Load Frequency Control in Multi-Area Power Systems Using Incremental Proportional–Integral–Derivative and Model-Free Adaptive Control

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.

This paper proposes a novel Improved Artificial Electric Field Algorithm (IAEF)-based AGC in a multi-area power system to minimize the area control error and to obtain an optimal solution. Here, four area AGC systems equipped with the PID controller are examined for the study processes. Also, an optimal gain value for the PID controller is obtained by employing an IAEFA; thus minimizing the area control error (ACE). In addition to this, nine different test functions are employed to evaluate the performances of the proposed IAEF-based AGC of a multi-area power system and are compared with several optimization approaches such as AEF algorithm, BBO Algorithm, PSO Algorithm as well as DE Algorithm. Also, the performance evaluation based on three different simulation strategies such as simulation based on time domain, Random disturbances of the AGC system as well as System Dynamics for maximum frequency deviation and Percentage Improvement for a four area power system. Here, the maximum frequency deviations for four areas are −0.0132, −0.0145, −0.0122 and −0.0120, respectively. Moreover, the percentage improvements with respect to the IAEF with respect to the PID controllers are 67.32%, 57.21%, 47.26% and 55.47%, respectively. The experimental analysis reveals that the proposed approach provides very less tie-line control error and better optimal tuning of the PID controller when compared with all other approaches.

In this paper, a decentralized load frequency control (LFC) is proposed for the frequency regulation of multi-area power systems applying optimized integral sliding mode control (OISMC) scheme. A modified particle swarm optimization (MPSO) algorithm is utilised to optimize the control variables of the proposed controller for improving the performance of frequency response. A comprehensive model of a multi-area power system is developed where each area is considered to power from conventional sources, renewable energy resources (RERs), and electrical vehicle (EV) aggregators. Different types of interconnectors have been considered to tie different power regions. This system is subjected to random generation and loading conditions, as well as model uncertainties, which cause its frequency and area control error (ACE) to deviate from their nominal values. A decentralised OISMC is implemented for the proposed model to keep the frequency variation within the nominal range. Further, a validation study investigates the performance of the proposed control technique in MATLAB simulation environment to mimic the real power system operation. This study includes different test scenarios considering the dynamic events such as variations in load demand, amount of generated power, participation factor of EV reserve, system parameters, disturbances, and time delay. The simulated results support better frequency regulation with the suggested OISMC compared to other alternative control approaches.

Electric vehicles (EVs) can be employed intelligently in Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) modes for Load Frequency Control (LFC) in the power grid. Due to the rapid variation of charging or discharging power, EVs can perform frequency regulation tasks and serve as distributed energy storage devices. In this paper, a linearized model of an interconnected multi-area power system is formulated in the restructured environment to include Electric Vehicle Aggregators (EVA). The DISCO Participation Matrix (DPM) is used to model Poolco and bilateral contracts in restructured multi-area power systems along with the participation of EVs. The faster dynamic response of EVs can effectively control the deviations in area frequencies imposed by the DISCOs load demand. The gains of the integral controller employed in each area are tuned by Real Coded Genetic Algorithm (RGA) by minimizing the integral square error function for area frequency deviations. The efficacy of power support from the EVs for load frequency control is validated by simulating different Poolco and bilateral transactions. It is shown that the EVs have a significant impact on obtaining optimal transient response for area frequencies, tie-line powers and power generation of conventional thermal generating units for any type of transaction in a restructured environment.

This paper is a study and review on application of Load Frequency Control(LFC), including two aspects: Due to the sudden load changes, intermittent wind power or large frequency fluctuation, power system's frequency deviates from the standard value, With the development of interconnected power grid and the electricity market, many optimization methods have emerged in modifying Automatic Generation Control(AGC) in order to improve system's frequency stability under different power disturbance. This paper also presents a Fuzzy-Genetic Algorithm(GA) controller based on PI controller. Using Fuzzy Control method to adjust the crossover probability, improve the convergence speed and accuracy. It can quickly adjust the system frequency deviation and ACE to zero, having strong robust performance and anti-interference for power disturbances.

A memory output sliding mode load frequency control (MOSMLFC) strategy is proposed for multi-area interconnected power systems under historical-frequency-triggered denial-of-service (DoS) attacks. Due to the use of the open network, the multi-area power system is prone to cyber-attacks. Different types of attack models have been built to describe the actual attack behavior, so that effective strategies can be quickly formulated in the event of an attack. Therefore, a historical-frequency-triggered DoS attacks model is presented from the perspective of attackers, with the aim of destroying the stable state of the multi-area power system. It is assumed that attackers determine the timing of DoS attacks by monitoring the operational status of multi-area power systems and designing the triggering condition with historical frequency. A MOSMLFC strategy is investigated to ensure the security performance of multi-area power systems under historical-frequency-triggered DoS attacks, which applies the memory output information of the power system to realize the controller design. The security condition of multi-area power systems under historical-frequency-triggered DoS attacks is obtained by Lyapunov’s theorem and linear matrix inequality (LMI). Numerical examples are tested over the IEEE 10-generator 39-bus system and the results prove the usefulness and superiority of the proposed method. Note to Practitioners—Load frequency control is widely applied in multi-area power systems to achieve a balance between the load demand and generation. Frequent cyber-attacks are a threat to the normal operation of the power system. It is therefore necessary to develop appropriate strategies to defend against cyber-attacks. So far, there have been many different forms of cyber-attacks. This has prompted defenders to build different types of attack models to describe the actual attack behavior in order to preemptively formulate appropriate defensive strategies. Smart attacker may notice that certain characteristics of the target system are important, such as the power system frequency. This motivates us to propose a historical frequency-triggered DoS attack model that contributes to a deep understanding of the impact of cyber-attacks on the power system. We propose a unique sliding mode control approach to ensure the stable performance of power system state and output simultaneously.

