﻿ 舰船综合电力系统暂态稳定性自适应控制技术
 舰船科学技术  2022, Vol. 44 Issue (9): 109-113    DOI: 10.3404/j.issn.1672-7649.2022.09.022 PDF

Adaptive control technology for transient stability of ship integrated power System
CHEN Fu-lan
Jiangsu Shipping College, Nantong 226010, China
Abstract: To research the adaptive control technology of transient stability of ship integrated power system, improve the effect of adaptive control of transient stability, ensure the stable operation of ship integrated power system, the mathematical model of ship integrated power system was designed, and the influencing parameters of transient stability were obtained, including generator power angle, speed, transient potential, magnetic field voltage and output electromagnetic power. A feedback fuzzy adaptive controller is designed by adaptive fuzzy method. The controller adapts to the parameters that affect the transient stability in the mathematical model. The improved bat algorithm was used to optimize the fuzzy rules of the controller and speed up the adaptive control. The experimental results show that this technology can effectively and adaptively control the transient stability of generator failures in different integrated power systems. After optimizing fuzzy rules, this technology can effectively shorten the adaptive control time, reduce the overshoot of adaptive control, and ensure the stable operation of ship integrated power system.
Key words: ship power system     transient stability     adaptive control     adaptive fuzzy     bat algorithm     fuzzy rules
0 引　言

1 基于反馈模糊控制器的舰船综合电力系统暂态稳定性自适应控制技术 1.1 舰船综合电力系统暂态稳定性数学模型

 \left\{ \begin{gathered} \delta ' = \omega - {\omega _0}，\hfill \\ \begin{aligned}\omega ' = & - \frac{1}{{2M}}\left( {D\omega - D{\omega _0} + \frac{\sin ( \delta ){\omega _0}U_q^l}{x_{d\sum }^l} + {\omega _0}{P_m} - {\omega _0}{P_\tau }} \right) + \\&\frac{{x_q}\sin ( \delta )}{{x_{q\sum }}x_{d\sum }^l} - \frac{x_d^l\sin ( \delta )}{{x_{q\sum }}x_{d\sum }^l} + {d_1}，\end{aligned}\hfill \\ U'^l_q = - \frac{x_{d\sum }U_q^l}{T_{d0}^lx_{d\sum }^l} + \frac{\cos ( \delta )x_d - \cos ( \delta )x_q^l}{T_{d0}^lx_{d\sum }^l} +\frac{{U_{ds}}}{T_{d0}^l} + \frac{u_f}{T_{d0}^l} + {d_2} 。\hfill \\ \end{gathered} \right. (1)

 $\left\{ \begin{gathered} \dot x = {\boldsymbol{A}}x + {\boldsymbol{B}}\left[ {H\left( x \right) + \left( {\omega ' + U'^l_q} \right)u} \right] ，\hfill \\ y = {\boldsymbol{C}}x 。\hfill \\ \end{gathered} \right.$ (2)

1.2 舰船综合电力系统暂态稳定性反馈模糊控制器设计

${x_j}$ ${O_{ij}}$ ，那么 ${\tilde h_i}\left( {x\left| {{\theta _i}} \right.} \right)$ $G_i^{}$ ，其中 ${x_j}$ 的隶属度函数和模糊集合为 ${\psi _{O_{ij}^{}}}$ $O_{ij}^{}$

${h_i}\left( {x\left| {{\theta _i}} \right.} \right)$ 的模糊规则为 $R_i^{}$ ${h_i}\left( {x\left| {{\theta _i}} \right.} \right)$ 的模糊集合为 $G_i^{} = {\tilde h_i}\left( {x\left| {{\theta _i}} \right.} \right)$ ；通过乘积推理与中心平均解模糊器[12]，设计舰船综合电力系统暂态稳定性自适应控制目标的模糊系统，公式如下：

 ${\tilde h_i}\left( {x\left| {{\theta _i}} \right.} \right) = \frac{{\displaystyle\sum\limits_{\lambda = 1}^{{m^2}} {{\theta _{i\lambda }}{\rho _i}\left( x \right)} }}{{\displaystyle\sum\limits_{\lambda = 1}^{{m^2}} {{\rho _i}\left( x \right)} }}。$ (3)

