Review of the Configuration and Transient Stability of Large-scale Renewable Energy Generation through Hybrid DC Transmission

Based on the complementary advantages of Line Commutated Converter(LCC)and Modular Multilevel Converter(MMC)in power grid applications,there are two types of hybrid DC system topologies:one is the parallel connection of LCC converter stations and MMC converter stations,and the other is the series connection of LCC and MMC converter stations within a single station.The hybrid DC transmission system faces broad application prospects and development potential in large-scale clean energy integration across regions and the construction of a new power system dominated by new energy sources in China.This paper first analyzes the system forms and topological characteristics of hybrid DC transmission,introducing the forms and topological characteristics of converter-level hybrid DC transmission systems and system-level hybrid DC transmission systems.Next,it analyzes the operating characteristics of LCC and MMC inverter-level hybrid DC transmission systems,provides insights into the transient stability of hybrid DC transmission systems,and typical fault ride-through control strategies.Finally,it summarizes the networking characteristics of the LCC-MMC series within the converter station hybrid DC transmission system,studies the transient characteristics and fault ridethrough control strategies under different fault types for the LCC-MMC series in the receiving-end converter station,and investigates the transient characteristics and fault ride-through control strategies under different fault types for the LCC-MMC series in the sending-end converter station.

Migratable Power System Transient Stability AssessmentMethod Based on Improved XGBoost

The data-driven transient stability assessment(TSA)of power systems can predict online real-time prediction by learning the temporal features before and after faults.However,the accuracy of the assessment is limited by the quality of the data and has weak transferability.Based on this,this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting(XGBoost)model.Firstly,the gradient detection method is employed to remove noise interference while maintaining the original time series trend.On this basis,a focal loss function is introduced to guide the training of theXGBoostmodel,enhancing the deep exploration of minority class samples to improve the accuracy of the model evaluation.Furthermore,to improve the generalization ability of the evaluation model,a transfer learning method based on model parameters and sample augmentation is proposed.The simulation analysis on the IEEE 39-bus system demonstrates that the proposed method,compared to the traditional machine learning-based transient stability assessment approach,achieves an average improvement of 2.16%in evaluation accuracy.Specifically,under scenarios involving changes in topology structure and operating conditions,the accuracy is enhanced by 3.65%and 3.11%,respectively.Moreover,the model updating efficiency is enhanced by 14–15 times,indicating the model’s transferable and adaptive capabilities across multiple scenarios.

Optimal Allocation of STATCOM to Enhance Transient Stability Using Imperialist Competitive Algorithm

With the daily expansion of global energy consumption,developing the power grids is of uttermost importance.However,building a new trans-mission line is costly and time-consuming,so utilizing the same lines with possible higher transmission capacity is very cost-effective.In this regard,to increase the capacity of the transmission lines,the flexible alternating current transmission system(FACTS)has been widely used in power grids in recent years by industrialized countries.One of the essential topics in electrical power systems is the reactive power compensation,and the FACTS plays a significant role in controlling the reactive power current in the power grid and the system voltage oscillations and stability.When a static synchronous compensator(STATCOM)is embedded in a power system to increase the bus voltage,a supplementary damping controller can be designed to enhance the system oscillation damping.Given the expansion of the grids in the power system,the complexity of their optimization and the extraordinary ability of the imperialist competitive algorithm(ICA)for solving such problems,in this paper,the ICA has been used to determine the optimal position and size of the FACTS devices.

A Nonlinear Coordinated Approach to Enhance the Transient Stability of Wind Energy-Based Power Systems

This paper proposes a novel framework that enables the simultaneous coordination of the controllers of doubly fed induction generators(DFIGs) and synchronous generators(SGs).The proposed coordination approach is based on the zero dynamics method aims at enhancing the transient stability of multi-machine power systems under a wide range of operating conditions. The proposed approach was implemented to the IEEE39-bus power systems. Transient stability margin measured in terms of critical clearing time along with eigenvalue analysis and time domain simulations were considered in the performance assessment. The obtained results were also compared to those achieved using a conventional power system stabilizer/power oscillation(PSS/POD) technique and the interconnection and damping assignment passivity-based controller(IDA-PBC). The performance analysis confirmed the ability of the proposed approach to enhance damping and improve system’s transient stability margin under a wide range of operating conditions.

