Load-frequency control scheme for power systems using smoothed switching structure theory

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 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.

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.

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.

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.

In this paper a fuzzy logic (FL) based load frequency controller (LFC) called discrete FuzzyP+FuzzyI+FuzzyD (FP+FI+FD) is proposed to ensure the stability of a multi-source power system in restructured environment. The whale optimization algorithm (WOA) is used for optimum designing the proposed control strategy to reduce fuzzy system effort and achieve the best performance of LFC task. Further, to improve the system performance, an interline power flow controller (IPFC) and superconducting magnetic energy system (SMES) is included in the system. Governor dead band, generation rate constraint, and time delay are considered as important physical constraints to get an accurate understanding of LFC task. The performance of the optimized FP+FI+FD controller is evaluated on a two area six-unit hydro-thermal power system under different operating conditions which take place in a deregulated power market and varying system parameters in comparison with the classical fuzzy PID controller. Simulation results shows that WOA based tuned FP+FI+FD based LFC controller are relatively robust and achieve good performance for a wide change in system parameters considering system physical constraints.

In the paper, based on a combination of the robust control approach and an adaptive control technique, a design procedure of a new robust adaptive controller is proposed for power system load-frequency control with system parametric uncertainties. The system parametric uncertainties are obtained by changing parameters by 30% to 50% simultaneously from their typical values. In the design of the proposed controller, only the bounds of system parameters are required. The motivation of combining the robust control with an adaptive control is to use the robust control approach to deal with the small parametric uncertainties and to use the adaptive control for large parametric uncertainties so that stability of the overall system and good performance can both be achieved for all admissible uncertainties. The simulation results demonstrate that for the example system the proposed load-frequency controller can achieve good performance even in the presence of the generation rate constraint (GRC).

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.

Information and communication technology tremendously facilitates the operation efficiency and economy of modern power systems in recent years. However, risks such as bandwidth constraints and malicious attacks threaten the secure load frequency control (LFC) of power systems. To mitigate such risks, this paper proposes distributed observer-based event-triggered LFC schemes for multi-area power systems under cyber attacks. Considering the practical situation that only local system output information may be available, distributed observer-based LFC schemes are designed. Meanwhile, to reduce the communication burden, an event-triggered mechanism is adopted to design control laws, where both static and dynamic event-triggered approaches are taken and the dynamic one is proved to be more economical in terms of control cost. Verifiable sufficient conditions are established to guarantee the stability of the closed-loop system in the presence of cyber attacks and the controller gains are explicitly derived. Finally, validation studies on a three-area interconnected power system are carried out to demonstrate the proposed control schemes. Note to Practitioners—Load frequency is a crucial index for evaluating the quality of electric energy and thus LFC has brought considerable attention in the area of power systems control. Although many achievements have been made on the LFC of multi-area power systems, the risks such as bandwidth constraints and malicious attacks affect the normal operation of LFC due to the interconnection between different power systems. To deal with these risks, this paper focuses on designing event-triggered LFC to guarantee the stability of the frequency deviation while reducing the communication burden and mitigating cyber attacks. More importantly, the proposed event-triggered LFC is designed based on an observer and can be implemented in a distributed manner, which is relatively practical in real applications. The results presented in this paper aim to provide a helpful reference for stable and secure LFC design of multi-area power systems, such that the corresponding application research can be promoted.

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.

This paper proposes a novel load frequency control (LFC) approach formulated on an optimal structure of the coefficient diagram method (CDM) in a two-area thermal power system. As part of a realistic analysis, nonlinearities related to governor dead band (GDB) and generation rate constraint (GRC) have been considered. In this article, a hybrid CDM method is combined with the optimization of its mathematical equations to achieve an innovative controller. Furthermore, a new metaheuristic optimization technique called the water cycle algorithm (WCA) is used to determine the optimal coefficients of the CDM controller. For the purpose of demonstrating the validity of the proposed scheme, a wide range of uncertainties in the dynamic parameters of a nonlinear power system were tested. In addition, a comparative study is presented between the results obtained from a classical integral, CDM alone, optimized fuzzy, optimized PID, and the suggested controller. In this new approach to improved control, algebraic support provides a robust and responsive controller that can provide fast and stable dynamic responses and effectively overcome the detrimental effects of nonlinearities and uncertainties in the parameters of the power system.

Fractional order phase lead-lag controller (FOPLC) is a generalization of classical phase lead-lag controller (CPLC) using fractional calculus. Nowadays, fractional order controllers (FOC) have found wide applications in various aspects of power system control and operation. In this paper, FOPLC are used in structure of flexible ac transmission system (FACTS) based frequency stabilizer to enhance the dynamic performance of automatic generation control (AGC) system. So, Thyristor controlled series capacitor (TCSC) based damping controller with FOPLC structure is proposed in coordination with secondary integral load frequency controller (LFC). The controller is optimized via an improved particle swarm optimization (IPSO) algorithm. The dynamic performance of the proposed controller is compared with CPLC using eigenvalue analysis and time domain simulations. An interconnected multi-source realistic power system with TCSC in series with the tie-line is investigated considering the nonlinearity effects of generation rate constraint (GRC) and governor dead band (GDB). Simulation results reveal that FOPLC based TCSC damping controller achieves superior dynamic performance under various load perturbation patterns in terms of decreased performance index, settling time and amplitude of oscillations. Furthermore, the robustness of the proposed controller has been tested for large uncertainties in system parameter and loading condition.