Power System Control Research Articles

Improving load frequency controller tuning with rat swarm optimization and porpoising feature detection for enhanced power system stability

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as “Porpoising”. This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.

Research on Coordinated Control and Hierarchical Partition Control Strategy of Distributed Generation in New Power System

Abstract In recent years, the structure of urban power systems has become more complex and the power flow is more changeable, which seriously hinders the optimal scheduling of urban power systems. Therefore, it is necessary to realize the coordinated control of distributed generation, flexible load, and reactive power equipment globally and locally, improve the utilization rate of distributed generation, give full play to the source and load of electric vehicles and energy storage devices, and realize the optimal scheduling of urban power system. This paper proposes a multi-time scale distributed power coordinated control and hierarchical partition control model, which realizes the coordinated optimal control of the new power system for distributed power, load, and energy storage. The effectiveness of the model in this paper is verified by IEEE33 node simulation.

Probabilistic Forecasting-Based Stochastic Nonlinear Model Predictive Control for Power Systems With Intermittent Renewables and Energy Storage

Probabilistic Forecasting-Based Stochastic Nonlinear Model Predictive Control for Power Systems With Intermittent Renewables and Energy Storage

Analysis of Load Frequency Control of Two Area Power System using Different Control Techniques

To maintain stability is a mandatory requirement of any kind of power network. Interconnected power systems have significant advantages in maintaining stability and frequency at their nominal values. The control of the interconnected power systems is performed by Automatic Generation Control (AGC), which has two parts: the Automatic Voltage Regulator (AVR) and Load Frequency Control (LFC). When a disturbance occurs in the system, Load Frequency Control (LFC) acts to re-establish the system into its steady state with zero error as well as the desired transient response characteristics. This paper exhibits the design and implementation of a PID and Fuzzy Logic Controller for two area power systems, taking the steady state error and percentage of overshoot, undershoot, and pre-shoot as comparison parameters. MATLAB/Simulink software was used to bring out the implementation and obtain results. System dynamic performance is observed for PID and Fuzzy Logic Controllers in Simulink. The comparison study indicated that the proposed Fuzzy Logic controller has better performance than the PID controller. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 62-79

Impact of ABT based control strategies on ALFC and AVR in DFIG-integrated interconnected power systems

To maintain a stable electricity supply in interconnected power grids, effective frequency and voltage regulation are required. Interconnected power systems use Automatic Load Frequency Control (ALFC) and Automatic Voltage Regulator (AVR) for this purpose. The integration of renewable sources, such as Doubly Fed Induction Generators (DFIGs), creates challenges for such control loops. This study investigates the impact of using various Frequency-Linked Pricing (FLP)-based operating strategies under Availability-Based Tariff (ABT) on combined ALFC and AVR in a two-area interconnected power system with DFIGs. FLP-based operating strategies under ABT react to frequency changes through price signals, which can act faster compared to traditional AGC methods that rely on communication between control centers. This quicker response helps maintain frequency and voltage during load disturbances. Most of the earlier research focused mostly on using only a single type of FLP-based operating strategy under ABT within the ALFC loop, neglecting the combined ALFC-AVR interaction. This work addresses this gap by evaluating three FLP-based operating strategies under ABT along with the traditional approach for their impact on frequency and voltage under step load disturbance. Simulation studies conducted in MATLAB 2022a demonstrate the effectiveness of the proposed strategies, providing valuable insights for improving frequency and voltage control in modern power systems. The investigation reveals that the strategy that considers the difference between the actual and reference values of the unscheduled interchange (UI) charge and the marginal cost of generation to produce the control command outperforms the other strategies and restores the frequency and voltage to their nominal values.

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

Application of Robust Super Twisting to Load Frequency Control of a Two-Area System Comprising Renewable Energy Resources

The main concern of the present article is to design a robust load frequency control for a two-area power system (TAPS) comprising renewable energy resources. Three different controllers are suggested. The first is based on a robust super twisting (ST) technique, which is an enhanced approach of the sliding mode control and is considered to be one of the most excellent control techniques. The second and the third are based on two recent metaheuristic techniques, namely the one-to-one based optimizer (OOBO) and hippopotamus optimizer (HO). The studied TAPS contains different energy resources, such as solar thermal, photovoltaic, wind energy, hydropower and energy storage in addition to other conventional sources. The OOBO and HO are used to determine the parameters of PI controllers, and the objective function is to minimize the integral square error of frequency and tie line power. The obtained results verify the high performance of the suggested three controllers with superiority to ST because of its intrinsic capability to cope with parameter changes.

Design of cascade P-P-FOPID controller based on marine predators algorithm for load frequency control of electric power systems

Design of cascade P-P-FOPID controller based on marine predators algorithm for load frequency control of electric power systems

Data-driven adaptive predictive frequency control for power systems with unknown and time-varying inertia

This paper proposes a novel data-based adaptive predictive frequency control method for multi-area power systems with unknown and time-varying inertia. Firstly, a data-based representation is built and updated at each instant based on behavioural system theory by using historical input–output data, where a moving horizon estimation method is used to deal with the unknown time-varying inertia issue adaptively. Then, the optimal frequency control signal is computed by solving an optimization problem under the framework of data-based predictive control. Simulation results on a power system with three control areas demonstrate the effectiveness of the proposed method.

