Iterative Learning Load Frequency Control of New Power System Based on Adaptive Event-Triggered Mechanism

In this paper, the pumped storage hydropower (PSH) is introduced into the conventional power system to be constructed as a load frequency control (LFC) model for frequency management of the auxiliary power system. First, a multi-area LFC systems with PSH participation by considering time delay is delicately constructed. The PSH is modelled as an auxiliary frequency adjustment tool that can achieve energy conversion to balance the system. Second, an adaptive event-triggered mechanism (AETM) is suggested as a means to enhance data transmission efficiency. Then, the Lyapunov theory is utilized to establish adequate conditions that guarantee prescribed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H$</tex> ∞and exponential mean-square stability for multi-area power systems. Finally, several simulations are used to show that the suggested strategy works.

Adaptive event-triggered PID load frequency control for multi-area interconnected wind power systems under aperiodic DoS attacks

It is eminent that iterative learning control algorithm is of high significance due to its speciality of tracking control for systems developed from real‐world phenomena that occurs repeatedly. Singular systems are well‐known for their applications in network analysis, biological systems, economic systems, social systems, engineering systems, time‐series analysis, and many other areas of science and technology. In this article, we investigate and analyze the convergence characteristic of PD‐type iterative learning control (ILC) scheme for linear discrete‐time singular system. We reformulate the discrete‐time singular system as a kind of algebraic input‐output transmission based on the lifted vector technique. The monotonic convergence has been deduced in the sense of 2‐norm for the first‐order as well as second‐order PD‐type ILC scheme. It is shown that the second‐order PD‐type ILC algorithm has good tracking performance than the first‐order on the whole time interval. Finally, we perform comparative analysis to validate the results numerically.

Sampled-data based discrete and fast load frequency control for power systems with wind power

In this work, two practical iterative learning control (ILC) schemes are analyzed and compared in frequency domain. Both ILC schemes consist of a feedback and a learning based feedforward compensation. Through analysis, we show that the difference in the learning updating mechanisms, in the sequel, exploit the circumstances where one ILC scheme can outperform the other. Based on the system Bode plots, we can easily check the learning convergence condition, the complementary property of feedback and feedforward compensation, and which ILC scheme can perform better. For the purpose of comparison and verification, both ILC schemes are applied to a real-time ball-and-beam system.

Load frequency control (LFC) plays an important role to maintain the system frequency and tie-line flow at their respective scheduled values during normal period in the power system. Generally, the conventional control strategies, such as PI and PID control algorithms are applied for LFC in practical power systems. However, good control performance could not be guaranteed by using such traditional control strategies when external disturbances occur. In order to overcome the drawbacks, fuzzy logic-based proportional integral (FLPI) controller is proposed for an efficient LFC performance in this paper. Considering the reason that various disturbances and uncertainty consisting of load variation, modeling error, and structure change exist in power system, fuzzy logic modeling method is used for improving the robust performance. And a PI control strategy based on Ziegler-Nichols is used to eliminate or reduce the frequency deviation, tie-line deviation, and area control error. Finally, simulation results demonstrate that the proposed method can stabilize the two-area power system; the performance indices including settling time and overshoots of the tie-line power and frequency deviations can also be improved through the proposed method.

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.

Load frequency control (LFC) continues to be a major problem in multi-area power systems, which is compounded by communication network issues and parametric uncertainties, that degrade controller performance. In this contribution, a fuzzy H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -iterative learning controller (FILC) is designed for decentralized LFC with little knowledge of the local power area's model and no knowledge of the external power areas' models. Further to this, time-varying communication delays, parametric variations, large disturbances, and non-identical power system area parameters are considered. The FILC strategy comprises a fuzzy strategy that quickly rejects large disturbances and drives the error to a designed tolerable band where an iterative learning control technique that is proven to be asymptotically stable with a prescribed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance achieves zero-error convergence. The proposed FILC is compared with that of the well-known PI algorithm for a three-area power system. Results indicate that the FILC performs significantly better than the PI controller under practical combinations of network problems, overlapping large disturbances in multiple areas and parameter variations.

In this article, the H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> dynamic triggering control design problem is concerned for a discrete-time switched control system with aperiodic and parameter uncertainty denial-of-service (DoS) attacks. First, by introducing dynamic trigger scheme, a switched delay system model under discrete-time can be established. Then, by integrating discrete piecewise Lyapunov-Krasovskii functional method with switch system method based on average dwell time (ADT), we obtain a criterion of preserving weighted H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> performance of the switched delay system. Derived from the criterion, since the DoS duration and frequency are constrained by upper/lower bounds, the dynamic output feedback control gain can be designed by solving a set of LMIs. Finally, by investigating a resilient H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> load frequency control (LFC) for a three-area power system, we verify the effectiveness of the method through numerical simulations.

This paper proposes a hierarchical hybrid control framework, termed Fuzzy Model Predictive Control with Iterative Learning (FMPC–IL), to enhance load–frequency control (LFC) in single-area hybrid power systems with high renewable penetration. The architecture integrates three layers: (i) a supervisory fuzzy logic controller (FLC) that adaptively tunes the MPC cost-function weights (Qⓜ,R)using the frequency error and its change, improving robustness under operating uncertainties; (ii) a constrained model predictive control (MPC) layer that computes the optimal control action while explicitly enforcing actuator and operational limits; and (iii) an iterative learning control (ILC) layer that exploits repetitive daily patterns in load and renewable profiles to generate a feedforward compensation signal updated cycle-to-cycle. The nonlinear governor–turbine limitations, including valve deadband, actuator saturation, and generation rate constraints (GRC), are represented in the simulation plant, whereas a linearized discrete-time model is used as the internal prediction model for MPC optimization. The proposed FMPC–IL strategy is evaluated in MATLAB/Simulink under stochastic load disturbances, renewable fluctuations, and parametric uncertainties. The results demonstrate improved frequency regulation and robustness compared with MPC, fuzzy-MPC, and MPC–IL baselines, particularly during severe disturbances and uncertainty scenarios.

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.

The modern power systems with industrial and commercial loads need to operate at constant frequency with reliable power. The load frequency control of an interconnected power system is being improved over the last few years. The goals of the LFC are to maintain stability improvement of power system. Fuzzy logic controller application of load frequency control (LFC) of a single area power system by using a fuzzy logic is presented. The study has been designed for a single area interconnected power system. The comparison between a conventional Proportional and Integral (PI) controller and the proposed artificial neural networks controller is showed that the proposed controller can generate the best dynamic response for a step load change. For this application, MATLAB-Simulink software is used.

For the load frequency control (LFC) of the interconnected power system, a two-level coordinated control frame based on multi-agent system is proposed to improve the frequency control performance of the power system. In the control frame, the upper-level agent, in a centralized manner, coordinates the lower-level LFC agents to deal with the conflict between narrowing the area control error and stabilizing the system frequency. The lower-level LFC agents, in the distributed cooperative manner, realize the mutual power support among the neighboring areas to guarantee the frequency stability in an emergency. To verify the validity of the proposed method, the numerical simulations are performed on the three-area interconnected power systems.

A dynamic wavelet network based adaptive load frequency control in power systems

Lots of iterative learning control (ILC) algorithms have been developed to improve tracking performance of systems that track same trajectory repetitively. In this paper, we use an open-closed-loop PD-type ILC scheme for the control of a class of nonlinear systems. Sufficient condition for guaranteeing the convergence of the learning scheme is given under a few reasonable assumptions, and then proved with linear operator theory. It is shown that the proportional coefficients of the ILC scheme is not a factor for guaranteeing the convergence, the condition is weaker than known results for similar ILC schemes.