Stability Of Power Grid Research Articles

Study on evolution characteristics of energy dissipation and vortex in pump-turbine during load rejection transition process

The widespread adoption of intermittent wind and solar energy sources disrupts the stability of power grids, necessitating frequent load rejection processes for pump-turbine. This jeopardizes the stability of pump-turbine units. To investigate the operating behavior of pump-turbine units during load rejection, this study focuses on a Francis reversible pump-turbine. Employing the results from a one-dimensional characteristic line method as boundary conditions, three-dimensional numerical simulations are conducted. The entropy production theory was employed to quantify energy loss in different components, including direct dissipation, turbulent dissipation, and wall shear stress. And the pressure load curves of runner blades at different spans are extracted. The flow characteristics, entropy production losses, and pressure loads within the passage are analyzed at various moments during the load rejection process. Results demonstrate that when a unit undergoes load rejection, it repeatedly transitions into and out of the S-characteristic region. The flow state within the passage is most unfavorable under reverse pump conditions, with the entropy production loss reaching its maximum value of 339 160 W/K. When the unit's rotational speed increased beyond 1.1nd, the unit's water thrust and torque underwent drastic changes. Moreover, the pressure load undergoes significant variations under runaway conditions, with the pressure load difference among various blades reaching up to 33 000 kPa. These findings provide a scientific basis for ensuring the safe and reliable operation of pump-turbine units during load rejection processes.

Strategic modelling for sustainable power grids: a comprehensive study on energy storage integration in India

Purpose This study aims to address the critical need for innovation in the power grid sector, driven by global carbon reduction commitments. It highlights the pivotal role of critical success factors (CSFs) in enhancing system adaptability and environmental mitigation within India’s power industry. Design/methodology/approach This research is grounded on transition management theory to identify and validate the CSFs necessary to integrate energy storage systems (ESS). Here, exploratory factor analysis (EFA) and total interpretive structural modeling (TISM) are integrated to evaluate the model’s effectiveness in reducing CO2 emissions while ensuring grid stability and flexibility. Findings The research develops a seven-level hierarchical model illustrating the interaction of ESS components for a stable power grid, clean energy and a profitable electric industry. It emphasizes the strategic significance of managing key factors to reduce CO2 emissions and ensure grid stability. The study recommends continuous monitoring at tactical and operational levels to enhance overall performance. Research limitations/implications The study provides policymakers with strategic insights for the successful implementation of smart grid initiatives, facilitating effective decarbonization of the electricity industry. Additionally, it offers a comprehensive framework for minimizing the environmental impact associated with electricity generation, thereby enhancing overall operational sustainability and efficiency. Originality/value The originality of this study lies in its integration of EFA and TISM for robust model assessment and the application of transition management theory to identify and validate CSFs in the integration of ESS. This approach offers a novel perspective on enhancing the sustainability and efficiency of power grids.

The application study of deep learning technology in intelligent power supervision systems

Abstract This study explores the application of a combined model incorporating bidirectional long short-term memory networks and gated recurrent units within intelligent power supervision systems, demonstrating its efficacy in recognizing anomalies within power grids. Based on historical operational data of two critical indicators—grid sectional security margin and generation-consumption balance margin—this paper constructs an anomaly detection model. This model effectively processes time series data, promptly identifying equipment malfunctions and abnormal load fluctuations, thereby enhancing the safety and stability of power grids.

A Coordinated Frequency Regulation Strategy Integrating Power Generation, Energy Storage, and DC Transmission for Offshore Wind Power MMC-HVDC Transmission Systems

With the increasing proportion of renewable energy in power grids, the inertia level and frequency regulation capability of modern power systems have declined. In response, this paper proposes a coordinated frequency regulation strategy integrating power generation, energy storage, and DC transmission for offshore wind power MMC-HVDC transmission systems, aimed at improving the frequency stability of onshore power grids. First, considering the inability of the receiving-end MMC-HVDC converter station under constant DC voltage control to directly respond to AC system frequency variations, a frequency regulation method is developed based on constant DC voltage control. The approach employs DC voltage as a transmission signal to coordinate the responses of wind turbines and energy storage systems. Subsequently, based on the energy storage configuration of the onshore renewable energy aggregation station, a secondary frequency regulation strategy is proposed. This strategy integrates offshore wind power, MMC-HVDC transmission system, and energy storage systems, balancing AC frequency regulation and the recovery of the state of charge (SOC) of the energy storage system. Finally, the proposed method is tested on a modified IEEE 39-bus system, the results demonstrate that the minimum frequency value can be in-creased by 37.5%, the system frequency can be restored to the initial state after secondary FM, and the results demonstrate its effectiveness.

