Power System Stability Research Articles

Nature-Inspired Optimization with Deep Learning-Enabled Sustainable Predictive Model for Intelligent Systems

Solar energy can be considered as an alternate solution to conventional energy sources. Short-term Photovoltaic (PV) Power Generation (PVPG) prediction methods are essential stabilize power integration among PV and smart grids. The PVPG generation process is highly dependent on climatic conditions and therefore high intermittent. Highly accurate PVPG prediction of PVPG acts based on the generation, transmission and dispersion of electricity, confirming the stability and dependability of power system. The current progress of Machine Learning (ML) and Deep Learning (DL) approaches enables for designing of accurate PVPG prediction models. In this view, this paper develops a new Badger Optimization with Deep Learning Enabled PV Power Generation Predictive (HBODL-PVPGP) model. The presented HBODL-PVPGP model enables to forecast of the PVPG process. To accomplish this, the HBODL-PVPGP model initially investigates the features depending upon intrinsic characteristics earlier in the learning process. In addition, the Bidirectional Gated Recurrent Unit (BiGRU) model is implemented for forecasting process. The performance of the BiGRU model can be improvised by the design of HBO-based hyperparameter tuning procedure. For ensuring the enhanced performance of the HBODL-PVPGP model, an extensive range of experimental study was effectuated and the results were investigated under various factors. The result highlighted the precipitated performance of the HBODL-PVPGP procedure on the current algorithms.

საქართველოს ელექტროენერგეტიკული სისტემის დინამიკური მდგრადობის ანალიზი ბატარეის ელექტროენერგიის დამაგროვებელ სისტემებში ინტეგრირებული ორმხრივი ნახევარგამტარული გარდამსახების და ენერგიის მართვის სისტემის გამოყენებით

In practice, a directional converter integrated into the battery energy storage systems - an inverter, which only supplies energy to the network for frequency and voltage regulation when necessary, and cannot receive energy from the network. Increasing the reliability of the stability of the electric power system will be especially effective when in the battery energy storage systems will be used a Bi-directional AC/DC Converter, which in turn includes a rectifier and an inverter, which will supply the grid through the energy management system and also consume energy from it if necessary through the upgraded SCADA/EMS. The high-level architecture of the Supervisory Control and Data Acquisition systems/Energy Management System includes a number of capabilities, including a dynamic stability analysis module, also "High numerical aperture extreme ultraviolet lithography - lithography of advanced semiconductor materials" - "High-NA-EUV lithography" provides opportunities for manufacturing such microcircuits, each nanocell includes a nanotransistor and a nanocapacitor, and their chipset size is 1 nanometer. The mentioned rate of scientific development provides an opportunity to create such technologies that will give wide opportunities to modern the battery energy storage systems to fully respond to the requirements that arise during the continuous operational management of the country's electric power system.

Simulation of Doubly Fed Induction generator (DFIG) for Steady state analysis when connected to a wind farm for power system stability

In this paper, a 2MW variable speed, pitch-regulated Doubly-fed Induction Generator (DFIG) with a speed range of 800-2000 rpm was studied for steady-state analysis. The DFIG was modelled in the Matlab/Simulink environment. The rotor-side converter utilized closed-loop stator flux-oriented vector control for managing the DFIG model. This method allows for rapid control and experimenting of grid-connected, variable speed DFIG wind turbines to examine their steady-state and energetic characteristics beneath ordinary and disturbed wind conditions when connected to a wind farm. The steady-state behavior of the wind turbine generator was derived at two different magnetizing levels: one with the reactive power of the stator equal to zero (Qs = 0), and the other with the direct current of the rotor equal to zero (Idr = 0). Simulation results show that the machine has higher efficiency when magnetized through the stator as compared with magnetization of the machine through the rotor. To come out with the DFIG transitory stability simulation results traditional controllers' for active and reactive power were compared with an adaptive adaptive tracking, self-tuned feedforward proportional integral regulating model for peak performance. Additionally, stability and instability were studied by solving the Swing equation using the Runge-Kutta method of order four. In a steady-state condition for the generator, the acceleration torque (Ta) reaches zero, which signifies that the mechanical torque (Tm) matches the electrical torque (Te). In the stability investigation, Tm is assumed to be constant. The findings provide valuable insights into the control strategies required for enhancing the reliability and efficiency of wind turbines in variable wind conditions.