Initial estimate of AC optimal power flow with graph neural networks

Optimal power flow (OPF) is a crucial task in power system management and control; accurate and time-efficient solutions for OPF are necessary to ensure cost-efficient and reliable power system operation. We introduce a novel solution to solving alternating current OPF (ACOPF), a nonlinear and nonconvex optimization problem, by combining the speed of a trained deep learning model with the accuracy of iterative solvers. The proposed framework uses a graph neural network (GNN) to exploit the graph structure of a power system in conjunction with proximal policy optimization, a deep reinforcement learning algorithm, to compute initial guesses for an interior point solver (IPS), providing a warm start, allowing the solver to converge in fewer iterations. Existing literature that explores warm start ACOPF solutions using machine learning choose to compute initial guesses that are trained to be feasible and cost-minimizing. Our approach trains the GNN-based reinforcement learning agent to produce an output that minimizes IPS convergence time by designing a reward function that is a function of the IPS convergence time. We evaluate the proposed framework using IEEE test case environments, using PyPower’s IPS-based ACOPF solver and a GNN-based framework that computes ACOPF solutions directly as baselines, demonstrating significantly improved convergence times.

Impact of network degradation on load frequency control of large interconnected power system

Due to the extensive geographical coverage of the power system, the load frequency control (LFC) performance is impacted by communication delays and packet loss that occur during the exchange of control and measurement signals. In the literature on LFC of large interconnected power systems, communication delays are often assumed to be arbitrary or time-varying values based on probability distributions or estimation models. Most studies neglect realistic delays based on the actual communication technology present in the system despite its practical significance. Furthermore, the impact of packet loss in LFC studies has been largely overlooked in the existing literature. This research work presents a comprehensive investigation into the impact of real-time communication delay and packet loss on LFC performance in large interconnected power systems. The simulation results demonstrate the significant impact of communication network impairments on LFC performance. The classical simulation results are validated using Real-Time Hardware-in-the-Loop (RT-HIL) simulation.

Investigation of reciprocal rank method for automatic generation control in two-area interconnected power system

In this article, the design and performance analysis of Jaya-assisted reciprocal rank-based proportional–integral–derivative controller with derivative filter (PIDn) is proposed for the automatic generation control (AGC) problem of two-area interconnected power systems. The filter with a derivative gain is used to nullify the impact of noise in the input signal. The integral of time multiplied square error (ITSE) of frequency deviations, tie-line power deviations, and area-control errors is the foundation of the goal function developed for tuning controller parameters (ACEs). A single overall objective function is created by combining these sub-objectives. ITSEs of two areas, ITSEs of tie-line power deviation, and ITSEs of ACEs of two areas are weighted and summarized to form the overall goal function. Each sub-relative goal’s importance in the control design is evaluated using the weights in the overall objective function. The weights in this article are determined using the reciprocal rank approach in a methodical manner, in contrast to earlier techniques where weights were either assumed equally by ignoring the relative relevance of sub-objectives or chosen at random. The resultant total objective function is minimized using the Jaya method. Six different test cases involving various load disturbances in the interconnected areas are used to evaluate the performance of the suggested Jaya optimizer-assisted reciprocal rank technique-based controller. Additionally, the performance of various controllers tuned with the sine cosine algorithm (SCA), salp swarm algorithm (SSA), symbiotic organisms search (SOS), Nelder–Mead simplex (NMS), and Luus–Jaakkola (LJ) algorithms is compared with that of the Jaya-tuned controller. The time domain specifications are provided for each of the six test scenarios. To display the differences in frequencies and tie-line power, the results are also shown. Statistical analysis is also provided to assess the suggested controller’s overall efficacy.

Distributed optimal load frequency control for multi-area power systems with controllable loads

With the proliferation of distributed energy resources, power imbalance may fluctuate rapidly with a large amount, which may make the conventional frequency control scheme not respond fast to the fluctuation and operate in an economically inefficient manner. This paper proposes a distributed optimal load frequency control (DOLFC) scheme for multi-area power systems with controllable loads, which optimally dispatches generators and controllable loads to participate the frequency regulation with cost minimization. It can restore the nominal frequency and only needs local measurements and local communication for implementation. With the DOLFC, we prove that the closed-loop LFC system asymptotically converges to an equilibrium point which minimizes the control cost of generators and controllable loads, and the equilibrium point is unique if and only if the multi-area power network is a tree. Compared with the conventional LFC and economic LFC in literature, the proposed DOLFC has higher economic efficiency and better control performance. Finally, a four-area power system is employed to illustrate the effectiveness of the proposed scheme by numerical simulations.

