Control Problems In Power Systems Research Articles

Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what the currently open theoretical and numerical challenges are in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided into “model-based” methods and “model-free” methods in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles, and energy storage.

Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach

Power system control and transient stability analysis play essential roles in secure system operation. Control of power systems typically involves highly nonlinear and complex dynamics. Most of the existing works address such problems with additional assumptions in system dynamics, leading to a requirement for a complete and general solution. This paper, therefore, proposes a novel control framework for various power system control and stability problems leveraging a learning-based approach. The proposed framework includes a two-module structure that iteratively and jointly learns the candidate Lyapunov function and control law via deep neural networks in a learning module. Meanwhile, it guides the learning procedure towards valid results satisfying Lyapunov conditions in a falsification module. The introduced termination criteria ensure provable system stability. This control framework is verified through several studies handling different types of power system control problems. The results show that the proposed framework is generalizable and can simplify the control design for complex power systems with the stability guarantee and enlarged region of attraction.

Accelerated Derivative-Free Deep Reinforcement Learning for Large-Scale Grid Emergency Voltage Control

Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operational conditions in power systems, existing methods have outstanding issues in terms of either speed, adaptiveness, or scalability. Deep reinforcement learning (DRL) was regarded and adopted as a promising approach for fast and adaptive grid stability control in recent years. However, existing DRL algorithms show two outstanding issues when being applied to power system control problems: 1) computational inefficiency that requires extensive training and tuning time; and 2) poor scalability making it difficult to scale to high dimensional control problems. To overcome these issues, an novel derivative-free DRL algorithm named PARS was developed and tailored for power system voltage stability control via load shedding. The method was tested on both the IEEE 39-bus and IEEE 300-bus systems, and the latter is by far the largest scale for such a study. Test results show that, compared to other methods including model-predictive control (MPC) and proximal policy optimization(PPO) methods, PARS shows better computational efficiency (faster convergence), more robustness in learning, excellent scalability and generalization capability.

An Approximate Model of Load Frequency Control Systems with Time Delay

In this paper we present an approximate model for load frequency control system with time delay. The load frequency control is one of the conventional power system control problems. In order to secure the stability of the grid the frequency must remain within its limited range which is achieved through the load frequency control. The load frequency control signals experience time delay that could destabilize the power systems. The presence of the time delay complicates the analysis of the load frequency control system. In this paper we present a stability method based on the Direct Frequency Response approximation for the time delay. This approximation transforms the transcendental time delay equation into linear equation. This results in a simple stability criterion for the loadfrequency control system with time delay. A one-area load frequency control system is chosen as a case study. The effectiveness of the proposed approximation has been tested through simulation and comparison with the published research work. By tracking the eigenvalues or using Routh's criterion the maximum delay margin can be estimated. The proposed stability criterion has been compared with the most recent methods and showed it is merit. The range of the PI controller parameters for a given time delay can be determined which is very important in practice.

DFIG-Based Wind Energy System Robust Optimal Control by Using of Novel LMI-Based Adaptive MPC

The wind turbine system is one of the most challenging control problems in power systems due to its fast and slow dynamics and the optimal and robust control of the doubly fed induction generator is particularly important in this system. To handle the desired performance under different conditions, this paper presents a new control method by combining the model predictive control method with the adaptive method. In the aforementioned method by decoupling the active and reactive power control, an adaptive method is used to estimate the uncertainty, disturbance, and nonlinear terms as well as the internal feedback design. Then, linear matrix inequality-based robust model predictive control is designed as external feedback. The Lyapunov method is used to demonstrate system stability under the proposed controller. The simulation results show the efficiency of the method under different scenarios.

Peer Review of Renewable Energy-based Adaptive Protection(s) & Relay Coordination Optimization Techniques

Background: Distributed Sustainable Energy Generation (DSEG) has become a leading trend in modern R&D to achieve practical, financial, and environmental benefits. With respect to the power system, the benefits are minimization of power failures, enhancement of energy quality & reliability, congestion relief, and smart/micro grid demand response. The widespread use of modern technologies encourages the use of DSEG for safe power production and power loss reduction. Besides, it also poses new challenges such as optimal placement of relays, protection device settings, and voltage control problems in power systems. : Among various solutions reported in the literature, a widely accepted solution seems to be the deployment of Fault Current Limiter (FCL) to minimize DSEG impacts. However, FCLs are an expensive option to restore relay coordination. The adaptive protection scheme provides benefits to the smart grid's advanced features and serves as an efficient solution to address new DSEG and FCL challenges. Objective: This paper presents an extensive review of the effect of DSEG on distribution systems from the safety point of view, different optimization techniques used for adaptive protections, optimal relay coordination, and merits & demerits of FCL. Methods: The paper discusses various recent optimization and hybrid optimization techniques used for solving the Optimal Relay Coordination model (ORC). Adaptive and FCL based non-adaptive techniques are also discussed to solve this model. An attempt has also been made to present a comparative study of various optimization techniques in tabular form, considering different bus models and their outcomes. Conclusion: Comparative study of these techniques shows that Water cycle optimization, hybrid whale-GWO and GSA-SQP algorithms give better results in solving the ORC problem. FCL based non-adaptive technique was found better as compared with adaptive techniques because a greater number of DGs can be integrated on a grid with less complexity.