Optimizing Power Quality in EV Chargers Using Advanced Quadrature Signal Generators and AI‐Driven Adaptive Filtering

ABSTRACTAs electric vehicle (EV) adoption grows, vehicle‐to‐grid (V2G) technology enables bidirectional power flow in grid‐interactive EV chargers. However, maintaining consistent power quality under nonideal grid conditions remains a challenge. Traditional PI controllers struggle to reduce total harmonic distortion (THD) and adjust to dynamic grid variations. This study explores machine learning techniques, including decision trees, artificial neural networks (ANN), and linear regression, as alternatives to conventional PI controllers. Decision trees emerge as the most advantageous due to their simplicity, interpretability, and ability to handle complex, nonlinear relationships with minimal data preprocessing. While ANN captures intricate patterns, it demands more computational resources and lacks transparency. Linear regression, though efficient, struggles with complex grid behaviors. The decision tree approach allows real‐time adaptive control, improving THD reduction and grid stability. Additionally, a CNISOGI filter is implemented to enhance harmonic attenuation and DC‐offset rejection. The system's effectiveness is validated through Matlab/Simulink simulations and a 1.1 kW hardware prototype. The results show that integrating decision tree‐based controllers with advanced filtering techniques can significantly enhance power quality, grid stability, and operational efficiency in future smart grids.

Improving Power Quality and Fault Ride-Through (FRT) in Grid-Connected Hybrid Energy Systems: Design and Optimization of Dynamic Voltage Restorers

The integration of renewable energy sources (RESs) into grid-connected systems has become pivotal in addressing climate change and building a sustainable, carbon-neutral society. However, challenges like power quality issues, grid stability, and fault ride-through (FRT) requirements remain significant. This study explores strategies to enhance FRT capabilities and improve power quality in hybrid energy systems. It emphasizes the role of advanced devices such as Dynamic Voltage Restorers (DVRs), EV charging stations, and energy storage solutions. The effectiveness of these technologies is analyzed under various fault scenarios, highlighting improvements in transient response, voltage stability, and harmonic reduction. Additionally, hybrid energy systems, combining multiple RESs and backup sources, are proposed as viable solutions to address intermittency and system reliability. MATLAB/SIMULINK simulations validate the proposed approaches, showcasing their effectiveness in mitigating grid disturbances and maintaining operational efficiency. The findings underline the importance of innovative control strategies and hybrid configurations in ensuring a resilient and sustainable energy infrastructure.

Power Grid Stability Analysis Based on Artificial Intelligence Convolutional Neural Networks

Power Grid Stability Analysis Based on Artificial Intelligence Convolutional Neural Networks

EV and PV penetration impact on grid with conservative voltage regulation and reactive voltage compensation.

As global interest grows in renewable energy sources, the impact of combined Electric Vehicle (EV) and PhotoVoltaic (PV) penetration on the power grid stability requires renewed attention, to incorporate new technologies to maintain the power quality under operational constraints. Energy-saving techniques such as Conservation Voltage Reduction (CVR) allow the power utilities to transmit voltage at a lower operation limit, increasing the generation margin to absorb the peak load demands. Increased reverse PV penetration results in grid overvoltage while EV charging absorbs the reactive power causing grid instability. Both overvoltage and loss of reactive power in the grid can be reduced by using CVR and reactive power injection techniques. A power electronic secondary var controller (SVC) can dynamically inject reactive power into selected grid buses. This work compares the voltage stability of an IEEE 33 bus system operating with and without CVR. The simulation studies analyzed the effects of EV penetration level, and PV hosting capacity with SVC compensation paired with and without conservation voltage reduction technique. The analysis results demonstrate that tandem usage of CVR and SVC maintains the grid voltage under operational limits, meets load and EV demand, and increases power efficiency and PV penetration.

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.

