Load Frequency Control System Research Articles

[formula omitted] sampled-data load frequency control with time-varying delays considering delay compensation

Time delays in load frequency control (LFC) systems may deteriorate the control performance or cause instability. How to mitigate such adverse effects of delays in a simple but effective way is worth investigating and motivates this work. For sampled-data LFC systems with time-varying delays, this paper introduces two delay compensation techniques, namely linear and division-based methods, aimed at enhancing the robustness of LFC. A stability criterion based on H∞ is derived to ensure LFC system stability under delay compensation. Building upon this criterion, we propose an H∞ controller optimization method to tune delay compensation controller gains offline by searching for a minimum H∞ disturbance attenuation level and effectively segmenting the geometric region formed by the delays. The effectiveness of the proposed delay compensation techniques and the controller design method is verified via numerical examples on an LFC system with both generation unit and energy storage participation.

Stability analysis of delayed load frequency control system based on a novel augmented functional

This article is concerned with the stability issue of PI-based load frequency control (LFC) systems with time-varying delays and load interference. Firstly, a novel augmented Lyapunov–Krasovskii functional (LKF) is developed, in which the single integral term includes the augmented s-dependent integral term of delay-partition. For the purpose of coordinating with the constructed LKF effectively, a generalised free-matrix-based integral inequality and quadratic inequality are utilised to estimate the functional derivatives accurately, so that the stability criterion of less conservative is obtained. Finally, the relationship between the PI gains and the delay margins is presented, and the effects of time delays and PI gains on the system dynamic performance are discussed simultaneously by simulation. The simulation results verify the validity of the proposed method.

Optimizing load frequency control in microgrid with vehicle-to-grid integration in Australia: Based on an enhanced control approach

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency.

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart Grid involves application of Information and Communication Technology (ICT) for monitoring, protection, operation, and control of interconnected power systems under various scenarios. Smart Grid Control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats, and attacks under central and distributed control architectures. Cyber Security of smart grid control, especially, Load Frequency Control (LFC) is an important issue that is addressed in this article. The state-of-the-art in cyber security and attacks on smart grid control and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic Vulnerability Assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for Vulnerability Assessment of the standard 39-bus New England test system and the 96 bus reliability test system to illustrate the concept of cyber security and Vulnerability Assessment of smart grid LFC.

Design of Optimal FOPI Controller for Two-Area Time-Delayed Load Frequency Control System with Demand Response

Design of Optimal FOPI Controller for Two-Area Time-Delayed Load Frequency Control System with Demand Response

DAR-LFC: A data-driven attack recovery mechanism for Load Frequency Control

In power systems, generation must be maintained in constant equilibrium with consumption. A key indicator for this balance is the frequency of the power grid. The load frequency control (LFC) system is responsible for maintaining the frequency close to its nominal value and the power deviation of tie-lines at their scheduled levels. However, the remote communication system of LFC exposes it to several cyber threats. A successful cyberattack against LFC attempts to affect the field measurements that are transferred though its remote control loop. In this work, a data-driven, attack recovery method is proposed against denial of service and false data injection attacks, called DAR-LFC. For this purpose, a deep neural network is developed that generates estimations of the area control error (ACE) signal. When a cyberattack against the LFC occurs, the proposed estimator can temporarily compute and replace the affected ACE, mitigating the effects of the cyberattacks. The effectiveness and the scalability of the DAR-LFC is verified on a single and a two area LFC simulations in MATLAB/Simulink.

Load Frequency Active Disturbance Rejection Control Based on Improved Particle Swarm Optimization

A load frequency control (LFC) system based on active disturbance rejection control (ADRC) is designed to solve the problem of frequency modulation caused by large-scale renewable energy grid connection. Traditional parameter-tuning methods are inefficient and often fail to achieve desired control outcomes. To overcome this, an improved Particle Swarm Optimization (PSO) algorithm is introduced, incorporating Levy flight and chaotic mapping. This enhanced algorithm combines the long step–length search capability of Levy flight with the rapid exploration of initial solution space using Tent chaotic mapping, enhancing PSO’s global search ability and addressing premature convergence issues. Simulation results demonstrate that the ADRC controller optimized by the improved algorithm exhibits greater robustness and smaller deviation compared to the original algorithm, showcasing its excellent control performance.