In this brief, a novel finite-time data-driven control algorithm to address the load frequency control (LFC) problem in multi-area power systems (MAPSs) with unknown system parameters. To enhance the system’s response speed in solving the LFC problem, a new finite-time performance cost function is introduced within the model-free adaptive control (MFAC) framework. This cost function ensures that the LFC problem is resolved within a finite time step. Furthermore, the proposed algorithm relies solely on real-time input and output data, without the need for an accurate system model or prior knowledge. The stability of the algorithm is rigorously validated through comprehensive theoretical analysis, and its effectiveness is confirmed through simulations of a three-area power system.

This paper presents a data-driven model-free adaptive containment control (MFACC) scheme for uncertain rehabilitation exoskeleton robots, where the robotic exoskeleton dynamics are uncertain with saturation constraints. To handle uncertainties of the robotic dynamics, a model-free adaptive control (MFAC) strategy is established by linearizing the robotic exoskeleton dynamics into an equivalent data model. Considering the integral additive effect of the traditional MFAC method, an improved MFAC controller is designed in this paper. Since actuators with saturation constraints constantly affect the safety of patients during rehabilitation training, we construct a new criterion function with active constraints for the critical function of the MFAC algorithm and adopt the Hildreth quadratic programming algorithm to find the constrained optimal solution to overcome this limitation. The proposed MFACC scheme is rigorously proven by the compression mapping method to demonstrate model-free stability. Finally, the proposed control scheme is verified to be effective by simulation studies of the robotic SimMechanics model.

This paper deals with fault-tolerant-based load frequency control in multi-area power systems. First, by using sliding mode observer technique, the system states and faults are figured out combined with the designed derivative and proportional gains. Upon the state estimates, sliding mode controller is synthesized to keep the asymptotical stability performance of the fault system.

This paper investigated the sliding mode load frequency control (LFC) for an multi-area power system (MPS) under deception attacks (DA). A Luenberger observer is designed to obtain the state estimate of MPS. By using the Lyapunov-Krasovskii method, a sliding mode surface (SMS) is designed to ensure the stability. Then the accessibility analysis ensures that the trajectory of the MPS can reach the specified SMS. Finally, the serviceability of the method is explained by providing a case study.

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 is one of the most important issues in multi area power systems. In this context, due to growing development of wind turbines in power systems, this type of renewable power sources can be used efficiently in frequency control. This paper applies doubly-fed induction generators (DFIGs) along with other conventional generation units for frequency performance enhancement after disturbances in multi-area power systems. Proportional integral (PI) controller is the conventional controller which is used in DFIGs to contribute in frequency control. The parameters of PI controller for DFIG are optimized by improved particle swarm optimization (IPSO) method. Simulation results in a three-area power system illustrate the efficiency of the DFIGs for frequency and tie-line power oscillations improvement.

In this paper, the current application status of gas turbine control technology is introduced, it presents the model free control theory and method in perspective of definition of model free control and model free adaptive control algorithms mainly existed and its advantages. This paper introduces some unsolved problems by using traditional control approaches. It gives reasons why the model free is suitable for the control of gas turbine. Afterwards, it describes how the model free adaptive control technology is applied to gas turbine and its related techniques in detail in this paper. In this part, it could provide theoretical foundation for applying model free control in actual gas turbine control system. Finally, conclusions and issues needed further research are put forward at the end of this paper.

This article addresses the design procedure and numerical validation of a robust fuzzy logic-based fine-tuning approach devised to enhance load frequency control capabilities in multi-area power systems. The founded robust fuzzy logic-based fine-tuning approach is intended for judicial parameter tuning of a proportional-integral controller encountering fault occurrences or severe changes in system loading conditions. Unlike conventional proportional-integral controllers that are generally designed for fixed operating conditions, the outlined approach demonstrates robust performance with power system uncertainties and disturbances. In this context, the projected robustness is principally emanated due to a fuzzy logic extensibility feature, furnishing the established framework to have a proliferated observability on control space. The design procedure of a robust fuzzy logic-based fine-tuning controller is substantially nourished by expert knowledge regarding the overall system performance. Apt fuzzified and defuzzified rule-based mechanisms are wisely included as the key building blocks of the fuzzy inference engine. Extensive numerical studies are launched in a thorough investigation of the proposed approach. As well, the robust fuzzy logic-based fine-tuning performance is compared with that of two extra robust methods, namely the H∞-iterative linear matrix inequality as well as the hybrid genetic algorithm and linear matrix inequality. The obtained results are deeply analyzed.

In order to alleviate the congestion between the side road and the freeway, a novel balancing control scheme is proposed based on model free adaptive predictive balancing control (MFAPBC). The proposed MFAPBC is a typical data driven control method, which merely utilize the measured input and output (I/O) data of side road and freeway to update the control input signal. Model free adaptive predictive control (MFAPC) has the advantages of model predictive control (MPC) and model free adaptive control (MFAC); that is, only the I/O data of the controlled plant are used to predict the system output sequence within the prediction horizon and to calculate the control input sequence within the control input horizon, without the controlled plant model involved. The effectiveness of the proposed MFAPBC method is test in simulation for side road and freeway system.