An Optimal Robust Controller Design for Automatic Voltage Regulator System Using Coefficient Diagram Method

Design of a robust controller for any control system is of paramount importance in the field of control system design. Despite parameter variations, the controller has to generate signals to enable satisfactory performance of the system. One of the recent developments of the robust controller design methods is the coefficient diagram method (CDM), which is an algebraic approach, wherein the characteristic polynomial along with the controller is simultaneously obtained from the design. A graph on semi-log axis, known as coefficient diagram, is a single tool used to analyze the key features of the system performance namely speed of response, stability and robustness. Although controller design using CDM has been used in many control applications, the method needs to be explored in the field of power system control problems. One of the crucial systems is the automatic voltage regulator (AVR) system. In this paper, a controller based on CDM has been designed for an AVR system and its robustness is analyzed in the presence of parameter variations. Also, the functioning of the system with conventional PID controller is contrasted with the performance of CDM-based controller. The results of the CDM-based controller are found to be better as compared to the former.

Performance analysis and optimization of power systems with spatially correlated noise

Based on stochastic differential equations (SDEs), we analyse the overall performance of heterogeneous power systems network, subject to spatially distributed and correlated noise with random initial conditions. We determine bounds on the H_2 norm of the heterogeneous system based on a closed-form of the norm of the homogeneous power system. Then, we formulate possible scenarios for performance optimization and link these to applications for network design and control problems in power systems. Our results are corroborated by numerical simulations from Kundur's four-machine two-area network after adaption to our setup.

Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

Game theoretic-based optimal load frequency control of power systems with network-induced delays

This paper presents a game theoretic-based load frequency control (LFC) scheme for power systems with network-induced delays. First, a dynamic model of two-area LFC systems is developed under consideration of bounded network-induced delays. Second, the optimal control problem of power systems with network-induced delays is formulated as a stochastic linear quadratic game. Then, by using differential games and Lyapunov theories, the game theoretic-based optimal load frequency controllers are designed for the system under consideration, and an algorithm is presented to make the desired optimal LFC gains solvable. Finally, a case study is carried out to show the effectiveness of the proposed method.

Modeling and Control of Combined Cycle Gas Turbines Using Rowen’s and Vournas’s Models

The gas turbine is known to feature low capital cost, high flexibility, high reliability without complexity, short delivery time, early commissioning and commercial operation, and fast starting as well as loading. The gas turbine is further recognized for its better environmental performance apparent in reducing air pollution as well as the greenhouse effect. The gas turbine can be configured as a simple (stand-alone) cycle plant or a part of one of the new technologies adopted in last decades for improvement which known as Cycle Power Plant. A Combined-Cycle Power Plant (CCPP) can be seen as coupling of a Gas Turbine (GT) and a Steam Turbine (ST) through a Heat Recovery Steam Generator (HRSG). Overall system efficiency can be greatly improved by linking together these two different thermal cycles. Recently, several gas turbine models have been suggested with different degree of complexity and success. The purpose of this paper is improving the behaviour of the gas turbine-based plants to investigate the associated power system control problems. Two gas turbine models will be used in this article which are Rowen’s and Vournas’s models. These models are studied when PID controller is used in its construction for speed and temperature control. Improving the gas turbine behaviour can be achieved by applying different artificial intelligent adaptation techniques on the PID controller of the dynamic models of Combined Cycle Power Plants (CCPPs). Tuning the parameters of a PID controller is very important. The well-known method to tune the coefficients of a PID controller is the Ziegler-Nichols method. This tuning method is very simple, but cannot guarantee to be always effective. For this reason, this paper investigates the tuning for a PID using Fuzzy, Genetic Algorithm and Particle Swarm Optimization methods. The results of tuning methods will be compared, analyzed and conclusion will be drawn out of the simulation results.

H ∞ control of single‐machine infinite bus power systems with superconducting magnetic energy storage based on energy‐shaping and backstepping

This article studies the H∞ control problem for single-machine infinite bus power systems with superconducting magnetic energy storage (SMES) via simultaneous design of generator excitation control and SMES control. In the control system design, the exciter dynamics and unknown external disturbances are considered for practical implementation in power systems. A new synthesis framework is proposed to address the robust control problem of power systems with SMES. First, from the energy perspective, the interconnection and damping assignment (IDA) approach is applied to improve the transient performance of power systems. Second, by introducing the idea of virtual control in the design process, the authors obtain the solution of a partial differential equation that is a crucial point and cannot be obtained by using the traditional IDA method. Third, in order to enhance the robustness of the closed-loop system, the authors study the H∞ control design that is challenging and difficult for power systems considering the above mentioned practical considerations. In addition, the authors use a novel equivalent generator model to avoid using unmeasurable infinite bus voltage. Simulation results also show the effectiveness and advantage of the proposed controller.

