Power System Stability Research Articles

GA and PSO Techniques Based Optimal Tuning of Power System Stabilizers in a PV Integrated Power Network

This paper involves the employment of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) methods for the optimal tuning of gain and time constant parameters of power system stabilizers in a power network with integrated Photovoltaic (PV) energy conversion systems. To examine the critical modes of the PV integrated system and the impact of the PV integration on various modes of oscillations, eigenvalue analysis is carried out under various PV penetration levels. The investigations indicate that the rotor angle oscillations following a disturbance have less damping as the PV penetration level increases. To enhance the small signal stability, the excitation systems are provided with the signal from power system stabilizers (PSS). The gain and time constant parameters of the PSS are tuned utilizing the GA and PSO methods and compared with the conventional proportional-integral (PI) PSS. The preliminary investigations on the PV integrated standard IEEE power system reveal that the PSO and GA-based PSS are significantly better than the PI-PSS, and further, PSO-PSS is marginally more effective than the GA-PSS in damping the rotor angle oscillations.

An Optimized Power-Angle and Excitation Dual Loop Virtual Power System Stabilizer for Enhanced MMC-VSG Control and Low-Frequency Oscillation Suppression

Modular Multilevel Converter Virtual Synchronous Generator (MMC-VSG) technology is gaining widespread attention for its ability to enhance the inertia and frequency stability of the power grid integrated with converter-interfaced renewable energy sources. However, the excitation voltage regulation in the MMC-VSG can generate equivalent negative damping torque and cause low-frequency oscillation problems similar to those in synchronous machines. This article aims to improve the system’s damping torque and minimize low-frequency oscillations by introducing a Virtual Power System Stabilizer (VPSS) into the power control loop. Building on the study of dynamic interactions between various control links of the MMC, this research establishes a reduced-order model (ROM) and a Phillips–Heffron state equation for the MMC-VSG single machine infinite bus system, using a hybrid modeling approach and a zero-pole truncation method. It also analyzes the mechanism of low-frequency oscillations in the MMC-VSG system through the damping torque method. The analysis reveals that the negative damping torque produced during the excitation voltage regulation process causes changes in the virtual power angle, which in turn increases the risk of low-frequency oscillation in the MMC-VSG. To address this issue, the article proposes an optimized control method for the MMC-VSG dual power loop architecture (power-angle/excitation) VPSS. This strategy compensates for the inadequate damping torque of a single loop VPSS and effectively suppresses low-frequency oscillations in the system.

A flexible perception method of thin smoke based on patch total bounded variation for buildings.

Early fire warning is critical to the safety and stability of power systems. However, current methods encounter challenges in capturing subtle features, limiting their effectiveness in providing timely alerts for potential fire hazards. To overcome this drawback, a novel detection algorithm for thin smoke was proposed to enhance early fire detection capabilities. The core is that the Patch-TBV feature was proposed first, and the total bounded variation (TBV) was computed at the patch level. This approach is rooted in the understanding that traditional methods struggle to detect minute variations in image characteristics, particularly in scenarios where the features are dispersed or subtle. By computing TBV at a more localized level, the algorithm proposed gains a finer granularity in assessing image quality, enabling it to capture subtle variations that might indicate the presence of smoke or early signs of a fire. Another key aspect that sets our algorithm apart is the incorporation of subtle variation magnification. This technique serves to magnify subtle features within the image, leveraging the computed TBV values. This magnification strategy is pivotal for improving the algorithm's precision in detecting subtle variations, especially in environments where smoke concentrations may be minimal or dispersed. To evaluate the algorithm's performance in real-world scenarios, a comprehensive dataset, named TIP, comprising 3,120 images was constructed. The dataset covers diverse conditions and potential challenges that might be encountered in practical applications. Experimental results confirm the robustness and effectiveness of the proposed algorithm, showcasing its ability to provide accurate and timely fire warnings in various contexts. In conclusion, our research not only identifies the limitations of existing methods in capturing subtle features for early fire detection but also proposes a sophisticated algorithm, integrating Patch-TBV and micro-variation amplification, to address these challenges. The algorithm's effectiveness and robustness are substantiated through extensive testing, demonstrating its potential as a valuable tool for enhancing fire safety in power systems and similar environments.