Research on Fault Prediction and Health Management of Grid-Connected Inverter based on Data Drive

Grid-connected inverter is the core component of photovoltaic power generation system, and its stable operation is very important to the overall performance of the system. However, the inverter is easily influenced by environmental factors and its own complexity, which leads to frequent failures and affects power generation efficiency and power grid stability. Firstly, an innovative fault prediction model of grid-connected inverter is constructed by using machine learning and deep learning technology. The prediction accuracy and robustness of the model are improved by integrating the prediction results of basic learners such as decision tree and support vector machine (SVM). The experimental results show that the integrated learning model achieves an accuracy of 0.95 and a F1 score of 0.94 under the optimal parameter configuration. At the same time, the Long Short Term Memory Network (LSTM) model is introduced to effectively capture the complex nonlinear relationship and time dependence in the data, which further improves the prediction performance, and the highest accuracy rate can reach 0.96. Based on the fault prediction model, a comprehensive health management strategy of grid-connected inverter is formulated in this paper. The strategy framework includes four links: data collection and monitoring, fault prediction and evaluation, preventive maintenance plan formulation, fault early warning and response. By monitoring the key parameters of the inverter in real time and comparing with the fault prediction model, the early warning and timely response of the fault can be realized. The experimental results show that after the implementation of health management strategy, the failure rate of inverter is significantly reduced, the response time of fault warning is significantly shortened, and the operation reliability is significantly improved.

Optimizing Integrating STATCOM technology into standard solar inverters in Retail or Residential Power

Abstract—Transforming Standard Solar Inverters into Inte- grating STATCOM Capabilities for Enhanced Reactive Power Management.This paper explores the potential of integrating STATCOM technology into standard solar inverters to improve power quality, grid stability, and renewable energy integration in homes.

Analysis of successive commutation failure influence on power angle stability of large power grid

Analysis of successive commutation failure influence on power angle stability of large power grid

Analysis of the evolution of power system scale and grid stability

Abstract To address the challenges faced in grid planning during the development phase of new power systems, an evolution model of the grid was constructed based on historical and future planning data of a regional power grid. Adopting a systematic perspective, this study statistically analyzed the changes in several indicators strongly related to safety and stability within the model. It quantitatively examined the correlation patterns between grid development scale and safety stability, investigating the evolution of grid-scale effects. The study found that the positive correlation between grid-scale and system stability is gradually weakening and suggested the timely adoption of effective technical measures to maintain or even enhance the safety and stability level of the grid.

An Optimal Load Frequency Control in a Two-Area Power System Using a Fractional Order Proportional- Integral-Derivative-Based Zebra Optimization Algorithm

Load frequency control systems are crucial for maintaining the stability and reliability of power grids. They help ensure that the power supply matches the demand, preventing fluctuations in grid frequency. However, Load frequency control systems have inherent limitations, such as the potential for instability and oscillation if not properly controlled. In this work, A fractional order proportional integral derivative controller is proposed to address this issue, which exhibits strong capabilities in managing parameter uncertainties, rejecting disturbances, and handling non-linear systems controllers. The novel approach in this search is the application of the zebra optimization algorithm to fine-tune controller parameters, a technique not previously used in load frequency control systems. In this Study the two-area power system with a reheat turbine is used a test case for fractional order proportional integral derivative controller, and the system is simulated using MATLAB/SIMULINK. The objective function integral time of square error is used that acts as a bridge of communication between the system's behavior and the control strategy. The performance of this controller is evaluated under disturbance 0.04. The results of the analysis demonstrated that the fractional order proportional integral derivative based zebra optimization algorithm controller outperformed the traditional fractional order proportional integral derivative controller in terms of three essential criteria: overshoot, settling time, and steady-state error. This research contributes to the advancement of load frequency control systems, ensuring reliable and stable power system operation.

A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques

The rapid growth of power electronics-based devices, such as electric vehicles and renewable energy systems, has introduced nonlinear components into power systems, generating high-frequency harmonics that distort current and voltage waveforms. These distortions pose significant risks to the stability of power grids, potentially leading to equipment malfunctions, reduced efficiency, and even system failures. To address these challenges, advanced harmonic detection, suppression, and estimation techniques are required to ensure the reliable operation of modern power systems. This paper comprehensively reviews the most widely used methods for managing harmonic distortions, focusing on recent harmonic detection, suppression, and estimation advancements. Key techniques, such as Fourier analysis and wavelet transforms, are compared alongside emerging machine learning-based approaches and adaptive filtering methods, which offer enhanced accuracy in real-time and dynamic environments. Additionally, advancements in harmonic suppression technologies, including passive, active, and hybrid filtering, are discussed for their effectiveness in mitigating harmonic impacts. Furthermore, the paper explores harmonic aggregation techniques that assess the cumulative impact of multiple harmonic sources and innovative estimation models that improve harmonic quantification under complex grid conditions. With the growing integration of renewable energy and electric vehicles, this review highlights the importance of advanced harmonic management strategies to ensure the safety, efficiency, and long-term stability of power systems.