A simulation tool for voltage control studies in power systems

An electrical power system is a large scale system composed of complicated and sophisticated combination of multiple electronic and electromechanical components. In general, these components are nonlinear. Power system is also characterized by a wide range of normal operating conditions which continuously vary. To help the designer study the voltage control problems in power systems, a simulation tool is presented in this paper. It is based on decomposition of the overall system simulation task into three subtasks so as to attain both computational efficiency and flexibility. The use of the proposed simulation tool in a voltage control problem is also presented.

Discrete Events in Power Systems

The relevance of Discrete Event System theory (whether synthetic or analytic) to power system analysis and control is not clear. However, based on several characterizations of the domain of discrete systems set forth in the January 1989 Special Issue of the IEEE Proceedings, on ’’Dynamics of discrete event systems‘‘ (Ho, 1989), we attempt to delineate several power system control problems that may be amenable to analysis by DEDS methodologies. While the author is not qualified to judge whether the latter can speak to any of the examples, it may be relevant that in all cases the problems are non-trivial and in need of further contributions toward improved solutions, especially in view of the ongoing radical restructuring of the industry. The continuous process that is instantiated in the functioning of a power system is observed via three more or less diverse variables. Two of these, the scaler ’’system‘‘ frequency and the vector system voltage profile, are in principle mensurable physical phenomena; the third, system security, is a quality represented by a set membership conditioned on an inferred relationship between the two physical system variables and future realizations of one or more classes of autonomous stochastic phenomena. (These latter may be internal or external to the physical system, such as, e.g., respectively, equipment failure mechanisms and weather events.)

Design of nonlinear robust excitation control for multimachine power systems

The nonlinear robust control problem of power systems is investigated in the paper. A novel controller for the generator excitation system is set up, based on a combination of H∞ control theory and an exact feedback linearisation approach. The properties of the controller are its complete decentralisation and robustness. The design is well suited to a multimachine system in that it is not based on a infinite bus approximation. Simulation performed on a 13-machine system shows that the proposed controller can not only improve the dynamic performance of the power system but also enhance the system stability, especially the large disturbance stability.

Power system voltage stability and security assessment

The purpose of this investigation is to review the recent research progress in voltage stability and security assessment of power systems. This paper presents the fundamental concepts of voltage stability, gives a short recall of the classical voltage theory and different criteria currently available for predicting voltage collapse problems in power systems, which have been associated with multiple power flow solutions, bifurcation, dynamics of tap changers, stochastic loads, singular values, etc. The various methods for studying voltage collapse are compared. The application of expert system techniques to assist in the decision-making process of the reactive power/voltage control problem of power systems and future research directions towards voltage stability and security assessment will also be proposed in this paper.

Real time implementation of a MIMO adaptive power system stabiliser

Power systems are non-linear and they are often subjected to random disturbances. Therefore stochastic controllers with on-line system identification are ideally suited to power system control problems. Experience with a real time implementation of an adaptive power system stabiliser to damp the dynamic oscillations of a power system is presented. A multi-input multi-output (MIMO) pole shifting control algorithm together with a least-square system identification is used. The system identification is improved using a variable forgetting factor in the recursive least-squares algorithm. The computation time was greatly reduced by streamlining the identification algorithm using the sparse nature of the matrices associated with the computation and by using parallel processing techniques. The controller was tested in real time using a physical model of a power system. The results show that the damping of the power system dynamic oscillations can be improved by using this controller.

A New Approach to Security Control of Power Systems — Local Protection Coordinated with System-Wide Operation

This paper describes and discusses a new concept for security control of power systems. The concept is called system protection and is intended to take advantage of the remote control system as well as of the local protection systems.The objective of the system-protection function is to counteract the propagation of disturbances from a system-wide point of view. This is achieved by using decentralized systems in various substations for local decisions and actions and a centralized system within the remote control center for acquisition of system-wide information and planning of emergency control policies. The local decisions and actions are based on local information, system-wide data, and emergency control policies. The latter two types of information are transferred from the remote control center. Consequently, the inherent possibilities for immediate actions in local systems are combined with the system-wide view of the remote control system. Hereby, an economically feasible solution to the security control problem of power systems is obtained.

Control Behaviour of the West European Power System Simulated on the Basis of a Detailed Dynamic Model

This paper reports on a dynamic power system model which describes the physical interaction of power plant units, transmission network, and load configurations. Based on two former dynamic models a detailed reconstruction of the dynamic behaviour of the West European Power System was made in which all main transmission lines and power plants are reproduced individually. The accuracy and the efficiency of this reconstruction with up to 413 power plants, 984 nodes, and a corresponding number of dynamic loads was verified by the comparison with real disturbance cases, e.g. the switching off of power plants and transmission lines. The developped and verified dynamic model is a valuable tool for the simulation of large power systems and due to this for the solution of power system control problems and operating tasks.

A Perspective on Adaptive Control of Power Systems

Potential applications of adaptive control theory to electrical power systems control are explored. In the first part of the paper, the status of adaptive control theory is reviewed. Current mechanisms and methods of electrical power systems control are then examined. The two areas in adaptive power system control that have received the most attention are adaptive generator exciter control and adaptive load-frequency control. Nine recent papers on adaptive exciter control are described, as also are three papers on adaptive load-frequency control. Potential applications of adaptive control to other power system control problems are noted.