Load Frequency Control of Interconnected Power System with non-linearities using PSO-based Fuzzy PID Control Approach

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.

BackgroundAutomatic generation control (AGC) of multi-area interconnected power system (IPS) is often designed with negligible cross-coupling between the load frequency control (LFC) and automatic voltage regulation (AVR) loops. This is because the AVR loop is considerably faster than that of LFC. However, with the introduction of slow optimal control action on the AVR, positive damping effect can be achieved on the LFC loop thereby improving the frequency control. In this paper, LFC synchronized with AVR in three-area IPS is proposed. Model predictive controller (MPC) configured in a dense distributed pattern, due to its online set-point tacking is used as the supplementary controller. The dynamics of the IPS subjected to multi-area step and random load disturbances are studied. The efficacy of the developed scheme is ascertained by simulating the disturbed system in MATLAB/Simulink.ResultsBased on the comparative analysis on the system responses, it is established that by cross-coupling the LFC loop with AVR, reductions of 66.45% and 59.09% in the frequency and tie-line power maximum deviations respectively are observed, while the respective settling times are found to be reduced by 29.68% and 22.77% when compared with the uncoordinated control scheme. In addition, the standard deviation and variance of the integral time absolute error of the system’s responses have reduced by 23.21% and 20.83% respectively compared to those obtained in a similar study.ConclusionsThe reduction in the maximum deviations and settling times in the system states indicates that introducing the voltage control via AVR loop has improved the frequency control significantly. While the lower standard deviation and variance of the integral time absolute error signify improvement in the robustness of the developed algorithm. However, this improvement is at the detriment of the controller size and computational complexity. In the uncoordinated control scheme, the control vector is one-dimensional, while in the coordinated scheme, the control vector is two-dimensional for each CA.

Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system

This paper presents an algorithm for load frequency control of multi-area power system. A more realistic models is used to represent the elements of power system. The frequency bias factor (B) is set optimally. The effect of generation rate constraints (GRC) and the dead band characteristics of the governor is taken into consideration. A simultaneous optimization of the fractional order (PIλ Dµ) control parameters, the speed regulation (R) of the governor and the frequency bias factor (B) is proposed for the first time. The objective function to be minimizes is a modified integral time absolute error (MITAE). The tuning of the control parameters is based on using cuckoo search algorithm (CSA) to minimize the objective function. The proposed algorithm is applied to a typical interconnected three-area power systems with a different levels of load step change. Results of simulation of the presented method are compared with those obtained by applying particle swarm optimization technique. The results obtained reveal the robustness of the suggested algorithm in terms of settling time and peak over shoot.

This paper presents a Fractional Order Proportional-Integral-Derivative (FOPID) controller optimized using Social Spider Optimization (SSO) algorithm to Load Frequency Control (LFC) of a two-area multi-source power system in the presence of Distributed Generations (DGs). Here, the effect of DGs consist of wind and diesel power plants for damping frequency deviation of two-area multi-source power system including thermal, hydro and gas units considering nonlinearities such as generation rate constraint and governor dead band has been investigated. The FOPID controller has three main control parameters as known gain, fractional orders of integrator and derivative part that provide the better performance in comparison with the PID controller. On the other hand, to achieve the good performance in the power system, tuning of these parameters is very important. Therefore, the SSO algorithm has been used to optimal tuning of the FOPID based LFC controller. One of the important specifications of the employed algorithm is that it can balance the local and global solutions in the search space that cause to overcome the local optimal and premature convergence. Also, a High Voltage Direct Current link (HVDC) is used to improve the dynamic performance of the LFC task. Finally, the proposed controller has been applied on a two case studies and analyzed based on investigated performance criteria. Results show the good effect of DGs for the solution of LFC task and enhancing system performance characteristics under different operating conditions and considering system nonlinearities.

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.

Nowadays, due to vulnerability of power system and upsurge in power demand, communication network is essential for secure operation, control and monitoring of power system. Time delay exists in communication channel can worsen the dynamic performance of closed loop of power system especially in load frequency control (LFC) problem. Due to time delay or some fault present on a system, some generator outage may take place from any well connected areas. So, in this paper an attempt is made to presents an impact of communication delay on frequency regulation of two area interconnected power system with outage of generator terminal from one area. Pade method is used to model the communication delay. A Proportional integral (PI) control based particle swarm optimization (PSO) technique is used to tune the controller gains. A series of simulation results are performed for various case studies, for the effectiveness of control design method mainly for dynamic response of system