$\tilde H\left( {x\left| \varphi \right.} \right) = {\left[ {{{\tilde h}_1}\left( {x\left| {{\theta _i}} \right.} \right), \cdots ,{{\tilde h}_n}\left( {x\left| {{\theta _n}} \right.} \right)} \right]^{\rm{T}}} = {\varphi ^{\rm{T}}}\rho \left( x \right)$ $\varphi = {\rm{diag}}\left[ {\theta _1^{\rm{T}}, \cdots ,\theta _n^{\rm{T}}} \right]$ ；建立舰船综合电力系统暂态稳定性控制目标的模糊自适应观测器，公式如下：

 $\left\{ \begin{gathered} \tilde {\dot {x}} = {\boldsymbol{A}}\tilde x + {\boldsymbol{B}}\left[ {\tilde H\left( {\tilde {x}\left| \varphi \right.} \right) + \left( {\omega ' + U'^l_q} \right)r - {r_a} - {r_s}} \right] + {{\boldsymbol{K}}_0}\left( {y - C\tilde x} \right)，\hfill \\ \tilde {y} = {\boldsymbol{C}}\tilde {x}。\hfill \\ \end{gathered} \right.$ (4)

 $\left\{ \begin{gathered} \dot {\hat {E}} = {\boldsymbol{A}}\hat {E} - {{\boldsymbol{K}}_0}{C^{\rm{T}}}\hat {E} + {\boldsymbol{B}}H\left( x \right) - {\boldsymbol{B}}\tilde {H}\left( {\tilde {x}\left| \varphi \right.} \right) + {\boldsymbol{B}}{r_a} + {\boldsymbol{B}}{r_s} ，\hfill \\ e = {\boldsymbol{C}}\hat {E} ，\hfill \\ \end{gathered} \right.$ (5)

${\varphi ^*}$ 的最佳参数估计值如下：

 ${\varphi ^*} = \mathop {\arg \min }\limits_{\varphi \in \Omega } \left[ {\sup \left\| {\tilde H\left( {\tilde x\left| \varphi \right.} \right) - H\left( x \right)} \right\|} \right]，$ (6)

 $w = \left[ {H\left( x \right) - \tilde H\left( {\tilde x\left| {{\varphi ^*}} \right.} \right)} \right]，$ (7)

 $\dot {\hat {E}} = {\boldsymbol{A}}\hat {E} - {{\boldsymbol{K}}_0}{\boldsymbol{C}}\hat {E} + {\boldsymbol{B}}\left[ {{{\bar {\Theta} }^{\rm{T}}}\rho \left( x \right) + w + {r_a} + {r_s}} \right] ，$ (8)

w为有界的，即 $\left\| w \right\| \leqslant S$ ，则反馈自适应模糊控制器的表达式如下：

 $\left\{ \begin{gathered} r = \frac{{\left[ { - \tilde {H}\left( {\tilde {x}\left| \rho \right.} \right) + {y^2} + K_C^{\rm{T}}\left( {\tilde {\dot {x}} - \tilde {x}} \right) + {r_a} + {r_s}} \right]}}{{\left( {\omega ' + U'^l_q} \right)}}，\hfill \\ {r_a} = K_0^{\rm{T}}{P_1}\left( {\tilde {\dot {x}} - \tilde {x}} \right)，\hfill \\ {r_s} = - {{\rm{sgn}}} \left( {{e^{\rm{T}}}{P_2}B} \right) 。\hfill \\ \end{gathered} \right.$ (9)

 $\dot {\hat E} = \left( {{\boldsymbol{A}} - {\boldsymbol{B}}{K_C}} \right)\left( {\tilde {\dot x} - \tilde x} \right) - {{\boldsymbol{K}}_0}{{\boldsymbol{C}}^{\rm{T}}}\hat E，$ (10)

 $\dot \varphi = \gamma \rho \left( {\tilde x} \right){B^{\rm{T}}}{{\boldsymbol{P}}_2}\dot {\hat E} = \gamma \rho \left( {\tilde x} \right)\hat e。$ (11)

