Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

This paper deals with the design of a novel fuzzy proportional–integral–derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching–learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.     

Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems

In the bacteria foraging optimization algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA–PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA–PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA–PSO algorithm has been tested for automatic generation control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with proportional-integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional integral time multiply absolute error (ITAE) based objective function is considered and the performance of hBFOA–PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tie-line power in the range of +50% to ?50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA–PSO optimized PI controller. The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of craziness based particle swarm optimization (CRAZYPSO) approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided.     

基于5要素试错更新算法SARSA(λ)的自动发电控制

本文提出了一种基于5要素试错更新算法SARSA(λ)强化学习的随机最优自动发电控制方法. 该方法不依赖任何系统模型和先验知识并通过试错机理寻求最优控制策略. 以控制性能标准(control performance standards, CPS)和区域控制偏差(areal control error, ACE)瞬时滚动值为基础设计了即时奖励函数, 有效提高了该方法的收敛速度和控制效果, 并在算法中融入了资格迹以解决二次调频过程的延时问题. 本文所提出的控制方法在进行状态空间搜索时, 能有效摆脱避免搜索较大扰动状态, 以此获得更佳的控制效果. 标准两区域和南方电网仿真模型研究表明, 本算法能给系统提供更加安全的控制策略, 具有比Q(λ)算法更好的控制性能, 有效提高CPS考核的合格率.     

自动发电控制系统(AGC)在克拉玛依电厂应用

详细介绍克拉玛依电厂自动发电控制系统(AGC)的网络结构及设计说明,以及对燃机和汽机的负荷协调控制方案进行描述和分析。     

Simulation study for automatic generation control of a multi-area power system by ANFIS approach

This paper deals with the application of artificial neural network (ANN) based ANFIS approach to automatic generation control (AGC) of a three unequal area hydrothermal system. The proposed ANFIS controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of ANN. Appropriate generation rate constraints (GRC) have been considered for the thermal and hydro plants. The hydro area is considered with an electric governor and thermal area is considered with reheat turbine. The design objective is to improve the frequency and tie-line power deviations of the interconnected system. 1% step load perturbation has been considered occurring either in any individual area or occurring simultaneously in all the areas. It is a maiden application of ANFIS approach to a three unequal area hydrothermal system with GRC considering perturbation in a single area as well as in all areas. The performance of the ANFIS controller is compared with the results of integral squared error (ISE) criterion based integral controller published previously. Simulation results are presented to show the improved performance of ANFIS controller in comparison with the conventional integral controller. The results indicate that the controllers exhibit better performance. In fact, ANFIS approach satisfies the load frequency control requirements with a reasonable dynamic response.     

Comparable investigation of backtracking search algorithm in automatic generation control for two area reheat interconnected thermal power system

This paper concentrates on the validation of metaheuristic algorithms like backtracking search optimization algorithm (BSA) and fruit fly optimization algorithm (FFA) for tuning a optimal PID controller for automatic generation control. For this purpose, a two area reheat interconnected thermal system with nonlinearities like generator rate constant (GRC), deadband and time delay are considered. The proposed work is implemented using MATLAB Simulink for various load conditions with objective functions for metaheuristic algorithms capturing signals from various positions of proposed model. The results obtained using two algorithms are compared and explored.     

基于AGC回路的超声波流量测量新方法

针对超声波流量测量系统干扰因素多、测量精度不高、不利于数据采集等特点,对该测量系统的AGC回路应用进行了研究。采用数学方法建立数学模型,并通过分析数学模型计算出AGC回路的可变增益和稳定时间,设计了相应的电路并分析了AGC回路对流量测量计算过程的影响。实际应用证明,该系统可以提供较高的流量测量精度。     

重叠分解补偿阵的选择及对控制性能的影响

在大系统包含原理的框架下,提出了一种新的重叠结构分解补偿阵的选择方法,并分析了不同补偿阵对互联系统控制性能的影响。新方法根据补偿阵的选择条件,将补偿阵应满足的基本约束转化为对补偿阵中矩阵子块的约束,然后按照控制要求利用遗传算法对补偿阵进行优化。将该补偿阵选择方法应用于两区域重叠互联电力系统的自动发电控制（AGC）中,仿真结果表明,在设计时采用该方法选择的补偿阵使系统的分散AGC较传统选择方法具有更好的性能指标。     

分布式多区域多能微网群协同AGC算法

综合能源多区域协同是电网发展趋势, 而核心问题是采用何种方法对多区域进行协同. 本文基于Q ($sigma $)融入了资格迹及双重Q学习, 提出一种面向多区域多能微网群的多智能体协同控制算法, 即DQ ($sigma ,lambda $), 避免传统强化学习动作探索值高估的同时, 来获取分布式多区域的协同. 通过对改进的IEEE两区域负荷频率控制模型及三区域多能微网群自动发电控制(Automatic generation control, AGC)模型仿真, 结果表明, 与传统方法相比, 所提算法具有快速收敛性和更优动态性能, 能获得分布式多区域多能微网群的协同.     