A Search for the Power System Stability Limit at a Specified Controlled Cutset by Applying a Flow Model

A Search for the Power System Stability Limit at a Specified Controlled Cutset by Applying a Flow Model

Enhanced Feature Extraction with AL-YOLOv9s Lightweight Model: Application in Key Component Recognition Within Highly Integrated Device Environments

In environments containing highly integrated devices, accurately monitoring the status of circuit breaker lockouts is essential for maintaining the stability of power systems. Traditional detection methods are often inadequate due to complex equipment configurations and severe operational challenges. This paper presents an enhanced detection model, the AL-YOLOv9s, which improves the efficiency and accuracy of detecting circuit breaker lockouts. The AL-YOLOv9s model is based on the advanced YOLOv9s algorithm and incorporates an enhanced efficient multi-scale attention module to boost feature extraction capabilities. It also integrates channel and spatial attention mechanisms to optimize the feature fusion process, thereby improving detection performance. Additionally, the model has been optimized to a size of 4.7M, making it suitable for lightweight field applications without compromising accuracy. Experimental results demonstrate that the AL-YOLOv9s model achieves high standards in accuracy and portability, thus offering an effective and practical solution for lockout detection.

The Influence of Stability in New Power Systems with the Addition of Phase Modulation Functions in Thermal Power Units

The addition of phase modulation function technology to thermal power units is one of the most effective measures to solve dynamic reactive power shortages in the construction process of new power systems. In this paper, the influence of the phase modulation function transformation of thermal power units on the stability of a new power system is studied. Firstly, the new power system stability index is deeply analyzed, and an evaluation system for power system transient stability is constructed from five key dimensions: transient voltage, static voltage, power angle stability, power flow characteristics, and grid support. Secondly, a fuzzy comprehensive evaluation method considering the subjective and objective comprehensive weights is proposed, and the influence of the phase modulation transformation of the thermal power unit on the stability of the receiving-end power grid is quantitatively analyzed. Finally, a CEPRI36 node example model was built based on the PSASP v.7.91.04.9258 (China Electric Power Research Institute, Beijing, China) platform to verify the accuracy and effectiveness of the proposed method. The results show that the proposed method can quantitatively analyze the impact of adding a phase modulation function to thermal power units on the stability of the power system. At the point of renewable energy connection, the static voltage stability index improved by 42.9%, the transient power angle stability index improved by 32.1%, the multi-feed effective short-circuit ratio index improved by 33.9%, and the comprehensive evaluation score improved by 14.7%. These results further indicate that adding a phase modulation function to thermal power units can provide a large amount of dynamic reactive power support and improve the voltage stability and operational flexibility of the system.

Short-Term Photovoltaic Power Forecasting Using PV Data and Sky Images in an Auto Cross Modal Correlation Attention Multimodal Framework

The accurate prediction of photovoltaic (PV) power generation is crucial for improving virtual power plant (VPP) efficiency and power system stability. However, short-term PV power forecasting remains highly challenging due to the significant impact of weather changes, especially the complexity of cloud motion. To this end, this paper proposes an end-to-end innovative deep learning framework for data fusion based on multimodal learning, which utilizes a new auto cross modal correlation attention (ACMCA) mechanism designed in this paper for feature extraction and fusion by combining historical PV power generation time-series data and sky image data, thereby enhancing the model’s prediction performance under complex weather conditions. In this paper, the effectiveness of the proposed model was verified through a large number of experiments, and the experimental results showed that the model’s forecast skill (FS) reached 24.2% under all weather conditions 15 min in advance, and 24.32% under cloudy conditions with the largest fluctuations. This paper also compared the model with a variety of existing unimodal and multimodal models, respectively. The experimental results showed that the model in this paper outperformed other benchmark methods in all indices under different weather conditions, demonstrating stronger adaptability and robustness.