Robust [formula omitted] control for LFC of discrete T–S fuzzy MAPS with DFIG and time-varying delays

The H∞ LFC control problem for a class of nonlinear power systems with time-varying delays is under study. Considering uncertainties arising from nonlinear issues such as the generation rate constraint (GRC) and governor dead band (GDB), as well as the high variability of renewable energy sources like wind power, the model is transformed into a discrete-time Takagi–Sugeno (T–S) fuzzy model with parameter uncertainty. By constructing a Lyapunov–Krasovskii functional, and employing difference inequalities and generalized cross-convex matrix inequalities, sufficient conditions for the asymptotic stability of power systems are provided. Based on the obtained conditions, a controller is designed to ensure the asymptotic stability of Multi-Area Power Systems (MAPS), with the performance index being H∞. Finally, simulation results demonstrate the correctness and effectiveness of the theorem.

Iterative LMI-Tuned PID Controller for Robust Decentralized Multi-Area Automatic Generation Control in Deregulated Electricity Markets

This study proposes an Iterative Linear Matrix Inequality (LMI)-Tuned Proportional-Integral-Derivative (PID) controller for robust decentralized AGC in the context of deregulated electricity markets. The study addresses the challenges posed by market uncertainties and diverse generation sources, aiming to enhance the stability and performance of power systems. This study is driven by the urgent need to develop robust AGC strategies that are adapted to the complex dynamics of decentralized power systems operating in deregulated markets. The conventional centralized control approaches are often inadequate to address the diverse and distributed nature of modern generation units. Furthermore, the intermittent and variable nature of renewable energy sources adds a layer of complexity that demands innovative control solutions. The impact of bilateral contracts on the dynamics is taken into consideration while modifying the conventional LFC of interconnected power networks. To model these bilateral contracts, the distribution company (DISCO) participation matrix (DPM) is presented, and the multi-area block diagram reflects these concepts. Simulation studies are conducted to evaluate and compare the performance of the proposed Iterative LMI-Tuned PID Controller and Conventional PID Controller. Results are analyzed against key performance metrics, considering scenarios reflective of diverse market conditions. Comparative studies with the conventional PID controller provide insights into the efficacy and robustness of the proposed approach.

Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning

Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.

Optimizing dynamic voltage restorers with Bee Optimization Algorithm for enhanced power quality in modern hydro turbine grids

ABSTRACT This study explores the optimization of dynamic voltage restorers (DVRs) within hydro turbine systems using the Bee Optimization Algorithm (BOA), focusing on enhancing power system stability and quality through advanced control strategies. Emphasizing total harmonic distortion (THD) minimization, the research addresses power quality challenges prevalent in hydroelectric power generation. Detailed simulations demonstrate how BOA effectively reduces THD, optimizing DVR performance in response to grid disturbances typical in renewable energy integrations. Findings validate the BOA's efficacy in improving voltage stability and underscore the potential of bio-inspired algorithms for smart grid applications in hydro settings. This approach not only supports the reliability of hydroelectric power systems but also opens new avenues for employing multi-objective optimization techniques to advance DVR functionality, contributing to the sustainable management of energy infrastructures.

Explicit formulas for the asymptotic variance of a linearized model of a Gaussian disturbance driven power system with a uniform damping-inertia ratio

Serious fluctuations caused by disturbances may lead to instability of power systems. With the disturbance modeled by a Brownian motion process, the fluctuations are often described by the asymptotic variance at the invariant probability distribution of an associated Gaussian stochastic process. Here, we derive the explicit formula of the variance matrix for the system with a uniform damping-inertia ratio at all the nodes, which enables us to analyze the influences of the system parameters on the fluctuations and investigate the fluctuation propagation in the network. With application to systems with complete graphs and star graphs, it is found that the variance of the frequency at the disturbed node is significantly bigger than those at all the other nodes. It is also shown that adding new nodes may prevent the propagation of fluctuations from the disturbed node to all the others. Finally, it is proven theoretically that larger line capacities accelerate the propagation of the frequency fluctuation and larger inertia of synchronous machines help suppress the fluctuations of the phase differences, however, these acceleration and suppression are quite limited.