Contribution to the Optimization of the Transient Stability of an Electric Power Transmission Network Using a Universal Power Flow Compensator Controlled by a Three-Stage Inverter

The use of an electrical network as close as possible to its limits can lead to its instability in the event of a high amplitude disturbance. The damping of system oscillations can be achieved by conventional means of voltage and speed regulation but also by FACTS (Flexible AC Transmission Systems) devices, which are increasingly used in power networks. In this work, optimal control coordination between a hybrid power flow controller and a three-level inverter was used to improve the transient stability of a transmission line. The UPFC is a combination of a serial compensator (SSSC) and a parallel compensator (STATCOM) both connected to a DC-LINK DC bus. The SSSC acts as a voltage source for the network and injects a voltage that can be adjusted in phase and amplitude in addition to the network voltage;the STATCOM acts as a current source. The approach used is tested in the Matlab Simulink environment on a single machine network. Optimal controller tuning gives a better transient stability improvement by reducing the transport angle oscillations from 248.17% to 9.85%.

Transient Stability Assessment of Synchronous Generator in Power System with High-Penetration Photovoltaics

As photovoltaic （PV） capacity in power system increases, the capacity of synchronous generator needs to be reduced relatively. This leads to the lower system inertia and the higher generator reactance, and hence the generator transient stability may negatively be affected. In particular, the impact on the transient stability may become more serious when the considerable amounts of PV systems are disconnected simultaneously during voltage sag. In this work, the generator transient stability in the power system with significant PV penetration is assessed by a numerical simulation. In order to assess the impact from various angles, simulation parameters such as levels of PV penetration, variety of power sources （inverter or rotational machine）, and existence of LVRT capability are considered. The simulation is performed by using PSCAD/EMTDC software.

Transient Stability Analysis of Power System with Photovoltaic Systems Installed

The impact of large-scale grid-connected PV （photovoltaics） on power system transient stability is discussed in this paper. In response to an increase of PV capacity, the capacity of conventional synchronous generator needs to be reduced relatively. This leads to the lower system inertia and the higher generator reactance, and hence, the power system transient stability may negatively be affected. In particular, the impact on the transient stability may become more serious when the considerable amounts of PV systems are disconnected simultaneously during voltage sag. In this work, the potential impact of significant PV penetration on the transient stability is assessed by a numerical simulation using PSCAD/EMTDC.

Transient Stability Analysis Using Transmission Line Measurement

The novel quantitative assessment method using transmission line measurement was developed. A new style of stability criterion was suggested which is based on the line measurement. The stability indices for lines, cutsets and power system according to features of transient energy in the lines were given, which not only provide a reliable and accurate assessment of the transient stability of power system, but also can be used to assess the effect of lines and cutsets on the transient stability and identify the weak transmission segment. Examples were presented by simulation on the IEEE-39 buses test system.

An Optimal DF Based Method for Transient Stability Analysis

The effect of energy on the natural environment has become increasingly severe as human consumption of fossil energy has increased.The capacity of the synchronous generators to keep working without losing synchronization when the system is exposed to severe faults such as short circuits is referred to as the power system’s transient stability.As the power system’s safe and stable operation and mechanism of action become more complicated,higher demands for accurate and rapid power system transient stability analysis are made.Current methods for analyzing transient stability are less accurate because they do not account formisclassification of unstable samples.As a result,this paper proposes a novel approach for analyzing transient stability.The key concept is to use deep forest(DF)and a neighborhood rough reduction approach together.Using the neighborhood rough sets,the original feature space is obtained by creating many optimal feature subsets at various granularity levels.Then,by deploying the DF cascade structure,the mapping connection between the transient stability state and the features is reinforced.The weighted voting technique is used in the learning process to increase the classification accuracy of unstable samples.When contrasted to current methods,simulation results indicate that the proposed approach outperforms them.

Transient Stability Analysis of Synchronous Generator in Power System with Renewable Power Sources Installed

The impact of large-scale grid-connected renewable power sources, such as wind generators and solar photovoitaic systems, on transient stability of synchronous generators is discussed in this paper. The permanent magnet synchronous generator with variable speed wind turbine is used in the simulation analysis as a wind generator model. The transient stability analysis is performed for IEEE 9-bus system model with high-penetration renewable power sources. The effect of FRT （fault ride-through） capability implemented for each power source on the transient stability is investigated.

Transient Stability Assessment of Synchronous Generator in Power System with High-Penetration Photovoltaics （Part 2）

In the previous paper [1], the transient stability of synchronous generator in power system with high-penetration PV （photovoltaic） was assessed by simulation analysis of a single-machine infinite-bus system model. Through the simulation analysis, we have obtained some conclusions in terms of the impact of high-penetration PV on the stability. However, for more accurate assessment of the transient stability, it is necessary to analyze various simulation models considering many other power system conditions. This paper presents the results of the analysis for the transient stability simulation performed for IEEE 9-bus system model, in which the effects of various conditions, such as variety of power sources （inverter or rotational machine）, load characteristics, existence of LVRT （low-voltage ride-through） capability and fault locations, on the transient stability are investigated.