Load frequency and voltage control of two area interconnected power system using covariance matrix adaptation evolution strategy based Aquila optimization optimized fast fuzzy-fractional order tilt integral derivative controller

In this paper, an optimal tuning method is given for a Fast Fuzzy-Fractional Order Tilt Integral Derivative Controller (HFF-FOTIDC) used in the Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR) systems of a two-area interconnected power system. LFC is in charge of managing the frequency and real power flows in the system. AVR is in charge of maintaining the voltage profile and managing the flow of reactive power. In Area 1, a step disturbance is used to study how the system works in a dynamic way. By looking at the system's voltage, power flow through the tie lines, and frequency, the experts can figure out how the system will react. The proposed control techniques combine two well established control logic, which are fast fuzzy control logic and fractional order tilt integral derivative control logic. The main goal is to decrease voltage and frequency changes caused by step disturbances and return them to their normal values. The simulation results show how good the suggested system is by comparing it to systems that use traditional proportional integral controllers and traditional Integral controllers. By using the finest tuning method for the fast fuzzy-fractional order tilt integral derivative controller, the designed system has better performance than standard controllers at reducing fluctuations and getting the desired frequency and voltage responses. The parameters of the proposed controller are optimized by using Covariance Matrix Adaptation Evolution Strategy based Aquila Optimization (CMAESAO) optimization technique.

Determination of the robust stability delay margins for the load frequency control systems

Bu çalışma, zaman gecikmesi içeren Yük Frekans Kontrol (YFK) sistemlerinde parametrik belirsizliklerin olması durumunda sistemin gürbüz kararlılık gecikme payı değerlerini hesaplamayı amaçlamaktadır. YFK sistemlerinde frekans kararlılığının sürdürülmesi bakımından çeşitli elektrik verilerinin ölçülerek kontrol merkezine iletilmesi ve kontrol merkezinden frekans kontrol servisine katılım sağlayan santrallere kontrol sinyallerinin iletilmesi gerekmektedir. Bu süreçte, haberleşme ağlarında veri iletimi nedeniyle, zaman gecikmeleri kaçınılmaz hale gelmektedir ve sistemin dinamik performansı ve kararlılığı olumsuz etkilenmektedir. Ayrıca, YFK sisteminin modellenmesinden kaynaklı ve güç sisteminde oluşabilecek belirsizlikler nedeniyle sistem parametrelerinin belirsizlikleri dikkate alınarak haberleşme ağı tabanlı gözlemlenebilecek gecikme değerleri üzerinde böylesi belirsizliklerin etkisi incelenmelidir. Bu amaçla, YFK sisteminde üstel terimin yok edilmesi yöntemi ile Kharitonov Teoremi birlikte kullanılarak sistemin gürbüz kararlılığını sağlayabilen gürbüz zaman gecikme payı değerleri teorik olarak hesaplanmıştır. Teorik sonuçların doğruluğu Matlab/Simulink programı ve QPmR (Quasi Poliynomial Mapping Root Finder) algoritması kullanılarak gösterilmiştir.

Load Frequency Control and Optimization of Load Allocation for Multi-Area Power System with Electric Vehicle Charging Load

The integration of electric vehicles (EVs) into power systems has introduced new challenges for load frequency control due to the additional charging load they impose. This research article investigates the design and analysis of automatic load frequency control in a two-area power system, considering the presence of EV charging load. The study employs Artificial Neural Network (ANN) based PI control to manage both traditional load demand and the dynamic charging requirements of EVs. Maintaining a stable power system frequency and balancing generation with the EV charging load have become crucial tasks. Automatic Generation Control (AGC) or Load Frequency Control (LFC) systems need to adapt and account for the variability and uncertainty associated with EV charging patterns. The integration of ANN-based PI control provides an intelligent and adaptive approach to address these challenges. Using MATLAB, a power system model is simulated to evaluate the effectiveness of the proposed control scheme. This investigation conducts a comparative analysis of the system's frequency responses under various scenarios, including different EV charging load profiles. It highlights the benefits and challenges of utilizing ANN-based PI control to manage the combined load of traditional demand and EV charging. Moreover, the load distribution among the distribution stations varied from 0.08% to 12.50% when compared between particle swarm optimization and genetic algorithm, respectively. By considering the dynamic interaction between power system operation and EV charging, this research aims to enhance the reliability, efficiency and sustainability of power systems in the context of evolving transportation trends and the increasing electrification of vehicles.

