Grid Conditions Research Articles

A Robust PCC Voltage Feedforward Control Strategy of LCL‐Type Converters under Weak Grid Condition

The point of common coupling (PCC) voltage feedforward control strategy can effectively reduce the startup inrush current and improve the anti‐interference ability of the LCL‐type converters, which is widely used in the renewable energy converters control field. With the high penetration of the new energy, the increase of grid impedance leads to the weakening of the grid. Under weak grid conditions, the PCC voltage feedforward control strategy will generate an additional feedback loop of grid‐side current, which has a great impact on the stability of the system. In this paper, the damping characteristic of the PCC voltage feedforward control strategy and the effect on the open loop characteristic of the system have been deeply analyzed. An improved PCC voltage feedforward control strategy based on a proportional plus band‐pass filter is proposed, which can effectively improve the phase margin and achieve active damping under weak grid conditions, therefore, the robustness of the converters can be effectively improved. A three‐phase LCL‐type AC/DC converter platform is built and comparative experiments are carried out to verify the effectiveness of the proposed algorithm. The experimental results are consistent with the theoretical analysis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Dynamic control of grid-following inverters using DC bus controller and power-sharing participating factors for improved stability

Integrating Grid-Following Inverters (GFLs) into power systems presents significant stability challenges, particularly in systems with reduced strength and high renewable energy penetration. This study delves into the dynamic interactions between parallel synchronous generators (SGs) and GFLs, highlighting the impact of reduced system inertia and transient stability margins. A novel DC bus controller is proposed to enhance the inertia and stability of GFLs during grid disturbances by dynamically adjusting power references based on load demand. Through mathematical modelling, small signal analysis, and MATLAB simulations, the study evaluates the effects of inverter-based resources (IBRs) on power system stability. The proposed GFL configuration demonstrates improved stability control, achieving an 80 % increase in stability under weak grid conditions. This paper provides insights into optimized control strategies, emphasizing the importance of power-sharing participation factors (PSPF) for effective GFL integration in modern power systems with high renewable energy penetration.

Mechanism Analysis of Low-Frequency Oscillation Caused by VSG from the Perspective of Vector Motion

Virtual synchronous generators (VSGs) have attracted widespread attention due to their advantage in supporting voltage and frequency of power systems. However, relevant studies have shown that a VSG has similar low-frequency oscillation as synchronous generators, which is more likely to occur under strong grid conditions. In this paper, the linearized mathematical model of a VSG is established by using small-signal analysis; based on this, the physical process of low-frequency oscillation of a VSG is explained from the perspective of vector motion. Firstly, the amplitude and phase motion of the current vector of a VSG under small disturbance are analyzed, then the mechanism of negative damping caused by terminal voltage control is revealed, and the reason why a VSG is more prone to instability under strong grid conditions is explained. Based on these, the influence of control and grid strength on the low-frequency oscillation of a VSG is analyzed. Studies show that the amplitude motion of the output current is the main cause of negative damping, and the oscillation can be suppressed by optimizing the value of key parameters.

Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity

Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.

A voltage sensorless technique for a shunt active power filter adopting band pass filter under abnormal supply conditions

Shunt active power filters (SAPFs) are effective in addressing power quality issues such as harmonics and voltage unbalance. During abnormal grid conditions, there will be a substantial variation in both the actual and reactive power of the system. Various SAPF control approaches were employed to address these issues. This paper presents an improved bandpass filter technique that serves multiple purposes: extracting the reference current harmonic for SAPF, providing reactive current for power factor correction, and functioning as a backup synchronization method in the event of a loss of bus voltage measurement. Furthermore, it should be minimally affected by the unbalance in supply voltage. The case study model and the proposed reference technique have been executed and validated using the MATLAB software environment.

Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.

Position Sensor-Less SRG Variable Speed WECS and an SECS-Based Microgrid Under Nonideal Grid and Load Conditions

Position Sensor-Less SRG Variable Speed WECS and an SECS-Based Microgrid Under Nonideal Grid and Load Conditions

Harmonics Suppression and Stability Improvement for Power Router in Nonideal Grid Conditions: A Multiport Passivity-Based Collaborative Control Scheme

Harmonics Suppression and Stability Improvement for Power Router in Nonideal Grid Conditions: A Multiport Passivity-Based Collaborative Control Scheme