Aspects of Relevance of Hybrid Power Plants in Control and Stability of Weak Grids

This paper reviews the possible contributions of Hybrid Power Plants (HPPs) to support weak grids while maintaining the desired system stability. Moving towards a converter-dominant power system with less inherent inertia and distant connections to the nearest synchronous generator, frequency and voltage controls are becoming more critical to ensure the stability of the weak grid. In this regard, state-of-the-art literature is reviewed for frequency and voltage controllers in single-technology power plants, like wind and solar power plants. The contribution of this paper lies in providing a clear overview of available literature in terms of frequency and voltage control stages, regardless of the utilized control method. On the other hand, focus has been put on the increased utilization of HPPs to provide more flexibility, increased availability, and reduced variability through the combination of various sources, i.e., wind, solar, and storage. Furthermore, investigating the specific capabilities and challenges of HPPs, this review shows that very little literature has been conducted on voltage control using HPPs. Finally, the aspect of relevance of HPPs is discussed in the control and stability of modern power systems.

Virtual inertia calculation and virtual power system stabiliser design for stability enhancement of virtual synchronous generator system under transient condition

Virtual inertia calculation and virtual power system stabiliser design for stability enhancement of virtual synchronous generator system under transient condition

A Novel IoT-Based Controlled Islanding Strategy for Enhanced Power System Stability and Resilience

Intentional controlled islanding (ICI) is a crucial strategy to avert power system collapse and blackouts caused by severe disturbances. This paper introduces an innovative IoT-based ICI strategy that identifies the optimal location for system segmentation during emergencies. Initially, the algorithm transmits essential data from phasor measurement units (PMUs) to the IoT cloud. Subsequently, it calculates the coherency index among all pairs of generators. Leveraging IoT technology increases system accessibility, enabling the real-time detection of changes in network topology post-disturbance and allowing the coherency index to adapt accordingly. A novel algorithm is then employed to group coherent generators based on relative coherency index values, eliminating the need to transfer data points elsewhere. The “where to island” subproblem is formulated as a mixed integer linear programming (MILP) model that aims to boost system transient stability by minimizing power flow interruptions in disconnected lines. The model incorporates constraints on generators’ coherency, island connectivity, and node exclusivity. The subsequent layer determines the optimal generation/load actions for each island to prevent system collapse post-separation. Signals from the IoT cloud are relayed to the circuit breakers at the terminals of the optimal cut-set to establish stable isolated islands. Additionally, controllable loads and generation controllers receive signals from the cloud to execute load and/or generation adjustments. The proposed system’s performance is assessed on the IEEE 39-bus system through time-domain simulations on DIgSILENT PowerFactory connected to the ThingSpeak cloud platform. The simulation results demonstrate the effectiveness of the proposed ICI strategy in boosting power system stability.

Enhancing vector control performance in DFIG-based wind turbine through ANFIS controller using real variable wind speed profile

This article focuses on enhancing vector control for a Doubly Fed Induction Generator (DFIG) through the integration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method and comparing it with the classical Proportional-Integral (PI) controller. In this study, the wind turbine operates on a DFIG connected to the AC power grid. The proposed vector-ANFIS controller is implemented on the rotor-side converter to regulate the current and achieve the desired torque for Maximum Power Point Tracking (MPPT). The system was modeled and simulated using MATLAB Simulink. The simulation employed a real variable wind speed profile to reflect a real-world scenario. our contribution was also to compress several hours of collected data into a matter of seconds, enabling us to simulate a multi-hour period and observe the results in MATLAB. The results demonstrate the superior performance of ANFIS compared to PI, indicating its potential utility in enhancing the power quality and stability of wind power systems.

Modeling, Control Study, and Power Management Strategy of a Hybrid Grid-Connected AC/DC Microgrid with High Integration of Renewable Energies and Green Hydrogen Sources