Attack Detection and Mitigation Scheme of Load Frequency Control Systems Against False Data Injection Attacks

Attack Detection and Mitigation Scheme of Load Frequency Control Systems Against False Data Injection Attacks

Design of control systems with multiple memoryless nonlinearities for inputs restricted in magnitude and slope

This paper develops a methodology for designing a control system composed of a linear time-invariant system interconnecting with multiple decoupled time-invariant memoryless nonlinearities. The design problem is to determine parameters of the system such that its outputs and the nonlinearity inputs always remain within prescribed bounds for all exogenous inputs whose magnitude and slope satisfy certain bounding conditions. By using Schauder fixed point theorem, we show that a design associated with a linear system is also a solution of the problem. Based on this, we further develop surrogate design criteria in the form of the inequalities that can readily be solved in practice. Sufficient conditions for the solvability of such inequalities are given for deadzone and saturation. To show the usefulness and the effectiveness of the methodology, a design example of a load frequency control system with time delay is carried out where deadzone and saturation are taken into account.

Renewable Energy‐Based Load Frequency Controller and Model with a Reduced Order for a Large‐Scale Power System

Power system stability relies heavily on load frequency control (LFC) because it ensures that the power generated in a grid matches the load demand. The conventional LFC methods are no longer adequate with the rising use of renewable energy sources like wind and solar power. These resources are intermittent, and their power output varies depending on weather conditions. As a result, incorporating renewable energy resources (RERs) into the LFC system necessitates a different approach. One method is to use advanced control algorithms that can respond quickly to changes in power output. Hence, this article proposes an autoencoder‐based jellyfish optimization developed in this framework to reduce the power outage and control voltage and frequency fluctuations. The LFC system can be optimized by combining autoencoder and jellyfish optimization techniques to effectively handle the variability and unpredictability of RERs while maintaining the power system's stability and reliability. This approach may also result in more efficient and cost‐effective power system operation by reducing the need for conventional power generation and minimizing the impact of RERs on the system.

Resilient Event-Triggered Load Frequency Control for Cyber-Physical Power Systems Under DoS Attacks

In cyber-physical power systems (CPPSs), additional control as the compensating control plays a very important part in load frequency control (LFC) systems. The additional control in the LFC system is constrained by limited communication resources and cyber-attacks, which may cause performance degradation or destabilize the system. Accordingly, this paper proposes resilient event-triggered LFC for CPPSs with an additional control loop under denial-of-service (DoS) attacks. Firstly, a resilient event-triggered communication scheme is presented to reduce the occupation of communication resources under DoS attacks in the additional control loop. Then, different from existing event-triggered LFC systems, this paper establishes a novel switched LFC system model, where the resilient event generator is integrated into an additional control loop when suffering from DoS attacks. It is the first time that the additional control loop of the LFC system simultaneously considers communication resources and cyber-security. Furthermore, we derive exponential stabilization criteria by applying the Lyapunov stability theory based on the established model. Criteria are derived to obtain the weighting matrix and controller gain simultaneously by applying the linear matrix inequality technique. Finally, a one-area and two multi-area CPPSs with the additional control loop under DoS attacks are used to testify the availability of the proposed resilient event-triggered LFC scheme.

Black Widow Optimization for Power System Load Frequency Control: A Comparative Study

This research paper mainly engrossed on developing a suitable novel tuning methodology named Black Widow Optimization (BWO) Algorithm for power system optimization problems. Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR), two of the most important control systems in the power system arena, are employed as test systems to assess the efficiency of the suggested BWO approach. Various analyses, such as transient analysis, are employed to evaluate the efficiency of the suggested BWO approach in LFC and AVR systems. robustness analysis and convergence analysis. The significant performance measures of test system such as output power of each generating unit, maximum peak, settling time, steady state error, rise time and voltage/frequency deviations of the frequency and voltage responses are considered for the comparison purposes. The comparative analysis clearly demonstrates that the proposed novel tuning methodology provides better transient performances subjected to Step Load Perturbations (SLP), Variable Load Perturbations (VLP), and robustness performances under a wide range of load and parameter changes ranging from -50% to +50%. The convergence analysis of BWO algorithm also performed in LFC and AVR test systems and results better convergence characteristics with minimal convergence time as well as least fitness value is obtained with the BWO algorithm.

Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System

Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.