An improved detached eddy simulation method for cavitation multiphase flow

The evolution of cavitation flow involves intense transient characteristics and complex multi-scale motions, significantly increasing the difficulty and computational cost of solving in separation zones. This study proposes an Improved Detached Eddy Simulation (IDES) method, based on a single-equation eddy-viscosity model, aimed at enhancing the resolution of small-scale cavitation flows. By incorporating the broad-spectrum von Karman scale concept, the method effectively improves the resolution capability for small-scale flows in separated zones. The performance of the IDES model was validated through three computational cases, and the main conclusions are as follows: The results of the triangular cylinder flows show that the IDES model demonstrates a resolution capability for large-scale turbulence comparable to the Large Eddy Simulation (LES) model. The orifice flow results indicate that activating the LES method with insufficient grid resolution is highly discouraged, whereas the IDES model performs more robustly under such conditions. For hydrofoil flows, the IDES model more accurately captures the temporal evolution of cavitation, avoiding the premature cavity shedding predicted by LES under coarse grid conditions. The decomposition results of the vorticity transport equation show that both the dilatation term and viscous term are significant contributors in the vorticity transport process, with their average contributions to vorticity reaching 49.25% and 39.99%, respectively. Vortices can be considered the primary controllers of cavitation dynamics, while the dilatation term and viscous term can be regarded as the main controllers of vorticity. Overall, the energy compensation mechanism of the IDES model, based on local flow information, gives it unique advantages in handling small-scale turbulence and cavitation phenomena, providing both high computational efficiency and accuracy.

Bi-Layer Model Predictive Control strategy for techno-economic operation of grid-connected microgrids

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy’s effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy’s effectiveness in optimizing BESS operations under real-world conditions.

Dual Second Order Generalized Integrator – Phase Locked Loop technique applied to a distorted grid-connected solar energy based on a Z-Source Inverter

In this paper, a Dual Second Order Generalized Integrator_Phase Locked Loop (DSOGI_PLL) technique applied to the Photovoltaic (PV) system supplying a Z-Source five-level Inverter (ZSI) is presented. The ZSI assures the increasing voltage of the PV system and provides the desired output DC at the input of the five-level Neutral Point Clamped Inverter (5L_NPCI), without any control which reduces the complexity of the overall system, this is due to the impedance network in its structure. We use Z-Source in order to eliminate the controlled DC bus as well as the use of the DC-DC Boost converter as an intermediary. A DSOGI technique is employed to control and optimize the energy quality, especially in distorted grid conditions and allows one to obtain a very low value of Total Harmonic Distortion (THD) which means lower peak currents, and higher efficiency. Low THD is an essential feature in power systems that international standards such as IEC 61000-3-2, and IEEE-519 set limits on the harmonic currents of various classes of power equipment connected to a distorted three-phase grid. Compared to the classic structure of inverters, the NPC five-level inverter presents a very high performance.

A new model reduction method based PBC control for grid-connected inverter with LCL-filter

Passivity-based control (PBC) exhibits robustness against parameters shift, providing a substantial level of stability. However, for the LCL-filtered grid-connected inverter (GCI), the conventional PBC (called C-PBC) controller has a narrow control bandwidth due to the control time delay, resulting in poor dynamic performance, especially in the weak grid state. This paper proposes a new PBC (called P-PBC) method to increase the closed-loop bandwidth of the system by setting an appropriate feedback proportional coefficient between the grid-side current and the inverter-side current. By extending the closed-loop bandwidth of the system, the proposed P-PBC method offers improved dynamic performance, particularly in challenging grid conditions. In addition, a state observer is also employed to reduce sensors, which saves costs and enhances the system's reliability. A 110 V/50 Hz/3 kW/3-phase experimental setup has been developed using the dSPACE DS1202 platform to validate the effectiveness of the proposed control method.

Robust higher order sliding mode control of grid‐forming converters with LCL filter in weak grid scenarios for fast frequency support

AbstractThis paper presents a nonlinear higher‐order sliding mode control (HO‐SMC) designed for a droop control‐based grid‐forming converter. In weak grid scenarios, where the rate of change of frequency is notably high, achieving a rapid frequency response becomes imperative. The stable operation of a grid‐forming converter using droop control, coupled with classical vector control that employs cascaded voltage and current loops (multiloop) with PI controllers, faces limitations when higher droop coefficients are applied. This constraint on the application of classical vector control in weak grid conditions necessitates alternative solutions. Operating as a grid‐forming converter, the grid‐connected converter with an LCL filter represents a second‐order system. HO‐SMC mitigates the switching challenges associated with conventional SMC by integrating robust feedback linearized control. A graphical method is proposed for designing the switching gain using Lyapunov's direct method to counteract the impact of a matched disturbance. The study demonstrates that the implementation of HO‐SMC in the grid‐forming converter enhances fast frequency response by increasing the gain margin of the power frequency loop. Finally, it is illustrated that the proposed control method also improves the transient response of the converter.

