Grid Conditions Research Articles

Generative artificial intelligence for distributed learning to enhance smart grid communication

Machine learning models are the backbone of smart grid optimization, but their effectiveness hinges on access to vast amounts of training data. However, smart grids face critical communication bottlenecks due to the ever-increasing volume of data from distributed sensors. This paper introduces a novel approach leveraging Generative Artificial Intelligence (GenAI), specifically a type of pre-trained Foundation Model (FM) architecture suitable for time series data due to its efficiency and privacy-preserving properties. These GenAI models are distributed to agents, or data holders, empowering them to fine-tune the foundation model with their local datasets. By fine-tuning the foundation model, the updated model can produce synthetic data that mirrors real-world grid conditions. The server aggregates fine-tuned model from all agents and then generates synthetic data which considers all data collected in the grid. This synthetic data can be used to train global machine learning models for specific tasks like anomaly detection and energy optimization. Then, the trained task models are distributed to agents in the grid to leverage them. The paper highlights the advantages of GenAI for smart grid communication, including reduced communication burden, enhanced privacy through anonymized data transmission, and improved efficiency and scalability. By enabling a distributed and intelligent communication architecture, GenAI introduces a novel way for a more secure, efficient, and sustainable energy future.

Design and Implementation of Robust H∞ Control for Improving Disturbance Rejection of Grid-Connected Three-Phase PWM Rectifiers

In response to the high performance requirements of pulse width modulation (PWM) converters in grid-connected power systems, H-Infinity (H∞) control has attracted significant research interest due to its robustness against parameter variations and external disturbances. In this work, an advanced robust H∞ control is proposed for a grid-connected three-phase PWM rectifier. A two-level control strategy is adopted, where cascaded H∞ controllers are designed to simultaneously regulate the DC bus voltage and input currents even under load disturbances and non-ideal grid conditions. As a result, unit power factor, stable DC bus voltage, and sinusoidal input currents with lower harmonics can be accurately achieved. The design methodology and stability of the proposed controller are verified through a comprehensive analysis. Simulation tests and experimental implementation on a dSPACE 1103 board demonstrate that the proposed control scheme can effectively enhance disturbance rejection performance under various operating conditions.

A Series Impedance Reshaping Control Method Considering PLL Dynamics for Grid-Connected Inverters Under Weak Grid Conditions

Grid-connected inverters are the key part in renewable energy power generation systems. Usually, phase-locked loop (PLL) is adopted in grid-connected inverters to achieve synchronization. However, prior-art works have revealed that the negative resistance behavior in the low-frequency band caused by PLLs may lead to system instability under weak grids. In this paper, first, a comprehensive small-signal output impedance modeling of the LCL-filtered single-phase grid-connected inverter is derived for stability analysis, where the digital control delays and PLL dynamics have been taken into account. Then, a simple but effective virtual series based impedance reshaping method is proposed. With that, the inverter system can operate stably even under a very weak grid (e.g., short-circuit ratio (SCR) is as low as 1.2). Compared with the traditional grid impedance compensation by virtually increasing grid resistance or reducing the grid inductance, the proposed method does not require grid impedance estimation and is robust against system parameter variations. Thus, its feasibility is improved. Finally, experimental tests are carried out on a single-phase 5-kW prototype, validating the effectiveness of the proposed scheme.

Control of Power Converters in Micro Grids

Owing to several operational drawbacks in the existing power management schemes for interlinked AC-DC micro grids which are either concerned about only on sharing power or regulation of voltage but not on both, so as to overcome these issues this paper is proposed. This proposed autonomous power management scheme will consider the specific loading condition of the DC micro grid before importing power from the interlinked AC microgrid. This strategy not only enables voltage regulation in the DC micro grid but also reduces the number of converters in operation which will in turn reduces the power transfer losses. Theproposed scheme is fully self governed as it holds on the plug-nplay features for generators and tie-converters. The performance of the proposed control scheme has been validated under different operating scenarios. The results reveal the usefulness of the proposed scheme in managing the shortcoming of power in the DC microgrid efficiently and autonomously, on the other hand maintaining the better voltage regulation in the DC micro grid. In this project a DG interfacing network and its control also to be simulated to analyze the system stability The results are verified through MATLAB/SIMULINK environment.

