A Novel Fuzzy Logic Based Load Frequency Control for Multi-Area Interconnected Power Systems

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.

This paper proposes a novel load frequency control (LFC) approach formulated on an optimal structure of the coefficient diagram method (CDM) in a two-area thermal power system. As part of a realistic analysis, nonlinearities related to governor dead band (GDB) and generation rate constraint (GRC) have been considered. In this article, a hybrid CDM method is combined with the optimization of its mathematical equations to achieve an innovative controller. Furthermore, a new metaheuristic optimization technique called the water cycle algorithm (WCA) is used to determine the optimal coefficients of the CDM controller. For the purpose of demonstrating the validity of the proposed scheme, a wide range of uncertainties in the dynamic parameters of a nonlinear power system were tested. In addition, a comparative study is presented between the results obtained from a classical integral, CDM alone, optimized fuzzy, optimized PID, and the suggested controller. In this new approach to improved control, algebraic support provides a robust and responsive controller that can provide fast and stable dynamic responses and effectively overcome the detrimental effects of nonlinearities and uncertainties in the parameters of the power system.

In this paper, control scheme for Load Frequency Control (LFC) problem in a three-area interconnected power system with Adaptive Neuro Fuzzy Inference System (ANFIS) controller is presented. Its performance is compared with that of conventional PID Controller and Fuzzy Logic Controllers. This power system consisting of a single reheat thermal unit, a double reheat nuclear unit and a hydro unit. The controllers are designed, trained and tuned. The robustness test and performance of these control schemes against step load change is examine methodically, simulated and compared using Matlab-Simulink.

In this paper a fuzzy logic (FL) based load frequency controller (LFC) called discrete FuzzyP+FuzzyI+FuzzyD (FP+FI+FD) is proposed to ensure the stability of a multi-source power system in restructured environment. The whale optimization algorithm (WOA) is used for optimum designing the proposed control strategy to reduce fuzzy system effort and achieve the best performance of LFC task. Further, to improve the system performance, an interline power flow controller (IPFC) and superconducting magnetic energy system (SMES) is included in the system. Governor dead band, generation rate constraint, and time delay are considered as important physical constraints to get an accurate understanding of LFC task. The performance of the optimized FP+FI+FD controller is evaluated on a two area six-unit hydro-thermal power system under different operating conditions which take place in a deregulated power market and varying system parameters in comparison with the classical fuzzy PID controller. Simulation results shows that WOA based tuned FP+FI+FD based LFC controller are relatively robust and achieve good performance for a wide change in system parameters considering system physical constraints.

Load frequency control is one of the most important issues in the power systems that many researches have been done for improving this problem until now, but most of the researches have not considered the power system model that is close to reality and nonlinear factors such as generation rate constraints, governor dead-band, boiler dynamic and time delays. Also because of the power system dynamic changes continuously, the uncertainty is undeniable in the system. In this article by consideration of these nonlinear effects and uncertainty, first the PI control factors are found by using the harmony search algorithm then by consideration of the uncertainty, optimized factors for PI controller are found each time and by forming a table that determines inputs and outputs for fuzzy-neural network, the designing and training of the fuzzy-neural network is done that has capability of controller coefficients regulation in various dynamics of system and presence of uncertainty. The proposed method by a fuzzy controller that is designed based on certified knowledge, has been compared and shown that has a very good performance such as lower settling time, higher speed, better damping, and steady-state error equal to 0.

ABSTRACTOne of the key challenges in interconnected power systems is developing an effective control strategy to mitigate frequency and power deviations caused by the intermittency of renewable energy sources (RESs) and varying load demands. This research introduces an innovative cascade control strategy featuring a PPD controller followed by a PI controller (PPD‐PI) for load frequency control (LFC) in a two‐area power system with photovoltaic (PV), wind, and thermal reheat power sources. The walrus optimization algorithm (WaOA) is employed to fine‐tune the parameters of both the PIDn and PPD‐PI controllers, with the goal of minimizing the integral time absolute error (ITAE). The study first applies the WaOA‐tuned PID with filter (PIDn) controller to showcase WaOA's effectiveness in LFC, achieving the lowest objective function value of 0.3862, surpassing MFO (0.3921) and GA (0.4127). The robustness of the WaOA‐tuned PPD‐PI controller is then evaluated under various conditions, including step load disturbances, random load patterns, and parameter uncertainties. The proposed controller achieves significant improvements, with a 36.8% reduction in ITAE compared to the second‐best CGO‐tuned PIDn‐PI controller in Case 2, and a 54.45% reduction in ITAE compared to the second‐best COA‐tuned PDn‐PI controller in Case 3. To further highlight the advantages of the proposed scheme, the analysis also includes nonlinearities such as governor dead band (GDB), boiler dynamics (BD), and generation rate constraints (GRC), along with sensitivity analysis and stability testing under a change in system parameters. The results strongly demonstrate the superior performance of the WaOA‐optimized PPD‐PI controller over existing methods.

This paper presents a novel approach for designing a decentralized controller for load frequency control of interconnected power areas.The proposed fuzzy logic load frequency controller (FLFC) has been designed to improve the dynamic performance of the frequency and tie line power under a sudden load change in the power areas.The effect of generation rate constraint (GRC) for both areas has been considered in the controller design.The proposed FLFC consists of two internal fuzzy logic controllers namely, the PDlike fuzzy logic controller and the PI-like fuzzy logic controller.The FLFC has been co-coordinated with the conventional integral controller.Time-domain simulations using MATALB/SIMULINK program has been performed to demonstrate the effectiveness of the proposed FLFC.The simulation results show that the proposed FLFC can provide good damping and reduce the overshoot even in the presence of the GRC.

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.

Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system

Nowadays due to increase in size, increasing demand and complexity in interconnected power system, load frequency control has become more significant. In general Proportional Integral [PI] controllers are used for Load frequency Control, which is generally tuned on the basis of classical approaches. Because of that they are incapable of obtaining good dynamic performance. Hence it is much more needed to have controllers which are capable of self-tuning in nature according to situations. For power system stabilization control fuzzy logic controllers can be used because of their ability to utilize expert knowledge and being adaptive in nature. Fuzzy Logic controller [Type-I] is a sophisticated technique that is easy to design and implement, nevertheless the determination of Membership Functions and control rules is an essential part of design. To achieve satisfactory Membership Functions and control rules, designer experience is necessary. In other hand, type-I fuzzy controller is further modified into Type-II Fuzzy controller by giving grading to Membership Functions which are themselves fuzzy which leads to provide new design degrees of freedom for handling uncertainties. In this paper, simulations are carried out for a two area thermal reheat power system.

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%.

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.

The use of renewable energy resources has created some problems for power systems. One of the most important of these is load frequency control (LFC). In this study, in order to solve the LFC problem, modern control methods were applied to a two area multi source interconnected power system. A photovoltaic solar power plant (PV-SPP) was also connected, in order to identify the harmful effects on the frequency of the system. A new Genetic-based Fuzzy Logic (GA-FL) controller was designed to control the frequency of the system. For comparison, conventional proportional-integral-derivative (PID), fuzzy logic (FL), and Genetic Algorithm (GA)-PID controllers were also designed. The new control method exhibited a better performance than the conventional and other modern control methods, because of the low overshoot and short settling time. All simulations were realized with the Matlab-Simulink program.

Improved load frequency control in power systems with high penetration of wind farms using robust fuzzy controller

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.