Direct-Axis Current Orientation-Based Grid Voltage Sensorless Control Strategy for Vienna Rectifier Under Unbalanced and Distorted Grid Conditions

Direct-Axis Current Orientation-Based Grid Voltage Sensorless Control Strategy for Vienna Rectifier Under Unbalanced and Distorted Grid Conditions

Virtual impedance optimization control strategy for wind turbine grid-forming converters in weak grid

Abstract Grid forming(GFM) technology is key to the effective consumption and grid-friendly integration of large-scale wind power. However, as the new type of power system sees a high penetration of new energy sources and a decrease in circuit capacity, leading to systems exhibiting weak grid characteristics, this paper studies the adaptive characteristics of grid-forming wind turbine converters under weak grid conditions. It investigates the dynamic relationship between power coupling characteristics and the system impedance ratio based on the virtual impedance method and proposes an impedance optimization method to improve the power decoupling accuracy and dynamic response capability of grid-forming converters. The research results show that the grid-forming controller can operate stably under weak grid conditions, an increase in grid impedance ratio leads to stronger power coupling intensity, and the voltage support capability of grid-forming converters weakens. The simulation verifies that the virtual impedance optimization strategy proposed in this paper can effectively improve the power coupling characteristics of the grid-forming converter and enhance the converter’s dynamic response performance under weak grid conditions.

Linear Time-Periodic theory-based novel stability analysis method for voltage-source converter under unbalanced grid conditions

Small-signal stability analysis is essential for the safe operation and parameter design of power electronic-dominated grids. Unbalanced conditions introduce more frequency-coupling terms, which are unneglectable in the stability assessment. This paper proposes a novel Linear Time-Periodic (LTP) theory-based stability analysis method and focuses on grid-connected voltage-source converters (VSCs) without improved unbalancing controls. Firstly, the analysis of fundamental solutions of the LTP system reveals that each LTP mode is characterized by a unique damping factor and multiple oscillation frequencies, defined by LTP eigenvalues and related transformation vectors, i.e., (λi,Vi(t)). Then, a method for calculating (λi,Vi(t)) is proposed using the characteristic matrix of the Harmonic State-Space (HSS) model. An iterative sorting method based on the time-domain interpretation of HSS eigenvalues/eigenvectors is proposed to determine accurate (λi,Vi(t)) in the case of the minimum truncation order. Finally, generalized definitions and calculation methods of the widely used indicators for modal analysis are presented to assess system stability and guide the parameter design. Numerical and simulation results verify the proposed stability analysis method and indicate its advantages compared to the existing Floquet and HSS methods.

Phase-Locked Loop Applications Under Weak Grid Conditions Assisted with Dual-Layer Filtering

Phase-Locked Loop Applications Under Weak Grid Conditions Assisted with Dual-Layer Filtering

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Deep learning-based evaluation of photovoltaic power generation

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

An improved derivative‐based phase‐locked loop for single‐phase grid synchronization under abnormal grid conditions

SummaryGenerating a quadrature signal in a single‐phase system using a second‐order generalized integrator (SOGI) requires accurate frequency information. The standard SOGI phase‐locked loop (PLL) includes frequency feedback to the SOGI. However, during grid abnormalities such as voltage sag/swell and phase angle jumps, the SOGI‐PLL faces frequency disturbances that propagate to the phase detector (PD) and affects the quadrature signal generator (QSG). Furthermore, the SOGI‐PLL having two loops dependent on each other creates loop coupling phenomena; either a change in phase or frequency affects each other. SOGI‐PLL is tuning sensitive, as the SOGI block has a gain that needs to be adjusted, increases the complexity, and affects the performance of the system. There is a trade‐off between SOGI gain and PLL parameters that needs to be considered for adequate parameter design to provide accurate grid synchronization while maintaining the stability of the system. To attain better performance, researchers have proposed derivative‐based PLL (DPLL). The conventional DPLL faces challenges to noise and harmonics amplification. This paper presents an improved DPLL for single‐phase grid synchronization under adverse grid conditions. The improved derivative‐based PLL (IDPLL) comprises two improved derivative‐based quadrature signal generator (IDQSG) blocks to extract the phase‐error information for accurately estimating phase and frequency. The detailed mathematical modeling and bode plot for the IDQSG and IDPLL are presented. The proposed IDQSG eliminates the requirement of gain tuning, hence reducing complexity. Moreover, there is no interdependent loop in the IDPLL, which significantly improves the dynamic performance. A hardware setup is developed to evaluate the performance of the system in real‐time. The experimental results are obtained using an field programmable gate array (FPGA)‐based controller.