Abstract This first quarter of the 21st century is increasingly marked by population growth, digital and industrial de- velopments, growing need for electricity supply, and climate change. All this, to name just a few, have made the establishment of a stable, flexible, controlled, well-designed, extensive and clean power system a necessity. Conse- quently, distributed microgrid generation based on alternative/renewable energies and/or low-carbon technologies has emerged. In this paper, we study the modeling, the control, and the power management strategy of a grid-connected hybrid Alternating/Direct Current (AC/DC) microgrid based on a wind turbine generation system using doubly fed induction generator, a photovoltaic generation system, and storage elements including hydrogen storage system and batteries. Adequate modeling is described, and the overall system monitoring is presented and applied to manage appropriate power sharing and to control active and reactive powers, in order to match load and weather fluctuation behaviour. Simulations are carried out using MATLAB/Simulink simulation tool. Simulations reveal convenient re- sults in terms of the bidirectional interlinking converter capabilities regarding power balance establishment between the two subgrids, reactive power compensation to ensure a unity power factor, and DC-bus voltage regulation at 1200 V. In addition, the primary and secondary controls are approved for each distributed generation of the studied system to attain the assigned power references, regardless of whether the subgrid is heavily or lightly loaded throughout the four considered case studies, showing satisfactory tracking and interacting performances, and thus stimulating a stable system implementation.

A novel controller algorithm to improve stability of power system based on a hybrid of fuzzy controller and Gray wolf optimization by coordinating PSS and TCSC with considering uncertainty

A novel controller algorithm to improve stability of power system based on a hybrid of fuzzy controller and Gray wolf optimization by coordinating PSS and TCSC with considering uncertainty

Low‐frequency oscillation damping strategy for power system based on virtual dual‐input power system stabilizer

AbstractTo keep pace with the construction of the new‐type power system, virtual synchronous generator control, as a classical method of virtual inertia control, has been widely adopted due to its electromechanical characteristics similar to synchronous generator. However, the introduction of rotor motion equations leads to low‐frequency oscillation issues in virtual synchronous generator units similar to synchronous machines. To address this challenge, this paper constructs the Phillips‐Heffron model of the virtual synchronous generator grid‐connected system and analyses the mechanism of low‐frequency oscillation in virtual synchronous generator through the damping torque method. Subsequently, a virtual dual‐input power system stabilizer is proposed by drawing inspiration from the design principles of the traditional dual‐input power system stabilizer to suppress low‐frequency oscillations in the power system. The structure of the virtual dual‐input power system stabilizer is provided, and the phase compensation method is used to optimize the parameters of the virtual dual‐input power system stabilizer. Finally, the effectiveness of the proposed virtual dual‐input power system stabilizer is verified by simulation comparison.

Control strategy of the novel stator free speed regulating wind turbine generation system.

Building a high-proportion renewable energy power system is a key measure to address the challenges of the energy revolution and climate change. However, current high-proportion renewable energy systems face issues of frequency instability and voltage fluctuations. To address these challenges, this paper proposes a novel topology for a stator free speed regulating wind turbine generation system. The stator free speed regulating machine connects a gearbox and a synchronous generator, forming a flexible drive chain that increases system inertia and enhances frequency stability in high-proportion renewable energy power systems. The synchronous generator at the end of the drive chain can be directly connected to the grid without the converter, thanks to the speed regulation provided by the stator free speed regulating machine, thus avoiding harmonic pollution caused by power electronic devices in traditional wind turbines, enhancing the reactive power support capability, and improving voltage stability in high-proportion renewable energy systems. Given that the proposed stator free speed regulating machine consists solely of a rotating inner and outer rotor without a stator, traditional motor control strategies are not applicable. Therefore, this paper focuses on developing control strategies for the stator free speed regulating machine, employing a dual closed-loop PI control strategy with an outer loop for speed and an inner loop for current, based on flux orientation of the outer rotor. Simulation experiments will validate the feasibility of the proposed stator free speed regulating wind turbine generation system topology and the effectiveness of the control strategy.

A Time-Limited Adaptive Reclosing Method in Active Distribution Networks Considering Anti-Islanding Protection

In active distribution networks (DNs), distributed energy resources (DERs) must be disconnected from the grid prior to automatic reclosing actions. Many scholars have proposed non-voltage checking reclosing methods, but a significant challenge arises; many substations lack line-side voltage transformers (LSVTs), making these schemes impractical. To address this, we introduce a time-limited adaptive automatic reclosing (TLAR) method that integrates DERs’ anti-islanding protection (AIP) with automatic reclosing. This method estimates the AIP action time using bus-side voltage measurements before the system-side protection (SSP) is tripped and adjusts the reclosing time accordingly to enhance power supply reliability. Simulations using PSCAD validate the method’s effectiveness. The TLAR method is well-suited for distribution lines without conditions for non-voltage checking, is cost-effective, easy to implement, and contributes to power system stability.