Optimizing the Load Frequency of a Two-Area Interlinked Power System using Artificial Intelligence Techniques

Power costs are increasing on a daily basis, generating changes in system frequency and causing serious concerns with system stability. It has become a major problem to offer customers with uninterrupted and high-quality power. To mitigate these issues, a linked power system's load distribution and network frequency should be constantly reviewed. Load frequency control adjusts the generator's energy output and tie line power between prescribed limits. As well as regulating generator output power, load frequency control also adjusts tie line power. The disturbance in the frequency due to different load changes is regulated using the proposed scheme. In this article, a two-area load frequency control system is constructed and evaluated using various control approaches, including a proportional integral derivative (PID) controller, a proportional integral (PI) controller, a fuzzy logic-based controller, and an Artificial Neural Network (ANN). The goal is to assess the power system's resilience under different loading conditions with these control schemes. The performance of the controllers is compared based on peak-undershoot, peak-overshoot, and settling time, focusing on tie line power and frequency response. To achieve this, the design is implemented using MATLAB/SIMULINK software.

Data-driven load frequency cooperative control for multi-area power system integrated with VSCs and EV aggregators under cyber-attacks

This paper proposes a cooperative load frequency control (LFC) strategy based on a multi-agent deep reinforcement learning (MADRL) framework for the multi-area power system in the presence of voltage source converters (VSCs) and electric vehicle (EV) aggregators under cyber-attacks. Different from the existing LFC model, a novel transfer function of VSCs is first improved by the space-vector technique and integrated with EV aggregators to develop a multi-area training environment. By installing the agent in different control areas and interacting state transition information between agents and the new environment, the MADRL-based control strategy is achieved for centralized training and decentralized execution. Thus, the proposed MADRL method can coordinate thermal turbines, VSCs, as well as EV aggregators in the different control areas. Furthermore, a suitable cyber-attack model that can circumvent bad data detection (BDD) is reconstructed according to the perspective of adversaries for the LFC system. Then the double critic networks and parameter updating policy are designed to eliminate and mitigate the fluctuations caused by cyber-attacks. The comparative simulation with other control strategies on a three-area test power system demonstrates the superior performance of the proposed MADRL-based approach.

Robust stability region analysis of time‐delayed load frequency control systems with EVs aggregator using Kharitonov theorem

AbstractThis study focuses on analyzing the robust stability regions and robustness margin of a time‐delayed load frequency control (LFC) system with Electric Vehicles (EVs) using the Kharitonov Theorem. Communication time delays in LFC systems can jeopardize stability and reliability, leading to suboptimal controller performance. Integrating EVs into LFC systems enhances dynamical stability but introduces additional complexity. Therefore, it is crucial to employ robust analysis and controller design techniques to ensure system stability. In this study, we utilize the Kharitonov Theorem to determine the robust stability regions and stability boundaries in the parameter plane of the Proportional‐Integral (PI) controller. By considering communication time delays and parametric uncertainties in the LFC system with EVs (LFC‐EVs), the robust PI controller gains using these methods are efficiently computed. To evaluate the performance of the theoretically computed robust controller parameters, time‐domain simulations are conducted.

Adaptive Event-Triggered Model Predictive Load Frequency Control for Power Systems

This paper investigates the adaptive event-triggered (AET) output feedback model predictive control (MPC) method for the load frequency control (LFC) problem of hybrid power system involving the thermal power and the photovoltaic power. First, an AET communication mechanism, which possesses the ability to dynamically adjust the trigger threshold parameter according to an adaptive law, is addressed to reducing the communication overhead, while keeping the required control performance; Second, the output feedback model predictive controller which is composed of an off-line designed state observer and an on-line solved optimization problem, is proposed using the techniques of quadratic boundedness and S-procedure. The state observer gain is obtained by off-line maximizing the trace of a matrix which is closely related to minimal positively invariant set; While the output feedback MPC strategy is acquired by expressing the infinite horizon control moves as a free control move and a linear feedback law based on the estimated state; Third, the recursive feasibility of the on-line optimization problem and the stability of the closed-loop system are analyzed by the fact that the estimated state and estimation error will converge to the presented minimal positively invariant set. Finally, MATLAB and its linear matrix inequality (LMI) toolbox are used to solve the optimization problem, and the LFC system with load disturbance is simulated and analyzed. The simulation results show that the proposed method is feasible and effective.