An improved gossip-based distributed control strategy of multi-objective combined economic emission dispatch in smart grids with stochastic communication link failures

This paper proposes a distributed control strategy, leveraging gossip-based communication, for combined economic emission dispatch (CEED) in smart grids. It optimizes economic dispatch while considering separate objectives for SO2, NOx, CO2 emissions. The network is modeled as a Bernoulli process to describe stochastic communication link failures. Utilizing the Analytic Hierarchy Process to determine the weights between multiple objectives in the CEED to aid decision-makers at thermal power plants in selecting appropriate solutions based on the actual conditions of power grid. Simulation results demonstrate the strategy effectiveness.

Grid-Impedance-Based Transient Current Control for Offshore Wind Turbines under Low-Voltage Fault

In order to enhance the transient stability of offshore wind turbines (OWTs) in marine energy systems, the grid codes stipulate that OWTs should possess the low-voltage ride-through (LVRT) ability of being grid-tied and injecting reactive current during grid fault. However, the grid-side converter (GSC) of OWTs may lose stability under weak grid or severe fault conditions due to inaccurate current references. To address this issue, a novel transient current control method is proposed to improve the transient stability of permanent-magnet-synchronous-generator (PMSG)-based OWTs. The feature of DC-link overvoltage is investigated and is alleviated by utilizing the GSC’s overcurrent capacity and chopper. Additionally, the equivalent circuit of the PMSG-based OWT connected to the onshore grid is derived based on Thevenin’s theorem. The feasible current region (FCR) is then determined, taking into account the GSC capacity, pre-fault power ability, LVRT requirement, and synchronization stability. Furthermore, a grid-impedance-based transient current control method is designed to enhance the fault ride-through performance and mitigate power oscillation of the OWT under various transient grid impedance and fault conditions. Finally, a simulation model is conducted using PSCAD v4.6.3 software to validate the effectiveness of the proposed method.

Grid synchronization method based on three novel types of third-order generalized integrators

Phase-locked loop (PLL) is widely used to estimate synchronous information such as amplitude and phase angle of grid voltage. It plays a crucial role in distributed renewable energy grid-connected power generation. However, the presence of grid harmonics and DC offset voltage (DCOV) can affect the accurate extraction of the fundamental voltage component, leading to phase-locked deviation. This paper introduces three novel or improved third-order generalized integrators (TOGI) with DCOV rejecting function to address this issue. A unified dual third-order generalized integrator (DTOGI) structure is designed based on the three TOGI structures. This DTOGI structure is further combined with a moving average filter (MAF) to propose three innovative three-phase PLLs. Unlike other PLL filtering methods that inadequately consider DCOV, the proposed PLL methods not only reject DCOV but also eliminate the fundamental voltage negative sequence component (FVNSC) and each order harmonic component. They demonstrate strong robustness and excellent filtering performance even under abnormal grid conditions. Among the three methods, the DII-TOGI based PLL exhibits superior dynamic performance compared to the other two methods, it can improve dynamic characteristics by about 10 % compared to the other two methods. The effectiveness of the proposed methods is verified through extensive Matlab/Simulink simulations and experiments.

