Applying structured singular values and a new LQR design to robust decentralized power system load frequency control

Decentralised power system load frequency control beyond the limit of diagonal dominance

In this paper a robust and adaptive temporal difference learning based MLP (TDMLP) neural network for power system load frequency control (LFC) is presented. Power systems, such as other industrial processes, are nonlinear and have parametric uncertainties that for controller design had to take the uncertainties into account. For this reason, in the design of LFC controller the idea of TDMLP neural network is being used. Some simulations with two interconnections are given to illustrate proposed method. Results on interconnected power system show that the proposed method not only is robust to increasing of load perturbations and operating point variations, but also it gives good dynamic response compared with traditional controllers. It guarantees the stability of the overall system even in the presence of generation rate constraint (GRC). To evaluate the usefulness of proposed method we compare the response of this method with RBF neural network and PID controller. Simulation results show the TDMLP has the better control performance than RBF neural network and PID controller.

The decentralised load-frequency controller design problem concerned is translated into an equivalent problem of decentralised controller design for a multi-input multi-output (MIMO) control system. It is shown that, subject to a condition based on the structured singular values, each local area load-frequency controller can be designed independently. The stability condition for the overall system can be stated as to achieve a sufficient interaction margin and a sufficient gain and phase margin defined in classical feedback theory during each independent design. It is demonstrated by computer simulations that within this general framework, very simple local controllers can be designed to achieve satisfactory performances for a sample two-area power system and a simplified four-area power system. Under the proposed framework based on the structured singular values, other design methods for local area controllers may also be applied. © IEE, 1998.

At present, simple and classical tuned controllers are widely used in the power system load frequency control (LFC) application. Existing LFC system parameters are usually tuned based on experiences, classical methods, and trial and error approaches, and they are incapable of providing good dynamic performance over a wide range of operating conditions and various load scenarios. Therefore, the novel modeling and control approaches are strongly required, to obtain a new trade-off between efficiency and robustness. Thus, the proposed techniques in this chapter are referred to be an adaptive control technique based on new optimization methods such as Jaya, Practical Swarm Optimization Algorithm, etc., which are used to make an on-line tuning of the LFC parameters in order to face the previous challenges in LFC. The system under study is a small microgrid with a renewable energy source and variable demand load. Digital simulation results are discussed.

With the increasing penetration of renewable energy and power electronic devices, the frequency fluctuation of the power system will increase sharply, the inertia will decrease significantly, the damping characteristics will be poor, and the frequency adjustment ability needs to be improved. In this paper, a DRL (deep reinforcement learning) based framework is propsed to solve the LFC (load frequency control) problem for power system. This paper first introduces the model of LFC and basic description of DRL. Then the flow of twin delayed deep deterministic policy gradient (TD3) algorithm and a general framework for load frequency control. Finally, the proposed method is verified through a single area LFC test system. The implementation demonstrates that the proposed method is effective in LFC problem.

In this study, a particle swarm optimization (PSO) based Multi-Agent Reinforcement Learning (MARL) approach for load frequency control (LFC) of multi-area power system over communication network considering communication delay and packet loss is presented. A networked control system (NCS) is framed to model a constant delay and packet loss using Markovian approach. The controller using PSO based MARL technique having two levels of controlled action in each area of power system act as an estimator agent and controller agent respectively. The proposed method for frequency regulation of multi-area power system is studied in detail and verified for different types of load disturbances. In addition, to check the system dynamic performance of electric power system a mean square errors (MSEs) of states with different value of communication delay are computed. The simulation results of this study is validated and compared with some pre-design methods available from literatures, along with capacity of multi agent system (MAS) for application of LFC in power system over communication network.

The load frequency control (LFC) problem has been one of the major subjects in electric power system design and operation. LFC is becoming much more significant today in accordance with increasing size, changing structure and complexity in interconnected power systems. Practice LFC systems use simple proportional-integral (PI) controllers, but parameters of PI controllers are usually tuned based on the trial-and-error approaches and they are incapable to obtain good dynamic performance under a wide range of operating conditions. For this problem, in this paper quantitative feedback theory (QFT) method is used for LFC problem. A multi-area electric power system with a wide range of parametric uncertainties is given to illustrate proposed method. To show effectiveness of proposed method, a classical PI type controller optimized by genetic algorithms (GA) is designed to compare with QFT controller. The simulation results visibly show the validity of QFT method in comparison with traditional method. Key words: Multi-area electric power system, electric power system load frequency control, robust control, quantitative feedback theory method.

Persistent electrical supply requires the power systems to be stable and reliable. Against varying load conditions, control strategies such as load frequency control (LFC) is a key mechanism to protect its stability. Traditional control strategies for LFC often face challenges due to system uncertainties, external disturbances, and nonlinearities. This paper presents an advanced approach to control load frequency and enhancing LFC in power systems by using sliding mode control (SMC). SMC offers powerful stability and robustness versus nonlinearities and perturbation, making it a promising approach for addressing the limitations of conventional control methods. We contemporary a comprehensive analysis of the SMC approach tailored for LFC, including the strategy and employment of the control algorithm. The proposed method makes use of a sliding/gliding surface to enable the system trajectories to be continuous on this surface despite parameter variations and external disturbances. Simulation results demonstrate significant improvements in frequency stability and system performance compared to conventional proportional-integral-derivative (PID) controllers. The paper also includes a comparative analysis of SMC with other modern control techniques, highlighting its advantages in terms of robustness and adaptability.

