm{exp}\left(\frac{2797.3}{{\theta }_{\text{oil}}+273}-\frac{2797.3}{{\theta }_{\text{oil,R}}+273}\right)$ (2)

${\theta }_{\text{hst}}={k}^{\frac{2m}{n+1}}{\mu }_{\text{pu}}^{\frac{n}{n\text{+1}}}\Delta {\theta }_{\text{hst,R}}\text{+}{\theta }_{\text{oil}}$ (3)

$V=\mathrm{exp}\left(\frac{B}{110+273}-\frac{B}{{\theta }_{\text{hst}}+273}\right)$ (4)

$T=N/V$ (5)

$T=N\mathrm{exp}\left(\frac{15000}{{\theta }_{\text{hst}}+273}-\frac{15000}{110+273}\right)$ (6)

$\lambda \left(k,{\theta }_{\text{a}}\right)=\frac{1}{T}N\mathrm{exp}\left(\frac{15000}{110+273}-\frac{15000}{{\theta }_{\text{hst}}+273}\right)$ (7)

2.2. 健康状态相依的潜伏性失效模型

$\lambda \left(\text{HI}\right)=k{\text{e}}^{-c\text{HI}}$ (8)

2.3. 健康状态相依的潜伏性失效模型

${\lambda }_{\text{t}}=\lambda \left(\text{HI}\right)+\lambda \left(k,{\theta }_{\text{a}}\right)$ (9)

${t}_{\text{t}}=\frac{\lambda \left(k,{\theta }_{a}\right){t}_{1}+\lambda \left(\text{HI}\right){t}_{2}+\lambda \left(k,{\theta }_{a}\right){t}_{1}\lambda \left(\text{HI}\right){t}_{2}}{{\lambda }_{\text{t}}}$ (10)

3. 输电线路安全裕度的表征配电网的短期风险评估

3.1. 用户失负荷时间期望(LLTE)

$\text{LLTE}=\frac{\underset{i\in \text{NS}}{\sum }\left({U}_{\text{LP,}i}{N}_{i}\right)}{\underset{i\in \text{NS}}{\sum }{N}_{i}}$ (11)

3.2. 用户失负荷概率期望(LLPE)

$\text{LLPE}=\frac{\underset{i\in \text{NS}}{\sum }\left({\lambda }_{\text{LP,}i}{N}_{i}\right)}{\underset{i\in \text{NS}}{\sum }{N}_{i}}$ (12)

3.3. 用户失负荷平均持续时间(LLDE)

$\text{LLDE}=\frac{\underset{i\in \text{NS}}{\sum }\left({U}_{\text{LP,}i}{N}_{i}\right)}{\underset{i\in \text{NS}}{\sum }\left({\lambda }_{\text{LP,}i}{N}_{i}\right)}$ (13)

Figure 1. Reliability assessment algorithm flow

4. 导线动态载流能力评估与运行温度预警

Figure 2. 24-hour ambient temperature curve

Table 1. Transformer short-term reliability model parameters

Figure 4. Transformer real-time failure rate curve

Figure 5. RBTS-BUS2 distribution network

case1：假设RBTS-BUS2系统中的变压器承载正常周期性负载；

case2：假设RBTS-BUS2系统中#11变压器承载超铭牌过负荷运行，其余变压器承载正常周期性负载；

case3：假设RBTS-BUS2系统中的#11变压器承载长期急救负载运行，其余变压器承载正常周期性负载；

case4：RBTS-BUS2系统中#11变压器承载短期急救负载运行，其余变压器承载周期性负载。

Table 2. Transformer short-term reliability model parameters

Figure 6. LPE in RBTS-BUS2 system

Figure 7. LLTE in RBTS-BUS2 system

Figure 8. LLDE in RBTS-BUS2 system

5. 结论

1) 变压器运行状态的动态变化会影响变压器的实时故障率，进而影响配电系统的可靠性评估指标，利用构建的可靠性指标有效地协助配电系统运行维护人员辨识配电网运行状态，为实现配电系统的故障预警提供底层基础。

2) 当变压器承载周期性负载以及计划超铭牌运行时，变压器的故障率主要由健康状态相依的潜伏性失效模型决定，当变压器承载长期急救性负载以及短期急救负载时，变压器的故障率主要由环境温度与负荷特性相依的实时故障率模型决定。

3) 基于变压器的短时可靠性模型的配电网可靠性评估，可获取配电网运行风险较大的时间点，可协助运行人员进行相应的系统调整，为实现配电网的故障预警以及故障快速排除提供基础。

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