Adaptive intelligent techniques for microgrid control systems: A survey

This paper introduces a survey on the adaptive and intelligent methods that have been applied to microgrids systems. Interestingly, the adaptive technique is effectively exercised in various control issues including stability, tracking error, and parameter uncertainties. Adaptive control has been extremely developed by using intelligent algorithms to automatically tune the control parameters namely fuzzy logic, particle swarm optimization, bacterial search algorithm, and etc. The objective is to evaluate and classify the design control methods and evaluation algorithms for the microgrid systems to maintain stability, reliability, and load variations by adjusting the controller parameters especially in standalone operation mode. The stability of islanded microgrids are constantly impacted by the related loads. A significant part of the research on an islanded microgrid involves droop control technique. In normal operation, distributed generation units and storage units provide power quality control. Once a shutdown is occurred, microgrid can be isolated from the main grid and operate in a local grid to support the local loads. Thus, distributed generations co-operate storage units to sustain the stability of the islanded microgrid.     

Evolution of intelligent control in adaptive systems

The evolution of adaptive control systems is considered from the viewpoint of the use of informatics and artificial intelligence (AI) tools. Some general principles of intellectronics (AI tools) are discussed taking into consideration adaptive control systems with non-linear dynamics operating under conditions of uncertainty. The principles are illustrated by an analytical review of adaptive-intelligent robot control and logical-probabilistic decision rules as applied to expert systems of adaptive control. Example of the use of intellectronics in adaptive systems are given.     

Artificial intelligence for operation and control: The case of microgrids

Research on artificial intelligence (AI) has advanced significantly in recent years. A variety of AI algorithms have shown great promise in a large number of applications for power system operation and control. This article examines the potential of applying AI in microgrids (MGs). Specifically, as MGs commonly employ onsite generation including an increasing penetration of non-dispatchable distributed energy resources (DERs) and require seamless transition between operation modes (e.g., grid-connected and island) for different operation scenarios, the energy management within an MG is particularly complicated. Many factors including lack of inertia needed for system stability, generation uncertainty from DERs, and complex MG network topology composition (e.g., AC, DC, and hybrid AC/DC MGs) contribute to the difficulty of microgrid energy management. AI techniques such as deep learning (DL) and deep reinforcement learning (DRL) have recently demonstrated their excellence in tackling problems pertinent to decision making, providing a possible solution to overcome the above-mentioned challenges. This article discusses the applications of AI to MG operation and control, with an emphasis on DL and DRL. We survey the available DL and DRL technologies and their applications to power grids. We also investigate the unique issues associated with MGs including their layered control architecture, single vs. networked structure, and topology optimization. Perspectives on the ongoing challenges and viable AI solutions to MG operation and control are presented.     

高精度伺服系统的CMAC学习控制研究

根据神经网络的特点，提出了基于CMAC网络的伺服系统在线学习控制方案，并给出了其再励学习算法。该方法无需离线训练，经在线学习得到系统的逆动态特性，并据此实时修改控制参数。引入鲁棒控制项，增强了学习算法的鲁棒性。在某单轴速率／位置转台上的应用结果表明，该方法可有效地改善伺服系统的跟踪精度和抗扰性能。     

Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization

Of the many kinds of renewable energy, wind power is low in cost and non-polluting, so it is especially well-suited to Taiwan. The Mai Liao Wind Farm is the most important wind farm in Taiwan, and forecasting the wind power output for national sustainable development continues to be a challenging research feature. In this study, we attempt to forecast the wind power data collected from the Mai Liao Wind Farm. Our forecast model is based on a Multi-Layer Perceptron Artificial Neural Network (MLP) model using the data collected at the Mai Liao Wind Farm over a period of five years from September 2002 to August 2007. We proposed a new algorithm, namely improved Simplified Swarm Optimization (iSSO), which improves Simplified Swarm Optimization (SSO) by justifying the weights and bias in training the MLP. The proposed iSSO combines Principal Component Analysis (PCA), Autocorrelation Function (AF) and Partial Autocorrelation Function (PAF) for the selection of features which increases the efficiency of the proposed model. The experimental results demonstrate that the performance of iSSO outperforms the other six most popular algorithms.     

