Passivity based distributed event-triggered load frequency control

Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

In this paper, the periodic event-triggered suboptimal control (PETSOC) method is developed for continuous-time linear systems. Different from event-triggered control, where the triggering condition is monitored continuously, the developed PETSOC method only verifies the triggering condition periodically at sampling instants, which further reduces computational resources. First, the control gain of the PETSOC is designed based on the algebraic Riccati equation. Subsequently, the periodic event-triggering condition is proposed for the suboptimal control method, which is only verified at sampling instants periodically. The sampling period is determined and analyzed based on the continuous form of the triggering condition. Moreover, the stability and the performance upper bound of the closed-loop system with the PETSOC are proved. Finally, the effectiveness of the developed PETSOC is validated through simulation on an unstable batch reactor.

In this paper, the dynamical response of the load frequency control problem in an interconnected power system is improved with a pragmatic viewpoint. In practice, access to all the state variables of system is not possible and also their measurements are not feasible. Usually only a reduced number of state variables or linear combinations thereof, are available. To resolve this difficulty, in this paper optimal output feedback method is proposed which uses only the measurable state variables. The optimal control is determined by minimizing a performance index under the proposed output feedback conditions. The effectiveness of the method is demonstrated on the two equal area interconnected thermal power system. The results indicate that the proposed controller exhibits better performance. In fact, the control system designed on this method satisfies the load frequency control requirements with a reasonable dynamic response. The proposed method has several desirable properties such as good sensitivity and robustness behavior.

This note considers the active secure event-triggered control(ETC) problem of hybrid power system under DoS attack. A combination of load frequency control (LFC) and virtual inertia control (VIC) is adopted to deal with the influence of uncertainty and lower inertia induced by renewable energy like wind and solar power. To active defend DoS attack interrupting communication of measurement and control, an interval secure event-triggered mechanism (ISETM) is proposed under software defined network (SDN). Both a triggering transmission and a secure triggering interval are generated where the triggering packet is transmitted over SDN data plane and the secure triggering interval is sent to SDN control plane regulating SDN cybersecurity mechanism. Under ISETM, multi-area hybrid power system is modeled by a delay system with two triggering conditions. Furthermore, interval secure event-triggered LFC-VIC of hybrid power system is formulated by a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty } $ </tex-math></inline-formula> control problem. A sufficient criterion of hybrid power system with the prescribed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty } $ </tex-math></inline-formula> performance level is derived by using Lyapunov-Krasovskii functional method. A co-designed method of ISETM and LFC-VIC gains is given by linear matrix inequalities (LMIs). Finally, a two-area hybrid power system is simulated to verify the validness of the proposed interval secure event-triggered control (ISETC) method.

This paper investigates the event-triggered model predictive control of positive systems with actuator saturation. Interval and polytopic uncertainties are imposed on the systems, respectively. First, a new model with actuator saturation obeying Bernoulli distribution is established, which is more general and powerful for describing the saturation phenomenon than the saturation in a deterministic way. Then, a linear event-triggering condition is constructed based on the state and error signal. Under the event-triggering condition, an interval estimate approach is presented to reach the positivity and stability of the systems. The saturation part in the controller is technically transformed into a non-saturation part. Thus, a linear programming approach is proposed to compute the event-triggered controller gain and the corresponding gain of attraction domain. A predictive algorithm is introduced for the computation of the event-triggered controller parameters. Finally, an example is provided to illustrate the validity of the design.

This paper presents multi area load frequency control (LFC) using artificial intelligence techniques in a power system comprising of non-reheat thermal power units for a three area interconnected power system. In this proposed scheme, fuzzy logic, Artificial Neural Network and Genetic Algorithm technique has been used to tune the classical PID controller for each area. Step load disturbance has been applied to assess its consequence on the dynamic performance of power system. The results are compared and appreciable change i.e minimum overshoot/undershoot, less settling time and a better response is achieved. For simulation of the controllers MATLAB simulation software is used.

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.