Current Controller Design of Grid-Connected Inverter with Incomplete Observation Considering L-/LC-Type Grid Impedance

This paper presents a current control design for stabilizing an inductive-capacitive-inductive (LCL)-filtered grid-connected inverter (GCI) system under uncertain grid impedance and distorted grid environment. To deal with the negative impact of grid impedance, LC-type grid impedance is considered in both the system model derivation and controller design process of an LCL-filtered GCI system. In addition, the integral and resonant control terms are also augmented into the system model in the synchronous reference frame to guarantee the reference tracking of zero steady-state error and good harmonic disturbance compensation of the grid-injected currents from GCI. By considering the effect of grid impedance on the control design process, an incomplete state feedback controller will be designed based on the linear-quadratic regulator (LQR) without damaging the asymptotic stabilization and robustness of the GCI system under uncertain grid impedance. By means of the closed-loop pole map evaluation, the asymptotic stability, robustness, and resonance-damping capability of the proposed current control scheme are confirmed even when all the system states are not available. In order to reduce the number of required sensors for the realization of the controller, a discrete-time current-type full-state observer is employed in this paper to estimate the system state variables with high precision. The feasibility and effectiveness of the proposed control scheme are demonstrated by the PSIM simulations and experiments by using a three-phase GCI prototype system under adverse grid conditions. The comprehensive evaluation results show that the designed control scheme maintains the stability and robustness of the LCL-filtered GCI when connecting to unexpected grids, such as harmonic distortion and L-type and LC-type grid impedances. As a result, the proposed control scheme successfully stabilizes the entire GCI system with high-quality grid-injected currents even when the GCI faces severe grid distortions and an extra grid dynamic caused by the L-type or LC-type grid impedance. Furthermore, low-order distortion harmonics come from the background grid voltages and are maintained as acceptable limits according to the IEEE Std. 1547-2003. Comparative test result with the conventional one also confirms the effectiveness of the proposed control scheme under LC-type grid impedance thanks to the consideration of LC grid impedance in the design process.

Learning-assisted optimization for transmission switching

The design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.

A Probabilistic Approach to Adaptive Protection in the Smart Grid

Smart grids are critical cyber-physical systems that are vital to our energy future. Smart grids’ fault resilience is dependent on the use of advanced protection systems that can reliably adapt to changing grid conditions. The vast amount of operational data generated and collected in smart grids can be used to develop these protection systems. However, given the safety-criticality of protection, the algorithms used to analyze this data must be stable, transparent, and easily interpretable to ensure the reliability of the protection decisions. Additionally, the protection decisions must be fast, selective, simple, and reliable. To address these challenges, this paper proposes a data-driven protection strategy, based on Gaussian Discriminant Analysis (GDA), for fault detection and isolation. This strategy minimizes the communication requirements for time-inverse relays, facilitates their coordination, and optimizes their settings. The interpretability of the protection decisions is a key focus of this paper. The method is demonstrated by showing how it can protect the medium-voltage CIGRE network as it transitions between islanded and grid-connected modes and radial and mesh topologies.

Neural network predictive control for fault detection and identification in DFIG with SMES for low voltage ride-through requirements

The increasing reliance on renewable energy sources brings to light the operational challenges of doubly fed induction generators (DFIGs), particularly under grid disturbances. This study introduces an innovative approach employing Neural Network Predictive Control (NNPC) for fault detection and identification (FDI) in DFIG systems, integrated with Superconducting Magnetic Energy Storage (SMES) to meet Low Voltage Ride-Through (LVRT) requirements. Our method uniquely combines NNPC for dynamic wind speed prediction and precise control of rotor-side and grid-side converters with the stability enhancement offered by SMES. Simulation results demonstrate a notable improvement in DFIG performance under variable wind conditions and grid failures. The system-maintained voltage stability at the Point of Common Coupling (PCC) with less than 5% deviation during transients and ensured consistent power output, marking a significant advancement in LVRT compliance. However, the model assumes steady-state grid conditions and does not account for extreme weather scenarios, indicating areas for future research. This study's findings contribute valuable insights into the robust operation of DFIGs within modern renewable energy grids.