Optimal Battery Storage Configuration for High-Proportion Renewable Power Systems Considering Minimum Inertia Requirements

With the continuous development of renewable energy worldwide, the issue of frequency stability in power systems has become increasingly serious. Enhancing the inertia level of power systems by configuring battery storage to provide virtual inertia has garnered significant research attention in academia. However, addressing the non-linear characteristics of frequency stability constraints, which complicate model solving, and managing the uncertainties associated with renewable energy and load, are the main challenges in planning energy storage for high-proportion renewable power systems. In this context, this paper proposes a battery storage configuration model for high-proportion renewable power systems that considers minimum inertia requirements and the uncertainties of wind and solar power. First, frequency stability constraints are transformed into minimum inertia constraints, primarily considering the rate of change of frequency (ROCOF) and nadir frequency (NF) indicators during the transformation process. Second, using historical wind and solar data, a time-series probability scenario set is constructed through clustering methods to model the uncertainties of wind and solar power. A stochastic optimization method is then adopted to establish a mixed-integer linear programming (MILP) model for the battery storage configuration of high-proportion renewable power systems, considering minimum inertia requirements and wind-solar uncertainties. Finally, through a modified IEEE-39 bus system, it was verified that the proposed method is more economical in addressing frequency stability issues in power systems with a high proportion of renewable energy compared to traditional scheduling methods.

A new optimal 3° of freedom fractional order proportion integral derivative controller with model predictive controller for frequency regulation in high penetrated renewable based interconnected system

The increasing use of renewable energy sources (RESs) in conventional power systems is making them more complex. RES, such as solar and wind energy, reduce system inertia, affecting power system (PS) stability and frequency aberrations. Thus, the modern PS faces new concerns that arise from integrating RESs and struggles to manage frequency and stability, which can cause power outages and blackouts and lower industry production. The intelligent control mechanism is critical to the PS's operation to ensure frequency under its complicated structure. This study suggests a new best 3-DoF fractional order proportional integral derivative-model predictive controller (3DoF-FOPIDN-MPC) for controlling frequency when there is a lot of load going on quickly. The 3DoF-FOPID controls the outer loop using the area control error (ACE) signal, while MPC controls the inner loop using the output signal from the 3DoF-FOPIDN and the area's total power output response. For optimal controller functionality, the salp swarm algorithm (SSA) extracts optimized controller settings. To validate the suggested controller, it is tested under various load-changing scenarios and compared to state-of-the-art controllers. For 1 % and 3 % load changes, the suggested controller settles in 1.1 s and 1.78 s, respectively.

An Evolutionary Game Model of Market Participants and Government in Carbon Trading Markets with Virtual Power Plant Strategies

The utilization of conventional energy sources commonly leads to heightened energy consumption and the generation of specific forms of environmental pollution. As an innovative power management and dispatch system, virtual power plants (VPPs) have the potential to significantly enhance the flexibility and stability of power systems, while supporting carbon reduction targets by integrating distributed energy resources (DERs), energy management systems (EMSs), and energy storage systems (ESSs), which have attracted much attention in the power industry in recent years. Consequently, it can effectively address the variability and management challenges introduced by renewable energy. Furthermore, optimizing power market dispatch and user-side power management plays a pivotal role in promoting the transition of the energy industry towards sustainable development. The current study highlights the unresolved issue of strategic decision-making among market participants, such as energy companies, generation companies, and power distribution companies, despite the potentially significant benefits of VPPs. These entities must carefully evaluate the costs and benefits associated with adopting a VPP. Additionally, governments face the complex task of assessing the feasibility and effectiveness of providing subsidies to incentivize VPP adoption. Previous research has not adequately explored the long-term evolution of these decisions in a dynamic market environment, leading to a lack of adequate understanding of optimal strategies for market participants and regulators. This paper addresses this critical research gap by introducing an innovative bilateral evolutionary game model that integrates VPP and carbon trading markets. By utilizing the model, simulation experiments are carried out to compare different strategic decisions and analyze the stability and long-term evolution of these strategies. Research findings indicate that the adoption of VPP technology by market participants, in conjunction with government policies, results in an average 90% increase in market participants’ earnings, while government revenues see a 35% rise. This approach provides an alternative method for understanding the dynamic interactions between market participants and government policy, offering both theoretical and practical insights. The findings significantly contribute to the literature by proposing a robust framework for integrating VPPs into electricity markets, while offering valuable guidance to policymakers and market participants in developing effective strategies to support the sustainable energy transition. The application of this model has not only enhanced the understanding of market dynamics in theory, but also provided quantitative support for strategic decisions under different market conditions in practice.