Event-triggered model-free adaptive predictive control for multi-area power systems under deception attacks

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 introduces an event triggering scheme for state feedback control of a class of stochastic systems which exists multiplicative noise affecting both state variables and control inputs. According to the state sampling error of the system state, the event trigger control scheme is developed to judge whether the output can be transmitted to the controller. Developing a state feedback controller that can make sure the closed-loop system is mean-square stable under given event triggering conditions is the aim of the control method. According to the linear matrix inequality (LMI), we propose a design method of event trigger control. Finally, the efficiency of the introduced method is verified by a numerical experiment.

Based on an event-triggered control strategy, the stabilization of a linear system with disturbance is the purpose of this article. In this article, we first introduce a newfangled event-triggered control with a linear diffusive term and discontinuous sign term. Then, a class of event-triggered conditions, whose threshold contains a constant term and a sampled state term, is provided for guaranteeing the stability of a disturbed linear system. The event could be directly triggered to change the control information by violating the event-triggered condition when packet losses during control information transmission are defaulting. This means that event-triggered control can markedly economize on resource waste and communication cost. In addition, the interexecution time, which is determined by the event-triggered condition, is proved to have a uniform positive lower bound to guarantee that Zeno behavior does not occur. In the presence of packet losses, we estimate the maximum allowable number of successive packet losses to maintain the desired performance of the system. Finally, the validity of the theoretical results is substantiated by an example.

Event-triggered networked control of a linear dynamical system is investigated. Specifically, the dynamical system and the controller are assumed to be connected through a communication channel. State and control input information packets between the system and the controller are attempted to be exchanged over the network only at time instants when certain triggering conditions are satisfied. We provide a probabilistic characterization for the link failures which allows us to model random packet losses due to unreliability in transmissions as well as those caused by malicious jamming attacks. We obtain conditions for the almost sure stability of the closed-loop system, and we illustrate the efficacy of our approach with a numerical example.

Load Frequency Control (LFC) for any power generating station is a subject of great concern for power system researchers. With the changes of load demand, frequency starts fluctuating which results in deviation in tie line power flow and frequency deviation at consumer end. To overcome this problem, many control techniques have been adopted. In early days fixed value integral/proportional-integral control, Optimal Control, Quantitative feedback theory, pole placement etc. methods were applied. In recent times, neural network, fuzzy logic, genetic algorithm controllers are replacing the conventional techniques. All the control techniques are used to find the optimal values of the PID/PI controller gain parameters (Kp, Ki, Kd) for which system stability is confirmed with minimum of Area Control Error (ACE). Differential Evolution (DE) which is a newer branch of genetic algorithms has been successfully applied in this problem. In this paper DE based PI controller has been implemented for Hydro-Thermal power plants to find out the optimal value of gain parameters for system stability. Nonlinearity has been considered in governor part of the thermal area for practical scenario. 1% step load changes have been applied to both areas simultaneously and individually to confirm its performance. Desired set of controller gain parameters (Kp, Ki) are selected based on eigenvalue and minimum value of Objective Function. All simulations are done in the MATLAB/SIMULINK environment.

In this note, robust consensus for a class of nonlinear second-order multiagent systems with uncertainties is investigated from an event-triggered control approach. An event-triggered distributed control protocol is designed to achieve semiglobal robust leaderless consensus for directed graphs. To guarantee the feasibility of the proposed scheme, the event-based consensus is converted to an event-based stabilization for analysis. Zeno behavior is avoided under the proposed event-triggered mechanism. The effectiveness of the proposed event-triggered scheme is demonstrated by numerical simulations.

This work presents a methodology for the design and analysis of an output feedback-based event-triggered networked control system for spatially-distributed processes with uncertain low-order dynamics, a finite number of spatially-distributed output measurements and sensor-controller communication constraints. Based on an approximate finite-dimensional system that captures the infinite-dimensional system's dominant dynamics, a model-based controller is initially designed and implemented. A state observer, with well-characterized state estimation error convergence properties, is also designed and embedded in the sensor to estimate the slow states of the infinite-dimensional system based on the available output measurements. The observer state is used to update the model state at times when sensor-controller communication is permitted. The update is triggered whenever a certain stability threshold on the model estimation error is breached. The threshold is explicitly characterized in terms of the controller and observer design parameters, the model uncertainty and the control actuator and measurement sensor locations. The connections between the developed output-feedback-based event-triggered control approach and the full-state feedback-based strategy are identified and discussed. Finally, the proposed methodology is illustrated using a simulation example.