Impact of Synchronous Condensers’ Ratings on Mitigating Subsynchronous Oscillations in Wind Farms

Subsynchronous oscillations have occurred in wind farms due to the high penetration of converter-based technology in power systems and may potentially lead to grid instability. As an effective solution, synchronous condensers, with their ability to control voltage and inject reactive power in the power system, are increasingly being adopted, as they can lead to the mitigation of such oscillations in weak grid conditions. However, the impact of synchronous condensers’ power ratings on system stability is a topic that requires further investigation. In fact, an improper selection of a synchronous condenser’s rating will not extinguish existing subsynchronous oscillations and may even cause the emergence of new oscillatory phenomena. This paper presents a novel examination of the impact that the synchronous condenser’s power rating has on the small-signal stability of a wind farm with existing subsynchronous oscillations while being connected to a weak grid. The wind farm’s model is developed using state-space modeling, centering on grid interconnection and incorporating the state-space submodel of a synchronous condenser to show its impact on subsynchronous oscillation mitigation. The stability analysis determines the optimal synchronous condenser’s power ratings for suppressing these oscillations in the wind farm model. The findings are corroborated through time domain simulations and fast-Fourier transformation (FFT) analysis, which further validate the stability effects of a synchronous condenser’s rating.

Optimizing Power Control for Dual Excited Synchronous Generators in Wind Turbine Systems

This study presents a MATLAB Simulink-based approach for optimizing power control in wind turbine systems with dual excited synchronous generators (DESGs), implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. DESGs offer advantages in efficiency and reliability, necessitating effective power control strategies. The proposed methodology integrates Simulink models of wind turbine dynamics, DESG behavior, and an ANFIS controller to optimize power output while ensuring stability and minimizing losses. Various factors such as wind speed variations and grid conditions are incorporated into the simulation environment. The ANFIS controller dynamically adjusts DESG control parameters based on real-time inputs, enhancing system adaptability and performance. Simulation graphs validate the efficiency of proposed approach in improving DESG performance and overall wind turbine system efficiency. This research contributes to advancing renewable energy technologies by leveraging MATLAB Simulink and ANFIS controllers for optimized power control in DESG-based wind turbine systems. Key Words: Wind power generation, Grid, DESG, MSC, GSC, and ANFIS.

An Effective Control Strategy for Single-Phase Single-Stage PV Grid-Tied Inverter Under Abnormal Grid Conditions

An Effective Control Strategy for Single-Phase Single-Stage PV Grid-Tied Inverter Under Abnormal Grid Conditions

The Current Sequence Components Extraction for Dual Synchronous Rotation Frame Current Control in Unbalanced Grid Conditions

The Current Sequence Components Extraction for Dual Synchronous Rotation Frame Current Control in Unbalanced Grid Conditions

Stability modeling of MMC-HVDC transmission system with differential flat control

AC asymmetry grid conditions put forward higher requirements for the stability control and performance of MMC-HVDC transmission systems. This thesis proposes a small-signal modeling method of an MMC-HVDC transmission system with differential flat control for AC asymmetry grid conditions, incorporating dynamic models of MMC internal characteristics, new control systems, circulation suppressors, and DC lines. To prove the accuracy of the pre-sended small-signal model, a two-terminal MMC-HVDC test system with differential flat control is constructed on the PSCAD/EMTDC platform.

Simulation of grid/standalone solar energy supplied reduced switch converter with optimal fuzzy logic controller using golden BallAlgorithm

This article presents the utilization of a shunt active power filter (SHAPF) in combination with an Energy Storage System (ESS) and a Solar Energy System (SES). Voltage source converters (VSC) are connected in parallel to a direct current (DC) bus. The membership function (MSF) of fuzzy logic controller (FLC) for the shunt control system is optimally adjusted using the golden balloptimization algorithm (GBOA). The present effort aims to achieve the following primary objectives: 1) Quick implementation to stabilize the voltage of the DC Link capacitor (DCLCV); 2) Mitigation of harmonics and improvement of power factor (PF); 3) Satisfactory performance under load as well as solar power varying conditions. The effectiveness of the optimally designed controller is evaluated by studying four test scenarios with grid and standalone conditions. The results are then compared to the existing sliding mode (SMC) and fuzzy logic controllers (FLC).