基于多步回溯Q学习的自动发电控制指令动态优化分配算法

单步Q学习在火电占优、机组时延较大的A动发电控制(AGC)功率指令动态优化分配中的应用表现出收敛速度慢等不足而影响最优策略的获取.具有多步预见能力的多步回溯Q学习(Q(λ))显式利用资格迹进行高效回溯操作,能够有效解决火电机组大时滞环节带来的延时回报问题,算法平均收敛时间较Q学习缩短50%以上.算法奖励函数引入调节费用...     

基于AGC的光伏电站一次调频控制

逆变器调节速度快,且利用光伏电站的调频能力可降低常规调频备用容量,光伏电站深入参与电网一次调频将是一种很好的选择。基于此,本文在光伏电站自动发电控制（AGC）系统基础上增加光伏电站有功-频率下垂控制特性,使光伏电站在无需额外硬件增加或改造基础上实现AGC和一次调频功能一体化集成;其次,增加一次调频和AGC配合策略,实现二者无缝配合;最后,针对有功功率跟踪能力的不足对有功分配策略进行改进,提高了有功功率控制精度,提升了调频贡献能力。经过西北电网组织的新能源电站快速调频试验的验证,基于光伏电站自动发电控制系统的光伏电站一次调频控制具备较好的响应速度和调频贡献能力,能够为电网频率稳定提供支撑作用。     

基于改进北方苍鹰算法与混合核极限学习机的齿轮箱故障诊断

针对行星齿轮箱故障诊断问题,本文提出了一种基于改进北方苍鹰优化(INGO)算法与混合核极限学习机(HKELM)的行星齿轮箱故障诊断方法.首先,引入Savitzky-Golay(SG)滤波对齿轮箱原始信号进行去噪.利用时变滤波经验模态分解(TVF-EMD)将去噪后的信号分解成多个本征模态函数(IMF),使用方差贡献率、相关系数和信息熵筛选出最优的IMF.将最优IMF重构后,对重构信号进行时间同步平均(TSA)去噪以减少故障诊断模型的数据计算量.将Tent混沌映射、混合正弦余弦算法和Levy飞行策略用于改进北方苍鹰优化(NGO)算法,得到一种新的INGO算法.同时,引入余弦因子以平衡正弦余弦算法的全局和局部开发能力.最后,利用INGO算法对HKELM进行优化,用以提高HKELM模型的故障诊断准确率.将所提方法应用于两个案例对模型进行检验,实验结果表明,本文所提方法具有可行性和优越性.     

基于阈值统计学习的差分进化引力搜索算法

为了改善基本差分进化算法在求解复杂优化问题时易出现早熟收敛、求解精度低以及进化后期收敛速度慢等缺陷,结合引力搜索算法的优点,提出一种基于阈值统计学习思想的混合差分进化引力搜索算法.该算法通过阈值统计学习的方式,充分利用差分进化算法的全局优化能力与引力搜索算法在进化后期的种群开发能力,在进化过程中根据2种策略在先前学习代数的成功率自适应选择较优策略生成下一代群体,保证种群在解空间中的探索与开发能力之间的平衡,以提高算法的全局寻优能力.对几个经典复杂测试函数的仿真结果表明:改进算法求解精度高、收敛速度快、鲁棒性强、能够有效避免早熟收敛问题.     

基于深度强化学习的分层自适应PID控制算法

比例积分微分(PID)控制在工业控制和机器人控制领域应用非常广泛. 然而, 其在实际应用中存在参数整定复杂、系统无法精准建模以及对被控对象变化敏感的问题. 为了解决这些问题, 本文提出了一种基于深度强化学习算法的分层自适应PID控制算法, 即TD3-PID, 用于移动机器人的自动控制. 其中, 上层控制器通过实时观测当前环境状态和系统状态实现对下层PID控制器参数和输出补偿量进行调整, 以实时补偿误差从而优化系统性能. 本文将所提出的TD3-PID控制器应用于4轮移动机器人轨迹跟踪任务并和其他控制方法进行了真实场景实验对比. 结果显示 TD3-PID控制器表现出更优越的动态响应性能和抗干扰能力, 整体响应误差显著减小, 在提高控制系统性能方面具有显著的优势.     

一种MIMO-OFDM系统的信道估计算法

导频辅助的最小平方（LS）算法是MIMO—OFDM系统中常用的信道估计方法,特点是易于实现,但精度不高。基于最小均方误差（MMSE）的信道估计算法性能较好,但复杂度高,难以实现。提出了一种基于离散傅里叶变换（DFT）的信道估计算法,首先将LS信道估计循环前缀长度以外的时域响应值置零,然后选择重要路径,再通过计算参考导频的相位偏移量来进行补偿,最终实现信道的估计。算法能够满足一定的估计精度,且便于实现。仿真数据证明了算法的有效性。