The growing integration of renewable distributed generators (DGs) poses significant frequency regulation challenges due to intermittency, uncertainties, and delays. This paper addresses these issues by designing and validating a robust Sliding Mode Controller (SMC) for Load Frequency Control (LFC) in complex power networks. The proposed control framework addresses a realistic multi-area system model incorporating nonlinearities such as Generation Rate Constraint (GRC), Governor Dead Band (GDB), and boiler dynamics, along with diversified generation units including thermal, hydro, gas, wind, solar photovoltaic, geothermal, and diesel-based DGs. To eliminate the chattering effect inherent in traditional SMC implementations, a saturation function is incorporated into the discontinuous control term. The SMC gains are optimally tuned using the Gannet Optimization Algorithm (GOA)—a novel bio-inspired technique modeled on the prey-capture dynamics of seabirds. Furthermore, a Modified GOA (MGOA) is proposed, where key parameters, such as the mass and velocity of gannets, are dynamically optimized using Particle Swarm Optimization (PSO) to enhance convergence precision. The comparative performance of GOA and MGOA is rigorously evaluated using six benchmark functions (e.g., Beale, Trid, Shekel), where MGOA consistently exhibits lower standard deviations and faster convergence rates, confirming its superiority in solution stability. Simulation studies conducted on MATLAB/Simulink reveal that the MGOA-SMC controller achieves superior transient performance compared to both PID and GOA-tuned SMC designs. Specifically, under a 1 % load disturbance, the proposed controller reduces frequency undershoot in area-1 from −0.7142×10−3Hz to−0.2678×10−3 Hz with an improvement of 62.5 %, overshoot in area-1 from 0.1627×10−3Hz to0.0315×10−3 Hz with an improvement of 80.6 %, and settling time from 0.15 sec to 0.02 sec with an improvement of 86.67 %. Similarly, tie-line power deviation in area-1 under the same load condition as improved by the proposed controller, such as undershoot from −0.2378×10−3p.u.MW to−0.0725×10−3p.u.MW with an improvement of 69.5 %, overshoot from 0.0543×10−3p.u.MW to 0.0267×10−3p.u.MW with an improvement of 50.8 %, and settling time from 0.06 sec to 0.02 sec with an improvement of 66.67 %. Robustness is further validated under scenarios involving nonlinearities, a 5-millisecond communication delay, random load variations, and ±20 % parameter perturbations, with minimal degradation in dynamic response. The sensitivity analysis confirms low standard deviation across performance indices, reinforcing the robustness of the control strategy. The standard deviation of undershoot, overshoot, and settling time for frequency in area-1 are (0.00065), (0.000084), and (1.85), respectively. Similarly, the standard deviation of undershoot, overshoot, and settling time for tie-line power are (0.00016), (0.00079), and (2.11), respectively. To demonstrate practical viability, the proposed system is implemented on an OPAL-RT 4510 real-time simulator. Hardware-in-the-loop results closely mirror the simulation outcomes, thereby validating the controller’s effectiveness in real-world environments. The study conclusively establishes that the MGOA-optimized SMC controller offers a scalable, computationally efficient, and resilient solution for load frequency regulation in nonlinear, DG-rich power systems. The proposed framework lays the groundwork for future extensions to address cyber-physical threats and decentralized control in smart grid applications.

This paper presents the effect of different controllers on Load Frequency Control (LFC) of a two area interconnected thermal power system for the advancement of dynamic performance. Time domain simulations are performed to investigate the performance of the system in the presence of generation rate constraint (GRC). The Controller consists of I, PI and PID compensator where the gain setting of proportional (P), integral (I) and derivative (D) constants are optimizes using Gravitational Search Algorithm (GSA) and the objective function used for minimization is Integral of Time multiplied with Absolute value of Error (ITAE). Results reveal that for a step load disturbance, PID controller gives the better dynamic performance of LFC on I and PI compensator and keep the frequency and tie-line power within the range; hence, it proves the effectiveness of GSA based PID compensator in two area interconnected thermal system.

Conventional control techniques such as proportional integral (PI) control and proportional integral derivative (PID) control are widely used as the load frequency controller in power system control applications; however, the increase in complexity of the power system is degrading the performance of classical control. Therefore, in this paper a new control approach using fractional calculus has been proposed for the load frequency control problem of two-area hydro-thermal power system. The paper presents an interconnected hydro-thermal power system working under different operating conditions introduced in the form of various nonlinearities. Four case studies have been presented in this paper considering transient analysis with (i) different loading conditions, (ii) parameter variations, (iii) different nonlinearities (governor dead band nonlinearity, time delay and generation rate constraints), and (iv) superconducting magnetic energy storage (SMES) device. A tilt integral derivative (TID) control has been presented as a secondary controller to stabilize the frequency deviations occurring in the two-area power system with the above-mentioned different operating conditions. The parameters of TID controller have been optimized using a differential evolution algorithm which solves an optimization problem formulated using the integral of time-weighted absolute error (ITAE) performance index. In addition, a comparison of dynamic system response obtained using TID control and PID control has been presented, which focusses on the better performance of TID control over PID control in an interconnected power system.