1.3 基于改进蝙蝠算法的自适应控制模糊规则优化

 $\left\{ \begin{gathered} v_{i'}^t = v_{i'}^{t - 1} + \left( {\textit{z}_{i'}^t - {\textit{z}_*}} \right)\left( {{Q_{\min }} + \varpi {Q_{\max }} - \varpi {Q_{\min }}} \right)，\hfill \\ \textit{z}_{i'}^t = \textit{z}_{i'}^{t - 1} + bv_{i'}^t ，\hfill \\ \end{gathered} \right.$ (12)

 ${{z}_{{\rm{new}}}} = {\textit{z}_{{\rm{old}}}} + \eta \mu {A^t}。$ (13)

 ${U'_{i',j'}}\left( {{z_{k'}}} \right) = \left\{ \begin{gathered} {Z_{\min }},Round\left[ {{U_{i',j'}}\left( {{z_{k'}}} \right)} \right] ，\hfill \\ {Z_{\max }},Round\left[ {{U_{i',j'}}\left( {{z_{k'}}} \right)} \right] > {Z_{\max }}。\hfill \\ \end{gathered} \right.$ (14)

2 实验分析

 图 1 舰船综合电力系统自适应控制效果 Fig. 1 Adaptive control effect of ship integrated power system

 图 2 各函数值分析结果 Fig. 2 Analysis results of each function value

 图 3 模糊规则优化前后的暂态电势自适应控制效果 Fig. 3 Adaptive control effect of transient electric potential before and after fuzzy rule optimization

3 结　语

 [1] 胡蝶. 船舶电力调速系统暂态稳定性分析[J]. 舰船科学技术, 2020, 42(2): 85-87. [2] 闫群民, 李玉娇. 基于多频段电力系统稳定器的电力系统暂态稳定性优化策略[J]. 现代电力, 2020, 37(2): 139-144. [3] 唐伟强, 龙文堃, 孙丽娟, 等. 基于聚类方法和神经网络的非线性系统多模型自适应控制[J]. 系统工程与电子技术, 2019, 41(9): 2100–2106 [4] 孙军伟, 李楠, 王延峰. 基于自适应控制的八个混沌系统的多级组合同步[J]. 计算机应用研究, 2020, 37(1): 188-192. [5] 汪慧玲. 基于两机并联非线性数学模型的舰船电力系统自适应控制器设计[J]. 舰船科学技术, 2019, 41(16): 88-90. [6] 迟福建, 刘聪, 申刚, 等. 端电压及功角双重稳定约束鲁棒自适应励磁控制[J]. 中国测试, 2019, 45(4): 129-134. [7] 张彦迪, 陈江宁. 舰船综合电力系统的设计与建模[J]. 计算机仿真, 2019, 36(8): 118-121. DOI:10.3969/j.issn.1006-9348.2019.08.024 [8] 霍江航, 姜向远, 栾义忠, 等. 基于L1自适应理论的AUV深度控制器设计[J]. 中国舰船研究, 2021, 16(5): 150-157. [9] 蔡卫江, 李雪锋, 赵士正. 基于STATCOM的功率振荡阻尼器和PSS控制器协调控制研究[J]. 中国工程机械学报, 2020, 18(1): 34-39. [10] 颜景斌, 杨晨, 常龙龙, 等. 虚拟同步发电机惯量阻尼协同自适应控制策略[J]. 哈尔滨理工大学学报, 2019, 24(6): 58-63. [11] 王岩, 王昕, 王振雷. 多变量周期系统的多模型二阶段自适应控制[J]. 控制理论与应用, 2021, 38(3): 391-397. [12] 巩磊, 王萌, 祝长生. 基于浸入不变流形的飞轮储能系统母线电压自适应非线性控制器[J]. 中国电机工程学报, 2020, 40(2): 623-634. [13] 朱劭璇, 王彤, 王增平, 等. 考虑主导不稳定平衡点变化的电力系统暂态稳定切机控制策略[J]. 电力系统保护与控制, 2021, 49(5): 20-28. [14] 王彤, 刘九良, 朱劭璇, 等. 基于随机森林的电力系统暂态稳定评估与紧急控制策略[J]. 电网技术, 2020, 44(12): 4694-4701.