On hybrid dynamical systems of differential–difference equations

In this paper, we define and study a class of linear hybrid dynamical systems characterized by differential–difference equations. We introduce two operators that facilitate the analysis of these systems and derive explicit formulas for their solutions. We examine the transfer function matrix and characteristic polynomial to assess stability. Our theoretical findings are supported by numerical examples, demonstrating their application in power systems stability analysis. Specifically, we substantiate our theory within the context of power systems stability analysis, incorporating elements of discrete behavior.

Comparative analysis and optimal allocation of virtual inertia from grid‐forming and grid‐following controlled ESSs

AbstractA broad consensus of neutralizing the carbon dioxide emissions facilitates the transition to the renewable energy power system. Meanwhile, the concerns about the volatility of renewable energies are growing as the rotational inertia of power system becomes inadequate. To maintain the frequency stability of power system, some studies for configuring inertia energy storage systems (ESSs) are carried out, mainly focusing on the allocation of virtual inertia from grid‐forming ESS. In contrast, the allocation of virtual inertia from grid‐following ESS has not been well elaborated and the differences in virtual inertia provided by these two modes are yet to be revealed. Based on ‐norm and Kron reduction, firstly, the state‐space model of post‐disturbance system is established, together with the transient performance evaluation. Then the inertia characteristics of both grid‐forming and grid‐following devices are formulated, followed by the unified gradient descent optimization method for allocating virtual inertia. A modified IEEE 39‐bus system and its time‐domain simulations help in the verification of the contribution of this paper. Through the comparative analysis of corresponding optimal results, the conclusions from two aspects are drawn: in terms of transient frequency support, the grid‐forming devices can provide no less than better inertia support; with the higher power capacity and similar energy capacity, the grid‐forming devices can relieve the response pressure of other generators by approximately .

Network control based load frequency control considering the transmission delay

AbstractThis study introduces a network‐based controller to regulate frequency within power systems. The primary objective of this investigation is to ensure the stability of power systems experiencing delays in control signal transmission. Initially, a modified networked predictor is proposed to anticipate future signals within a decentralized load frequency control framework. Subsequently, a timestamp technique is employed to actively compensate for the impact of transmission delays. Additionally, an event trigger mechanism is incorporated to complement the extended state observer and optimize signal transmission resource usage. The outcomes of the study indicate that the proposed method effectively mitigates delay effects while maintaining superior performance. Moreover, the proposed method remarkably reduces the required transmission resources.

An Enhanced Continuation Power Flow Method Using Hybrid Parameterization

The rapid integration of renewable energy sources and the increasing complexity of modern power systems urge the development of advanced methods for ensuring power system stability. This paper presents a novel continuation power flow (CPF) method that combines two well-known parameterization techniques: natural parameterization and arc-length parameterization. The proposed hybrid approach significantly improves computational efficiency, reducing processing time by 32.76% compared to conventional methods while maintaining high accuracy. The method enables faster and more reliable stability assessments by efficiently managing the complexities and uncertainties, particularly in grids with high penetration of renewable energy.

Bidirectional long‐short‐term memory‐based fractional power system stabilizer: Design, simulation, and real‐time validation

AbstractPower oscillations in modern power grids are inherent phenomena that may threaten system reliability. Therefore, to ensure acceptable system reliability, effective damping of power oscillations is inevitably required. In this context, this article introduces a novel approach to designing fractional power system stabilizer (FPSS) for effective damping of power oscillations. Bidirectional long‐short‐term memory (Bi‐LSTM) approach is adopted to predict the parameters of FPSS. The conventional phase compensation technique is used to train Bi‐LSTM network. To validate the efficacy of FPSS, different test scenarios of contingent operating conditions are simulated for the system. Comparative analysis is carried out with conventional power system stabilizers (PSSs) and optimization‐based PSS techniques. Additionally, a test scenario is performed against existing deep neural network‐based PSS methods to ascertain the robustness of the proposed PSS. Furthermore, the performance of the proposed Bi‐LSTM‐based FPSS is validated in real‐time simulation using an interfaced OPAL‐RT OP5700 hardware device.

Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses

Accurately predicting electricity load is crucial for maintaining the stability of modern power systems. Most studies focus on deterministic predictions, while uncertainty prediction and demand responses (DR) need further research. This paper proposes Bilinear-DRTFT, a load forecasting model that supports uncertainty and considers DR. To comprehensively evaluate model performance, this study combines five deterministic evaluation metrics and three uncertainty evaluation metrics on the test set. The research findings include:1. Incorporating DR effectively enhances the model's performance in both deterministic and uncertainty prediction; 2. Model performance continually improves with refined DR, achieving better uncertainty reduction when incorporating dynamic DR signals considering critical peak electricity prices, reaching expected performance at 80 % and 90 % confidence levels with narrow PINAW; 3. After adding DR, high-quality feature engineering provides the basis for the simplification of feedforward network; 4. Analyzing monthly uncertainty predictions reveals specific time periods on which DR policies should focus. These conclusions are validated in two experimental regions.

Optimal Reactive Power Dispatch Using Artificial Gorilla Troops Optimizer Considering Voltage Stability

The power system has been expanded to supply and fulfil the consumers’ requirements for reliability, affordability, and power quality. Power loss reduction and voltage stability enhancement are important points and have been considered interesting subjects for researchers and utilities. Furthermore, reactive power plays an important role in power system stability, security, and voltage improvement, and it is known as reactive power dispatch (RPD). In this paper, a newly developed meta-heuristic optimization technique that inspired the gorilla troop’s social intelligence in nature is applied. It is named Artificial Gorilla Troop Optimization (GTO). In addition, GTO is utilized to solve the optimal reactive power dispatch (ORPD) problem, whose real active power and voltage deviation reduction are the objective functions of this study. Generator voltage, transformer tap-changers, and reactive power compensators are the controlled variables that are optimized for achieving the minimum real power loss and bus voltage deviation. To illustrate the efficiency and performance of the proposed algorithm, IEEE 14-bus and 30-bus systems are employed. Moreover, the obtained results are compared with those obtained with other three already existing optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). Obviously, the proposed approach can prove the optimal values of controlled variables in solving the ORPD problem by giving the minimum real power loss and voltage deviation than those from compared techniques with less computation time.

Improvement of low-frequency oscillation damping in power systems using a deep learning technique

Over the last few years, machine learning tools have significantly progressed and attracted extensive applications in many parts of contemporary life. The power sector is one of the leading domains implementing such appealing and effective technologies for diverse applications as a part of the digital transformation of electric networks. A power system's low-frequency oscillation (LFO) is a non-threatening but slow-burning problem that might cause complete network failure unless adequately handled. This article proposes a state-of-the-art procedure of LFO damping in electric power networks via the sine cosine algorithm and deep learning (DL) technique. It uses two networks of power systems, in which the synchronous generator is fitted with a power system stabilizer (PSS) in the case of the first network; in the other, the synchronous machine is conjoined to the PSS that coordinates with a unified power flow controller. The proposal is developed based on the statistical assessment of the analyzed networks to improve the LFO damping via real-time adjustment of PSS parameters/variables. The proposed technique was evaluated using power system stability performance measuring criteria, such as the eigenvalue and minimum damping ratio. In the end, the effectivity of the stability-gaining procedure is also tested by time-domain simulation to implement in real-time. The study also dealt with a comparative investigation and discussion of the findings of some published works to conclude the capability of the proposed DL tool for stability improvement of the system in real-time by removing undesirable LFOs.

A novel offshore wind power prediction model based on TCN-DANet-sparse transformer and considering spatio-temporal coupling in multiple wind farms

Offshore wind power capacity is growing, leading to larger clustered farms. Accurately predicting offshore wind power capacity is crucial for power system stability; however, current studies often overlook neighbouring installations. To address this, this study presents the Temporal Convolutional Network-Dual Attention Network-Sparse Transformer (TCN-DANet-Sparse Transformer) model, which considers the spatiotemporal coupling of multiple wind farms. Before detailing our model, we review the existing prediction methods, noting their limitations in capturing interconnected adjacent wind farms. Our model integrates spatial information from nearby farms to enhance prediction reliability. Through Pearson Correlation Coefficient analysis, we explore the temporal and spatial coupling features. Using overlapping sliding windows, we partition farms into subsequences, processed with TCN-DANet for efficient spatio-temporal feature extraction. These features are then input into the Sparse Transformer to improve the computational efficiency. Validated using a dataset from Kächele et al., our model outperforms the baseline on the London Wind Farm. In spring, for Case 1, the mean square error (MSE) of the main model decreased by 43.19 % compared to that of the TCN-DANet-transformer model. Similarly, for Case 2, the MSE of the main model is reduced by 41.69 %.