Hybrid Analytical and Data-Driven Model Based Instance-Transfer Method for Power System Online Transient Stability Assessment

Data-driven methods are widely recognized and generate conducive results for online transient stability assessment.However,the tedious and time-consuming process of sample collection is often overlooked.The functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode,thereby highlighting the importance of collection efficiency.As a means to achieve high sample collection efficiency following the operation mode change,we propose a novel instance-transfer method based on compression and matching strategy,which facilitates the direct acquisition of useful previous samples,used for creating the new sample base.Additionally,we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection,where features of analytical modeling with special significance are introduced into data-driven methods.At the same time,a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models,enabling fast and accurate post-disturbance transient stability assessment.As a paradigm,we consider a scheme for online critical clearing time estimation,where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part,respectively.Derived results validate the credible efficacy of the proposed method.

Transient Stability Analysis of Grid-connected Converters in Wind Turbine Systems Based on Linear Lyapunov Function and Reverse-time Trajectory

As the proportion of converter-interfaced renewable energy resources in the power system is increasing,the strength of the power grid at the connection point of wind turbine generators(WTGs)is gradually weakening.Existing research has shown that when connected with the weak grid,the stability of the traditional grid-following controlled converters will deteriorate,and they are prone to unstable phenomena such as oscillation.Due to the limitations of linear analysis that cannot sufficiently capture the stability phenomena,transient stability must be investigated.So far,standalone time-domain simulations or analytical Lyapunov stability criteria have been used to investigate transient stability.However,the time-domain simulations have proven to be computationally too heavy,while analytical methods are difficult to formulate for larger systems,require many modelling assumptions,and are often conservative in estimating the stability boundary.This paper proposes and demonstrates an innovative approach to estimating the transient stability boundary via combining the linear Lyapunov function and the reverse-time trajectory technique.The proposed methodology eliminates the need of time-consuming simulations and the conservative nature of Lyapunov functions.This study brings out the clear distinction between the stability boundaries with different post-fault active current ramp rate controls.At the same time,it provides a new perspective on critical clearing time for wind turbine systems.The stability boundary is verified using time-domain simulation studies.

Transient Stability Analysis and Design of VSGs with Different DC-Link Voltage Controllers

Virtual synchronous control has been widely studied for the advantages of emulating inertia for voltage source converters (VSCs). A constant dc-link voltage is usually assumed in existing literature to estimate transient stability of virtual synchronous generators (VSGs). However, actual power supply in the dc-side of VSGs is limited and different dc-link voltage controllers are needed to achieve power balance between DC side and AC side. Addition of dc-link voltage controller has great influence on transient behavior of VSGs, which has not been investigated by previous research. To fill this gap, this paper gives insights into the effect of dc-link voltage dynamics on transient stability of VSGs. First, two typical kinds of VSGs with dc-link voltage controllers are introduced. Then, mathematical models considering dc-link dynamics are established and the effect of dc-link voltage controllers on transient synchronization stability of VSGs is revealed through equal area criterion (EAC). It is found that dc-link voltage controller would reduce stability margin of VSGs and design-oriented transient stability analysis is carried out quantitively using critical clearing time (CCT). Finally, simulation results are given to validate correctness of theoretical analysis.

Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints

Preventive transient stability control is an effective measure for the power system to withstand high-probability severe contingencies.It is mathematically an optimal power flow problem with transient stability constraints.Due to the constraints involved for differential algebraic equations of transient stability,it is difficult and time-consuming to solve this problem.To address these issues,this paper presents a novel deep reinforcement learning(DRL)framework for preventive transient stability control of power systems.A distributed deep deterministic policy gradient is utilized to train a DRL agent that can learn its control policy through massive interactions with a grid simulator.Once properly trained,the DRL agent can instantaneously provide effective strategies to adjust the system to a safe operating position with a near-optimal operational cost.The effectiveness of the proposed method is verified through numerical experiments conducted on a New England 39-bus system and NPCC 140-bus system.