Fractional order PID controller for load frequency control in a deregulated hybrid power system using Aquila Optimization

This paper presents an innovative approach for automatic generation control for power system under a deregulated setting. The main objective of this work is to optimally tune the parameters of the fractional-order controller using the newly developed Aquila Optimizer (AO) to enhance system performance. A test system comprising a thermal power plant, a hydroelectric system, a gas turbine-based power plant, and wind energy sources is examined under deregulated environment. The study emphasizes the minimization of frequency variations, tie line deviations, and area control errors during diverse operational shifts. The proposed control strategy explores the response of generators in a hybrid deregulated power system, emphasizing the critical role of properly tuned Fractional Order Proportional-Integral-Derivative (FOPID) controllers in ensuring system stability. The potential and effectiveness of the proposed algorithm are compared with particle swarm optimization (PSO) and whale optimization algorithm (WOA) based controller performance for the same test system. The objective function for optimization is set as the minimization of the integral time and absolute error (ITAE) performance index. Furthermore, the efficacy of the proposed technique is compared with the Unified Power Flow Controller (UPFC) and its superiority is validated. Performance evaluation of the hybrid power system is conducted under Poolco agreement, bilateral agreement, and varying operating conditions. Comparative assessments reveal the superiority of the AO-driven FOPID over other techniques, demonstrating improved system metrics, including frequencies across different areas, tie-line power variations, and generator outputs.

Design and Analysis of Load Frequency Control for a Two-Area Power System Using Conventional PID and FPID Controllers

Power system stability is required to maintain a continuous balance between power generation and load demand. Frequency control is also a major function of automatic generation control and one of the most important control problems in power system design and operation. So, a robust control system must be implemented for controlling the actual power and frequency. This paper mainly focuses on the design of a two-area power system with a rated frequency of 50 Hz, which is used in Myanmar, using the fuzzy-PID method to control the load frequency. It also aims to imply from one area to another when demand suddenly increases or decreases. It involves applying mathematical formulas and models to analyze how an automatic generation control works. The plan is first implemented in a single area before being modified to a two-area power system with and without controllers. The output results are simulated with MATLAB/ SIMULINK.

A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Frequency regulation in Multi-region Interconnected Power Systems (MIPS), incorporating wind turbine systems, energy storage units, and demand response, is a challenging control problem. The problem involves maintaining grid stability, integrating variable renewable energy sources, enhancing grid resilience, optimizing energy storage and demand response capabilities, and ensuring regulatory compliance. Effective frequency regulation is a key problem for reliable and sustainable power system operation. In this paper, complex and uncertain dynamics in various components of a MIPS are modeled using multiple first-order dynamic fractional-order Type-2 Fuzzy Logic Systems (T2-FLSs). The models are evaluated, and the best possible dynamic model is chosen. With the best possible model, an optimal controller is designed. The stability and optimality analysis are presented through a Linear Matrix Inequality (LMI) approach. The adaptive laws of T2-FLSs are derived such that some LMI-based conditions are satisfied. The designed controller does not depend on the dynamics of MIPS. The uncertainties of time-varying load, wind energy, and solar power are modeled using T2-FLSs. The suggested LMI technique presents adaptation laws for T2-FLSs to ensure stability in the presage of natural disturbances and errors. The designed scheme is validated by applying to a practical 39-bus IEEE test system that includes the wind farms, demand response, and energy storage systems. The simulation results under various conditions verify the usefulness of the suggested controller. The comparisons with conventional controllers demonstrate that the suggested approach is more effective under uncertainties and natural perturbations.

Distributed Remote Secure ADP-Based Control for Interconnected Power Systems Under Cyber-Attacks

Distributed Remote Secure ADP-Based Control for Interconnected Power Systems Under Cyber-Attacks

An effective updating scheme based DETM robust LFC under non-ideal communication network

This paper proposes an effective control mechanism including decentralized event-triggered conditions with a coincident control signal updating strategy, which dramatically reduces massive amount of data transmission and remarkably saves network bandwidth. Meanwhile, an asymmetric Lyapunov–Krasovskii functional (LKF) with relaxed conditions is proposed to investigate the robust performance of load frequency control (LFC) in power system. The development is capable of dealing with the frequency fluctuation of power system by utilizing LKF stability theory and stochastic analysis techniques under non-ideal communication network, such as unexpected loads, network delays and random deception attacks. Furthermore, the well-designed examples are presented to assess the effectiveness of this control strategy with quantitative analyses on: (i) implementation of control designing; (ii) comparison of communication efficiency using different trigger mechanisms; and (iii) evaluation of H∞ performance under different attack probabilities and delay upper bounds.

Research on Intelligent Control and Optimization Methods in Power Systems

Research on Intelligent Control and Optimization Methods in Power Systems

A Comprehensive Review of Load Frequency Control Technologies

Load frequency control (LFC) is one of the most important tools in power system control. LFC is an auxiliary service related to the short-term balance of energy and frequency of power systems. As such, it allows the acquisition of a central role in enabling electricity exchanges and providing better conditions. The classification of LFC can be carried out from different angles: we can enumerate, among others, the type of control used. The following types of control are presented in this review: classical, optimal, and robust control. More advanced controls can also be used for classification: fuzzy logic control, ANN control, genetic algorithms, PSO control, etc. The influence of renewables and power control tools like FACTS is also considered as a category to be analyzed. The last classifications are related to two important subjects—the influence of DC links on LFC efficiency and the dangers of cyberattacks on the LFC.