This paper focuses on using the Bees Algorithm (BA) to tune the parameters of the proposed Fuzzy Proportional–Integral–Derivative with Filtered derivative (Fuzzy PIDF), Fractional Order PID (FOPID) controller and classical PID controller developed to stabilize and balance the frequency in the Great Britain (GB) power system at rated value. These controllers are proposed to meet the requirements of the GB Security and Quality of Supply Standard (GB-SQSS), which requires frequency to be brought back to its nominal value after a disturbance within a specified time. This work is extended to employ the proposed fuzzy structure controller in a dual-area interconnected power system. In comparison with controllers tuned by Particle Swarm Optimization (PSO) and Teaching Learning-Based Optimization (TLBO) used for the same systems, simulation results show that the Fuzzy PIDF tuned by BA is able to significantly reduce the deviation in the frequency and tie-line power when a sudden disturbance is applied. Furthermore, the applied controllers tuned by BA including the Fuzzy PIDF prove their high robustness against a wide range of system parametric uncertainties and different load disturbances.

This chapter presents a comparison of the performance of integral (I) and proportional-integral-derivative (PID) controllers in frequency stabilization or load frequency control (LFC) of two-area interconnected power system considering generation rate constraints (GRCs) with Doubly fed induction generator (DFIG)-based wind energy. Two mathematically models are identified for investigations. Power system model 1 is two-area interconnected power system which contains two identical non-reheat thermal plants without DFIG participation. Whereas, power system model 2 contains two identical non-reheat thermal plants with dynamic participation of DFIG at both areas. Moreover, Harris Hawks Optimizer (HHO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA) are applied to find the optimal values of the controller settings mentioned above. The effectiveness of the proposed controllers, which are optimally designed by several optimization techniques (i.e., HHO, SSA, and SCA) is tested and verified through an interconnected power system comprises two identical non-reheat thermal power plants with/without DFIG participation. Time-domain simulation results of the studied power system with all mentioned optimization techniques are carried out using Matlab/Simulink® software to validate the robustness of the proposed controllers.

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.

This study focuses on designing an effective intelligent control method to stabilize the net frequency against load variations in multi-control-area interconnected power systems. Conventional controllers (e.g. Integral, PI, and PID) achieve only poor control performance with high overshoots and long settling times. They could be replaced with intelligent regulators that can update controller parameters for better control quality. The control strategy is based on fuzzy logic, which is one of the most effective intelligent strategies and can be a perfect substitute for such conventional controllers when dealing with network frequency stability problems. This paper proposes a kind of fuzzy logic controller based on the PID principle with a 49-rule set suitable to completely solve the problem of load frequency control in a two-area thermal power system. Such a novel PID-like fuzzy logic controller with modified scaling factors can be applied in various practical scenarios of an interconnected power system, namely varying load change conditions, changing system parameters in the range of ±50%, and considering Governor Dead-Band (GDB) along with Generation Rate Constraint (GRC) nonlinearities and time delay. Through the simulation results implemented in Matlab/Simulink software, this study demonstrates the effectiveness and feasibility of the proposed fuzzy logic controller over several counterparts in dealing with the load-frequency control of a practical interconnected power system considering the aforesaid conditions.

This paper presents dynamic performance comparison of a fuzzy logic-based proportional, integral, and derivative controller (FPID) with different membership functions such as triangular, trapezoidal, and Gaussian for load frequency control (LFC) in an interconnected two-area thermal power system. The parameters of controller are optimized by using spider monkey optimization (SMO) algorithm. The superiority of the proposed algorithm is established by comparing the results with popularly used algorithms like particle swarm optimization (PSO) and teaching–learning-based optimization (TLBO). Initially, the linearized model of the system is considered with reheat turbine; then, the study is extended by imposing nonlinearity such as generation rate constraints (GRC) and governor dead band (GDB). The result comparison is analyzed using various time domain specifications like peak undershoot, peak overshoot, and settling time of different area frequencies and tie-line power deviation between them applying a step load perturbation (SLP) of 1%.