Static information, K-neighbor, and self-attention aggregated scheme: a transient stability prediction model with enhanced interpretability

Data-driven preventive scanning for transient stability assessment(DTSA)is a faster and more efficient solution than time-domain simulation(TDS).However,most current methods cannot balance generalization to different topolo-gies and interpretability,with simple output.A model that conforms to the physical mechanism and richer label for transient stability can increase confidence in DTSA.Thus a static-information,k-neighbor,and self-attention aggre-gated schema(SKETCH)is proposed in this paper.Taking only static measurements as input,SKETCH gives several explanations that are consistent with the physical mechanisms of TSA and provides results for all generator stability while predicting system stability.A module based on the self-attention mechanism is designed to solve the locality problem of a graph neural network(GNN),achieving subgraph equivalence outside the k-order neighborhood.Test re sults on the IEEE 39-bus system and IEEE 300-bus system indicate the superiority of SKETCH and also demonstrate the rich sample interpretation results.

Data-driven Transient Stability Assessment Using Sparse PMU Sampling and Online Self-check Function

Artificial intelligence technologies provide a newapproach for the real-time transient stability assessment (TSA)of large-scale power systems. In this paper, we propose a datadriven transient stability assessment model (DTSA) that combinesdifferent AI algorithms. A pre-AI based on the time-delay neuralnetwork is designed to locate the dominant buses for installingthe phase measurement units (PMUs) and reducing the datadimension. A post-AI is designed based on the bidirectionallong-short-term memory network to generate an accurate TSAwith sparse PUM sampling. An online self-check function of theonline TSA’s validity when the power system changes is furtheradded by comparing the results of the pre-AI and the post-AI.The IEEE 39-bus system and the 300-bus AC/DC hybrid systemestablished by referring to China’s existing power system areadopted to verify the proposed method. Results indicate that theproposed method can effectively reduce the computation costswith ensured TSA accuracy as well as provide feedback forits applicability. The DTSA provides new insights for properlyintegrating varied AI algorithms to solve practical problems inmodern power systems.

Data-driven Estimation for a Region of Attraction for Transient Stability Using the Koopman Operator

This paper presents a novel Koopman Operator based framework to estimate the region of attraction for power system transient stability analysis.The Koopman eigenfunctions are used to numerically construct a Lyapunov function.Then the level set of the function is utilized to estimate the boundary of the region of attraction.The method provides a systematic method to construct the Lyapunov function with data sampled from the state space,which suits any power system models and is easy to use compared to traditional Lyapunov direct methods.In addition,the constructed Lyapunov function can capture the geometric properties of the region of attraction,thus providing useful information about the instability modes.The method has been verified by a simple illustrative example and three power system models,including a voltage source converter interfaced system to analyze the large signal synchronizing instability induced by the phase lock loop dynamics.The proposed method provides an alternative approach to understanding the geometric properties and estimating the boundary of the region of attraction of power systems in a data driven manner.Index Terms-Koopman operator,lyapunov function,power system transient stability,region of attraction.

Transient stability probability of a power system incorporating a wind farm

An analytical approach for probabilistic evaluation of transient stability of a power system incorporating a wind farm is presented in this study. Based on the fact that the boundary of practical dynamic security region(PDSR) of a power system with double fed induction generators(DFIG) can be approximated by one or few hyper-planes in nodal power injection space, transient stability criterion for given configurations of pre-fault, fault-on and post-fault of a power system is to be expressed by certain expressions of linear combination of nodal injection vector and the transient stability probability(TSP) is further obtained with a much more simplified expression than the complex integral. Furthermore, considering uncertainties of nodal injection power including wind power and load, TSP is calculated analytically by Cornish-Fisher expansion, which can provide reliable evaluation results with high accuracy and much less computing time compared with Monte Carlo simulation. TSP and its visualization can further help operators and planners be aware of the degree of stability or instability and find critical components to monitor and reinforce. Test results on the New England 10-generators and 39-buses power system show the method's effectiveness and significance for probabilistic security assessment.

Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

Transient stability assessment(TSA)is of great importance in power system operation and control.One of the usual tasks in TSA is to estimate the critical clearing time(CCT)of a given fault under the given network topology and pre-fault power flow.Data-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of samples.However,the influence of network topology on CCT is hard to be analyzed and is often ignored,which makes the models inaccurate and unpractical.In this paper,a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the problem.The model is a weighted sum of several sub-models.Each sub-model only uses the data of one topology to construct a kernel regressor.The weights are determined by both the topological similarity and numerical similarity between the samples.The similarities are decided by the parameters in Mahalanobis distance,and the parameters are to be trained.To reduce the model complexity,sub-models within the same topology category share the same parameters.When estimating CCT,the model uses not only the sub-model which the sample topology belongs to,but also other sub-models.Thus,it avoids the problem that there may be too few data under some topologies.It also efficiently utilizes information of data under all the topologies.Moreover,its decision-making process is clear and understandable,and an effective training algorithm is also designed.Test results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.