Incentive Determination for Demand Response Considering Internal Rate of Return

The rapid expansion of renewable energy sources has led to increased instability in the power grid of Jeju Island, leading to the implementation of the plus demand response (DR) system, which aims to boost electricity consumption during curtailment periods. However, the frequency of curtailment owing to the increased utilization of renewable energy is outpacing the implementation of plus DR, highlighting the need for additional resources, such as energy storage systems (ESS). High initial investment costs have been the primary hindrance to the adoption of ESS by DR-participating companies but have not been fully considered in earlier studies on DR incentive determination. Therefore, this study proposes an algorithm for calculating appropriate incentives for plus DR participation considering the investment costs required for ESS. Based on actual load data, incentives are determined using an iterative mixed-integer programming (MIP) optimization method that progressively adjusts the incentive level to address the overall nonlinearity arising from both the multiplication of variables and the nonlinear characteristics of the internal rate of return (IRR), ensuring that the target IRR is achieved. A case study on the impact of factors such as IRR, ESS costs, and fluctuations in electricity rates on incentive calculations demonstrated that plus DR incentives required to achieve IRR targets of 5%, 10%, and 15% have increased linearly from 142.2 KRW/kWh to 363.0 KRW/kWh, confirming that the appropriate incentive level can be effectively determined based on ESS investment costs and target IRR. This result could help promote ESS adoption among DR companies and plus DR participation, thereby enhancing power grid stability.

Performance evaluation of a low-voltage SVC utilizing IoT streamed data for distribution systems

This paper presents the design, implementation, and performance evaluation of a novel 50 kvar Static Var Compensator (SVC) integrated with an Internet of Things (IoT) controller for a low-voltage power distribution system in Moscow. This integration enhances real-time monitoring and control capabilities over traditional SVC systems. The study focuses on advanced control systems for reactive power compensation, voltage stabilization, and overall system efficiency. Data collected over two months demonstrate the SVC’s effectiveness in maintaining a stable power factor close to unity and reducing reactive power demand. The device successfully kept voltage levels within acceptable limits across all phases, reducing fluctuations and ensuring balanced voltage levels. Key findings include enhanced reactive power compensation, voltage stabilization (minimum voltage not less than 218 V), improved system efficiency (reactive power demand from the source near zero most of the time), and significant improvements in power quality and grid stability. The case study at Kosinskaya Street, Moscow, confirms the device’s role in improving power quality and grid stability. These results support the broader deployment of IoT-integrated SVC technology in low-voltage power distribution systems.

Equivalent measurement method for ice thickness of transmission lines

Abstract Icing on power transmission lines poses a serious threat to the safety and stability of power grids. Traditional manual patrols are inefficient and unable to effectively monitor ice conditions on the lines. This paper presents a novel ice-monitoring device that employs cylindrical arrays to predict and monitor icing parameters. By measuring the increase in ice weight on multiple cylindrical surfaces, environmental conditions such as wind speed, temperature, and water droplet content are determined. The device combines advanced modeling techniques to provide real-time and precise data, facilitating timely warnings and preventive actions. Experimental results have confirmed the accuracy of this method, offering a reliable solution for managing ice-related risks in power transmission systems.

Co-evolutionary dynamics for two adaptively coupled Theta neurons.

Natural and technological networks exhibit dynamics that can lead to complex cooperative behaviors, such as synchronization in coupled oscillators and rhythmic activity in neuronal networks. Understanding these collective dynamics is crucial for deciphering a range of phenomena from brain activity to power grid stability. Recent interest in co-evolutionary networks has highlighted the intricate interplay between dynamics on and of the network with mixed time scales. Here, we explore the collective behavior of excitable oscillators in a simple network of two Theta neurons with adaptive coupling without self-interaction. Through a combination of bifurcation analysis and numerical simulations, we seek to understand how the level of adaptivity in the coupling strength, a, influences the dynamics. We first investigate the dynamics possible in the non-adaptive limit; our bifurcation analysis reveals stability regions of quiescence and spiking behaviors, where the spiking frequencies mode-lock in a variety of configurations. Second, as we increase the adaptivity a, we observe a widening of the associated Arnol'd tongues, which may overlap and give room for multi-stable configurations. For larger adaptivity, the mode-locked regions may further undergo a period-doubling cascade into chaos. Our findings contribute to the mathematical theory of adaptive networks and offer insights into the potential mechanisms underlying neuronal communication and synchronization.

Impact of Slack Bus Compensation on Voltage Stability in Power Grids Integrated with Electric Vehicles: A Machine Learning Approach for Intelligent Management

Impact of Slack Bus Compensation on Voltage Stability in Power Grids Integrated with Electric Vehicles: A Machine Learning Approach for Intelligent Management