An architecture for the intelligent detection and alleviation of overloaded transmission lines

A new control strategy is proposed that offers intelligent solutions to the problem of alleviating overloaded lines. The subsystems used consist of artificial neural networks, a holographic associative memory, and a knowledge based expert system.     

基于深度强化学习的电网自主控制与决策技术

高比例可再生能源和电力电子设备渗透率的不断增加给电力系统运行与调控带来诸多挑战。本文基于深度强化学习技术（深度确定策略梯度, DDPG）提出了具有在线学习功能的电网自主优化控制与决策框架，即“电网脑”系统；通过不断的学习和经验累积，AI智能体可以在亚秒级时间内根据实时量测数据给出调控指令及预期效果。该系统近期可用于辅助调度员决策，远期可为自动调度提供技术手段。本文以电网电压和联络线潮流控制为例，从多方面详细介绍了自主调控的方法，包括问题描述、控制目标和样本设定、奖惩机制定义、状态空间和控制动作集定义、算法实现流程等。大量的数值仿真实验验证了所提方法强大的学习能力以及应用于电力系统自主控制与决策的可行性。     

人工智能在电气自动化控制方面的实践运用分析

随着人工智能的发展,第四次工业革命应时而来。从历代工业革命的发展来看,每次工业革命都昭示着整个时代的全面变革与进步,更为人类社会经济发展与人类文明建设进程带来巨大的机遇和挑战。电气系统的自动化控制技术显然是第四次工业革命所带来的时代硕果,更是人工智能应用的典型领域之一。本文从电气自动化控制方面分析了人工智能的实践和应用,以期为人工智能在我国电气自动化控制方面的应用带来一定的推动和指导作用。     

数控系统技术的发展新趋势

基于对传统数控技术的剖析，探讨了数控系统技术发展的新趋势。文章在回顾数控系统技术演进基训上，重点讨论了CIMS技术、图形图像处理技术及人工智能控制技术等在数控系统中的应用趋势。     

人工智能与模糊控制在电力系统继电保护应用的研究现状及前景

对人工智能及模糊控制在电力系统继电保护中应用的国内外研究现状进行了综述 ,指出了需要进一步研究的方向和主要内容     

Adaptive fuzzy constraint control for switched nonlinear systems in nonstrict feedback form

In this paper, the adaptive fuzzy controller design problem is investigated for a class of switched nonlinear systems in nonstrict feedback form, in which the unknown functions are considered and are approximated. Moreover, the system states are constrained in corresponding compact. By using Barrier Lypunov function method and backstepping technique, the adaptive fuzzy controller is designed such that all the signals in the closed-loop system are bounded, the system output can track the desired signal to small compact, and all the system states satisfy the constraint conditions. Finally, the simulation results show the effectiveness of the proposed method.     

Neuroadaptive output feedback control for nonlinear nonnegative dynamical systems with actuator amplitude and integral constraints

A neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the control amplitude as well as the integral of the control input over a given time interval are constrained, and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and an integral drug dosing constraint over a specified time period. Copyright © 2010 John Wiley & Sons, Ltd.     

基于智能多代理的能量协调控制在直流微网中的应用

提出一种基于智能多代理技术的能量协调控制方法,用于直流微网的能量管理与电压控制.对直流微网的架构进行设计,实现对光伏电池、燃料电池及蓄电池的数学建模.设计了一个2层的智能多代理系统,对直流微网的并网、解列,以及光伏电池、燃料电池、负荷及蓄电池的能量管理进行协调控制.从微源和负荷各种状态中提取了8个特征量及13种不同动作作为神经网络的输入和输出参数,训练并实现了控制中心Agent的决策器.通过MATLAB/Simulink仿真对光照变化、并网时负荷增加后断网及孤岛时负荷增加后并网这3种算例进行分析,验证了该智能多代理能量协调控制策略的可行性.     