In this paper an optimal load-frequency controller for a nonlinear power system is proposed. The mathematical model of the power system consists of one area with several power plants, a few concentrated loads and a transmission network, along with simplified models of the neighbouring areas. Firstly, a substitute linear model is derived, with its parameters being identified from the responses of the nonlinear model. That model is used for load-frequency control (LFC) algorithm synthesis, which is based on discrete-time sliding mode control. Due to a non-minimum phase behaviour of hydro power plants, full-state feedback sliding mode controller must be used. Therefore, an estimation method based on fast output sampling is proposed for estimating the unmeasured system states and disturbances. Finally, the controller parameters are optimized using a genetic algorithm. Simulation results show that the proposed control algorithm with the proposed estimation technique can be used for LFC in a nonlinear power system.

The hybrid power system (HPS) owing to its stochastic generating sources always needs a robust load frequency controller (LFC) for its reliable operation. This paper proposes a moth flame optimized fractional order proportional-integral-derivative controller with filter (FOPIDF) for frequency regulation of HPS. The simulation is carried out to validate the superiority of the combination of UC-UPFC over only UC. Different operating conditions like step change in load, random variation of demand is considered for validation purpose. Stability of the proposed system is also verified for momentary abnormal condition.

This paper describes a nonlinear radial basis function neural networks (RBFNN) controller based on /spl mu/ synthesis technique to load frequency control (LFC) of the power systems. Power systems such as other industrial plants have some uncertainties and deviations due to multivariable operating conditions and load variations that for controller design had to take the uncertainties into account For this reason, in design of the proposed loud frequency controller the idea of /spl mu/ synthesis theory is being used. The motivation of using the /spl mu/-based robust controller for training of the RBFNN controller is to take the large parametric uncertainties and modeling error into account The proposed controller is effective and can guarantee the stability of overall system in the presence of plant parameter changes and system nonlinearities. The simulation results on a two-area power system show that the proposed RBFNN controller gives good dynamic responses and is superior to the conventional PI and /spl mu/-based robust controllers.

A design scheme of smoothed switching structure theory based load-frequency control for power systems including parameter uncertainties is presented in this paper. However, knowledge about the bounds of the parameter uncertainties is required for controller design. The basic concept and design scheme of switching structure control are briefly discussed. The design process of load-frequency control using a linearized model with parameter uncertainties, which are obtained by simultaneously changing parameters by 55% to 95% from their nominal values, consists of key steps including both approximating continuous control law with sliding mode property, and the special switching hyperplane to enhance the system stability degree. The simulation results show that the proposed controller can not only improve the dynamic stability of the power system but also achieves good performance for all admissible uncertainties.

In this paper, a wide area measurement, centralized, load frequency control using model predictive control (MPC) is presented for multi-area power systems. A multivariable constrained MPC was used to calculate optimal control actions including generation rate constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional MPC was employed. Time-based simulation studies were performed on a three-area power system, and the results were then compared with decentralized MPC and classical PI controller. The results show that the proposed MPC scheme offers significantly better performance against load disturbance. Copyright © 2013 John Wiley & Sons, Ltd.

This paper proposes fully decentralized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> optimal dynamic state estimators (DSEs) for load frequency control (LFC) of interconnected power generating systems and provides its comparative and extensive study with respect to three other types of DSEs. To this end, we present the dynamic model of a single area of the power-generating unit occurring from the interconnected power systems. By noting the fact that each area is affected by the frequency deviations in other areas, and it is quite difficult to obtain any property of the load changes in power systems, we characterize the disturbances in the LFC of power systems as bounded persistent signals. As a candidate for DSEs for LFC of power systems, the unknown input observer (UIO), Kalman filter (KF), and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> optimal DSE are considered, and their limitations are analyzed in depth. In connection with this, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> optimal DSE, in which the maximum magnitude of estimation error for the worst bounded persistent disturbances is minimized, is proposed as the most effective state estimator. Finally, the practical validity and effectiveness of the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> optimal DSE are demonstrated through some comparative simulations for a three-area power generating system.

AIn this paper, impact of communication delay on distributed load frequency control (dis-LFC) of multi-area interconnected power system (MAIPS) is investigated. Load frequency control (LFC), as one of ancillary services, is aimed at maintaining system frequency and inter-area tie-line power close to the scheduled values, by load reference set-point manipulation and consideration of the system constraints. Centralized LFC (cen-LFC) requires inherent communication bandwidth limitations, stability and computational complexity, as such, it is not a good technique for the control of large-scale and geographically wide power systems. To decrease the system dimensionality and increase performance efficiency, distributed and decentralized control techniques are adopted. In distributed LFC (dis-LFC) of MAIPS, each control area (CA) is equipped with a local controller and are made to exchange their control actions by communication with controllers in the neighboring areas. The delay in this communication can affect the performance of the LFC scheme and in a worst case deteriorates power system stability. To investigate the impact of this delay, model predictive controller (MPC) is employed in the presence of constraints and external disturbances to serve as LFC tracking control. The scheme discretizes the system and solves an on-line optimization at each time sample. The system is subjected to communication delay between the CAs, and the response to the step load perturbation with and without the delay. Time-based simulations were used on a three-area MAIPS in MATLAB/SIMULINK environment to verify the investigations. The overshoot and settling time in the results reveals deterioration of the control performance with delay. Also, the dis-LFC led to zero steady states errors for frequency deviations and enhanced the MAIPS’ performance. With this achievement, MPC proved its constraints handling capability, online rolling optimization and ability to predict future behavior of systems.