Application of Some Modern Techniques in Load Frequency Control in Power Systems

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

The increasing reliance on renewable energy sources (RES), such as photovoltaic (PV) and wind power, has introduced significant challenges in maintaining the frequency stability of modern power grids due to their intermittent and variable nature. This study proposes a hybrid approach that combines particle swarm optimization (PSO) and adaptive neuro-fuzzy inference system (ANFIS) for frequency regulation in hybrid power systems. The method leverages electric vehicles (EVs) operating in vehicle-to-grid (V2G) mode to enhance grid stability. PSO is utilized to optimize the parameters of the ANFIS model, enabling intelligent and adaptive control of power flows between the grid, renewable sources, and EVs. The proposed system mitigates frequency deviations caused by PV and wind power generation fluctuations by dynamically coordinating EVs' charging and discharging rates. Simulation results under various scenarios, including sudden changes in renewable output and load demand, demonstrate that the ANFIS-PSO approach effectively reduces frequency deviations and maintains stability within permissible limits.

Load frequency control (LFC) is vital for balancing power generation with fluctuating power demand. It ensures that the system frequency and tie-line power are maintained at their desired levels. The design of an LFC system is crucial for the automation of power systems. This article introduces a Fuzzy Proportional-Integral-Derivative (FPID) control approach for LFC in an interconnected reheat thermal power system, utilizing a Particle Swarm Optimization (PSO) method and incorporating the Generation Rate Constraint (GRC) as nonlinearities. The proposed controller offers the advantage of effectively managing system nonlinearities while being better than classical controllers. The response of the FPID controller is contrasted with both the conventional Proportional-Integral (PI) controller and the Fractional Order PID (FOPID) controller, in scenarios both including and excluding the Generation Rate Constraint (GRC). The response of the system is analyzed by considering load disturbances in each region of the integrated power network.

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 presents the comparative study of parameter tuning of proportional-integral-derivative controller in load frequency control in a single area multi-source power system as well as multi-source multi-area power system. It is shown that simple particle swarm optimization (SPSO), due to its premature convergence to a local minimum, is not able to provide an optimum value of the controller parameters. As a solution to this problem, a modified version of SPSO (MPSO) is proposed and discussed. The idea is to avoid particles from converging prematurely to a local minimum and cover the entire search space in an efficient way. MPSO provides far better result than the SPSO. Initially, a multi-source single area power system having integral controllers for each i.e., thermal, hydro and gas is examined and controller parameters are obtained using MPSO algorithm. Then a multi-source multi-area power system in presence of AC-DC tie-lines is studied. Results obtained by these algorithms are compared with the other existing optimization algorithm such as differential evolution and method based on optimal control theory.

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.

This paper aims at hardware-in-loop (HIL) implementation of load frequency control (LFC) and automatic voltage regulation (AVR) schemes for a single area power system. The single area power system consists of a synchronous generator based non-reheat thermal area unit serving linear loads. In order to minimize the power mismatch between the generation and load, LFC & AVR control strategies have been developed considering the dynamics of thermal area power system. The control strategies have been developed to operate in a continuous mode of operation using conventional Proportional-Integral (PI) controller. 3%, 8% & 10% variations of load has been considered to validate system performance at various loading levels. HIL implementation has been achieved using dPSACE MicroLabBox which interfaces the controller developed in MATLAB/Simulink platform to the hardware components.

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.

Given the changing nature of power systems, it is challenging to optimize the controller for controlling load frequency problems. Distributed power generating sources and power system reorganization with multi-sources and multi-stakeholders make traditional load frequency control approaches unsuitable for current power systems. This research provides the comparative analysis of regulation of the load frequency in a multiple-area deregulated electricity system with the help of soft computing. In a reorganized electrical system, the major objectives of load frequency control (LFC) are to set up system frequency into acceptable limit, swiftly return the frequency to the setpoint, reduce tie-line power flow fluctuations across adjacent control zones, and track load demand agreements. To achieve LFC's goals, proportional integral derivative (PID) gain values must be tuned, for optimization purpose, soft computational methods are used in this present work. MATLAB/Simulink simulation results show that soft computing controllers can keep tie line power interchange within contracted constraints and frequency variation within the allowed range. This article compares auto tuned PID, genetic algorithm (GA), and particle swarm optimization (PSO) controllers in unregulated circumstances, load frequency regulation of two-area power systems.

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.

In industry or any area increasing load is a vast problem for power generation plants due to increase in demand for power. So making balance between generation and demand is the operating principle of load frequency control (LFC). The reliable operation of a large interconnected power system necessarily requires an Automatic Generation Control (AGC). The objective of AGC is to regulate the power output of Generators within a specified area in response to change in the system frequency, tie line power or relation of the two to each other, so as to maintain the scheduled system frequency and power interchange in the other are within the prescribed limits. This paper presents the use of conventional PI controller and artificial intelligence to study the load frequency control of interconnected power system. In the proposed scheme, a control methodology is developed using conventional PI controller and Fuzzy Logic controller (FLC) for interconnected hydro-thermal power system. The control strategies guarantees that the steady state error of frequencies and inadvertent interchange of tie-lines power are maintained in a given tolerance limitations. The performances of the controllers are simulated using MATLAB/SIMULINK package. A comparison of Fuzzy controller and PI controller based approaches shows the superiority of proposed Fuzzy logic controller for step change in loading conditions. The simulation results also tabulated as a comparative performance in view of settling time and peak over shoot.

In this paper, Proportional Integral Derivative (PID) controller is designed using Differential Evolution (DE) algorithm to Load Frequency Control (LFC) in three areas of an interconnected power system. The proposed controller has appropriate dynamic response, so it increases damping in transient state in unhealthy conditions. Different generators have been used in three areas. Area 1 includes thermal non-reheat generator and two thermal reheat generators; area 2 includes hydro and thermal non-reheat generators, and area 3 includes hydro and thermal reheat generators. In order to evaluate the performance of the controller, Sim/Matlab software is used. Simulation results show that the controller designed using DE algorithm is not affected by load changes, disturbance, or system parameters changes. Comparing the results of proposed algorithm with other load frequency control algorithms, such as PSO and GA, it has been found that this method has a more appropriate response and satisfactory performance

This paper deals with the importance of load frequency control (LFC) for single and interconnected power system. It is important to keep the frequency of the system and the inter area tie power stable. The goal of LFC is to maintain the frequency, the desired power output and to control the change in tie line power between control areas in an interconnected power system. Two different techniques have been implemented in this paper which are model predictive controller MPC and conventional proportional plus integral controller PI for LFC of single area power system as well as two area system. To investigate the powerful and robustness of model predictive control (MPC) technique for LFC problems in electric power system, time simulations is performed. The obtained results indicate that the MPC provide better damping to the single area system as well as two-area system in case of load disturbance compared with conventional PI controller. The studied single-area and two-area systems are modelled and simulated using Simulink/Matlab software.

In this paper, an improved particle swarm optimization (IPSO) based load frequency control (LFC) of a single area power system is presented. Although the Particle Swarm Optimization approaches have several advantages, they can still have drawbacks like local optimal trapping due to premature convergence(i.e. exploration problem). This paper proposes an improved PSO framework adopting a crossover operation scheme to increase exploitation capability of PSO. The study has been realized for control of a single area interconnected power system with IPSO optimized self-tuning PID controller. The comparison between a conventional Proportional-Integral (PI) controller and the proposed PSO based controller showed that the proposed controller can generate a better transient response for a step load change. For this application, MATLAB-Simulink software is used.