An interpretation of neural networks as inference engines with application to transformer failure diagnosis

An artificial neural network concept has been developed for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). A new methodology for mapping the neural network into a rule-based inference system is described. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a Fuzzy Inference System. Some studies are reported, illustrating the good results obtained.     

Model reference adaptive iterative learning control for linear systems

In this paper, we propose a model reference adaptive control (MRAC) strategy for continuous‐time single‐input single‐output (SISO) linear time‐invariant (LTI) systems with unknown parameters, performing repetitive tasks. This is achieved through the introduction of a discrete‐type parametric adaptation law in the ‘iteration domain’, which is directly obtained from the continuous‐time parametric adaptation law used in standard MRAC schemes. In fact, at the first iteration, we apply a standard MRAC to the system under consideration, while for the subsequent iterations, the parameters are appropriately updated along the iteration‐axis, in order to enhance the tracking performance from iteration to iteration. This approach is referred to as the model reference adaptive iterative learning control (MRAILC). In the case of systems with relative degree one, we obtain a pointwise convergence of the tracking error to zero, over the whole finite time interval, when the number of iterations tends to infinity. In the general case, i.e. systems with arbitrary relative degree, we show that the tracking error converges to a prescribed small domain around zero, over the whole finite time interval, when the number of iterations tends to infinity. It is worth noting that this approach allows: (1) to extend existing MRAC schemes, in a straightforward manner, to repetitive systems; (2) to avoid the use of the output time derivatives, which are generally required in traditional iterative learning control (ILC) strategies dealing with systems with high relative degree; (3) to handle systems with multiple tracking objectives (i.e. the desired trajectory can be iteration‐varying). Finally, simulation results are carried out to support the theoretical development. Copyright © 2006 John Wiley & Sons, Ltd.     

Hybrid adaptive control for non‐linear uncertain impulsive dynamical systems

A direct hybrid adaptive control framework for non‐linear uncertain hybrid dynamical systems is developed. The proposed hybrid adaptive control framework is Lyapunov‐based and guarantees partial asymptotic stability of the closed‐loop hybrid system; that is, asymptotic stability with respect to part of the closed‐loop system states associated with the hybrid plant states. Furthermore, hybrid adaptive controllers guaranteeing attraction of the closed‐loop system plant states are also developed. Finally, two numerical examples are provided to demonstrate the efficacy of the proposed hybrid adaptive stabilization approach. Copyright © 2004 John Wiley & Sons, Ltd.     

Overview of progress in quantum systems control

The development of the theory on quantum systems control in the last 20 years is reviewed in detail. The research on the controllability of quantum systems is first introduced, then the study on the quantum open-loop control methods often used for controlling simple quantum systems is analyzed briefly. The learning control method and the feedback control method are mainly discussed for they are two important methods in quantum systems control and their advantages and disadvantages are presented. According to the trends in quantum systems control development, the paper predicts the future trends of its development and applications. A complete design procedure necessary for the quantum control system is presented. Finally, several vital problems hindering the advancement of quantum control are pointed out. Translated from Chinese Journal of Quantum Electronics, 2003, 20(1): 1–9 [译自:: 量子电子学报]     

Adaptive control of unactuated dynamical systems through interconnections: Stability and performance guarantees

This article studies control and performance enforcement for a class of uncertain dynamical systems that consist of actuated and unactuated portions physically interconnected to each other. The proposed approach stabilizes the overall interconnected system in the presence of unknown physical interconnections as well as system uncertainties. Performance guarantees are enforced, individually, on the actuated as well as unactuated portions of the interconnected system via this approach. For enforcing these performance guarantees, a set-theoretic model reference adaptive control approach is used, in conjunction with linear matrix inequalities, to restrict the respective system error trajectories of the actuated and unactuated dynamics inside a priori, user-defined compact sets. The efficacy of the proposed approach is demonstrated using simulations.     

人工智能技术在电力系统继电保护中的应用

90年代以来,电力系统保护领域内的研究工作转向人工智能的应用,本文简要介绍了一些人工智能技术如专家系统、暂态保护、人工神经网络、模糊集理论、小波分析等在电力系统继电保护领域中应用,分析了各种方法的优缺点,并提出今后的发展方向。