A Fast and Accurate Method for dq Impedance Modeling of Power Electronics Systems Based on Finite Differences

This paper presents a finite-difference-based method for numerically deriving the DQ impedance model of power electronics-based power systems, specifically tailored for stability analysis. The proposed method offers a computationally efficient alternative to traditional approaches by directly applying finite-difference approximations to the large-signal dynamic system, without relying on repetitive time-domain simulations or small-signal analytical models. This method eliminates the need for additional models or complex procedures to compute the steady-state solution, streamlining the impedance modeling process. The accuracy, efficiency, and precision of the proposed method are evaluated through comparative studies with analytical and time-domain perturbation methods. Results demonstrate that the proposed approach provides accuracy comparable to analytical models while significantly reducing computational effort, outperforming perturbation methods in both speed and precision. These findings highlight the practical value of the proposed method for real-time and large-scale system analysis, making it a robust tool for power systems stability assessment.

Optimization algorithm of power system line loss management using big data analytics

As global energy demand continues to rise and renewable energy sources develop rapidly, the operational efficiency and stability of power systems have emerged as primary challenges in energy management. Line loss within these systems is a critical factor for both energy efficiency and economic performance. This study leverages an electric energy data management platform that facilitates the sharing of archival information, the development of line loss calculation models, and the automated computation of electricity and line loss formulas. This ensures accurate and real-time calculations of line losses in the power grid, supporting multi-time scale analyses and providing timely, comprehensive data for effective line loss management. The platform utilizes theoretical line loss values to identify anomalies, which are categorized into five types: topological relationships, archival information, data collection, electricity metering, and consumption behavior. In response to the abnormal monthly power imbalance rate of 220 kV and 110 KV stations, and the − 3.684% exceeding the − 1% assessment limit, the designed line loss management system service layer does not need to go deep into the bottom layer of the power system. It hides the complexity of the power grid through middleware and provides data, application, and security services.

A multi-scale and multi-modal convolutional neural network for condition monitoring of transmission line

Abstract Monitoring the fatigue damage of transmission lines is crucial for stable power system operation. However, existing model-driven methods face challenges such as high computational complexity and reliance on expert knowledge, while data-driven methods require large amounts of abnormal state data. To address these issues, a multi-scale and multi-modal convolutional neural network (CNN) is proposed for real-time condition monitoring of transmission lines. Key steps include: firstly, empirical Fourier decomposition is used to decompose the original signals, extracting multi-scale state information at different frequency scales. Then, time-domain, frequency-domain, and time–frequency domain analyses are performed on the decomposed signals to capture multi-modal information. Based on this, a multi-modal fusion network is proposed based on a CNN to extract shallow and deep features, with a fully connected layer used for multi-modal feature fusion. Notably, the algorithm is implemented on a microprocessor for practical application. Experimental results show that the proposed model achieves a diagnostic accuracy of 93.06%, outperforming classical networks. It also surpasses models trained solely on time, frequency, or time–frequency features by 25.18%, 21.8%, and 19.3%, respectively.

Deep-RNN based model for short-time forecasting photovoltaic power generation using IoT

Global warming is one of the most significant issues of the century due to climate change caused by increased carbon emissions resulting from the exploitation of fossil fuels. Consequently, renewable energies are considered an alternative that promotes cleaner production and offers a substantial reduction in carbon emissions. Therefore, accurately forecasting photovoltaic (PV) power generation is crucial for controlling and distributing electrical inventory and ensuring the stability and reliability of power systems. In this paper, we develop a model for forecasting short-term PV power generation based on deep Recurrent Neural Networks (deep-RNNs). To improve efficiency, our model uses weather and PV generation dataset on-site collected in real-time using IoT technology. Specifically, by leveraging deep-RNN, particularly the long short-term memory network (LSTM) and gated recurrent units (GRU), which excel at capturing long-term dependencies in time series data, this article proposes a combination of LSTM and GRU models to take advantage of both in different weather conditions. The results of the experiments show that the LSTM-GRU model that has been proposed performs better in PV power forecasting than both the LSTM and GRU models together.