A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

Load Frequency Control (LFC) Strategies in Renewable Energy‐Based Hybrid Power Systems:A Review

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

Load Frequency Control in Two Area Power System using ANFIS

The load-frequency control (LFC) is used to restore the balance between load and generation in each control area by means of speed control. The main goal of LFC is to minimize the transient deviations and steady state error to zero in advance. This paper investigated LFC using proportional integral (PI) Controller and Adaptive Neuro Fuzzy Inference System (ANFIS) for two area system. The results of the two controllers are compared using MATLAB/Simulink software package. Comparison results of conventional PI controller and Adaptive Neuro Fuzzy inference System are presented. Keywords: Load Frequency Control, Adaptive Neuro Fuzzy Inference System (ANFIS), PI controller

An optimal artificial neural network controller for load frequency control of a four-area interconnected power system

In this paper, an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity is presented. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprised of transfer function models of twothermal units, one nuclear unit and one hydro unit. The controller model is developed by considering generation rate constraint (GRC) of different units as a non-linearity. The typical system parameters obtained from IEEE press power engineering series and EPRI books. The robustness, effectiveness, and performance of the proposed optimal ANN controller for a step load change and random load change in the system is simulated through using MATLAB-Simulink. The time response characteristics are compared with that obtained from the proportional, integral and derivative (PID) controller and non-linear autoregressive-moving average (NARMA-L2) controller. The results show that the algorithm developed for proposed controller has a superiority in accuracy as compared to other two controllers

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

Frequency Control of Microgrid Network using Intelligent Techniques – ANN, PSO and ANFIS

The electric grid is a complex system that transmits electricity from the point of generation to the point of consumption. According to the IEA, worldwide energy-related carbon emissions in 2021 will be 36.3Gt, 60% greater than at the start of the industrial revolution. Researchers have used intelligent solutions for power system frequency regulation to ensure that the system\u27s frequency is maintained. A proper frequency control of the microgrid necessitates the modeling and study of the systems. To emulate the operation of the human brain, frequency control employs a variety of artificial intelligence-based computer algorithms. This thesis generates a complete state space model of a microgrid composed of solar power plants, wind turbines, battery storage systems, and backup generators. The system frequency control was created for this system and analyzed against a benchmark PID controller utilizing several intelligent controllers such as PSO optimized PID, ANN, and ANFIS. The suggested intelligent frequency controllers were be simulated and validated using MATLAB/ Simulink

Situational Intelligence for Improving Power System Operations Under High Penetration of Photovoltaics

Nowadays, power grid operators are experiencing challenges and pressures to balance the interconnected grid frequency with rapidly increasing photovoltaic (PV) power penetration levels. PV sources are variable and intermittent. To mitigate the effect of this intermittency, power system frequency is regulated towards its security limits. Under aforementioned stressed regimes, frequency oscillations are inevitable, especially during disturbances and may lead to costly consequences as brownout or blackout. Hence, the power system operations need to be improved to make the appropriate decision in time. Specifically, concurrent or beforehand power system precise frequencies simplified straightforward-to-comprehend power system visualizations and cooperated well-performed automatic generation controls (AGC) for multiple areas are needed for operation centers to enhance. The first study in this dissertation focuses on developing frequency prediction general structures for PV and phasor measurement units integrated electric grids to improve the situational awareness (SA) of the power system operation center in making normal and emergency decisions ahead of time. Thus, in this dissertation, a frequency situational intelligence (FSI) methodology capable of multi-bus type and multi-timescale prediction is presented based on the cellular computational network (CCN) structure with a multi-layer proception (MLP) and a generalized neuron (GN) algorithms. The results present that both CCMLPN and CCGNN can provide precise multi-timescale frequency predictions. Moreover, the CCGNN has a superior performance than the CCMLPN. The second study of this dissertation is to improve the SA of the operation centers by developing the online visualization tool based on the synchronous generator vulnerability index (GVI) and the corresponding power system vulnerability index (SVI) considering dynamic PV penetration. The GVI and SVI are developed by the coherency grouping results of synchronous generator using K-Harmonic Means Clustering (KHMC) algorithm. Furthermore, the CCGNN based FSI method has been implemented for the online coherency grouping procedure to achieve a faster-than-real-time grouping performance. Last but not the least, the multi-area AGCs under different PV integrated power system operating conditions are investigated on the multi-area multi-source interconnected testbed, especially with severe load disturbances. Furthermore, an onward asynchronous tuning method and a two-step (synchronous) tuning method utilizing particle swarm optimization algorithm are developed to refine the multi-area AGCs, which provide more opportunities for power system balancing authorities to interconnect freely and to utilize more PV power. In summary, a number of methods for improving the interconnected power system situational intelligence for a high level of PV power penetration have been presented in this dissertation

Artificial Intelligence-based Control Techniques for HVDC Systems

The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PD

USING AN INTELLIGENT ANFIS-ONLINE CONTROLLER FOR STATCOM IN IMPROVING DYNAMIC VOLTAGE STABILITY

This research has introduced the intelligent ANFIS-Online controller of STATCOM for improving the dynamic voltage on the power network under a 3-phase short circuit fault. The ANFIS-Online is made using an artificial neural network identifier. And based on the identifier, the premise and consequent parameters of ANFIS are adjusted timely. To demonstrate the performance of the suggested controller, the transient waves are shown to describe the effectiveness of the intelligent ANFIS-Online controller to enhance the transient response of the research system under a 3-phase short circuit fault. It's shown that the suggested intelligent ANFIS-Online controller has provided waves better than the other controllers such as ANFIS controller, ANFIS-PSO controller, ANFIS-GA controller for STATCOM equipment to enhance transient voltage stability