Impedance remodeling control strategy of grid-connected inverter with inertia-damping phase-locked loop under extremely weak grid

The operation of the grid-connected inverter (GCI) in weak grid conditions presents a risk of instability due to the presence of high grid impedance and the negative impedance effect of the phase-locked loop (PLL). In response to this issue, this paper introduces an impedance remodeling control strategy for the GCI utilizing the inertia-damping phase-locked loop (ID-PLL). Firstly, the proposed ID-PLL exhibits certain inertia and damping properties in comparison to the traditional PLL (T-PLL), resulting in an enhanced phase margin of the GCI system's equivalent output impedance. Consequently, the adaptation range of the grid impedance increases from 12mH (SCR = 3.00) to 24mH (SCR = 1.37). Next, from the perspective of impedance analysis, this study remodels the PLL impedance and inverter control loop impedance, further improving the amplitude and phase margin of the system's output impedance. As a result, the adaptation range of the grid impedance extends to 33mH (SCR = 1.00), and the transition of grid-connected currents becomes smooth and free of overshoot. Finally, the effectiveness of the proposed control strategy is validated through theoretical analysis and experimental verification.

Performance Analysis of Photovoltaic and Wind Turbine Grid-Connected Systems under LVRT Conditions

The integration of grid-connected renewable energy systems has gained significant attention and introduces several challenges and considerations. One of these challenges is ensuring the reliable and stable operation of these systems under various grid conditions. For example, faults at the grid could lead problems such as DC-link over-voltage and AC over-current that may cause disconnection or damage to inverter. This paper presents a comprehensive analysis of the performance of photovoltaic (PV) and doubly-fed induction generator (DFIG) wind turbine grid-connected systems under low voltage ride-through (LVRT) conditions. The study aims to investigate their behavior, and stability during LVRT events and provide insights for enhancing their grid integration capabilities. The PV and DFIG systems are modelled and simulated using MATLAB Simulink under three difference conditions, with and without using reactive current injection and DC chopper circuit. Various performance parameters, including grid voltage, grid current, DC-link voltage, active power, and reactive power, are analyzed to assess the system's behavior and compare their responses. The principal results reveal distinct performance characteristics of the PV and DFIG systems. The PV system shows higher overshoot currents, over-voltage, and significant drops in active power during fault occurrences, while the DFIG system exhibits lower overshoot currents and better stability in the DC-link voltage. Reactive power responses differ between the systems, with the PV system demonstrating a higher capability for support. The implementation of DC chopper shows more effective in the reduction of DC-link voltage and overshoot grid current in the PV system compared to the DFIG system.

A smart parametrisation for robust adaptive PI controller applied on renewable energy power generation systems under weak and uttermost weak grid conditions

The increasing penetration of renewable energy systems into the global energy matrix is a necessity for sustainable development. The benefits impact the well-being of humanity and the environment directly. However, these power generation systems demand reliable controllers to regulate the current injection into the electrical grid. Therefore, this paper presents a smart parametrisation for a robust adaptive proportional integral controller applied to grid-injected current control of a voltage inverter source with an LCL filter. Controller parameter optimisation and the initialisation of their gains are performed using the particle swarm optimisation algorithm. Three cost functions are assessed on controller parametrisation, considering the current tracking error minimisation and control action limitations: integral of absolute error (IAE) + integral of absolute control action (IAU), integral of square error (ISE) + IAU, and integral of the time-weighted absolute error (ITAE) + IAU. A framework for performance analysis and an in-depth discussion are presented, providing a systematic decision-support methodology for controller configuration. The function determined as the best one is implemented experimentally and compared with a designer’s experience-based robust adaptive proportional integral controller using a hardware-in-the-loop test bench, where the improvements in controller performance are corroborated, contributing to the enhancement of energy quality in renewable energy systems. In addition, the controller’s robustness is evaluated on a weak grid and an uttermost weak grid, where it presents high robustness and global stability.